References：Gong, P.*, Li, X.C., Wang, J.*, Bai, Y., Chen, B., Hu, T.Y., Liu, X.P., Xu, B., Yang, J., Zhang, W., & Zhou, Y.Y. 2020. Annual maps of global artificial impervious areas (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510. doi: 10.1016/j.rse.2019.111510.The original data (Version 2024) is available from Star Cloud Data Service Platform at https://data-starcloud.pcl.ac.cn/resource/13.

The Global Man-made Impervious Surface (GMIS) Dataset From Landsat consists of global estimates of fractional impervious cover derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. The GMIS dataset consists of two components: 1) global percent of impervious cover; and 2) per-pixel associated uncertainty for the global impervious cover. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. The spatial extent covers the entire globe except Antarctica and some small islands. This dataset is one of the first global, 30m datasets of man-made impervious cover to be derived from the GLS data for 2010 and is a companion dataset to the Global Human Built-up And Settlement Extent (HBASE) dataset. The dataset is expected to have a rather broad spectrum of users, from those wishing to examine/study the fine details of urban land cover over the globe at full 30m resolution to global modelers trying to understand the climate/environmental impacts of man-made surfaces at continental to global scales. For example, the data are applicable to local modeling studies of urban impacts on the energy, water, and carbon cycles, as well as analyses at the individual country level.

Version 3 Global Urban Canopy Parameters DatasetThis dataset provides Global Urban Canopy Parameters (GloUCP) at a 1-km resolution for approximately the year 2020, which is derived from the global three-dimensional building footprint (3D-GloBFP) dataset generated by Che et al. (2024). The data is divided into 288 tiles, each stored in a compressed file, covering 15°×15° geographic regions. The corresponding geographic area for each compressed file can be identified from its filename (e.g., GloUCP-X105_119.Y15_29.zip contains data for the region spanning 105°E-120°E and 15°N-30°N, while GloUCP-X-180_-166.Y-60_-46.zip corresponds to the region between 180°W-165°W and 60°S-45°S).Each compressed file includes 255 geogrid binary format files, with each file representing a 1°×1° region. The specific geographic coverage of each file can be determined by consulting the index file and the file names, as explained in the WRF (Weather Research and Forecasting) User Guide.(e.g., 35161-35280.14281-14400 contains data for the region spanning 113°E-114°E and 29°N-30°N).Global 1km Resolution Impervious Surface Fraction DataIn addition, this dataset provides two forms of impervious surface data for the year 2020, both derived from the Global Artificial Impervious Area (GAIA) dataset developed by Gong et al. (2020). The first is the impervious surface fraction data, a continuous variable at 1 km spatial resolution, representing the proportion of impervious surfaces within each grid cell, with values ranging from 0 to 1. The second is the impervious surface mask data, a binary classification derived from the fraction data by applying a threshold of 0.01, where a value of 1 denotes impervious surfaces and 0 denotes non-impervious surfaces. These datasets are provided alongside the GloUCP dataset and can support consistent land cover/use classification in WRF simulations.APP: GloUCP ProcessorTo streamline data retrieval and facilitate dataset download, we developed a dedicated GloUCP Data Application. Users may specify the latitude and longitude bounds of their study area (e.g., 110°E–115°E, 25°N–30°N), based on which the application will automatically identify, retrieve, and merge the corresponding tiles along with the index.References:Che, Y., Li, X., Liu, X.*, Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., and Dai, Y. 2024: 3D-GloBFP: the first global three-dimensional building footprint dataset, Earth System Science Data, 16, 5357–5374. doi: 10.5194/essd-16-5357-2024.Gong, P.*, Li, X.C., Wang, J.*, Bai, Y., Chen, B., Hu, T.Y., Liu, X.P., Xu, B., Yang, J., Zhang, W., & Zhou, Y.Y. 2020. Annual maps of global artificial impervious areas (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510. doi: 10.1016/j.rse.2019.111510.

High-resolution impervious surface area (ISA) mapping is critical for a wide range of applications, including sustainable urban planning, flood risk assessment, and land use monitoring. However, the production of meter ISA products has long relied on commercial very high-resolution (VHR) satellite imagery, which is cost-prohibitive and geographically limited. To this end, we propose a novel joint framework by integrating super-resolution and segmentation, called JointSeg, which enables the generation of 1-meter ISA maps directly from freely available Sentinel-2 imagery (10m resolution). JointSeg was trained on multimodal cross-resolution inputs, offering a scalable and affordable alternative to traditional approaches. This synergistic design enables gradual resolution enhancement from 10m to 1m while preserving fine-grained spatial textures, and ensures high classification fidelity through effective cross-scale feature fusion. This method has been successfully applied to the Yangtze River Economic Belt (YREB), a region characterized by complex urban–rural patterns and diverse topography. As a result, a comprehensive ISA mapping product for 2021, referred to as ISA-1, was generated, covering an area of over 2.2 million square kilometers. Quantitative comparisons against the 10m ESA WorldCover and other benchmark products reveal that ISA-1 achieves an F1-score of 85.71%, outperforming bilinear-interpolation-based segmentation by 9.5%, and surpassing other ISA datasets by 21.43%-61.07%. In densely urbanized areas (e.g., Suzhou, Nanjing), ISA-1 reduces ISA overestimation through improved discrimination of green spaces and water bodies. Conversely, in mountainous regions (e.g., Ganzi, Zhaotong), it identifies significantly more ISA due to its enhanced ability to detect fragmented anthropogenic features such as rural roads and sparse settlements, demonstrating its robustness across diverse landscapes. Moreover, we present biennial ISA maps from 2017 to 2023, capturing spatiotemporal urbanization dynamics across representative cities. The results highlight distinct regional growth patterns: rapid expansion in upstream cities, moderate growth in midstream regions, and saturation in downstream metropolitan areas. These findings underscore the framework’s utility for continuous, high-resolution urban monitoring using only open-access data. In summary, our method establishes a transformative approach to ISA mapping by overcoming the resolution limitations of medium-resolution imagery. This technique opens new possibilities for large-scale, fine-resolution ISA monitoring without the dependency on expensive VHR data, thus supporting global efforts in sustainable development and urban resilience planning.

The Global Human Built-up And Settlement Extent (HBASE) Dataset from Landsat is a global map of HBASE derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. The HBASE dataset consists of two layers: 1) the HBASE mask; and 2) the pixel-wise probability of HBASE. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. The spatial extent covers the entire globe except Antarctica and some small islands. This dataset is one of the first global, 30m datasets of urban extent to be derived from the GLS data for 2010 and is a companion dataset to the Global Man-made Impervious Surface (GMIS) dataset. The HBASE mask was created for post-processing of the GMIS dataset, but can also be utilized by users needing a binary map. The dataset is expected to have a rather broad spectrum of users, from those wishing to examine/study the fine details of urban land cover over the globe at full 30m resolution to global modelers trying to understand the climate/environmental impacts of man-made surfaces at continental to global scales. For example, the data are applicable to local modeling studies of urban impacts on the energy, water, and carbon cycles, as well as analyses at the individual country level.

Based on 30x30 meter Landsat imagery from 2010. Extracted from NASA's Global Man-made Impervious Surface Dataset V1. Null values were removed and the dataset was clipped to the Salish Sea Bioregion boundary from the Salish Sea Atlas. All processing and analysis was completed using the NAD 83 UTM Zone 10N projection and coordinate system.

This dataset includes year-on-year data on urban construction land changes in five countries in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) from 1985 to 2018. The data has a spatial resolution of 30m and a temporal resolution of one year. It is derived from the Global Artificial Impervious Area (GAIA) change data extracted from Landsat images from 1985 to 2018 (Gong Peng et al.). The researchers evaluated 7 sets of data every 5 years from 1985 to 2015. The average overall accuracy is over 90%, and it is the only urban construction land dataset spanning 30 years.

This U.S. Geological Survey (USGS) metadata release consists of 17 different spatial layers in GeoTIFF format. They are: 1) average water capacity (AWC.zip), 2) percent sand (Sand.zip), 3) percent silt (Silt.zip), 4) percent clay (Clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (Snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (Imperv.zip), 14) snow depletion curve numbers (Snow.zip), 15) rooting depth (RootDepth.zip), 16) permeability values (Lithology_exp_Konly_Project.zip), and 17) water bodies. All data cover the National Hydrologic Model's (NHM) version 1.1 domain. The NHM is a modeling infrastructure consisting of three main parts: 1) an underlying geospatial fabric of modeling units (hydrologic response units and stream segments) with an associated parameter database, 2) a model input data archive, and 3) a repository of the physical model simulation code bases (Regan and others, 2014). The NHM has been used for a variety of applications since its initial development.The 250-meter (m) raster data sets for soils are derived from the OpenGeoHub's LandGIS data (Hengl, 2018). The 30-meter raster of land use and land cover data are a simplified re-classification version of the North American Land-Change Monitoring System (NALCMS, Latifovic and others, 2012) data following the guidance in Viger and Leavesley (2007). This layer was used to derive rasters representing dominant vegetative cover type, snow, summer and winter rain interception values, leaf cover and loss, and rooting depth. The impervious data was compiled from the Global Man-made Impervious Surface (GMIS) Dataset from Landsat, v1 (NASA, 2010). The tree canopy data was compiled from MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006, (Carroll and others, 2017). The snow depletion data was compiled from data by Liston and others (2009) and further processed using methods by Sexstone and others (2020). All file formats are in GeoTIFF (Geograhpic Tagged Imaged Format).

The past few decades have witnessed unprecedented global urbanisation, with direct or indirect impacts on global cultural heritage sites. Research on the spatial relationship between cultural heritage sites and urban areas has provided a new perspective for understanding the impact processes between them, which have previously been discussed at the regional scale. In the article related, we analyse the spatial relationship between World Cultural Heritage sites and neighbouring towns through systematic observations at the global scale and attempt to model change processes and identify impact mechanisms. We adopt spatial analysis and spatial statistics to analyse the changing characteristics of the spatial relationship between World Cultural Heritage sites and neighbouring towns from 1990 to 2018 and to analyse the impact processes at different spatial and temporal scales by combining indicators such as income levels and urbanisation rates at national scales. This study provides a basis for development plans and policies in urban design, especially those that are sensitive to cultural heritage, and may also provide ideas and references for heritage conservation against the background of urbanisation. The dataset of distance between heritage sites and neighbouring towns (1990-2018), which is the core of this study, is provided here for reference and use by anyone who requires it. The table relates to cultural heritages that have been outside the urban areas between 1990-2018, a total of 523 items. The data items represent the standard ID, name, country, region, location (latitude, longitude) of the heritages sites, distance between heritages sites and neighbouring towns (in km, 1990, 1995, 2000, 2005, 2010, 2015, 2018), and distance variation between the World Heritage Sites and neighbouring towns from 1990-2018 (in km). The data for distance analysis are heritage attribute data and the Global Urban Boundary (GUB) dataset, which is derived from the artificial impervious surface mapping product (Global Artificial Impervious Area (GAIA)). Please refer to the article https://www.sciencedirect.com/science/article/abs/pii/S0034425719305292. Using the ArcGIS near analysis of proximity toolset, we calculated the distance between the input element and the nearest element in another layer or element class within a specified search radius. The method involves concise steps, rapid calculations and accurate results; the search radius is determined from the data, thus avoiding the problem of blindly determining the search range. The calculation steps are as follows:1. Data inputThe basis for setting the search radius was that experimentally, the farthest distance from a city boundary of all cultural heritage sites worldwide from 1990 to 2018 is 3,918 km, so it is reasonable to choose a search radius of 4,000 km.The basis for choosing the GEODESIC method is because it takes into account the curvature of the spheroid and correctly deals with data near the dateline and poles. The default method PLANAR, on the other hand, calculates only planar distances.2. Data outputOnce the near tool has been run, the output item fields are automatically added to the World Cultural Heritage table.3. Data cleaning and statisticsBy observing and sampling the initial distance data and comparing them with the actual situation, we found that the distance calculation results had extreme values and unreasonable situations caused by the boundary extraction error. Therefore, we observed the changes in images of different years. After individual verification, the least squares fitting of some original data was carried out, a small amount of missing data was supplemented, and the extreme value was reasonably corrected.

This study developed a method to estimate building height for all of China based on the Advanced Land Observing Satellite (ALOS) World 3D-30 m (AW3D30) DSM and other ancillary data including the Global Artificial Impervious Area (GAIA) dataset, the NASADEM dataset and the Global Roads Inventory Project (GRIP) dataset. The proposed method enabled us to accurately estimate building height with a special slope correction algorithm, improving the accuracy of building height estimation. The outcome of our procedure is a map of building height for China at a spatial resolution of 30 m. Compared to field-measured building height data and reference building height data from Baidu map, results indicate that the proposed method performed well (root mean square error (RMSE) of 4.26 m and 4.98 m, respectively). The new building height map of China contributes to the improved management of urban areas and further studies of urban environments.Reference: https://doi.org/10.1016/j.isprsjprs.2022.01.022.

The Geospatial Fabric is a dataset of spatial modeling units for use within the National Hydrologic Model that covers Alaska, and most major river basins that flow in from Canada. This U.S. Geological Survey (USGS) data release consists of the geospatial fabric features and other related datasets created to expand the National Hydrologic Model to Alaska. This U.S. Geological Survey (USGS) child item consists of 17 different spatial layers in GeoTIFF format for Alaska. They are 1) average water capacity (awc.zip), 2) percent sand (sand.zip), 3) percent silt (silt.zip), 4) percent clay (clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (imperv.zip), 14) snow depletion curve numbers (CV_INT.zip), 15) rooting depth (RootDepth.zip), 16) permeability values (Lithology_exp_Konly_Project.zip), and 17) water bodies (wbg.zip). All data cover the National Hydrologic Model's (NHM) Alaskan domain. The 250-meter (m) raster datasets for soils (in sand.zip, silt.zip, clay.zip, TEXT_PRMS.zip) are derived from the Zonodo data (Hengl, 2018). The 30-meter raster of land use and land cover data are a simplified re-classification version of the North American Land-Change Monitoring System (NALCMS, Latifovic and others, 2012) data following the guidance and crosswalk table (crosswalk.csv) in Viger and Leavesley (2007). This layer was used to derive rasters representing dominant vegetative cover type, snow, summer and winter rain interception values, leaf cover and loss, and rooting depth. The impervious data were compiled from the Global Man-made Impervious Surface (GMIS) Dataset from Landsat, v1 (Brown de Colstoun, 2010). The tree canopy data were compiled from MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006, (Sexton and others, 2013). The snow depletion data was compiled from data by Liston (2009) and further processed using methods provided in a snow depletion table (SDC_table.csv) by Sexstone and others (2020). All file formats are in GeoTIFF (Geograhpic Tagged Imaged Format).

The Sample can drive classification algorithms, thus is a prerequisite for accurate classification. Coastal areas are located in the transitional zone between land and sea, requiring more samples to describe diverse land covers. However, there are scarce studies sharing their sample datasets, leading to a repeat of the time-consuming and laborious sampling procedure. To alleviate the problem, we share a sample set with a total of 16,444 sample points derived from a study of mapping mangroves of China. The sample set contains a total of 10 categories, which are described as follows. 1) The mangroves refer to “true mangroves” (excluding the associate mangrove species). In sampling mangroves, we used the data from the China Mangrove Conservation Network (CMCN, http://www.china-mangrove.org/), a non-governmental organization aiming to promote mangrove ecosystems. The CMCN provides an interactive map that can be annotated by volunteers with text or photos to record mangrove status at a location. Although the locations were shifted due to coordinate system differences and positioning errors, mangroves could be found around the mangrove locations depicted by the CMCN’s map on Google Earth images. There is a total of 1887 mangrove samples. 2) The cropland is dominated by paddy rice. We collected a total 1383 points according to its neat arrangement based on Google Earth images. 3) Coastal forests neighboring mangroves are mostly salt-tolerant, such as Cocos nucifera Linn., Hibiscus tiliaceus Linn., and Cerbera manghas Linn. We collected a total 1158 samples according to their distance to the shoreline based on Google Earth images. 4) Terrestrial forests are forests far from the shoreline, and are intolerant to salt. By visual inspection on Google Earth, we sampled 1269 points based on their appearances and distances to the shoreline. 5) For the grass category, we collected 1282 samples by visual judgement on Google Earth. 6) Saltmarsh, dominated by Spartina alterniflora, covering large areas of tidal flats in China. We collected 2065 samples according to Google Earth images. 7) The tidal flats category was represented by 1517 samples, which were sampled using the most recent global tidal flat map for 2014–2016 and were visually corrected. 8) The “sand or rock” category refers to sandy and pebble beaches or rocky coasts exposed to air, which are not habitats of mangroves. We collected 1622 samples on Google Earth based on visual inspection. 9) For the permanent water category, samples were first randomly sampled from a threshold result of NDWI (> 0.2), and then were visually corrected. A total of 2056 samples were obtained. 10) As to the artificial impervious surfaces category, we randomly sampled from a threshold result corresponding to normal difference built-up index (NDBI) (> 0.1), and corrected them based on Google Earth. The artificial impervious surface category was represented by 2205 samples. This sample dataset covers the low-altitude coastal area of five Provinces (Hainan, Guangdong, Fujian, Zhejiang, and Taiwan), one Autonomous region (Guangxi), and two Special Administrative Regions (Macau and Hong Kong) (see “study_area.shp” in the zip for details). It can be used to train models for coastal land cover classification, and to evaluate classification results. In addition to mangroves, it can also be used in identifying tidal flats, mapping salt marsh, extracting water bodies, and other related applications.Compared with the V1 version, we added a validation dataset for mangrove maps (Mangrove map validation dataset.rar), and thus can evaluate mangrove maps under the same dataset, which benefit the comparison of different mangrove maps. The validation dataset contains 10 shp files, in which each shp file contains 600 mangrove samples (cls_new field = 1) and 600 non-mangrove samples (cls_new field = 0).Compared with the V2 version, we added two classes of forest near water and grass near water, in addition to suppress the prevalent misclassified patches due to the spectral similarity between mangroves and those classes.

This U.S. Geological Survey (USGS) metadata record consists of 17 different spatial layers in GeoTIFF format for the Hawaii. They are: 1) average water capacity (awc.zip), 2) percent sand (sand.zip), 3) percent silt (silt.zip), 4) percent clay (clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (imperv.zip), 14) snow depletion curve numbers (CV_INT.zip), 15) rooting depth (RootDepth.zip), 16) permeability values (Lithology_exp_Konly_Project.zip), and 17) water bodies. All data cover the National Hydrologic Model's (NHM) version 1.1 Alaskan domain. The NHM is a modeling infrastructure consisting of three main parts: 1) an underlying geospatial fabric of modeling units (hydrologic response units and stream segments) with an associated parameter database, 2) a model input data archive, and 3) a repository of the physical model simulation code bases (Regan and others, 2014). The NHM has been used for a variety of applications since its initial development.The 250-meter (m) raster data sets for soils are derived from the OpenGeoHub's LandGIS data (Hengl, 2018). The 30-meter raster of land use and land cover data are a simplified re-classification version of the North American Land-Change Monitoring System (NALCMS, Latifovic and others, 2012) data following the guidance and crosswalk table (CrossWalk.xslx) in Viger and Leavesley (2007). This layer was used to derive rasters representing dominant vegetative cover type, snow, summer and winter rain interception values, leaf cover and loss, and rooting depth. The impervious data was compiled from the Global Man-made Impervious Surface (GMIS) Dataset from Landsat, v1 (NASA, 2010). The tree canopy data was compiled from MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006, (Carroll and others, 2017). The snow depletion data was compiled from data by Liston and others (2009) and further processed using methods provided in a snow depletion table (SDC.xslx) by Sexstone and others (2020). All file formats are in GeoTIFF (Geograhpic Tagged Imaged Format).

This U.S. Geological Survey (USGS) metadata record consists of 17 different spatial layers in GeoTIFF format for the Hawaii. They are: 1) average water capacity (awc.zip), 2) percent sand (sand.zip), 3) percent silt (silt.zip), 4) percent clay (clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (imperv.zip), 14) snow depletion curve numbers (CV_INT.zip), 15) rooting depth (RootDepth.zip), 16) permeability values (Lithology_exp_Konly_Project.zip), and 17) water bodies. All data cover the National Hydrologic Model's (NHM) version 1.1 Alaskan domain. The NHM is a modeling infrastructure consisting of three main parts: 1) an underlying geospatial fabric of modeling units (hydrologic response units and stream segments) with an associated parameter database, 2) a model input data archive, and 3) a repository of the physical model simulation code bases (Regan and others, 2014). The NHM has been used for a variety of applications since its initial development.The 250-meter (m) raster data sets for soils are derived from the OpenGeoHub's LandGIS data (Hengl, 2018). The 30-meter raster of land use and land cover data are a simplified re-classification version of the North American Land-Change Monitoring System (NALCMS, Latifovic and others, 2012) data following the guidance and crosswalk table (CrossWalk.xslx) in Viger and Leavesley (2007). This layer was used to derive rasters representing dominant vegetative cover type, snow, summer and winter rain interception values, leaf cover and loss, and rooting depth. The impervious data was compiled from the Global Man-made Impervious Surface (GMIS) Dataset from Landsat, v1 (NASA, 2010). The tree canopy data was compiled from MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006, (Carroll and others, 2017). The snow depletion data was compiled from data by Liston and others (2009) and further processed using methods provided in a snow depletion table (SDC.xslx) by Sexstone and others (2020). All file formats are in GeoTIFF (Geograhpic Tagged Imaged Format).

Trophic interactions play a key role in maintaining ecological balance. In urban environments, avian predation has been demonstrated to be particularly important due to its effects on community structure, pest control, and nutrient cycling. As humanity relies upon ecosystem services for sustenance, and with 70% of the global population projected to reside in urban areas by 2050, understanding the impact of urbanization on avian predation is becoming increasingly important. This study investigates the impacts of urban microclimates, shaped by impervious surfaces and green/blue infrastructure, and human-induced disturbances, on avian predation in urban areas, with the aim of testing the increased disturbance hypothesis. To assess the avian predation rate, plasticine caterpillars were placed in Quercus robur trees in the city of Amsterdam for a period of two months. The analyses evaluated the impact of artificial lighting at night, human population density, the urban heat island effect, impervious surfaces, vegetation, noise pollution, and water bodies on predation rates. The results indicated a substantial increase in predation during the second month, which was likely caused by an increase in naïve fledglings or elevated ambient temperatures. Noise pollution was identified as the most frequent and robust predictor of predation, consistently leading to a reduction in predation rates, possibly due to avoidance behavior. Other predictors exhibited substantial temporal and spatial variability. The variables related to urbanization increased predation in the initial month, suggesting that insectivorous birds prey on areas with higher illumination and temperature. However, the effect diminished in the subsequent month, potentially due to the increased daylight hours or a reduction in heating effects. During the second month, all predictors exhibited a negative effect on predation, thereby supporting the increasing disturbance hypothesis. These findings underscore the complex relationship between urban factors and avian predation, emphasizing the necessity for mitigation efforts in urban planning. Methods Study Site The city of Amsterdam (The Netherlands) was selected as the study site for this experiment due to several factors. The city has a high human population density with 5.336 people per km2 (CBS 2023), a significant vegetation presence, numerous water canals, and a negligible elevation difference throughout the city that might affect trophic interactions (Dean et al. 2024). In consideration of the findings presented by Hernández-Agüero et al. (2020), which demonstrated predation differences between tree species, this study exclusively utilized Quercus robur trees. The trees were selected from a georeferenced list of all trees in Amsterdam. The selection criteria included species and a maximum height of twelve meters, which was necessary for us to reach the branches. A total of 2,882 Q. robur trees were identified as eligible from a list of 259,431 trees in Amsterdam. The human population density, NDVI index and water percentage surrounding each tree were estimated, and the resulting ranges were divided into five categories. Only one tree per category of each was randomly selected. Ultimately, 38 trees were chosen based on variations in HPD, vegetation, and water presence (Figure 1). Avian Predation Data Collection The field experiment involved the placement of artificial plasticine caterpillars (N = 114) of three distinct colors as a proxy for prey organisms in Q. robur trees (N = 38) throughout Amsterdam. The coloration of prey organisms can influence the detection and selection of prey by avian species, as these animals primarily detect prey through visual cues (Ruxton et al. 2018). By using three different colors in our experiment, we ensured sufficient variability in predation pressure among study sites, at least for one color, independent of the time interval between tree visits, as is common in similar studies (e.g. Alonso-Crespo & Hernández-Agüero 2023, Hernández-Agüero et al 2024b). This approach permitted the comparison of predation levels between trees, even in instances where predation for a particular color was either nearly absent or so high as to preclude the observation of differences in the number of attacks. All colors demonstrated sufficient variability among trees, and thus no color was excluded from the analysis. The models were placed in week 15 (2024), with two reviews occurring at four-week intervals. This was done to account for potential temporal variability, with avian predation rates being highest in the summer months, thereby strengthening the potential for seasonal variability (Hernández-Agüero et al. 2020). During the reviews, we identified attack marks at the coarse taxonomic level with the assistance of the standardization proposed by Low et al. (2014). First, the avian predation marks were recorded, and the caterpillars were molded back to their original shape. The methodology employed was similar to that used by Alonso-Crespo and Hernández-Agüero (2023) (see Appendix Figure A1), with the caterpillars attached to tree branches with metal wires (diameter 0.5 mm). The average length of each caterpillar was approximately 30mm, with a diameter of 4mm. Given that invertebrate predation rates are higher when placed near the ground (Lövei and Ferrante 2017), and our objective was to observe avian predation, the caterpillars were placed at heights ranging between 1.5 and 2 meters. The plasticine caterpillar models were non-toxic and unscented, consisting of a mixture of waxes, inert substances, and colored pigments (STAEDTLER MARS GmbH & Co KG 2017). This material addresses the concerns raised by of Rößler et al. (2018) regarding the avoidance of polyvinyl chloride due to potential ingestion hazards. All models were molded exclusively by hand, resulting in slight differences between the models. However, this is the most reasonable method for creating caterpillar-like shapes with plasticine (Bateman et al. 2017). Spatial Analyses The averages of the urbanization-related predictors, vegetation presence, and water presence were calculated through zonal statistics in QGIS version 3.34.3 (QGIS 2023). This was conducted for buffers of 200 meters surrounding the trees where measurements were taken, in accordance with the methodology described by Valdés-Correcher et al. (2022). In addition to the 200-meter buffers, larger buffer zones of 400, 600, 800, and 1,000 meters were constructed in order to account for the dynamic nature of avian movement, and to examine the effects of these predictors across spatial scales. We requested satellite imagery from the Sentinel-II satellite through Copernicus, and the European Space Agency (ESA 2023) processed all satellite imagery. The MultiSpectral Instrument (MSI), comprising 13 spectral bands, and the high spatial resolution of Sentinel-II (10m/20m/60m) facilitate precise remote sensing analyses. The selection of imagery was based on several criteria to ensure its usability for reliable remote sensing. These criteria included the necessity for the imagery to originate from the same satellite, for the sensing periods to fall as close to the measurements as possible, and for cloud coverage to be limited to below 5%. These criteria adhere to the guidelines for remote sensing set forth by Lefsky and Cohen (2003) and Rembold et al. (2020). As this study incorporated remote sensing analyses for both vegetation and water infrastructures through the Normalized Difference Vegetation Index/NDVI (see Appendix Equation A1) and the Normalized Difference Water Index/NDWI (see Appendix Equation A2), and given that it was necessary to isolate vegetation and water areas to prevent misguided averages in their respective analyses, a remote sensing analysis on impervious surfaces through the Normalized Difference Built-up Index/NDBI (see Appendix Equation A3) was also conducted. The isolation of water, impervious surfaces, and vegetation was achieved by identifying the predictors and reclassifying them. Subsequently, inversions of the predictors were employed to isolate the respective predictors. Moreover, we acquired population data for Amsterdam from WorldPop (2018) to assess HPD (people/hectare), with a resolution of 100 x 100 meters. For ALAN, we extracted the modelled light emissions at night in 2022 (Watt/cm2/steradian) provided by the National Oceanic and Atmospheric Administration (NOAA) using the Visible Infrared Imager Radiometer Suite (VIIRS). This model was subsequently modified by the National Institute for Public Health and the Environment (RIVM in Dutch; RIVM 2023). We acquired noise data from RIVM (2020), which provides estimated noise pollution/Lden (level day-evening-night) in 2021 in dB. Anthropogenic noise pollution is defined as artificial noise originating from air traffic, industry, neighborhoods, rail traffic, and road traffic (Radford et al. 2012). The model encompassed all widely accepted primary sources of noise pollution, with the exception of noise from neighborhoods. Data Analyses We assessed the associations between standardized averages of the outlined predictors and the avian predation rates recorded with the plasticine caterpillars in R environment version 4.2.2 (R Core Team 2022), including packages ‘car’ (Fox and Weisberg 2019), ‘ggplot2’ (Wickham 2016), ‘Hmisc’ (Harrell 2024), ‘lme4’ (Bates et al. 2015), ‘MuMIn’ (Barton 2021), and ‘visreg’ (Breheny and Burchett 2017). A correlation analysis was conducted between variables within the 600-meter buffer zone, as this is the buffer zone of intermediate size. An additional check for multicollinearity was conducted using the Variance Inflation Factor to confirm the efficacy of the multicollinearity correction (see Appendix Table A2). As HPD, impervious surface, UHI effect, ALAN and NDVI exhibited high correlation coefficients (above ± 0.8; Table A1 in

The Share of Built-up (SBU) layer for the reference year 2018 represents share (percentage) of built-up (IBU) for the reference year 2018 in an aggregated version of 100m spatial resolution for the EEA38 countries and the United Kingdom. The production of the high resolution imperviousness layers is coordinated by the EEA in the frame of the EU Copernicus programme.

The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product.

The 100 meter aggregate raster (fully conformant with the EEA reference grid) is provided as a full EEA38 and United Kingdom mosaic.

The data used to characterize inorganic pollution come from the global cumulative impacts assessment on habitats and available on the NCEAS online data repository.

Inorganic pollution was modeled using impervious surface area (i.e. artificial surfaces such as paved roads) under the assumption that most of this pollution source comes from urban runoff. Inorganic pollution originating from point-sources or in areas lacking paved roads is therefore not captured by this layer. The data obtained was aggregated at the watershed scale.

It is possible to consult the scientific report of the eDrivers project: Characterizing Exposure to and Sharing Knowledge of Drivers of Environmental Change in the St. Lawrence System in Canada and the additional data. It is possible to consult the application eDrivers.

REFERENCE:

• Halpern, B., Frazier, M., Potapenko, J. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat Commun 6, 7615 (2015). https://doi.org/10.1038/ncomms8615