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13 results for “urban land cover”
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Urban-Rural Temperature Data-relation between land-cover and the Urban Heat Island in San Juan, Puerto Rico
Our objective in this study is to quantify the UHI created by the San Juan Metropolitan Area over space and time using temperature data collected by mobile and fixed-station measurements. We used the fixed-station measurements to examine the relation between average temperature at a given location and the density of vegetation located upwind. We then regressed temperatures against regional land-cover to predict future temperature with projected land-cover change. Our data show the existence of a nocturnal UHI, with average nighttime urban-rural temperature differences (ΔTU-R) of up to 3.02°C. Each of the stations listed in this excel file were used to calculate the urban heat island created by the San Juan Metropolitan Area. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Land cover, landscape metrics and typology of European cities for Urban Forest Ecosystem Services (UFES) evaluation
<p>The data refers to the paper "<em>Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities</em>"</p> <p>The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them.</p> <p>The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.</p> <p> </p> <p>The datasets are a csv file (<code>Metrics.csv</code>) and a shapefile (<code>UFES.shp</code>) of polygons with attributes.</p> <ul> <li> <p><code>UFES.shp</code> attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA.</p> </li> <li> <p><code>Metrics.csv</code> contains the FUA codes, the land cover and landscape metrics used in the HCA.</p> </li> </ul> <p> </p>
Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019
<p>A CCDC algorithm was used to produce 13 sets of geographical maps. Each set represents one capital city of Saudi Arabia: Riyadh (which also serves as the country’s capital), Buridah, Ha’il, Dammam, Makkah, Madinah, Arar, Skakah Tabuk, Albaha, Abha, Jazan, and Najran. Each of the 13 datasets contains 35 annual maps from 1985 to 2019. The file format of these datasets is the .hdr file. The dataset is free to download. </p> <p>In the case of using the urban land cover maps, please cites the website and the paper:</p> <ol> <li>Aljaddani AH, Song X-P, Zhu Z. Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series. <em>Remote Sensing</em>. 2022; 14(10):2382. https://doi.org/10.3390/rs14102382</li> <li>Aljaddani, Amal H, Song, Xiao-Peng, & Zhu, Zhe. (2022). Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019 (Version: 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6210073</li> </ol> <p> </p> <p> </p>
Exploring the Relationship Between Land Cover Classifications and Urban Heat Island Intensity
<p>This dataset is a collection of data and results from a research project conducted by NASA SEES Interns. The research project aimed to study urban heat islands and their relationship with land cover observations. This dataset upload consists of 12 files. One file is a poster pdf that includes all the information needed about the project. The other 11 files are png images of heatmaps, bar graphs, scatter plots, and tables used in the analysis of our project. For quick reference, the abstract to this project is below:</p> <p><strong>The urban heat island (UHI) effect refers to the phenomenon in which urban areas experience higher temperatures compared to their rural counterparts. This research aims to quantify and examine the UHI effect within three areas of interest (AOIs) by utilizing LANDSAT imagery. In addition, this study seeks to explore the relationship between land cover classifications, which represent the most green (rural) and the most urban areas, and the intensity of the UHI effect. To achieve this, temperature data from local weather stations are analyzed, and statistical methods are employed to determine whether a correlation exists between the difference in land cover classifications and the intensity of the UHI effect, as determined by the average temperature difference between urban and rural areas. Google Earth Engine is used to visualize LANDSAT data from 2013 to 2022 in the months of July and August for each AOI. Subsequently, the data is compared with the land cover classifications from Collect Earth Online using statistical models in Microsoft Excel. These tools were used to take data from three pre-selected areas of interest in GLOBE Observer. The data findings from this analysis suggest that the more tree cover and rural an area is according to our classification method, the lower the UHI intensity. On the other hand, the higher the urban area, the higher the UHI intensity. By beginning this research, we have reinforced the validity of land cover classifications, and we now have the capability to generally predict the UHI intensity of locations based on their classifications. Overall, this investigation aims to contribute to a better understanding of the GLOBE land cover classifications and their potential indications of UHI intensity.</strong></p>
Urban scavenging: Vertebrates display greater sensitivity to land-cover and garden vegetation cover than invertebrates
<p>1. Scavenging removes carrion or littered food waste from the environment, providing ecosystem services including nutrient cycling, reduced pathogen spread, and reduced waste management costs. These services are particularly important in urban environments, where high human population densities result in increased littered food waste. It is unclear how the magnitude of scavenging across urban-rural gradients is influenced by agent (vertebrates and invertebrates), land-cover type and patch size.</p> <p>2. We investigated scavenging provision by vertebrates and invertebrates across a gradient of urbanisation in 37 woodlands and 35 domestic gardens across Liverpool, UK. Sites were selected using random stratification across a gradient of urbanisation based on impervious surface cover. At each site, four different bait types were deployed either within vertebrate exclusion cages or exposed to vertebrates and invertebrates. The percentage dry weight loss of bait after 48 hours was used to quantify scavenging provision.</p> <p>3. Data were analysed using general linear mixed effects models (Gaussian error distribution) and a full model approach to assess 1) the relative contributions of vertebrates and invertebrates across an urban-rural gradient; 2) variation in scavenging between woodlands and gardens; 3) the effects of semi-natural vegetation cover on scavenging provision. We also consider patch size as a preliminary assessment of how fragmentation influences scavenging.</p> <p>4. Vertebrates contributed substantially more to scavenging provision than invertebrates across the urbanisation gradient. Vertebrate scavenging was substantially greater in woodlands than gardens, invertebrate scavenging was similar in both land-cover types. Scavenging increased with patch size in gardens, but not woodlands. Vertebrate scavenging increased with patch size, while invertebrate scavenging decreased. Within gardens, vertebrate scavenging increased with semi-natural vegetation cover.</p> <p>5. Scavenging-mediated ecosystem services are important in urban areas where food littering is frequent, and efforts should be made to facilitate these services. Urban woodlands and gardens both make important contributions to scavenging-mediated ecosystem services in urban areas, but woodlands' contributions are much greater. There is a need to increase the cover of semi-natural vegetation in gardens to increase their contributions, and protect and expand woodlands, especially in areas with a high demand for ecosystem services reliant on scavenging.</p>
Mitigating urban heat island through neighboring rural land cover: Dataset
<p>A dataset of the manuscript "Mitigating urban heat island through neighboring rural land cover". Includes: Land surface temperature acquisition code (Google Earth Engine) - <strong><em>Average_LST_GEE.sh</em></strong>; Codes for calculating urban development intensity - <em><strong>UrbanDevelopmentIntensity.py</strong></em>; Regression, Machine Learning, Interpretable Machine Learning Code - <em><strong>All</strong><strong><em>R</em>egression.py, SHAP.py, ALE.py</strong></em>; a zip file containing the data used in the calculations - <em><strong>OperationalData</strong></em>.<em><strong>rar.</strong></em></p>
Essential Urban Land Cover Category across the Conterminous United States
<p>This dataset, titled "Essential Urban Land Cover Category across the Conterminous United States", contains high-resolution urban land cover maps for urban areas across the Conterminous United States produced in the manuscript "Large-scale High-resolution Essential Urban Land Cover Category (EULCC) Mapping Using a Semantic-Augmented and Noise-Tolerant Approach". The land cover maps are organized by city, with each city’s data stored as a separate <span>TIFF </span>file. These city-level files are grouped by state, and the dataset provided here is the compressed files of the state-level folders.</p> <p>The raster data values range from 1 to 9 in the land cover maps, representing different essential urban land cover categories as follows:</p> <table> <tbody> <tr> <td>Values</td> <td>Classes</td> </tr> <tr> <td>1</td> <td>Building</td> </tr> <tr> <td>2</td> <td>Tree</td> </tr> <tr> <td>3</td> <td>Grass/Shrub</td> </tr> <tr> <td>4</td> <td>Parking lot</td> </tr> <tr> <td>5</td> <td>Road</td> </tr> <tr> <td>6</td> <td>Water</td> </tr> <tr> <td>7</td> <td>Barren</td> </tr> <tr> <td>8</td> <td>Agriculture</td> </tr> <tr> <td>9</td> <td>Others</td> </tr> </tbody> </table> <p> </p>
Urban scavenging: Vertebrates display greater sensitivity to land-cover and garden vegetation cover than invertebrates
Open the record for dataset details and reuse information.
Evaluating water and energy fluxes across three distinct land cover types in a desert urban environment
Urbanization impacts surface energy and water balances across multiple spatial and temporal scales, which can be particularly important in desert cities where resources are limited. Urban climate observations are limited, especially over a variety of locations that represent urban land cover. To help address the lack of observations over different urban land cover types, a mobile eddy covariance tower (ECT) was deployed at three different locations in the Phoenix metropolitan area, representing a xeric landscape (drip irrigated palo verde trees with gravel), a parking lot, and a mesic landscape (sprinkler irrigated turf grass). In this project, data obtained from the mobile ECT deployments will be coupled with data from an eddy covariance tower managed by CAP LTER in the Maryvale suburb of Phoenix, Arizona. Data is processed to obtain energy and water fluxes, which are controlled by land surface characteristics, over the four distinct land cover types.
Annual Land Use and Urban Land Cover: Ethiopia, Nigeria, and South Africa, 2016-2020
This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format.
Urban Land Cover Maps for Mekelle, Ethiopia and Polokwane, South Africa, 2020
This dataset consists of very high resolution urban land cover maps for two African cities, Mekelle, Ethiopia and Polokwane, South Africa for 2020. Maps were generated from Planet SuperDove satellite imagery at 3.125-m spatial resolution, and Worldview-3 satellite imagery (Maxar Techologies) at two spatial resolutions, 2 m for multispectral imagery and 0.5-m spatial resolution for pansharpened imagery. An object-based image classification approach was used to produce a multi-class land cover product for each image source. The aim of this work was to support fine scale urban land cover analyses and comparative assessments between different high resolution satellite imagery sources. The data are provided in shapefile format.
Essential Urban Land Cover Category Dataset - Samples
<p>This dataset, titled "Essential Urban Land Cover Category Dataset - Samples", contains sample data for five representative cities produced in the manuscript "Large-scale High-resolution Essential Urban Land Cover Category (EULCC) Mapping Using a Semantic-Augmented and Noise-Tolerant Approach". The raster data values range from 1 to 9, representing different essential urban land cover categories as follows:</p> <table> <tbody> <tr> <td>Values</td> <td>Classes</td> </tr> <tr> <td>1</td> <td>Building</td> </tr> <tr> <td>2</td> <td>Tree</td> </tr> <tr> <td>3</td> <td>Grass/Shrub</td> </tr> <tr> <td>4</td> <td>Parking lot</td> </tr> <tr> <td>5</td> <td>Road</td> </tr> <tr> <td>6</td> <td>Water</td> </tr> <tr> <td>7</td> <td>Barren</td> </tr> <tr> <td>8</td> <td>Agriculture</td> </tr> <tr> <td>9</td> <td>Others</td> </tr> </tbody> </table> <p> </p>
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