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20 results for “impervious surface”
Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Impervious surface and race in Los Angeles County, 2019
This dataset was used to analyze differences in impervious surface levels by race while also accounting for other sociodemographic factors and land use in Los Angeles County. Data was downloaded from the websites of the U.S. Geological Survey, U.S. Census Bureau, and Southern California Association of Governments. Due to redlining and disinvestment in neighborhoods of color, there are generally fewer green spaces and greater urban heat island effects in neighborhoods of color throughout the U.S., as documented in the environmental justice literature. Replacing impervious surfaces like concrete and asphalt with green stormwater infrastructure can help increase green space and reduce urban heat while also meeting local stormwater goals. This research supports existing findings and ties it to the local stormwater context in Los Angeles County. Based on the results of this research, local decision-makers should consider racial demographics when prioritizing where to remove impervious surface and replacing with green stormwater infrastructure.
Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer
As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land-cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10, 30, 60 and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (about 7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0 to 100%, each) in a mid-sized city in the Upper Midwest, USA. Variability in daytime air temperature within the urban landscape averaged 3.5 degreeC (range 1.1 to 5.7 degreeC). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale, and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1C (range 1.2 to 3.0 degreeC), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.
Concrete habitat: Impervious surface in nest vicinity is associated with avian fitness decline in two urban adapters
<p>The conversion of natural habitats to impervious surfaces in cities affects biotic and abiotic attributes of urban ecosystems. Detailed information on the gradual influence of impervious surfaces on reproductive success, however, is lacking. Using five years of nestbox-breeding great tit and blue tit breeding data collected across various habitat types within and outside a Central-Eastern European capital city, we quantified the impact of impervious surfaces on avian reproductive success. Impervious surfaces strongly and negatively covaried with the number of fledged young in both species: a 50% increase in impervious surface resulted in 2.93 (95% CI: -4.27; -1.58) fewer blue tit offspring fledging the nest, and 3.51 (95% CI: -4.79; -2.23) fewer great tit offspring fledging the nest, thus halving the reproductive output of two widespread urban species. These results provide benchmark values of avian productivity for ecologists and urban policy makers, and for the management of urban areas.</p>
Improving 30-meter global impervious surface area (GISA) mapping: New method and dataset
<p>Timely and accurate monitoring of impervious surface areas (ISA) is crucial for effective urban planning and sustainable development. Recent advances in remote sensing technologies have enabled global ISA mapping at fine spatial resolution (<30 m) over long time spans (>30 years), offering the opportunity to track global ISA dynamics. However, existing 30 m global long-term ISA datasets suffer from omission and commission issues, affecting their accuracy in practical applications. To address these challenges, we proposed a novel global longterm ISA mapping method and generated a new 30 m global ISA dataset from 1985 to 2021, namely GISA-new. Specifically, to reduce ISA omissions, a multi-temporal Continuous Change Detection and Classification (CCDC) algorithm that accounts for newly added ISA regions (NA-CCDC) was proposed to enhance the diversity and representativeness of the training samples. Meanwhile, a multi-scale iterative (MIA) method was proposed to automatically remove global commissions of various sizes and types. Finally, we collected two independent test datasets with over 100,000 test samples globally for accuracy assessment. Results showed that GISA-new out performed other existing global ISA datasets, such as GISA, WSF-evo, GAIA, and GAUD, achieving the highest overall accuracy (93.12 %), the lowest omission errors (10.50 %), and the lowest commission errors (3.52 %). Furthermore, the spatial distribution of global ISA omissions and commissions was analyzed, revealing more mapping uncertainties in the Northern Hemisphere. In general, the proposed method in this study effectively addressed global ISA omissions and removed commissions at different scales. The generated high-quality GISAnew can serve as a fundamental parameter for a more comprehensive understanding of global urbanization.</p>
Mapping 10-m global impervious surface area (GISA-10m) using multi-source geospatial data
<p>Artificial impervious surface area (ISA) documents human footprints. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA mapping at 10-m resolution is still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and the crowdsourcing OpenStreetMap (OSM), 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. Combined with over 2.7 million Sentinel optical and radar images on the Google Earth Engine, we produced the 10 m global ISA dataset (GISA-10m). Based on the test samples that are independent to the training set, GISA-10m embraced an overall accuracy greater than 86%. In addition, the GISA-10m was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was demonstrated. </p>
Concrete habitat: Impervious surface and avian fitness decline in two urban adapters
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Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform
<p>An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.</p>
urban impervious surface area (UISA) data in Shanghai in 2015 and 2018
<p>This data cite from Kuang, W., Zhang, S., Li, X., and Lu, D. (2000–2018). A 30 M Resolution Dataset of China’s Urban Impervious Surface Area and green Space, 2000-2018. Earth Syst. Sci. Data 13, 63–82. doi:10.5194/essd-13-63-2021</p>
Impervious surface cover and number of restaurants shape diet variation in an urban carnivore
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The formation process, mechanism, and attribution of urban impervious surface thermal runoff
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GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
<p>A novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform.</p>
Data from: Herbivory on the pedunculate oak along an urbanization gradient in Europe: effects of impervious surface, local tree cover and insect feeding guild
<p><span>Urbanization is recognized as an important driver of the diversity and abundance of tree associated insect herbivores, but its consequences for insect herbivory are controversial. A likely source of variability among studies is the insufficient consideration of intra-urban variability in forest cover. W</span>ith the help of citizen scientists, we investigated the independent and interactive effect of urbanization and local canopy cover on insect herbivory in the pedunculate oak (<em>Quercus robur</em>) throughout most of its geographic range in Europe. We found that the damage caused by chewing insect herbivores as well as the incidence of leaf-mining and gall-inducing herbivores consistently decreased with increasing urbanization around focal oaks. Herbivory by chewing herbivores increased with increasing forest cover, regardless of urbanization. In contrast, an increase in local canopy cover buffered the negative effect of urbanization on leaf-miners and strengthened its effect on gall-inducers. These results show the complexity of plant-herbivore interactions in urbanized areas, highlighting that the presence of local canopy cover within cities has the potential to attenuate or modify the effect of urbanization on biotic interactions.</p>
Data from: Herbivory on the pedunculate oak along an urbanization gradient in Europe: effects of impervious surface, local tree cover and insect feeding guild
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Spectrally unmixed percent of impervious surface, soil, and vegetation cover in central Arizona-Phoenix, year 2000
Urban land covers (e.g., cement parking lots, asphalt roads, shingle rooftops, grass, tress, exposed soil) can only be recorded as either present or absent in each pixel when using traditional per-pixel classifiers. Sub-pixel analysis approaches that can provide the relative fraction of surface covers within a pixel may be a potential solution to effectively identifying urban impervious areas. Spectral mixture analysis approach is probably the most commonly used approach that models image spectra as spatial average of spectral signatures from two or more surface features. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA) approach addresses these issues by allowing endmembers to vary on a per pixel basis. The MESMA technique was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Field spectra of vegetation, soil, and impervious surface areas collected with the use of a fine resolution Quickbird image and pixel purity index tool in ENVI software were modeled as reference endmembers in addition to photometric shade that was incorporated in every model. This study employs thirty endmembers and six hundred and sixty spectral models to identify soil, impervious, vegetation, and shade in the Phoenix metropolitan area. The mean RMS error for the selected land use land cover classes range from 0.003 to 0.018. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.7052, 0.7249, and 0.8184 respectively.
Urban impervious surface expansion intensifies heavy rainfall in Beijing's rainy season
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Global Man-made Impervious Surface (GMIS) Dataset From Landsat
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.
California HOLC grades with NDVI, impervious surface, and iNaturalist data (2017-2022)
<p>HOLC grade maps linked to iNaturalist data across six taxonomic clades from (2017-2022)</p>
The impact of impervious surface expansion in Indonesia from 1992 to 2020 on terrestrial vertebrates
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