text:
Plants (botany),Satellites,Soils,Surface structure,Surface Structure,Terrestrial landscape,Topography,Total nitrogen content},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\ustin_middleton_2021_current_and_near-term_advances_in_earth_observation_for_ecological_applications.pdf}
}

@article{valentini2019,
  title = {New Tree Monitoring Systems: From {{Industry}} 4.0 to {{Nature}} 4.0},
  shorttitle = {New Tree Monitoring Systems},
  author = {Valentini, Riccardo and Belelli Marchesini, Luca and Gianelle, Damiano and Sala, Giovanna and Yarovslavtsev, Alexey and Vasenev, Viacheslav and Castaldi, Simona},
  date = {2019-11-29},
  journaltitle = {Annals of Silvicultural Research},
  volume = {43},
  number = {2},
  issn = {2284354X},
  doi = {10.12899/asr-1847},
  url = {https://doi.org/10.12899/asr-1847},
  urldate = {2021-08-14},
  abstract = {Recently, Internet of Things (IoT) technologies have grown rapidly and represent now a unique opportunity to improve our environmental monitoring capabilities at extremely low costs. IoT is a new system of thinking in which objects, animals or people are equipped with unique identifiers and transfer data to a network without requiring human-to-human or human-to-computer interaction. IoT has evolved from the convergence of wireless technologies, microelectromechanical systems (MEMS) and the Internet. The development of these technologies in environmental monitoring domains allows real-time data transmission and numerous lowcost monitoring points. We have designed a new device, the TreeTalker©, which is capable of measuring water transport in trees, diametrical growth, spectral characteristics of leaves and microclimatic parameters and transmit data in semi-real time. Here we introduce the device’s features, provide an example of monitored data from a field test site and discuss the application of this new technology to tree monitoring in various contexts, from forest to urban green infrastructures management and ecological research.},
  langid = {english},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\H7S8P632\\Valentini et al. - 2019 - New tree monitoring systems from Industry 4.0 to .pdf}
}

@article{vartanian2014,
  title = {Assessing the {{Impact}} of {{Field}} of {{View}} on {{Monitoring Understory}} and {{Overstory Phenology Using Digital Repeat Photography}}},
  author = {Vartanian, M and Nijland, W and Coops, N C and Bater, C and Wulder, M A and Stenhouse, G},
  date = {2014},
  volume = {40},
  pages = {8},
  abstract = {Phenological patterns of the components within forest ecosystems, such as understory vegetation, are important indicators of climate variability, productivity, and additional ecosystem services such as food and habitat availability for wildlife. Proximal sensing systems can provide detailed phenological records at a relatively low cost. As interest in these datasets increases, we need additional information regarding the effect of different approaches on the scale of observations and camera field of view. In this research, we examine the impact of field of view on the capacity of cameras to detect changes in phenology of individual species in an image time series. We examine two co-located series of oblique images acquired using a fine and broad field of view and compare a number of phenological indicators, including the start and end of season derived for individual plant species. Our results indicate both fine and broad field of view camera systems are highly effective at detecting key markers of plant phenology with no significant differences between the two. This result supports environmental monitoring using cost-effective broad field of view cameras, or even—subject to some constraints—readily available camera stations installed for tourism or traffic monitoring.},
  langid = {english},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\LBLKKCGE\\Vartanian et al. - 2014 - Assessing the Impact of Field of View on Monitorin.pdf}
}

@article{Walker201542,
  title = {Land Surface Phenology along Urban to Rural Gradients in the {{U}}.{{S}}. {{Great Plains}}},
  author = {Walker, J.J. and de Beurs, K.M. and Henebry, G.M.},
  options = {useprefix=true},
  date = {2015},
  journaltitle = {Remote Sensing of Environment},
  volume = {165},
  pages = {42--52},
  doi = {10.1016/j.rse.2015.04.019},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929317289&doi=10.1016%2fj.rse.2015.04.019&partnerID=40&md5=3db366fdc8b818f3795eed4d6aa32e7f},
  abstract = {The elevated surface and air temperatures of urban environments can influence the timing of vegetation growth dynamics within and across city boundaries. We examined patterns of land surface phenology (LSP) throughout the U.S. Great Plains region, which contains diverse metropolitan areas embedded within a predominately agricultural landscape. We assembled a time series (2002-2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-Adjusted Reflectance (NBAR) data and land surface temperature data at 500m and 1000m spatial resolution, respectively. We derived measures of the vegetated land surface and the thermal regime of the growing season at 8-day intervals using the Normalized Difference Vegetation Index (NDVI) and Accumulated Growing Degree-Days (AGDD). Fitting the convex quadratic LSP model of NDVI as a function of AGDD yielded two phenometrics - Peak NDVI and Thermal Time to Peak - and one model fit metric - the adjusted coefficient of determination (r2¡inf¿adj¡/inf¿) - for each pixel per growing season. We linked the phenometrics with impervious surface area (ISA) data extracted from the U.S. Geological Survey National Land Cover Dataset (NLCD) to characterize differences in timing and amplitude of peak greenness between urban areas and their surrounding landscapes. Our results reveal the broad control of climatic conditions and moisture availability on phenological patterns across urban to rural gradients, with drier, southern cities displaying more varied responses of peak greenness timing and amplitude to urban intensity. © 2015.},
  affiliation = {Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States; Department of Geography and Environmental Sustainability, The University of Oklahoma, Norman, OK, United States},
  author_keywords = {Great plains; Land surface temperature (LSP); MODIS; NDVI; Phenology; Urban heat island (UHI)},
  document_type = {Article},
  source = {Scopus}
}

@article{wang2020a,
  title = {Characterizing Urbanization-Induced Land Surface Phenology Change from Time-Series Remotely Sensed Images at Fine Spatio-Temporal Scale: {{A}} Case Study in {{Nanjing}}, {{China}} (2001–2018)},
  author = {Wang, X. and Du, P. and Chen, D. and Lin, C. and Zheng, H. and Guo, S.},
  date = {2020},
  journaltitle = {Journal of Cleaner Production},
  volume = {274},
  doi = {10.1016/j.jclepro.2020.122487},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088378785&doi=10.1016%2fj.jclepro.2020.122487&partnerID=40&md5=18d1c94e9c0f7e9efaa18c79e3cf72f9},
  abstract = {Continuous urban expansion and its influence on land surface phenology (LSP) have been gaining considerable attention due to their impacts on climate change, carbon cycling and human health. Most previous studies have investigated the effects of urbanization on LSP from the regional to global scales by using coarse-resolution remotely sensed images and fixed land cover boundary maps. However, the influences of urbanization on LSP are also important at the local level, particularly in and around rapidly urbanizing cities as LSP is closely linked to the local ecology and people's health. To analyze the dynamic of urbanization and its effect on LSP at the local scale, this study proposed a novel framework of characterizing LSP with consideration of continuous urban expansion based on all available time-series Landsat and Moderate Resolution Image Spectroradiometer (MODIS) Reflectance and Enhanced Vegetation Index (EVI) composite data. The proposed approach was capable of evaluating LSP impacts of urbanization from three aspects: 1) the phenology difference between dynamic annual urban and rural areas; 2) the different rates of phenology trend between the permanent urban and rural areas; and 3) the phenology shifts in the areas that were urbanized during the period. This approach was applied to Nanjing, China and the experimental results indicated that when compared with the rural areas, the phenology cycle started 0.59 ± 0.58 days earlier (start of season, SOS) and ended 1.65 ± 1.55 days later (end of season, EOS) in urban areas, accordingly leading to increased growing season length (GSL) by 2.77 ± 2.61 days. Besides, the experiments also revealed that 70.81\% SOS and 78.79\% EOS in the permanent urban-rural areas were provided with delayed phenology trends, along with 80.72\% of these regions tending to have a prolonged GSL. Furthermore, the rates of SOS and EOS delay in rural areas were 0.12 ± 0.12 day/year and 0.02 ± 0.02 day/year faster than those in urban areas, and the rate of GSL prolonging in urban regions was 0.04 ± 0.04 day/year faster than that in rural counterparts. For the urbanized regions in Nanjing, after experiencing conversion to the impervious surface, SOS and EOS delayed in 65.77\% and 70.33\% of the regions, accompanied by extended GSL in 70.83\% of the regions. Overall, this research proposed a novel approach of analyzing urbanization implications for LSP at the city scale and demonstrated its priority of taking continuous land cover change into account. © 2020 Elsevier Ltd},
  affiliation = {School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing, Jiangsu 210023, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, China; Department of Geography and Planning, Queen's University, Kingston, ON K7L 3N6, Canada},
  art_number = {122487},
  author_keywords = {Ensemble continuous change detection and classification; Impervious surface; Land surface phenology; Landsat; MODIS; Urbanization},
  document_type = {Article},
  source = {Scopus},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\wang_et_al_2020_characterizing_urbanization-induced_land_surface_phenology_change_from.pdf}
}

@article{wang2021,
  title = {Optimization of Residential Green Space for Environmental Sustainability and Property Appreciation in Metropolitan {{Phoenix}}, {{Arizona}}},
  author = {Wang, C. and Turner, V.K. and Wentz, E.A. and Zhao, Q. and Myint, S.W.},
  date = {2021},
  journaltitle = {Science of the Total Environment},
  volume = {763},
  publisher = {{Elsevier B.V.}},
  issn = {00489697},
  doi = {10.1016/j.scitotenv.2020.144605},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098721489&doi=10.1016%2fj.scitotenv.2020.144605&partnerID=40&md5=7f7ae78020da40fb43b0ef71e01361b8},
  abstract = {Cities in arid and semi-arid regions have been exploring urban sustainability policies, such as lowering the vegetation coverage to reduce residential outdoor water use. Meanwhile, urban residents express concerns that such policies could potentially impact home prices regardless of the reduced water costs because studies have shown that there is a positive correlation between vegetation coverage and home values. On the other hand, lower vegetation coverage in arid and semi-arid desert regions could increase surface temperatures, and consequently increases energy costs. The question is therefore where the point in which residential outdoor water use can be minimized without overly increasing surface temperatures and negatively impacting home values. This study examines the impacts of spatial composition of different vegetation types on land surface temperature (LST), outdoor water use (OWU), and property sales value (PSV) in 302 local residential communities in the Phoenix metropolitan area, Arizona using remotely sensed data and regression analysis. In addition, the spatial composition of vegetation cover was optimized to achieve a relatively lower LST and OWU and maintain a relatively higher PSV at the same time. We found that drought-tolerant landscaping that is composed of mostly shrubs and trees adapted to the desert environment is the most water efficient way to reduce LST, but grass contributes to a higher PSV. Research findings suggest that different residential landscaping strategies may be better suited for different neighborhoods and goal sets can be used by urban planners and city managers to better design urban residential landscaping for more efficient water conservation and urban heat mitigation for desert cities. © 2020 Elsevier B.V.},
  langid = {english},
  keywords = {Arid and semi-arid regions,Atmospheric temperature,Costs,Environmental sustainability,Housing,Land surface temperature,Land use,Landforms,Phoenix,Phoenix metropolitan,Positive correlations,Remotely sensed data,Residential communities,Surface properties,Surface temperatures,Sustainable development,Urban sustainability,Vegetation,Water conservation}
}

@article{ward2007,
  title = {Geospatial Methods Provide Timely and Comprehensive Urban Forest Information},
  author = {Ward, Kathleen T. and Johnson, Gary R.},
  date = {2007},
  journaltitle = {Urban Forestry \& Urban Greening. 6: 15-22.},
  volume = {6},
  url = {https://www.fs.usda.gov/treesearch/pubs/11443},
  urldate = {2021-01-11},
  langid = {english},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\ward_johnson_2007_geospatial_methods_provide_timely_and_comprehensive_urban_forest_information.pdf;C\:\\Users\\ahurl\\Zotero\\storage\\TATXGYMH\\11443.html}
}

@article{way2010,
  title = {Differential Responses to Changes in Growth Temperature between Trees from Different Functional Groups and Biomes: A Review and Synthesis of Data},
  shorttitle = {Differential Responses to Changes in Growth Temperature between Trees from Different Functional Groups and Biomes},
  author = {Way, Danielle A. and Oren, Ram},
  date = {2010-06-01},
  journaltitle = {Tree Physiology},
  shortjournal = {Tree Physiol},
  volume = {30},
  number = {6},
  pages = {669--688},
  publisher = {{Oxford Academic}},
  issn = {0829-318X},
  doi = {10.1093/treephys/tpq015},
  url = {https://academic.oup.com/treephys/article/30/6/669/1614202},
  urldate = {2020-11-06},
  abstract = {Abstract.  The response of tree growth to a change in temperature may differ in predictable ways. Trees with conservative growth strategies may have little abil},
  langid = {english},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\way_oren_2010_differential_responses_to_changes_in_growth_temperature_between_trees_from.pdf;C\:\\Users\\ahurl\\Zotero\\storage\\X8QQHF3S\\1614202.html}
}

@inproceedings{wegner2016,
  title = {Cataloging {{Public Objects Using Aerial}} and {{Street-Level Images}} — {{Urban Trees}}},
  booktitle = {2016 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {Wegner, Jan D. and Branson, Steve and Hall, David and Schindler, Konrad and Perona, Pietro},
  date = {2016-06},
  pages = {6014--6023},
  publisher = {{IEEE}},
  location = {{Las Vegas, NV, USA}},
  doi = {10.1109/CVPR.2016.647},
  url = {http://ieeexplore.ieee.org/document/7781016/},
  urldate = {2021-01-11},
  abstract = {Each corner of the inhabited world is imaged from multiple viewpoints with increasing frequency. Online map services like Google Maps or Here Maps provide direct access to huge amounts of densely sampled, georeferenced images from street view and aerial perspective. There is an opportunity to design computer vision systems that will help us search, catalog and monitor public infrastructure, buildings and artifacts. We explore the architecture and feasibility of such a system. The main technical challenge is combining test time information from multiple views of each geographic location (e.g., aerial and street views). We implement two modules: det2geo, which detects the set of locations of objects belonging to a given category, and geo2cat, which computes the fine-grained category of the object at a given location. We introduce a solution that adapts state-ofthe-art CNN-based object detectors and classifiers. We test our method on “Pasadena Urban Trees”, a new dataset of 80,000 trees with geographic and species annotations, and show that combining multiple views significantly improves both tree detection and tree species classification, rivaling human performance.},
  eventtitle = {2016 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  isbn = {978-1-4673-8851-1},
  langid = {english},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\2JN5S2GP\\Wegner et al. - Supplementary Material of CVPR 2016 publication C.pdf;C\:\\Users\\ahurl\\Zotero\\storage\\MDWZQBSW\\Wegner et al. - 2016 - Cataloging Public Objects Using Aerial and Street-.pdf}
}

@article{weinberger2015,
  title = {A {{Review}} of {{Spatial Variation}} of {{Allergenic Tree Pollen Within Cities}}},
  author = {Weinberger, Kate R and Kinney, Patrick L and Lovasi, Gina S},
  date = {2015},
  pages = {13},
  abstract = {Urban trees provide a range of environmental and public health benefits. However, urban trees may also have an adverse effect on human health by increasing exposure to pollen. Many types of tree pollen are considered to be allergens and have been linked to various manifestations of allergic disease, including allergic sensitization, exacerbation of allergic rhinitis, and exacerbation of allergic asthma. An emerging body of literature suggests that the amount of pollen deposited annually varies widely over small spatial scales. While the health impacts of spatial variation in tree pollen within metropolitan areas could be large, the current literature has not been systematically reviewed. To fill this gap in knowledge, this review synthesizes existing evidence on how tree pollen is distributed on an intra-urban spatial scale. A better understanding of the spatial distribution of allergenic tree pollen within urban environments and its relation to health could inform increasingly common urban tree planting programs.},
  langid = {english},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\PJPQSCFW\\Weinberger et al. - 2015 - A Review of Spatial Variation of Allergenic Tree P.pdf}
}

@article{weinberger2018,
  title = {Levels and Determinants of Tree Pollen in {{New York City}}},
  author = {Weinberger, Kate R. and Kinney, Patrick L. and Robinson, Guy S. and Sheehan, Daniel and Kheirbek, Iyad and Matte, Thomas D. and Lovasi, Gina S.},
  date = {2018-03},
  journaltitle = {Journal of Exposure Science \& Environmental Epidemiology},
  shortjournal = {J Expo Sci Environ Epidemiol},
  volume = {28},
  number = {2},
  pages = {119--124},
  publisher = {{Nature Publishing Group}},
  issn = {1559-064X},
  doi = {10.1038/jes.2016.72},
  url = {https://www.nature.com/articles/jes201672},
  urldate = {2021-06-07},
  abstract = {Exposure to allergenic tree pollen is a risk factor for multiple allergic disease outcomes. Little is known about how tree pollen levels vary within cities and whether such variation affects the development or exacerbation of allergic disease. Accordingly, we collected integrated pollen samples at uniform height at 45 sites across New York City during the 2013 pollen season. We used these monitoring results in combination with adjacent land use data to develop a land use regression model for tree pollen. We evaluated four types of land use variables for inclusion in the model: tree canopy, distributed building height (a measure of building volume density), elevation, and distance to water. When included alone in the model, percent tree canopy cover within a 0.5\,km radial buffer explained 39\% of the variance in tree pollen (1.9\% increase in tree pollen per one-percentage point increase in tree canopy cover, P{$<$}0.0001). The inclusion of additional variables did not improve model fit. We conclude that intra-urban variation in tree canopy is an important driver of tree pollen exposure. Land use regression models can be used to incorporate spatial variation in tree pollen exposure in studies of allergic disease outcomes.},
  issue = {2},
  langid = {english},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\weinberger_et_al_2018_levels_and_determinants_of_tree_pollen_in_new_york_city.pdf;C\:\\Users\\ahurl\\Zotero\\storage\\7PP5YNQF\\jes201672.html}
}

@article{weinstein2020,
  title = {{{DeepForest}}: {{A Python}} Package for {{RGB}} Deep Learning Tree Crown Delineation},
  shorttitle = {{{DeepForest}}},
  author = {Weinstein, Ben G. and Marconi, Sergio and Aubry‐Kientz, Mélaine and Vincent, Gregoire and Senyondo, Henry and White, Ethan P.},
  date = {2020},
  journaltitle = {Methods in Ecology and Evolution},
  volume = {11},
  number = {12},
  pages = {1743--1751},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13472},
  url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472},
  urldate = {2021-01-11},
  abstract = {Remote sensing of forested landscapes can transform the speed, scale and cost of forest research. The delineation of individual trees in remote sensing images is an essential task in forest analysis. Here we introduce a new Python package, DeepForest that detects individual trees in high resolution RGB imagery using deep learning. While deep learning has proven highly effective in a range of computer vision tasks, it requires large amounts of training data that are typically difficult to obtain in ecological studies. DeepForest overcomes this limitation by including a model pretrained on over 30 million algorithmically generated crowns from 22 forests and fine-tuned using 10,000 hand-labelled crowns from six forests. The package supports the application of this general model to new data, fine tuning the model to new datasets with user labelled crowns, training new models and evaluating model predictions. This simplifies the process of using and retraining deep learning models for a range of forests, sensors and spatial resolutions. We illustrate the workflow of DeepForest using data from the National Ecological Observatory Network, a tropical forest in French Guiana, and street trees from Portland, Oregon.},
  langid = {english},
  keywords = {crown delineation,deep learning,forests,NEON,remote sensing,RGB,tree crowns},
  annotation = {\_eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13472},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\weinstein_et_al_2020_deepforest.pdf;C\:\\Users\\ahurl\\Zotero\\storage\\2EA2TWSP\\2041-210X.html}
}

@article{werchan2017,
  title = {Spatial Distribution of Allergenic Pollen through a Large Metropolitan Area},
  author = {Werchan, Barbora and Werchan, Matthias and Mücke, Hans-Guido and Gauger, Ulrich and Simoleit, Anke and Zuberbier, Torsten and Bergmann, Karl-Christian},
  date = {2017-03-18},
  journaltitle = {Environmental Monitoring and Assessment},
  shortjournal = {Environ Monit Assess},
  volume = {189},
  number = {4},
  pages = {169},
  issn = {1573-2959},
  doi = {10.1007/s10661-017-5876-8},
  url = {https://doi.org/10.1007/s10661-017-5876-8},
  urldate = {2021-06-21},
  abstract = {For nearly a decade, the majority of the world’s population has been living in cities, including a considerable percentage of people suffering from pollen allergy. The increasing concentration of people in cities results in larger populations being exposed to allergenic pollen at the same time. There is almost no information about spatial distribution of pollen within cities as well as a lack of information about the possible impact to human health. To obtain this increasing need for pollen exposure studies on an intra-urban scale, a novelty screening network of 14 weekly changed pollen traps was established within a large metropolitan area—Berlin, Germany. Gravimetric pollen traps were placed at a uniform street-level height from March until October 2014. Three important allergenic pollen types for Central Europe—birch (Betula), grasses (Poaceae), and mugwort (Artemisia)—were monitored. Remarkable spatial and temporal variations of pollen sedimentation within the city and the influences by urban local sources are shown. The observed differences between the trap with the overall highest and the trap with the overall lowest amount of pollen sedimentation were in the case of birch pollen 245\%, grass pollen 306\%, and mugwort pollen 1962\%. Differences of this magnitude can probably lead to different health impacts on allergy sufferers in one city. Therefore, pollen should be monitored preferably in two or more appropriate locations within large cities and as a part of natural air quality regulations.},
  langid = {english},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\werchan_et_al_2017_spatial_distribution_of_allergenic_pollen_through_a_large_metropolitan_area.pdf}
}

@article{werchan2018,
  title = {Spatial Distribution of Pollen-Induced Symptoms within a Large Metropolitan Area—{{Berlin}}, {{Germany}}},
  author = {Werchan, Barbora and Werchan, Matthias and Mücke, Hans-Guido and Bergmann, Karl-Christian},
  date = {2018-12-01},
  journaltitle = {Aerobiologia},
  shortjournal = {Aerobiologia},
  volume = {34},
  number = {4},
  pages = {539--556},
  issn = {1573-3025},
  doi = {10.1007/s10453-018-9529-3},
  url = {https://doi.org/10.1007/s10453-018-9529-3},
  urldate = {2021-06-21},
  abstract = {Large spatial differences in the distribution of three allergologically relevant pollen types for Central Europe—birch, grass, and mugwort—are revealed within a large metropolitan area—Berlin, Germany. The purpose of the study is an examination of the hypothesis that these different pollen exposure conditions can cause different degrees of pollen-induced symptoms within the city. Pollen data from 14 gravimetric traps and one volumetric trap in Berlin and anonymously reported pollen-induced symptom data from the online-based self-documentation tool “Patient’s Hayfever Diary” (PHD) are used for the analysis of temporal and spatial variations of the severity of the overall total symptoms. Geographically localised symptom data are linked to the nearest pollen trap. Statistical analysis is performed using Kendall’s Tau-b. Higher amounts of monitored birch and grass pollen in the peripheral areas of Berlin induce stronger symptoms in PHD users located within suburbs than those located in the city centre. There is no statistical relationship between the varying presence of mugwort pollen in the air and the severity of symptoms. Spatial differences in the pollen-induced symptom severity within a large city coinciding with spatial differences in birch and grass pollen depositions are shown for the first time. Therefore, pollen data from a single trap may not provide an appropriate explanation for differences in pollen-induced symptoms across the city. More detailed and reliable information about the exposure to allergenic pollen can be addressed by installing further traps in order to improve the knowledge about pollen exposure within cities.},
  langid = {english},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\werchan_et_al_2018_spatial_distribution_of_pollen-induced_symptoms_within_a_large_metropolitan.pdf}
}

@article{werchan2018a,
  title = {The Pollator: A~Personal Pollen Sampling Device},
  shorttitle = {The Pollator},
  author = {Werchan, Matthias and Sehlinger, Torsten and Goergen, Frank and Bergmann, Karl-Christian},
  date = {2018-02-01},
  journaltitle = {Allergo Journal International},
  shortjournal = {Allergo J Int},
  volume = {27},
  number = {1},
  pages = {1--3},
  issn = {2197-0378},
  doi = {10.1007/s40629-017-0034-y},
  url = {https://doi.org/10.1007/s40629-017-0034-y},
  urldate = {2021-06-21},
  langid = {english},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\werchan_et_al_2018_the_pollator.pdf}
}

@article{Weryszko-Chmielewska2019,
  title = {Response of {{Tilia}} Sp. {{L}}. to Climate Warming in Urban Conditions – {{Phenological}} and Aerobiological Studies},
  author = {Weryszko-Chmielewska, E. and Piotrowska-Weryszko, K. and Dąbrowska, A.},
  date = {2019},
  journaltitle = {Urban Forestry and Urban Greening},
  volume = {43},
  doi = {10.1016/j.ufug.2019.126369},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067579714&doi=10.1016%2fj.ufug.2019.126369&partnerID=40&md5=89011958672793f9b076b6fe93f12311},
  abstract = {Lindens belong to valued trees that are frequently planted in urban green areas. Nevertheless, during their flowering they can pose a risk to sensitive people in cities due to their allergenic pollen. This paper presents the results of a 3-year phenological study (2016–2018) and an 18-year aerobiological study (2001–2018) conducted in Lublin (Poland). The flowering phenology data for the following five species: T. americana, T. cordata, T. × euchlora, T. platyphyllos, and T. tomentosa, were compared with the data regarding Tilia pollen seasons. It was shown that the largest amounts of airborne Tilia pollen grains occur during the full flowering period of T. cordata and that the flowering sequence of the linden species studied is constant during the growing season: T. platyphyllos, T. americana, T. cordata, T. × euchlora, and T. tomentosa. We found pollen seasons to start earlier by 14 days and to be extended by 15 days. Throughout the study period, the annual Tilia pollen sum did not change significantly. However, in 2018 in which there was a significant increase in temperature above the average for the previous 17 years, flowering and pollen release distinctly accelerated, and the annual pollen sum increased more than three times compared to the average for the previous 17 years. In central-eastern Poland, the risk of pollen allergy due to the presence of Tilia pollen occurs in May, June, and July. We recorded the highest linden pollen concentrations in June. Due to the acceleration of flowering and pollen release in linden trees as a result of global warming, the incidence of pollen allergy in central Europe can be expected to become more intense already in the second half of May. Therefore, linden plantings in new urban green areas should be planned to be established at some distance from residential estates. © 2019 Elsevier GmbH},
  affiliation = {Department of Botany and Plant Physiology, University of Life Sciences in Lublin, Akademicka 15, Lublin, 20-950, Poland; Botanical Garden, Maria Curie-Skłodowska University, Sławinkowska 3, Lublin, 20-810, Poland},
  art_number = {126369},
  author_keywords = {Acceleration; Allergy risk; Global warming; Linden; Pollen seasons; Time of blooming; Urban greenery},
  document_type = {Article},
  source = {Scopus}
}

@article{White2002260,
  title = {Satellite Evidence of Phenological Differences between Urbanized and Rural Areas of the Eastern {{United States}} Deciduous Broadleaf Forest},
  author = {White, M.A. and Nemani, R.R. and Thornton, P.E. and Running, S.W.},
  date = {2002},
  journaltitle = {Ecosystems},
  volume = {5},
  number = {3},
  pages = {260--273},
  doi = {10.1007/s10021-001-0070-8},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036262807&doi=10.1007%2fs10021-001-0070-8&partnerID=40&md5=f7789aea2fd474ce60f1c2c6af8c957e},
  abstract = {We used a 10-year record (1990-99) of composited and cloud-screened reflectances from the Advanced Very High Resolution Radiometer (AVHRR) to test for phenological differences between urban and rural areas in the eastern United States deciduous broadleaf forest (DBF). We hypothesized that well-documented urban heat island effects would be associated with alterations in temperature-sensitive vegetation phenology. Our objectives were thus (a) to investigate possible differences in the start of the growing season (SOS) and end of the growing season (EOS) between the urban and DBF land covers, (b) to investigate related differences in greenness amplitude and fractional cover, and (c) to develop a generalized additive model (GAM) to predict the spatial variation of observed differences. By analyzing individual 1° latitude by 1° longitude blocks, we found that, on average, urbanization is associated with a growing season expansion of 7.6 days. Most of this effect is caused by an earlier SOS in urban areas. In all cases, urban regions had lower fractional cover and greenness amplitude. The GAM model failed to produce a viable model for differences in EOS, probably because it is dominated by photoperiod controls with only a minor temperature impact. SOS differences were predicted with an accuracy of about 2.4 days, with a GAM consisting of smoothed functions of mean annual average temperature, urban fractional cover, and the urban vs DBF greenness amplitude difference. We speculate that evidence of a phenological response to warming indicates that global warming, without reduction in DBF vegetation cover and greenness amplitude, may increase carbon sequestration in mesic deciduous forests.},
  affiliation = {Department of Geography and Earth Resources, Old Main Hall 5240, Utah State University, Logan, UT 84322-5240, United States; NTSG/Forestry, University of Montana, Missoula, MT 59812, United States; National Center for Atmospheric Research, Climate and Global Dynamics Division, Boulder, CO 80307, United States},
  author_keywords = {Advanced Very High Resolution Radiometer (AVHRR); Deciduous broadleaf forest; Normalized difference vegetation index (NDVI); Phenology; Urban climate; Urbanization},
  document_type = {Article},
  source = {Scopus},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\white_et_al_2002_satellite_evidence_of_phenological_differences_between_urbanized_and_rural.pdf}
}

@article{white2005,
  title = {A Global Framework for Monitoring Phenological Responses to Climate Change},
  author = {White, M.A. and Hoffman, F. and Hargrove, W.W. and Nemani, R.R.},
  date = {2005},
  journaltitle = {Geophysical Research Letters},
  volume = {32},
  number = {4},
  pages = {1--4},
  issn = {00948276},
  doi = {10.1029/2004GL021961},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-19744363696&doi=10.1029%2f2004GL021961&partnerID=40&md5=698f18a1f36a76e2309e95b2af564083},
  abbrev_source_title = {Geophys. Res. Lett.},
  abstract = {Remote sensing of vegetation phenology is an important method with which to monitor terrestrial responses to climate change, but most approaches include signals from multiple forcings, such as mixed phenological signals from multiple biomes, urbanization, political changes, shifts in agricultural practices, and disturbances. Consequently, it is difficult to extract a clear signal from the usually assumed forcing: climate change. Here, using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels whose wavelet power spectrum was consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity. Remaining phenoregions represented areas with a minimized probability of non-climatic forcings and form elemental units for long-term phenological monitoring. Copyright 2005 by the American Geophysical Union.},
  affiliation = {Department of Aquatic Watershed and Earth Resources, Utah State University, Old Main Hall 5240, Logan, UT 84322-5240, United States; Computer Science and Mathematics, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States; Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States; NASA Ames Research Center, Moffett Field, CA 94087, United States},
  coden = {GPRLA},
  correspondence_address1 = {White, M.A.; Department of Aquatic Watershed and Earth Resources, Utah State University, Old Main Hall 5240, Logan, UT 84322-5240, United States; email: mikew@cc.usu.edu},
  document_type = {Article},
  langid = {english},
  source = {Scopus},
  keywords = {Agricultural practices,Biomass,climate change,Climate change,Data reduction,global perspective,Landcover homogeneity,Monitoring,Normalized Difference Vegetation Index (NDVI),Normalized difference vegetation index datum,Phenological signals,phenology,Political changes,Probability,remote sensing,Remote sensing,Self-similar cluster,Signal processing,Spectrum analysis,vegetation,Vegetation,Vegetation phenology,Wavelet power spectra,Wavelet transforms}
}

@article{White20051,
  title = {A Global Framework for Monitoring Phenological Responses to Climate Change},
  author = {White, M.A. and Hoffman, F. and Hargrove, W.W. and Nemani, R.R.},
  date = {2005},
  journaltitle = {Geophysical Research Letters},
  volume = {32},
  number = {4},
  pages = {1--4},
  doi = {10.1029/2004GL021961},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-19744363696&doi=10.1029%2f2004GL021961&partnerID=40&md5=698f18a1f36a76e2309e95b2af564083},
  abstract = {Remote sensing of vegetation phenology is an important method with which to monitor terrestrial responses to climate change, but most approaches include signals from multiple forcings, such as mixed phenological signals from multiple biomes, urbanization, political changes, shifts in agricultural practices, and disturbances. Consequently, it is difficult to extract a clear signal from the usually assumed forcing: climate change. Here, using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels whose wavelet power spectrum was consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity. Remaining phenoregions represented areas with a minimized probability of non-climatic forcings and form elemental units for long-term phenological monitoring. Copyright 2005 by the American Geophysical Union.},
  affiliation = {Department of Aquatic Watershed and Earth Resources, Utah State University, Old Main Hall 5240, Logan, UT 84322-5240, United States; Computer Science and Mathematics, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States; Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States; NASA Ames Research Center, Moffett Field, CA 94087, United States},
  document_type = {Article},
  source = {Scopus},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\white_et_al_2005_a_global_framework_for_monitoring_phenological_responses_to_climate_change.pdf}
}

@article{wohlfahrt2019,
  title = {The Urban Imprint on Plant Phenology},
  author = {Wohlfahrt, Georg and Tomelleri, Enrico and Hammerle, Albin},
  date = {2019-12},
  journaltitle = {Nature Ecology \& Evolution},
  shortjournal = {Nat Ecol Evol},
  volume = {3},
  number = {12},
  pages = {1668--1674},
  issn = {2397-334X},
  doi = {10.1038/s41559-019-1017-9},
  url = {https://www.nature.com/articles/s41559-019-1017-9},
  urldate = {2020-02-11},
  abstract = {Urbanization gradients do not make good field laboratories for predicting climate warming effects on phenology, according to the analysis of a 30 year dataset on plant phenology and temperature in different urbanized settings.},
  langid = {english},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\MFRDGSU5\\Wohlfahrt et al_2019_The urban imprint on plant phenology.pdf;C\:\\Users\\ahurl\\Zotero\\storage\\HSXWN8NZ\\s41559-019-1017-9.html}
}

@article{wolkovich2014,
  title = {Progress towards an Interdisciplinary Science of Plant Phenology: Building Predictions across Space, Time and Species Diversity},
  shorttitle = {Progress towards an Interdisciplinary Science of Plant Phenology},
  author = {Wolkovich, Elizabeth M. and Cook, Benjamin I. and Davies, T. Jonathan},
  date = {2014},
  journaltitle = {New Phytologist},
  volume = {201},
  number = {4},
  pages = {1156--1162},
  issn = {1469-8137},
  doi = {10.1111/nph.12599},
  url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.12599},
  urldate = {2021-06-07},
  abstract = {Climate change has brought renewed interest in the study of plant phenology – the timing of life history events. Data on shifting phenologies with warming have accumulated rapidly, yet research has been comparatively slow to explain the diversity of phenological responses observed across latitudes, growing seasons and species. Here, we outline recent efforts to synthesize perspectives on plant phenology across the fields of ecology, climate science and evolution. We highlight three major axes that vary among these disciplines: relative focus on abiotic versus biotic drivers of phenology, on plastic versus genetic drivers of intraspecific variation, and on cross-species versus autecological approaches. Recent interdisciplinary efforts, building on data covering diverse species and climate space, have found a greater role of temperature in controlling phenology at higher latitudes and for early-flowering species in temperate systems. These efforts have also made progress in understanding the tremendous diversity of responses across species by incorporating evolutionary relatedness, and linking phenological flexibility to invasions and plant performance. Future research with a focus on data collection in areas outside the temperate mid-latitudes and across species' ranges, alongside better integration of how risk and investment shape plant phenology, offers promise for further progress.},
  langid = {english},
  keywords = {climate change,cues,investment,local adaptation,phenology,plant ecology,plasticity,risk},
  annotation = {\_eprint: https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.12599},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\wolkovich_et_al_2014_progress_towards_an_interdisciplinary_science_of_plant_phenology.pdf;C\:\\Users\\ahurl\\Zotero\\storage\\L56A4IHQ\\nph.html}
}

@article{xiao2017,
  title = {Monitoring Vegetation Cover in {{Chongqing}} between 2001 and 2010 Using Remote Sensing Data},
  author = {Xiao, Q. and Tao, J. and Xiao, Y. and Qian, F.},
  date = {2017},
  journaltitle = {Environmental Monitoring and Assessment},
  volume = {189},
  number = {10},
  publisher = {{Springer International Publishing}},
  issn = {01676369},
  doi = {10.1007/s10661-017-6210-1},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029225287&doi=10.1007%2fs10661-017-6210-1&partnerID=40&md5=511f0f4644b3a70246eb527a4dabf022},
  abbrev_source_title = {Environ. Monit. Assess.},
  abstract = {In this study, we applied asymmetric Gaussian function fitting to reconstruct a high-quality MODIS normalized difference vegetation index (NDVI) time series dataset. Following this, we retrieved vegetation cover data from the Chongqing area between 2001 and 2010 using this dataset, applying a dimidiate pixel method. We then used several analytical indices to analyze spatial and temporal changes and trends related to these changes. We determined that a reconstruction of the MODIS NDVI dataset using asymmetric Gaussian fitting in conjunction with a data quality weight coefficient improved data quality and created a foundation for accurate estimations of vegetation cover. We also determined that vegetation cover in the Chongqing area decreased gradually from east to west. During the 10-year study period, vegetation cover in the Chongqing area generally increased, changing from low to high coverage. This increase in vegetation cover was mainly the result of ecological protection policies and improving climate conditions. We also found that changes in vegetation cover were mainly the result of urban construction and afforestation initiatives, but vegetation cover improved overall. © 2017, Springer International Publishing AG.},
  affiliation = {Key Laboratory of Eco-environments in Three Gorges Reservoir Region (Ministry of Education), Chongqing Key Laboratory of Plant Ecology, and Resources Research in Three Gorges Reservoir Region, School of Life Sciences, Southwest University, Chongqing, 400715, China; Chongqing College of Arts and Sciences, Chongqing, 402160, China; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China},
  art_number = {493},
  author_keywords = {Dimidiate pixel model; Dynamic change analysis; TIMESAT; Vegetation cover},
  coden = {EMASD},
  correspondence_address1 = {Tao, J.; Key Laboratory of Eco-environments in Three Gorges Reservoir Region (Ministry of Education), Chongqing Key Laboratory of Plant Ecology, and Resources Research in Three Gorges Reservoir Region, School of Life Sciences, Southwest UniversityChina; email: taojianping@163.com},
  document_type = {Article},
  funding_details = {31570612},
  funding_text_1 = {Acknowledgments This study was supported by the National Foundation of Natural Sciences of China (No. 31570612).We would like to thank Brian DOONAN for his help in writing this paper as well as to the journal editors and anonymous reviewers for their comments on an earlier version of this manuscript.},
  langid = {english},
  pubmed_id = {28884302},
  source = {Scopus},
  keywords = {afforestation,China,Chongqing,climate,Climate,Conservation of Natural Resources,Dimidiate pixel methods,Dynamic changes,Ecological protection,environmental monitoring,Environmental Monitoring,environmental protection,Models,MODIS,monitoring system,NDVI,Normalized difference vegetation index time series,pixel,Pixel models,Pixels,plant,Plants,procedures,Radiometers,Reforestation,remote sensing,Remote sensing,Remote Sensing Technology,satellite data,season,Seasons,Spatial and temporal changes,spatiotemporal analysis,Theoretical,theoretical model,time series analysis,TIMESAT,trends,urbanization,Urbanization,vegetation,Vegetation,vegetation cover,Vegetation cover}
}

@article{xing2020,
  title = {An Effective High Spatiotemporal Resolution Ndvi Fusion Model Based on Histogram Clustering},
  author = {Xing, X. and Yan, C. and Jia, Y. and Jia, H. and Lu, J. and Luo, G.},
  date = {2020},
  journaltitle = {Remote Sensing},
  volume = {12},
  number = {22},
  pages = {1--21},
  publisher = {{MDPI AG}},
  issn = {20724292},
  doi = {10.3390/rs12223774},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096201521&doi=10.3390%2frs12223774&partnerID=40&md5=a0904522a81313e21554e4e984b1a94c},
  abbrev_source_title = {Remote Sens.},
  abstract = {The normalized difference vegetation index (NDVI) is a powerful tool for understanding past vegetation, monitoring the current state, and predicting its future. Due to technological and budget limitations, the existing global NDVI time-series data cannot simultaneously meet the needs of high spatial and temporal resolution. This study proposes a high spatiotemporal resolution NDVI fusion model based on histogram clustering (NDVI\textsubscript{F}MHC), which uses a new spatiotemporal fusion framework to predict phenological and shape changes. Meanwhile, this model also uses four strategies to reduce error, including the construction of an overdetermined linear mixed model, multiscale prediction, residual distribution, and Gaussian filtering. Five groups of real MODIS\textsubscript{N}DVI and Landsat\textsubscript{N}DVI datasets were used to verify the predictive performance of the NDVI\textsubscript{F}MHC. The results indicate that NDVI\textsubscript{F}MHC has higher accuracy and robustness in forest areas (r = 0.9488 and ADD = 0.0229) and cultivated land areas (r = 0.9493 and ADD = 0.0605), while the prediction effect is relatively weak in areas subject to shape changes, such as flooded areas (r = 0.8450 and ADD = 0.0968), urban areas (r = 0.8855 and ADD = 0.0756), and fire areas (r = 0.8417 and ADD = 0.0749). Compared with ESTARFM, NDVI\textsubscript{L}MGM, and FSDAF, NDVI\textsubscript{F}MHC has the highest prediction accuracy, the best spatial detail retention, and the strongest ability to capture shape changes. Therefore, the NDVI\textsubscript{F}MHC can obtain NDVI time-series data with high spatiotemporal resolution, which can be used to realize long-term land surface dynamic process research in a complex environment. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
  affiliation = {Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China; University of Chinese Academy of Sciences, Beijing, 100049, China; National Earth System Science Data Center, Beijing, 100020, China; Department of Architectural Engineering, Binzhou University, Binzhou, 256603, China; Guizhou Provincial Key Laboratory of Geographic State Monitoring of Watershed, Guizhou Education University, Guiyang, 550018, China},
  art_number = {3774},
  author_keywords = {Histogram clustering; Landsat; MODIS; Normalized difference vegetation index (NDVI); Spatiotemporal fusion framework},
  correspondence_address1 = {Yan, C.; Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, National Earth System Science Data CenterChina; email: yancz@lzb.ac.cn},
  document_type = {Article},
  funding_details = {National Natural Science Foundation of ChinaNational Natural Science Foundation of China, NSFC, 41971277, 41730752},
  funding_text_1 = {Funding: This research was funded by the Natural Science Foundation of China (Grant No. 41730752 and 41971277) and Key Program of Guizhou Provincial Science and Technology Foundation ([2018]2200).},
  langid = {english},
  source = {Scopus},
  keywords = {Budget control,Forecasting,Graphic methods,Histogram clustering,Multiscale predictions,Normalized difference vegetation index,Predictive performance,Residual distribution,Spatial and temporal resolutions,Spatio-temporal fusions,Spatio-temporal resolution,Time series,Vegetation}
}

@article{Xu20061777,
  title = {Responses of {{Shenyang}} Urban Tree Phenology to Climate Warming},
  author = {Xu, W. and He, X. and Chen, W. and Hu, J. and Wen, H.},
  date = {2006},
  journaltitle = {Chinese Journal of Applied Ecology},
  volume = {17},
  number = {10},
  pages = {1777--1781},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33846418384&partnerID=40&md5=e4d9b12630dc2c26d54648d3e1b1af43},
  abstract = {By using statistic and linear regression methods, this paper studied the last 40 years responses of Shenyang urban tree phenology to climate warming. The results showed that there was a significant correlation between the duration of tree dormancy and the mean air temperature in winter. Appropriate cold condition was beneficial to bud break, and a significant negative correlation was observed between the outset of sprouting and the mean air temperature in winter and early spring. Leaf expansion started 15 days after sprouting, which was mainly affected by the mean air temperature in spring but had no correlation with the temperature in winter. The air temperature within 20 ∼ 80 days and especially 20 ∼ 40 days before flowering had a significant effect on the outset of flowering, 1. e., an increasing temperature in spring could advance the outset of flowering. Both sprouting and leaf expansion were negatively correlated with cold index (CI), but no significant correlation was observed between the outset of flowering and CI. An increase of mean annual air temperature by 1 °C would advance the outset of sprouting by 9 days, leaf expansion by 10 days, and outset of flowering by 5 days.},
  affiliation = {Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China},
  author_keywords = {Climate change; Phenological period; Response; Urban forest},
  document_type = {Article},
  source = {Scopus}
}

@article{Xu20081461,
  title = {Relationships between Spring Phenology of {{Shenyang}} Urban Forest and Global Climate Warming in Last 40 Years},
  author = {Xu, W.-D. and He, X.-Y. and Chen, W. and Liu, C.-F. and Zhao, G.-L. and Zhou, Y. and Wen, H.},
  date = {2008},
  journaltitle = {Chinese Journal of Ecology},
  volume = {27},
  number = {9},
  pages = {1461--1468},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-55849129541&partnerID=40&md5=7ee27bbe813d0606aaa9b0f8fcefac41},
  abstract = {In last 40 years (1960-2005), the spring phenology of Shenyang urban forest was affected by global warming. The mean annual air temperature of Shenyang had an overall increase, with the increment being 0.96 °C. Before 1982, it was relative cold and the maximal of the decrease was 1.95 °C ; while after 1982, it was relative warm and the maximal increment was 1.60 °C. The air temperature was increased in the whole year, with the maximal increment in winter (2.3 °C) and spring (1.35 °C). The spring phenological phase of main tree species in Shenyang urban forest presented itself later in relatively cold period, but in advance in the relatively warm period. The dates of the beginning and ending of the spring phenological phase had a close synchronous relationship, and correlated negatively with the length of the seasonal rhythm of phenology. There was a significant negative correlation between the date of budding and the air temperature in winter and early spring. The higher the air temperature in winter and early spring, the earlier the tree budded. The date of sprouting new leaves was mainly affected by the air temperature in spring and had less correlation with the temperature in winter, while that of early flowering was mainly affected by the air temperature 20-80 days (especially 20-40 days) before early flowering. Coldness index (CI) had significant positive correlations with the dates of budding and sprouting new leaves, but less correlation with the date of early flowering. It was predicted that under the conditions of elevated CO2 concentration and air temperature, the dates of budding, sprouting new leaves, and early flowering of the main tree species in Shenyang urban forest would be advanced for 40.41, 43.08, and 24.13 days, respectively.},
  affiliation = {Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; School of Forestry, Shenyang Agricultural University, Shenyang 110061, China},
  author_keywords = {Global climate change; Plant phenology; Spring phenological change; Urban forest},
  document_type = {Article},
  source = {Scopus}
}

@article{xu2013,
  title = {A Remote Sensing Urban Ecological Index and Its Application},
  author = {Xu, H.Q.},
  date = {2013},
  journaltitle = {Shengtai Xuebao/ Acta Ecologica Sinica},
  volume = {33},
  number = {24},
  pages = {7853--7862},
  issn = {10000933},
  doi = {10.5846/stxb201208301223},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891894279&doi=10.5846%2fstxb201208301223&partnerID=40&md5=669db69c49843816e81af0c67103c754},
  abbrev_source_title = {Shengtai Xuebao Acta Ecol. Sin.},
  abstract = {Urban ecological status is closely related to the quality of human life. Timely, precisely and objectively understanding urban ecological status has become an increasing concern in the world. To meet this requirement, this paper develops a remote sensing based ecological index (RSEI) for the measure of urban ecology. As an index for the assessment of urban ecological status, the RSEI aims to integrate four important ecological indicators which are frequently used in evaluating urban ecology. These are greenness, wetness, dryness, and heat. The four indicators can be represented respectively by four remote sensing indices or components, which are the normalized difference vegetation index (NDVI), normalized difference built-up and soil index (NDBSI), wetness component of the tasseled cap transformation (Wet), and land surface temperature (LST). Instead of a simple addition or weighted addition of the four indicators, the principal component analysis (PCA) was utilized to compress the four indicators into one to construct the index for assessing overall urban ecological status. After careful comparison of the four principal components (PC1 to PC4) derived from the four indicators, the first principal component (PC1) was found to have integrated most of the information of the four indicators. This suggested that the PC1 can more effectively represent the four indicators than any of the other three components, i.e., PC2, PC3 and PC4. Accordingly, the new index, RSEI, was formed using the PC1 derived from the four factors and thus can measure greenness, wetness, dryness and heat of an urban ecosystem. Obviously, the RSEI is entirely based on remote sensing data and mostly on natural ecological factors. The calculation of the index is totally free of artificial interference, such as assigning a threshold value or weight value during the computing procedure. Therefore, the RSEI can assess the urban ecological status more objectively and easily. With the change detection technique, the proposed index can also be used to monitor the change of the ecological status of an urban area between different years. In practice, the index was successfully applied in a multi-temporal ecological status assessment of Fuzhou' s urban area in Fujian province, southeastern China. Results show that Fuzhou has witnessed ecological degradation during the study period from 2001 to 2009. This is indicated by a decline of the RSEI value from 0. 579 in 2001 to 0. 529 in 2009. The fast expansion of the Fuzhou's urban area was attributed to the ecological degradation in the study duration, which caused the increase in NDBSI and LST and the decrease in NDVI and Wet. This in turn resulted in the decline of the RSEI as the index is the function of the four factors. Nevertheless, a RSEI-based change detection has revealed that the ecological quality of the urban center of Fuzhou was improved in spite of the overall ecological degradation in the urban area during the study years.},
  affiliation = {College of Environment and Resources, Fuzhou University, Institute of Remote Sensing Information Engineering, Fuzhou 350108, China},
  author_keywords = {Fuzhou city; Principal component analysis; Remote sensing; Rsei; Urban ecology},
  correspondence_address1 = {Xu, H. Q.; College of Environment and Resources, Fuzhou University, Institute of Remote Sensing Information Engineering, Fuzhou 350108, China},
  document_type = {Article},
  langid = {english},
  source = {Scopus},
  keywords = {China,environmental degradation,Fujian,Fuzhou,land surface,NDVI,principal component analysis,remote sensing,surface temperature,urban area,urban ecosystem}
}

@article{Xu2014,
  title = {Leaf Phenological Characters of Main Tree Species in Urban Forest of {{Shenyang}}},
  author = {Xu, S. and Xu, W. and Chen, W. and He, X. and Huang, Y. and Wen, H.},
  date = {2014},
  journaltitle = {PLoS ONE},
  volume = {9},
  number = {6},
  doi = {10.1371/journal.pone.0099277},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903513570&doi=10.1371%2fjournal.pone.0099277&partnerID=40&md5=b436b020cb54b7630739e3f8afc71c0a},
  abstract = {Background: Plant leaves, as the main photosynthetic organs and the high energy converters among primary producers in terrestrial ecosystems, have attracted significant research attention. Leaf lifespan is an adaptive characteristic formed by plants to obtain the maximum carbon in the long-term adaption process. It determines important functional and structural characteristics exhibited in the environmental adaptation of plants. However, the leaf lifespan and leaf characteristics of urban forests were not studied up to now. Methods: By using statistic, linear regression methods and correlation analysis, leaf phenological characters of main tree species in urban forest of Shenyang were observed for five years to obtain the leafing phenology (including leafing start time, end time, and duration), defoliating phenology (including defoliation start time, end time, and duration), and the leaf lifespan of the main tree species. Moreover, the relationships between temperature and leafing phenology, defoliating phenology, and leaf lifespan were analyzed. Findings: The timing of leafing differed greatly among species. The early leafing species would have relatively early end of leafing; the longer it took to the end of leafing would have a later time of completed leafing. The timing of defoliation among different species varied significantly, the early defoliation species would have relatively longer duration of defoliation. If the mean temperature rise for 1°C in spring, the time of leafing would experience 5 days earlier in spring. If the mean temperature decline for 1°C, the time of defoliation would experience 3 days delay in autumn. Interpretation: There is significant correlation between leaf longevity and the time of leafing and defoliation. According to correlation analysis and regression analysis, there is significant correlation between temperature and leafing and defoliation phenology. Early leafing species would have a longer life span and consequently have advantage on carbon accumulation compared with later defoliation species. © 2014 Xu et al.},
  affiliation = {State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China},
  art_number = {e99277},
  document_type = {Article},
  source = {Scopus},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\xu_et_al_2014_leaf_phenological_characters_of_main_tree_species_in_urban_forest_of_shenyang.pdf}
}

@article{xu2020,
  title = {A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images},
  author = {Xu, Z. and Zhou, Y. and Wang, S. and Wang, L. and Li, F. and Wang, S. and Wang, Z.},
  date = {2020},
  journaltitle = {Remote Sensing},
  volume = {12},
  number = {22},
  pages = {1--18},
  publisher = {{MDPI AG}},
  issn = {20724292},
  doi = {10.3390/rs12223845},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096413935&doi=10.3390%2frs12223845&partnerID=40&md5=44f884e5bcaeac7b803ae336d39a83c7},
  abbrev_source_title = {Remote Sens.},
  abstract = {The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48\%, 4.77\%, and 3.93\%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
  affiliation = {Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; University of Chinese Academy of Sciences, Beijing, 100094, China; The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, 050081, China},
  art_number = {3845},
  author_keywords = {Deep learning; Focal Tversky Loss; High-resolution remote sensing images; HRNet; Phenology; Urban green space classification},
  correspondence_address1 = {Zhou, Y.; Aerospace Information Research Institute, Chinese Academy of SciencesChina; email: zhouyi@aircas.ac.cn},
  document_type = {Article},
  funding_details = {2017YFB0504000, 200266},
  funding_text_1 = {Funding: This research was funded by the National Key R\&D Program of China (Grant number: 2017YFB0504000) and the project “Regional Eco-environment Monitoring System” (Grant number: 200266).},
  langid = {english},
  source = {Scopus},
  keywords = {Classification (of information),Classification accuracy,Classification methods,Deep learning,Ecological environments,Ecology,Forestry,High resolution image,High resolution remote sensing images,High resolution technology,Image classification,Image enhancement,Intelligent classification,Learning systems,Remote sensing,Space optics,Spatial informations,Vegetation}
}

@article{Xu20201,
  title = {A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images},
  author = {Xu, Z. and Zhou, Y. and Wang, S. and Wang, L. and Li, F. and Wang, S. and Wang, Z.},
  date = {2020},
  journaltitle = {Remote Sensing},
  volume = {12},
  number = {22},
  pages = {1--18},
  doi = {10.3390/rs12223845},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096413935&doi=10.3390%2frs12223845&partnerID=40&md5=44f884e5bcaeac7b803ae336d39a83c7},
  abstract = {The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48\%, 4.77\%, and 3.93\%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
  affiliation = {Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; University of Chinese Academy of Sciences, Beijing, 100094, China; The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, 050081, China},
  art_number = {3845},
  author_keywords = {Deep learning; Focal Tversky Loss; High-resolution remote sensing images; HRNet; Phenology; Urban green space classification},
  document_type = {Article},
  source = {Scopus},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\xu_et_al_2020_a_novel_intelligent_classification_method_for_urban_green_space_based_on.pdf}
}

@article{Yan2018230,
  title = {Mapping Vegetation Functional Types in Urban Areas with {{WorldView-2}} Imagery: {{Integrating}} Object-Based Classification with Phenology},
  author = {Yan, J. and Zhou, W. and Han, L. and Qian, Y.},
  date = {2018},
  journaltitle = {Urban Forestry and Urban Greening},
  volume = {31},
  pages = {230--240},
  doi = {10.1016/j.ufug.2018.01.021},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044101548&doi=10.1016%2fj.ufug.2018.01.021&partnerID=40&md5=d9f147c8c98e8c507172d2068505c93b},
  abstract = {Mapping urban vegetation is a prerequisite to accurately understanding landscape patterns and ecological services provided by urban vegetation. However, the uncertainties in fine-scale vegetation biodiversity mapping still exist in capturing vegetation functional types efficiently at fine scale. To facilitate the application of fine-scale vegetation spatial configuration used for urban landscape planning and ecosystem service valuation, we present an approach integrating object-based classification with vegetation phenology for fine-scale vegetation functional type mapping in compact city of Beijing, China. The phenological information derived from two WorldView-2 imagery scenes, acquired on 14 September 2012 and 26 November 2012, was used to aid in the classification of tree functional types and grass. Then we further compared the approach to that of using only one WorldView imagery. We found WorldView-2 imagery can be successfully applied to map functional types of urban vegetation with its high spatial resolution and relatively high spectral resolution. The application of the vegetation phenology into classification greatly improved the overall accuracy of classification from 82.3\% to 91.1\%. In particular, the accuracies of vegetation types was improved by from 10\% to 13.26\%. The approach integrating vegetation phenology with high-resolution remote sensed images provides an efficient tool to incorporate multi-temporal data into fine-scale urban classification. © 2018 Elsevier GmbH},
  affiliation = {State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China},
  author_keywords = {Multi-seasonal images; Urban land cover; Vegetation functional type; Vegetation phenology; WorldView-2 imagery},
  document_type = {Article},
  source = {Scopus}
}

@article{yan2021,
  title = {Exploring {{Google Street View}} with Deep Learning for Crop Type Mapping},
  author = {Yan, Yulin and Ryu, Youngryel},
  date = {2021-01-01},
  journaltitle = {ISPRS Journal of Photogrammetry and Remote Sensing},
  shortjournal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {171},
  pages = {278--296},
  issn = {0924-2716},
  doi = {10.1016/j.isprsjprs.2020.11.022},
  url = {http://www.sciencedirect.com/science/article/pii/S0924271620303294},
  urldate = {2021-01-26},
  abstract = {Ground reference data are an essential prerequisite for supervised crop mapping. The lack of a low-cost and efficient ground referencing method results in pervasively limited reference data and hinders crop classification. In this study, we apply a convolutional neural network (CNN) model to explore the efficacy of automatic ground truthing via Google Street View (GSV) images in two distinct farming regions: Illinois and the Central Valley in California. We demonstrate the feasibility and reliability of our new ground referencing technique by performing pixel-based crop mapping at the state level using the cloud-based Google Earth Engine platform. The mapping results are evaluated using the United States Department of Agriculture (USDA) crop data layer (CDL) products. From~\textasciitilde ~130,000 GSV images, the CNN model identified~\textasciitilde ~9,400 target crop images. These images are well classified into crop types, including alfalfa, almond, corn, cotton, grape, rice, soybean, and pistachio. The overall GSV image classification accuracy is 92\% for the Central Valley and 97\% for Illinois. Subsequently, we shifted the image geographical coordinates 2–3 times in a certain direction to produce 31,829 crop reference points: 17,358 in Illinois, and 14,471 in the Central Valley. Evaluation of the mapping results with CDL products revealed satisfactory coherence. GSV-derived mapping results capture the general pattern of crop type distributions for 2011–2019. The overall agreement between CDL products and our mapping results is indicated by R2 values of 0.44–0.99 for the Central Valley and 0.81–0.98 for Illinois. To show the applicational value of the proposed method in other countries, we further mapped rice paddy (2014–2018) in South Korea which yielded fairly well outcomes (R2~=~0.91). These results indicate that GSV images used with a deep learning model offer an efficient and cost-effective alternative method for ground referencing, in many regions of the world.},
  langid = {english},
  keywords = {Crop type mapping,Deep learning,Google Earth Engine,Google Street View,Ground referencing},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\EL2NIF6Z\\S0924271620303294.html}
}

@inproceedings{yang2009,
  title = {Tree Detection from Aerial Imagery},
  booktitle = {Proceedings of the 17th {{ACM SIGSPATIAL International Conference}} on {{Advances}} in {{Geographic Information Systems}} - {{GIS}} '09},
  author = {Yang, Lin and Wu, Xiaqing and Praun, Emil and Ma, Xiaoxu},
  date = {2009},
  pages = {131},
  publisher = {{ACM Press}},
  location = {{Seattle, Washington}},
  doi = {10.1145/1653771.1653792},
  url = {http://portal.acm.org/citation.cfm?doid=1653771.1653792},
  urldate = {2021-02-06},
  eventtitle = {The 17th {{ACM SIGSPATIAL International Conference}}},
  isbn = {978-1-60558-649-6},
  langid = {english},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\yang_et_al_2009_tree_detection_from_aerial_imagery.pdf}
}

@article{Yang2020,
  title = {Spatiotemporal Patterns of Vegetation Phenology along the Urban–Rural Gradient in {{Coastal Dalian}}, {{China}}},
  author = {Yang, J. and Luo, X. and Jin, C. and Xiao, X. and Xia, J.},
  date = {2020},
  journaltitle = {Urban Forestry and Urban Greening},
  volume = {54},
  doi = {10.1016/j.ufug.2020.126784},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087892199&doi=10.1016%2fj.ufug.2020.126784&partnerID=40&md5=6a249106bd8fb97fc26b0c2d557a706c},
  abstract = {Most studies on vegetation phenology along the urban–rural gradient (URG) have focused on inland cities, with a comparative lack of research on coastal cities despite their different climatic background. We used the normalized difference vegetation index (NDVI), land surface temperature (LST), and land cover data to determine spatiotemporal patterns in vegetation phenology with respect to LST along the URG in China's coastal \hspace{0pt}\hspace{0pt}Dalian sub-province, with a focus on the main city of Dalian and three sub-cities (Pulandian, Wafangdian, and Zhuanghe). Our results were well-correlated with MODIS Land Cover Dynamics Product (MCD12Q2) reference data and matched patterns found in previous studies, indicating that the amplitude method of TIMESAT for obtaining vegetation phenology is practical. Start of growing season (SOS) and end of growing season (EOS) of urban areas were earlier and later than rural areas, respectively. The four urban areas had dissimilar vegetation types and urbanization levels leading to different changes in SOS and EOS along the URG; the average △SOS (the difference in SOS along the URG) and △EOS (the difference in EOS along the URG) of the main and sub-cities were 7.4 and 5.0 d, respectively. Changes in LST along the URG exhibited a non-linear relationship, with the maximum usually appearing 6–8 km from the urban areas. There was a strong linear relationship between vegetation phenology and LST along the URG. The winter–spring and yearly LSTs were negatively correlated with SOS, with both having roughly similar effects. The fall and yearly LSTs had significantly positive correlations with EOS, with the latter having a stronger effect. This study will be helpful for understanding climatic changes arising from urbanization in coastal areas and improving the management and productivity of the ecological environment. © 2020},
  affiliation = {Human Settlements Research Center, Liaoning Normal University, Dalian, 116029, China; Jangho Architecture College, Northeastern University, Shenyang, 110169, China; Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, United States; School of Earth and Planetary Sciences (EPS), Curtin University, Perth, 65630, Australia},
  art_number = {126784},
  author_keywords = {Coastal cities; End of growing season; Start of growing season; Urban heat island},
  document_type = {Article},
  source = {Scopus}
}

@article{yang2021,
  title = {The Woody Plant Diversity and Landscape Pattern of Fine-Resolution Urban Forest along a Distance Gradient from Points of Interest in {{Qingdao}}},
  author = {Yang, J. and Li, X. and Li, S. and Liang, H. and Lu, H.},
  date = {2021},
  journaltitle = {Ecological Indicators},
  volume = {122},
  publisher = {{Elsevier B.V.}},
  issn = {1470160X},
  doi = {10.1016/j.ecolind.2020.107326},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098714168&doi=10.1016%2fj.ecolind.2020.107326&partnerID=40&md5=320b15dfab295da65bf53a82aa1e13cd},
  abstract = {Points of interest (POIs) are closely related to urbanization and human mobilities, and would consequently affect the spatial distribution of urban forest and its species diversity. However, the issue on what the relationships between POIs and urbanization, and how do POIs affect urban forest was rarely mentioned in previous studies. In our research, a total of 82,978 POI records (reclassed to 7 categories of catering and restaurants, commercial areas, educational and institutional areas, public services, residential areas, scenic spots and shopping areas) were collected to analyze their influence on woody plant diversity and urban forest landscape metrics according to a distance gradient using field measurements of diversity and imagery delineated urban forest patches. The results revealed that the urbanization intensity (computed as impervious surface area proportion) was linearly decreased with the increase of distance from POIs except for scenic spots. When it close to POIs, the woody plant species richness, Shannon-Wiener diversity and evenness gradually increased, whereas the Simpson's diversity decreased. Meanwhile, the urban forest patches delineated from fine-resolution images got smaller, less shape complexity and fragmented with mean patch area (AREA\_MN), area-weighted mean shape index (SHAPE\_AM) and area-weighted mean contiguity index (CONTIG\_AM) decreased and patch density (PD) increased. This study can improve our understanding on the diversity, distribution of urban forest and its relationship with human activities, and guide us in urban forest planning and management. © 2020 The Authors},
  langid = {english},
  keywords = {Distance gradients,Field measurement,Fine-resolution images,Forestry,Impervious surface area,Landscape pattern,Plants (botany),Points of interest,Residential areas,Woody plant species}
}

@article{Yuan2020,
  title = {Vegetation Green up under the Influence of Daily Minimum Temperature and Urbanization in the {{Yellow River Basin}}, {{China}}},
  author = {Yuan, M. and Wang, L. and Lin, A. and Liu, Z. and Li, Q. and Qu, S.},
  date = {2020},
  journaltitle = {Ecological Indicators},
  volume = {108},
  doi = {10.1016/j.ecolind.2019.105760},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072574331&doi=10.1016%2fj.ecolind.2019.105760&partnerID=40&md5=116ee0726a40aec5b95e75202f02f4f8},
  abstract = {Climate conditions are the major driving factors of vegetation phenology. However, there is limited effort to monitor dynamics of vegetation phenology and its responses to climate change and urbanization. Using NDVI data from 1982 to 2015, this study investigated the spatiotemporal change of spring green-up date (GUD) across the Yellow River Basin (YRB) and estimated the possible effects of different climatic factors on it. Additionally, the urban-rural differences in GUD and its linkage to spring land surface temperature (LST) for YRB's major cities over 2001–2015 were investigated. The results showed that the GUD significantly advanced at a rate of 0.42 days yr−1 and delayed spatially from southeast to northwest. The interannual variations in regionally averaged GUD were driven mainly by preseason min-temperature (Tmin). Spatially, the effect of preseason Tmin was strongest in the central and western region. The confounding effects of preseason maximum temperature (Tmax) and precipitation jointly affected the GUD, while insolation had a weak impact on GUD. Moreover, the sensitivity of GUD to preseason Tmin and precipitation weakened with an increasing mean annual preseason precipitation gradient, but the sensitivity to preseason Tmax was gradually enhanced. Furthermore, the difference in the GUD between urban and rural areas presented a significant logarithmic relationship with the distance away from the urban center, and it was strongly related to the regional LST. Our findings confirmed the importance of Tmin and urbanization in regulating changes in GUD and further suggested that LST should be considered to develop an improved model of GUD under future climate change and urban development. © 2019 Elsevier Ltd},
  affiliation = {School of Resources and Environmental Sciences, Wuhan University, Wuhan, 430079, China; Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China},
  art_number = {105760},
  author_keywords = {Asymmetric warming; Plant phenology; Urban heat island},
  document_type = {Article},
  source = {Scopus}
}

@article{Zhang2004,
  title = {The Footprint of Urban Climates on Vegetation Phenology},
  author = {Zhang, X. and Friedl, M.A. and Schaaf, C.B. and Strahler, A.H. and Schneider, A.},
  date = {2004},
  journaltitle = {Geophysical Research Letters},
  volume = {31},
  number = {12},
  pages = {L12209 1-4},
  doi = {10.1029/2004GL020137},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-6044238216&doi=10.1029%2f2004GL020137&partnerID=40&md5=c102f0315a0826afa3f96c760b993a97},
  abstract = {Human activity, through changing land use and other activities, is the most fundamental source of environmental change on the Earth. Urbanization and the resultant "urban heat islands" provide a means for evaluating the effect of climate warming on vegetation phenology. Using data from the Moderate Resolution Imaging Spectroradiometer, we analyzed urban-rural differences in vegetation phenological transition dates and land surface temperatures for urban areas larger than 10 km2 in eastern North America. The results show that the effect of urban climates on vegetation phenology decays exponentially with distance from urban areas with substantial influence up to 10 km beyond the edge of urban land cover, and that the ecological "footprint" of urban climates is about 2.4 times that of urban land use in eastern North America. The net effect is an increase in the growing season by about 15 days in urban areas relative to adjacent unaffected rural areas. Copyright 2004 by the American Geophysical Union.},
  affiliation = {Department of Geography, Center for Remote Sensing, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, United States},
  document_type = {Article},
  source = {Scopus},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\zhang_et_al_2004_the_footprint_of_urban_climates_on_vegetation_phenology.pdf}
}

@article{Zhang20041133,
  title = {Climate Controls on Vegetation Phenological Patterns in Northern Mid- and High Latitudes Inferred from {{MODIS}} Data},
  author = {Zhang, X. and Friedl, M.A. and Schaaf, C.B. and Strahler, A.H.},
  date = {2004},
  journaltitle = {Global Change Biology},
  volume = {10},
  number = {7},
  pages = {1133--1145},
  doi = {10.1111/j.1529-8817.2003.00784.x},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-3242667702&doi=10.1111%2fj.1529-8817.2003.00784.x&partnerID=40&md5=a3bce75d3d3dab4df362d5326cf58815},
  abstract = {Recent studies using both field measurements and satellite-derived-vegetation indices have demonstrated that global warming is influencing vegetation growth and phenology. To accurately predict the future response of vegetation to climate variation, a thorough understanding of vegetation phenological cycles and their relationship to temperature and precipitation is required. In this paper, vegetation phenological transition dates identified using data from the moderate-resolution imaging spectroradiometer (MODIS) in 2001 are linked with MODIS land surface temperature (LST) data from the northern hemisphere between 35°N and 70°N. The results show well-defined patterns dependent on latitude, in which vegetation greenup gradually migrates northward starting in March, and dormancy spreads southward from late September. Among natural vegetation land-cover types, the growing-season length for forests is strongly correlated with variation in mean annual LST. For urban areas, the onset of greenup is 4-9 days earlier on average, and the onset of dormancy is about 2-16 days later, relative to adjacent natural vegetation. This difference (especially for urban vs. forests) is apparently related to urban heat island effects that result in both the average spring temperature and the mean annual temperature in urban areas being about 1-3°C higher relative to rural areas. The results also indicate that urban heat island effects on vegetation phenology are stronger in North America than in Europe and Asia. Finally, the onset of forest greenup at continental scales can be effectively described using a thermal time-chilling model, which can be used to infer the delay or advance of greenup onset in relation to climatic warming at global scale. © 2004 Blackwell Publishing Ltd.},
  affiliation = {Department of Geography, Center for Remote Sensing, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, United States},
  author_keywords = {Land surface temperature; MODIS; Phenology model; Urban heat island; Vegetation phenology},
  document_type = {Article},
  source = {Scopus},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\zhang_et_al_2004_climate_controls_on_vegetation_phenological_patterns_in_northern_mid-_and_high.pdf}
}

@article{zhang2019a,
  title = {Estimation of Vegetation Productivity Using a {{Landsat}} 8 Time Series in a Heavily Urbanized Area, Central {{China}}},
  author = {Zhang, M. and Lin, H. and Wang, G. and Sun, H. and Cai, Y.},
  date = {2019},
  journaltitle = {Remote Sensing},
  volume = {11},
  number = {2},
  publisher = {{MDPI AG}},
  issn = {20724292},
  doi = {10.3390/rs11020133},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060738461&doi=10.3390%2frs11020133&partnerID=40&md5=b9d1e250d5c54f5df551f79c1fb3137a},
  abbrev_source_title = {Remote Sens.},
  abstract = {Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie-Ames-Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2.yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas. © 2019 by the authors.},
  affiliation = {Research Center of Forest Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China; Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, 410004, China; Key Lab. of State Forestry Admin. on Forest Resources Management and Monitoring in Southern Area, Changsha, 410004, China; Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, United States},
  art_number = {133},
  author_keywords = {CASA; Fusion model; High spatio-temporal; NDVI; NPP},
  correspondence_address1 = {Zhang, M.; Research Center of Forest Remote Sensing and Information Engineering, Central South University of Forestry and TechnologyChina; email: mengzhang@csu.edu.cn},
  document_type = {Retracted},
  funding_details = {Scientific Research Foundation of Hunan Provincial Education DepartmentScientific Research Foundation of Hunan Provincial Education Department, 17A225},
  funding_text_1 = {Funding: This work was supported by the Twelfth Five-Year Plan Pioneering project of the High Technology Plan of the National Department of Science and Technology (2012AA102001), and the Scientific Research Fund of Hunan Provincial Education Department (17A225): Synergy Simulation of multi-resolution remote sensing data for city vegetation carbon mapping and uncertainty analysis.},
  langid = {english},
  source = {Scopus},
  keywords = {Carbon,Carnegie-ames-stanford approach (CASA),CASA,Errors,Fusion model,Mean square error,Meteorology,NDVI,Remote sensing,Spatial and temporal resolutions,Spatio temporal,Spatio-temporal resolution,Time series,Timeseries Normalized difference vegetation index (NDVI),Uncertainty analysis,Vegetation}
}

@article{Zhang2021,
  title = {Urban Forest Responses to Climate Change: {{A}} Case Study in {{Canberra}}},
  author = {Zhang, B. and Brack, C.L.},
  date = {2021},
  journaltitle = {Urban Forestry and Urban Greening},
  volume = {57},
  doi = {10.1016/j.ufug.2020.126910},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096987920&doi=10.1016%2fj.ufug.2020.126910&partnerID=40&md5=f0939f9b42f6ebe87f05e5cc156a7812},
  abstract = {Climate change is affecting tree growth and vitality, including potentially accelerating growth rate or exacerbating drought stress. Physiological and phenological changes of a tree may eventually disrupt the beneficial effects that it provides to the ecosystem (‘ecosystem services’). This research examined how the urban forest in Canberra has responded to climate change in the last 20 years. A road survey was conducted to document the growth of commonly planted trees across the city, which was then compared with a tree growth model (DISMUT) that reflected growth around the turn of the 20th century. The results suggest that urban trees have various mechanisms whereby they respond to climate change, and these mechanisms are partially based on tolerance to heat and drought, leaf phenology and age. In general, trees with high tolerance, evergreen leaves and young age grow faster and bigger than expected, while drought-intolerant, deciduous and older trees are more likely to exhibit restricted size. Water deficit is one of the main threats to tree vitality in Canberra. Species alleviate water deficit stress by forming dead branches or showing crown dieback. However, these symptoms undermine the ecosystem services that urban trees can provide. Urban forest management should be modified to adapt to the changes in trees, including accelerated growth and severe stress symptoms. DISMUT models can be updated to incorporate the systematic deviations in growth to improve predictions and scenario planning. © 2020 Elsevier GmbH},
  affiliation = {Fenner School of Environment \& Society, Australian National University, Australia},
  art_number = {126910},
  author_keywords = {Climate change; DISMUT; Drought stress; Heat stress; Tree growth},
  document_type = {Article},
  source = {Scopus},
  file = {C\:\\Users\\ahurl\\transfer\\OneDrive\\_literature\\zhang_brack_2021_urban_forest_responses_to_climate_change2.pdf}
}

@article{zhao2016,
  title = {Prevalent Vegetation Growth Enhancement in Urban Environment},
  author = {Zhao, Shuqing and Liu, Shuguang and Zhou, Decheng},
  date = {2016-05-31},
  journaltitle = {Proceedings of the National Academy of Sciences},
  shortjournal = {PNAS},
  volume = {113},
  number = {22},
  eprint = {27185955},
  eprinttype = {pmid},
  pages = {6313--6318},
  publisher = {{National Academy of Sciences}},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1602312113},
  url = {https://www.pnas.org/content/113/22/6313},
  urldate = {2020-03-06},
  abstract = {Urbanization, a dominant global demographic trend, leads to various changes in environments (e.g., atmospheric CO2 increase, urban heat island). Cities experience global change decades ahead of other systems so that they are natural laboratories for studying responses of other nonurban biological ecosystems to future global change. However, the impacts of urbanization on vegetation growth are not well understood. Here, we developed a general conceptual framework for quantifying the impacts of urbanization on vegetation growth and applied it in 32 Chinese cities. Results indicated that vegetation growth, as surrogated by satellite-observed vegetation index, decreased along urban intensity across all cities. At the same time, vegetation growth was enhanced at 85\% of the places along the intensity gradient, and the relative enhancement increased with urban intensity. This growth enhancement offset about 40\% of direct loss of vegetation productivity caused by replacing productive vegetated surfaces with nonproductive impervious surfaces. In light of current and previous field studies, we conclude that vegetation growth enhancement is prevalent in urban settings. Urban environments do provide ideal natural laboratories to observe biological responses to environmental changes that are difficult to mimic in manipulative experiments. However, one should be careful in extrapolating the finding to nonurban environments because urban vegetation is usually intensively managed, and attribution of the responses to diverse driving forces will be challenging but must be pursued.},
  langid = {english},
  keywords = {direct impact,indirect impact,urban–rural gradients,urbanization,vegetation growth},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\XU7T6DEN\\Zhao et al_2016_Prevalent vegetation growth enhancement in urban environment.pdf;C\:\\Users\\ahurl\\Zotero\\storage\\FVMW7L3W\\6313.html}
}

@article{Zhaoling20204137,
  title = {Spatio-Temporal Change of Urban-Rural Vegetation Phenology and Its Response to Land Surface Temperature in {{Northeast China}}},
  author = {Zhaoling, H. and Hui, D. and Fei, H. and Erzhu, L.},
  date = {2020},
  journaltitle = {Shengtai Xuebao/ Acta Ecologica Sinica},
  volume = {40},
  number = {12},
  pages = {4137--4145},
  doi = {10.5846/stxb201812252806},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090099110&doi=10.5846%2fstxb201812252806&partnerID=40&md5=5c722ce8ed579507eef9aac1359c7e81},
  abstract = {Three large cities in Northeast China (Shenyang, Changchun and Harbin) and their surrounding rural areas are taken as the study units. The wavelet transform method is used to reduce the noise data of long time series Moderate-resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) data and reconstructed smooth EVI curves in pixel scale. Based on EVI curves, the key vegetation phenological parameter indexes from 2009 to 2016, namely the start of growing season (SOS) and the end of growing season (EOS) are extracted by using the dynamic threshold method. The spatio-temporal change of vegetation phenology and their responses to surface temperature in each study unit are analyzed. The results show that the differences in the spatial distribution of the SOS and the EOS in each study unit are significant between urban area and rural area. The area where a smaller value of SOS is taken is consistent with the area where a larger value of EOS is taken, and vice versa. The spatial distribution of vegetation phenology indexes and their geographical locations (either urban or rural) are highly consistent. The closer the actual location of each pixel is to its urban center, the smaller the SOS value is and the larger the EOS value is, which indicates an early start and a late end of the growing season, that is, a longer growing season. From 2009 to 2016, the average annual SOS values in these 3 cities form an early center in the urban areas and show a tendency of gradual delay from the urban area to the rural area, whereas the average annual EOS values form a late center in urban areas and show a tendency of gradual advance from the urban area to the niral area. The SOS is between the 99th and the 112nd day in urban areas, while the SOS is between the 125th day and the 135th day in rural areas. The time difference of urban-rural SOS is about 3 weeks. The EOS is between the 293rd and the 300th day in urban areas, while the EOS is between the 284th and the 286th day in rural areas. The urban EOS is approximately 2 weeks later than that in rural areas. The inter-annual change trend of vegetation phenological parameter indexes in each study unit has some similarities, that is, SOS shows an early trend with time, and the trend of SOS inter- A nnual change in urban and rural areas is consistent but with different rale. The maximum of the SOS value during the study period occurred in 2012 in the study areas, which was the coldest year in terms of vegetation phonological indexes and this coincided with the climatic phenomena of extreme weather such as blizzard and low temperature, which were affected by the cold wave in the year. The average annual land surface temperature ( LST) of each study unit is significantly correlated with the corresponding vegetation phenological indexes. There is a significant negative correlation between SOS and LST, and a high positive correlation between EOS and LST. The higher the average temperature of the same vegetation phenological period, the earlier the start time of vegetation growth season, and the later the end time of vegetation growth season. © 2020 Science Press. All rights reserved.},
  affiliation = {School of Geography, Geomatics \& Manning, Jiangsu Normal University, Xuzhou, 221116, China},
  author_keywords = {Land surface temperature; Modis evi; Northeast China; Spatio-temporal change; Vegetation phenology},
  document_type = {Article},
  source = {Scopus}
}

@article{zheng2018a,
  title = {Evaluation of {{RGB}}, {{Color-Infrared}} and {{Multispectral Images Acquired}} from {{Unmanned Aerial Systems}} for the {{Estimation}} of {{Nitrogen Accumulation}} in {{Rice}}},
  author = {Zheng, Hengbiao and Cheng, Tao and Li, Dong and Zhou, Xiang and Yao, Xia and Tian, Yongchao and Cao, Weixing and Zhu, Yan},
  date = {2018-05-25},
  journaltitle = {Remote Sensing},
  shortjournal = {Remote Sensing},
  volume = {10},
  number = {6},
  pages = {824},
  issn = {2072-4292},
  doi = {10.3390/rs10060824},
  url = {http://www.mdpi.com/2072-4292/10/6/824},
  urldate = {2021-07-14},
  abstract = {Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management.},
  langid = {english},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\CMLUC33Q\\Zheng et al. - 2018 - Evaluation of RGB, Color-Infrared and Multispectra.pdf}
}

@article{zipper2016,
  title = {Urban Heat Island Impacts on Plant Phenology: Intra-Urban Variability and Response to Land Cover},
  shorttitle = {Urban Heat Island Impacts on Plant Phenology},
  author = {Zipper, Samuel C. and Schatz, Jason and Singh, Aditya and Kucharik, Christopher J. and Townsend, Philip A. and Loheide, Steven P.},
  date = {2016-05},
  journaltitle = {Environmental Research Letters},
  shortjournal = {Environ. Res. Lett.},
  volume = {11},
  number = {5},
  pages = {054023},
  publisher = {{IOP Publishing}},
  issn = {1748-9326},
  doi = {10.1088/1748-9326/11/5/054023},
  url = {https://doi.org/10.1088%2F1748-9326%2F11%2F5%2F054023},
  urldate = {2020-04-06},
  abstract = {Despite documented intra-urban heterogeneity in the urban heat island (UHI) effect, little is known about spatial or temporal variability in plant response to the UHI. Using an automated temperature sensor network in conjunction with Landsat-derived remotely sensed estimates of start/end of the growing season, we investigate the impacts of the UHI on plant phenology in the city of Madison WI (USA) for the 2012–2014 growing seasons. Median urban growing season length (GSL) estimated from temperature sensors is ∼5 d longer than surrounding rural areas, and UHI impacts on GSL are relatively consistent from year-to-year. Parks within urban areas experience a subdued expression of GSL lengthening resulting from interactions between the UHI and a park cool island effect. Across all growing seasons, impervious cover in the area surrounding each temperature sensor explains {$>$}50\% of observed variability in phenology. Comparisons between long-term estimates of annual mean phenological timing, derived from remote sensing, and temperature-based estimates of individual growing seasons show no relationship at the individual sensor level. The magnitude of disagreement between temperature-based and remotely sensed phenology is a function of impervious and grass cover surrounding the sensor, suggesting that realized GSL is controlled by both local land cover and micrometeorological conditions.},
  langid = {english},
  file = {C\:\\Users\\ahurl\\Zotero\\storage\\4F2HYA7R\\Zipper et al_2016_Urban heat island impacts on plant phenology.pdf}
}

@inproceedings{Zoran2017,
  title = {Urban Green Land Cover Changes and Their Relation to Climatic Variables in an Anthropogenically Impacted Area},
  author = {Zoran, M.A. and Dida, A.I.},
  date = {2017},
  series = {Proceedings of {{SPIE}} - {{The International Society}} for {{Optical Engineering}}},
  volume = {10431},
  doi = {10.1117/12.2278019},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040051310&doi=10.1117%2f12.2278019&partnerID=40&md5=9d166c348e57462aa4a4793be610fdb7},
  abstract = {Urban green areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index timeseries provide a useful way to monitor urban vegetation phenological variations. This study quantitatively describes Normalized Difference Vegetation Index NDVI) /Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) temporal changes for Bucharest metropolitan region land cover in Romania from the perspective of vegetation phenology and its relation with climate changes and extreme climate events. The time series from 2000 to 2016 of the NOAA AVHRR and MODIS Terra/Aqua satellite data were analyzed to extract anomalies. Time series of climatic variables were also analyzed through anomaly detection techniques and the Fourier Transform. Correlations between NDVI/EVI time series and climatic variables were computed. Temperature, rainfall and radiation were significantly correlated with almost all land-cover classes for the harmonic analysis amplitude term. However, vegetation phenology was not correlated with climatic variables for the harmonic analysis phase term suggesting a delay between climatic variations and vegetation response. Training and validation were based on a reference dataset collected from IKONOS high resolution remote sensing data. The mean detection accuracy for period 2000- 2016 was assessed to be of 87\%, with a reasonable balance between change commission errors (19.3\%), change omission errors (24.7\%), and Kappa coefficient of 0.73. This paper demonstrates the potential of moderate - and high resolution, multispectral imagery to map and monitor the evolution of the physical urban green land cover under climate and anthropogenic pressure. © 2017 SPIE.},
  affiliation = {National Institute of R and D for Optoelectronics, MG5 Bucharest -Magurele, 077125, Romania; University Transylvania of Brasov, Faculty of Forest Sciences, Brasov, 500123, Romania},
  art_number = {104310W},
  author_keywords = {Bucharest; climate impacts; LAI; NDVI/EVI; time series anomaly; urban green; vegetation phenology},
  document_type = {Conference Paper},
  source = {Scopus}
}