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10 results for “LULC”
Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020
## overview The project extends the long-term, LULC datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six land-use/land-cover (LULC) maps at 30 m resolution were previously created from 1985 to 2010 at five-year intervals (Zhang and Li 2017). This project updates that suite with maps for 2015 and 2020. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The maps comprise 11 land-use/land-cover classes with an overall accuracy of 89.1% for 2015 and 89.6% for 2020. ## literature cited - Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).
TimeSpec4LULC: A Smart-Global Dataset of Multi-Spectral Time Series of MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to perform LULC Mapping
<p>TimeSpec4LULC is a smart open-source global dataset of multi-spectral time series for 29 Land Use and Land Cover (LULC) classes ready to train machine learning models. It was built based on the seven spectral bands of the MODIS sensors at 500 m resolution from 2000 to 2021 (262 observations in each time series). Then, was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE).</p> <p>TimeSpec4LULC contains two datasets: the original dataset distributed over 6,076,531 pixels, and the balanced subset of the original dataset distributed over 29000 pixels.</p> <p>The original dataset contains 30 folders, namely "Metadata", and 29 folders corresponding to the 29 LULC classes. The folder "Metadata" holds 29 different CSV files describing the metadata of the 29 LULC classes. The remaining 29 folders contain the time series data for the 29 LULC classes. Each folder holds 262 CSV files corresponding to the 262 months. Inside each CSV file, we provide the seven values of the spectral bands as well as the coordinates for all the LULC class-related pixels.</p> <p>The balanced subset of the original dataset contains the metadata and the time series data for 1000 pixels per class representative of the globe. It holds 29 different JSON files following the names of the 29 LULC classes.</p> <p>The features of the dataset are:</p> <p>- ".geo": the geometry and coordinates (longitude and latitude) of the pixel center.</p> <p>- "ADM0_Code": the GAUL country code.</p> <p>- "ADM1_Code": the GAUL first-level administrative unit code.</p> <p>- GHM_Index": the average of the global human modification index.</p> <p>- "Products_Agreement_Percentage": the agreement percentage over the 15 global LULC products available in GEE.</p> <p>- "Temporal_Availability_Percentage": the percentage of non-missing values in each band.</p> <p>- "Pixel_TS": the time series values of the seven spectral bands.</p>
Maps and Validation result for LULC map of Pakyong sub-division, East Sikkim
<p>The repository contains the maps and images pertaining to the study area and the validation results.</p>
the state vectors and transition matrixes of the LULC classification in Nanjing for modified Sankey chart
<p><strong>The original data of the state vectors and transition matrixes of the LULC classification in Nanjing used to create the modified Sankey charts.</strong></p>
LULC input and CLM5 carbon output "Chemistry-albedo feedbacks offset up to a third of forestation's CO2 removal benefits."
<p>Land use land cover (LULC) files for the CESM and UKESM simulations performed for the study "Chemistry-albedo feedbacks from reforestation reduce climate benefits and crop yields. The input data was processed to make it compatible for UKESM as described in Weber et al (2022) https://doi.org/10.5194/egusphere-2022-748.</p>
Crowd and community sourcing to update authoritative LULC data in urban areas
<p>The French National Mapping Agency (Institut National de l'Information Géographique et Forestière - IGN) is responsible for producing and maintaining the spatial data sets for all of France. At the same time, they must satisfy the needs of different stakeholders who are responsible for decisions at multiple levels from local to national. IGN produces many different maps including detailed road networks and land cover/land use maps over time. The information contained in these maps is crucial for many of the decisions made about urban planning, resource management and landscape restoration as well as other environmental issues in France. Recently, IGN has started the process of creating a high-resolution land use land cover (LULC) maps, aimed at developing smart and accurate monitoring services of LULC over time. To help update and validate the French LULC database, citizens and interested stakeholders can contribute using the <a href="https://paysages.ign.fr/">Paysages</a> mobile and web applications. This approach presents an opportunity to evaluate the integration of citizens in the IGN process of updating and validating LULC data.</p> <p><strong>Dataset 1: Change detection validation 2019</strong></p> <p>This dataset contains web-based validations of changes detected by time series (2016 – 2019) analysis of Sentinel-2 satellite imagery. Validation was conducted using two high resolution orthophotos from respectively 2016 and 2019 as reference data. Two tools have been used: <a href="https://paysages.ign.fr/">Paysages</a> web application and <a href="https://laco-wiki.net/">LACO-Wiki</a>. Both tools used the same validation design: blind validation and the same options. For each detected change, contributors are asked to validate if there is a change and if it is the case then to choose a LU or LC class from a pre-defined list of classes.</p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of the change detection: 2016-2019.</li> <li>Time period of data collection: February 2019-December 2019</li> <li>Total number of contributors: 105</li> <li>Number of validated changes: 1048; each change was validated by between 1 to 6 contributors.</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 1- Change validation locations.png, 1-Change validation 2019 – Attributes.csv, 1-Change validation 2019.csv, 1-Change validation 2019.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a>, and <a href="https://www.geoville.com/">GeoVille</a>.</p> <p><strong>Dataset 2: Land use classification 2019</strong></p> <p>The aim of this data collection campaign was to improve the LU classification of authoritative LULC data (<a href="https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#ocs-ge">OCS-GE 2016</a> ©IGN) for built-up area. Using the Paysages web platform, contributors are asked to choose a land use value among a list of pre-defined values for each location. </p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of data collection: August 2019</li> <li>Types of contributors: Surveyors from the production department of IGN</li> <li>Total number of contributors: 5</li> <li>Total number of observations: 2711</li> <li><a href="https://geoservices.ign.fr/ressources_documentaires/Espace_documentaire/BASES_VECTORIELLES/OCS_GE/DC_OCS_GE_1-1.pdf">Data specifications of the OCS-GE</a> ©IGN</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 2- LU classification points.png, 2-LU classification 2019 – Attributes.csv, 2-LU classification 2019.csv, 2-LU classification 2019.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a> and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a>.</p> <p><strong>Dataset 3: In-situ validation 2018</strong></p> <p>The aim of this data collection campaign was to collect in-situ (ground-based) information, using the Paysages mobile application, to update authoritative LULC data. Contributors visit pre-determined locations, take photographs, of the point location and in the four cardinal directions away from the point and answer a few questions with respect with the task. Two tasks were defined: </p> <ul> <li>Classify the point by choosing a LU class between three classes: industrial (US2), commercial (US3) or residential (US5).</li> <li>Validate changes detected by the LandSense Change Detection Service: for each new detected change, the contributor was requested to validate the change and choose a LU and LC class from a pre-defined list of classes.</li> </ul> <p>The dataset has the following characteristics </p> <ul> <li>Time period of data collection: June 2018 – October 2018</li> <li>Types of contributors: students from the School of Agricultural and Life Sciences and citizens</li> <li>Total number of contributors: 26</li> <li>Total number of observations: 281</li> <li>Total number of photos: 421</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 3- Insitu locations.png, 3- Insitu validation 2018 – Attributes.csv, 3- Insitu validation 2018.csv, 3- Insitu validation 2018.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Land use and land cover (LULC) classification of the CAP LTER study area using 2010 Landsat imagery
The land use and land cover (LULC) mapping generated from the 30 meter resolution Landsat TM5 is prepared for CAP LTER analyses. The series of products includes three levels LULC classifications, from coarser land-cover types to finer, hybrid LULC types, arranged in three thematic maps with contrasting numbers of LULC categories: (1) 12, (2) 15, and (3) 21. The percent of vegetation cover (vegetation fraction) in the residential area is provided. The image has a resampled spatial resolution of 15 meters due to the image classification procedure.
High-resolution (30m) LULC images for Nanjing
<p># High-resolution (30m) LULC images for Nanjing</p> <ul> <li>resolution: 30m</li> <li>range: from 1985-2015</li> <li>interval: 5 years</li> <li>coordinate reference systems: EPSG:4326(wgs 1984), EPSG:32650 (wgs 1984 utm zone 50n)</li> <li>finished date: 2019/08/30</li> <li>available / original published on <a href="https://code.earthengine.google.com/?asset=users/XiaolongLiu/Nanjing/nanjing_lulc">Google Earth Engine Assets</a></li> </ul> <p># Land use land cover types</p> <p>| value | type|<br> |---------------|-----------------|<br> | 0 | urban and buit-up lands |<br> | 1 | Water bodies |<br> | 2 | Mixed forests |<br> | 3 | Cropland |<br> | 4 | Grass lands |</p>
LULC_test_Upload
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Climate simulations of impact of LULC on mid-Holocene climate
<p>Extracted data from climate simulations by Smith et al., 2016 with and without landuse-landcover changes at 6000 yr BP., used to construct Figure 8 in <strong>Harrison, S.P.</strong>, Gaillard, M-J., Stocker, B., Vander Linden, M., Klein Goldewijk, K., Boles, O., Braconnot, P., Dawson, A., Fluet-Chouinard, E., Kaplan, J.O., Kastner, T., Pausata, F.S.R., Robinson, E., Whitehouse, N., Madella, M., Morrison, K.D., 2019. Development and testing of scenarios for implementing Holocene LULC in Earth System Model Experiments. <em>Geoscientific Model Development</em> <em>Discussions, </em><strong><a href="https://doi.org/10.5194/gmd-2019-125%20%20%20%207">https://doi.org/10.5194/gmd-2019-125 7</a></strong></p>
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