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4 results for “Deforestation drivers”
Data and code for the publication: "Deforestation as an anthropogenic driver of mercury pollution"
<p>A. Feinberg, Sep 2023<br> arifeinberg@gmail.com</p> <p>Essential data and code for the publication: Feinberg et al. : Deforestation as an anthropogenic driver of mercury pollution</p> <p>The directories include:<br> 1) analysis_scripts/ - all analysis scripts used to produce input data and figures for paper<br> 2) Erosion_data/ - Erosion model (GloSEM) output<br> 3) GC_code/ - Archived GEOS-Chem code used to simulate the runs in this paper<br> 4) GC_data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs:<br> HIST - run0311<br> BAU - run0312<br> GOV- run0313<br> SAV - run0315<br> RFR - run0314<br> Deforesting different regions for EF calculations:<br> DFR_Afrotropic - run0321<br> DFR_Indomalayan - run0322<br> DFR_China - run0323<br> DFR_Neotropic - run0324<br> DFR_Palearctic - run0325<br> DFR_Australasia - run0326<br> DFR_Nearctic - run0327<br> DFR_Amazon = SAV - run0315</p> <p>5) input_data/ - input data used to run GEOS-Chem</p> <p>Please refer to other README.md files within sub-directories and contact me for any questions</p>
Labelled dataset to classify direct deforestation drivers in Cameroon
<p><strong>Overview</strong></p> <p>This dataset includes the images (visible bands for Landsat-8 or NICFI PlanetScope), auxiliary data (infrared, NCEP, forest gain, OpenStreetMap, SRTM, GFW), and data about forest loss (Global Forest Change) used to train, validate and test a model to classify direct deforestation drivers in Cameroon. </p> <p><strong>Description of the files</strong></p> <ul> <li>'my_examples_landsat_final_detailed.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes: ‘Oil palm plantation’, ‘Timber plantation’, ‘Fruit plantation (e.g. banana)’, ‘Rubber plantation’, ‘Other large-scale plantation (e.g. tea, sugarcane)’, ‘Grassland/Shrubland’, ‘Small-scale oil palm plantation’, ‘Small-scale maize plantation’, ‘Other small-scale agriculture’, ‘Mining’, ‘Selective logging’, ‘Infrastructure’, ‘Wildfire’, ‘Hunting’, ‘Other’)</li> <li>'my_examples_planet_final_detailed.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes)</li> <li>'my_examples_landsat_final.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes: 'Plantation', 'Grassland/Shrubland', 'Smallholder agriculture', 'Other')</li> <li>'my_examples_planet_final.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes)</li> <li>'my_examples_landsat_detailed_timeseries.zip': Landsat-8 images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis </li> <li>'my_examples_planet_detailed_timeseries.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis</li> <li> <p>‘labels.zip’: in csv files, the labels for each image in each folder described above (image identified by folder and coordinates or ‘path’) and matches the format of the csv files used as inputs to train, validate and test our classification model</p> <p>For ‘labels.zip’, we have subfolders for Landsat and PlanetScope. Then, for each type of imagery, we have subfolders for ‘detailed’, ‘groups’ and ‘time series’ which correspond to the different ‘my_examples’ folders listed above. </p> <p>For each folder, subfolders named with the coordinates of the centre of the images contain each:<br>• A folder ‘images’, with a sub-folder ‘visible’ containing the PNG RGB image; and a sub-folder ‘infrared’ containing the infrared bands in a NPY file.<br>• A folder ‘auxiliary’ with topographic and forest gain information in a NPY format, OpenStreetMap and peat data in a JSON format, and a sub-folder ‘ncep’ containing all data from NCEP in a NPY format.<br>• The forest loss pickle file delimiting the area of forest loss.</p> </li> </ul> <p><strong>Details about the images</strong></p> <ul> <li> <p>For Landsat-8 data (courtesy of the U.S. Geological Survey), this dataset contains 332x 332 pixels RGB calibrated top-of-atmosphere (TOA) reflectance images pan-sharpened to a 15 m resolution (less than 20% cloud cover)</p> </li> <li> <p>For NICFI PlanetScope data (catalog owner: Planet), this dataset contains 332x 332 pixels monthly RGB composite with a 4.77 m resolution</p> </li> </ul> <p><strong>Details about the auxiliary data</strong></p> <ul> <li>Forest gain from GFC: 30-m resolution, yearly data for 2000-2021, downloaded via Google Earth Engine</li> <li>Near infrared, shortwave infrared 1 and 2 bands from Landsat-8 TOA: 30-m resolution, data every 16 days for 2013-2023, downloaded via Google Earth Engine and selected using the same process as for Landsat-8 RGB images</li> <li> From NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products: surface level albedo and volumetric soil moisture content (depths: 0.1 m, 0.4 m, 1.0 m, 2.0m) in 0.01%; radiative fluxes (clear-sky longwave flux downward and upward, clear-sky solar flux downward and upward, direct evaporation from bare soil, longwave and shortwave radiation flux downward and upward, latent, ground and sensible heat net flux), potential evaporation rate, and sublimation in W/m²; humidity (specific, maximum specific, minimum specific) in 10-4 kg/kg; ground level precipitation in 0.1 mm; air pressure at surface level in 10 Pa; wind level (u and v component) in 0.01 m/s, water runoff at surface level in 232.01 kg/ m²; temperature in K: 22264-m resolution, available four times a day for 2011-2023, downloaded directly from the NOAA website and selected the mean of the monthly mean over 5 years before the forest loss event, the monthly maximum over 5 years before the forest loss event, and the monthly minimum over 5 years before the forest loss event for each parameter</li> <li>Closest street and closest city from OpenStreetMap in km: directly downloaded with the Nominatim API</li> <li>Altitude in m, slope and aspect in 0.01° from Shuttle Radar Topography Mission (SRTM): 30-m resolution, measured for 2000, downloaded via Google Earth Engine</li> <li>Presence of peat from GFW: 232-m resolution, measured for 2017, directly downloaded on the GFW website</li> </ul> <p><strong>Details about Global Forest Change</strong></p> <p>For each image, there is a corresponding 'forest_loss_region' .pkl file delimiting a forest loss region polygon from Global Forest Change (GFC). GFC consists of annual maps of forest cover loss with a 30-m resolution. </p> <p><strong>License</strong></p> <p>The NICFI PlanetScope images fall under the same license as the <a href="https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf">NICFI data program license agreement</a> (data in 'my_examples_planet_final.zip', 'my_examples_planet_final_detailed.zip', 'my_examples_planet_detailed_timeseries.zip': subfolders '[coordinates]'>'images'>'visible'). </p> <p><a href="https://wiki.osmfoundation.org/wiki/Licence/Attribution_Guidelines">OpenStreetMap®</a> is open data, licensed under the <a href="https://opendatacommons.org/licenses/odbl/1-0/">Open Data Commons Open Database License (ODbL)</a> by the OpenStreetMap Foundation (OSMF) (data in all 'my_examples' folders: subfolders '[coordinates]'>'auxiliary'>'closest_city.json'/'closest_street.json'). The documentation is licensed under the <a href="https://creativecommons.org/licenses/by-sa/2.0/">Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0)</a>.</p> <p>The rest of the data is under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. The data has been transformed following the code that can be found via this link: <a href="https://github.com/aedebus/Cam-ForestNet">https://github.com/aedebus/Cam-ForestNet</a> (in 'prepare_files').</p> <p> </p>
Tropical deforestation drivers 2008-2019, 100m, IIASA - accompanying file to https://doi.org/10.3389/fcosc.2022.830248
<p>The zip file contains the map used for the statistics calculated in the publication 'A Continental Assessment of the Drivers of Tropical Deforestation With a Focus on Protected Areas', available at <a href="https://doi.org/10.3389/fcosc.2022.830248">https://doi.org/10.3389/fcosc.2022.830248</a>. </p> <p>This is a first version of the drivers of forest loss map, It is an allocation of crowdsourced data on predominant drivers of forest loss (data available here <a href="https://doi.org/10.1038/s41597-022-01227-3">https://doi.org/10.1038/s41597-022-01227-3</a>) to the Hansen et al (2013) dataset of global tree loss (v 1.7), set to a resolution of 100x100 m.</p> <p>Furthermore, the classes 'Other subsistence agriculture' and 'Shifting cultivation' are parts of the 'Subsistence agriculture' class on the original crowdsourced data, separated by extracting shifting agriculture according to Heinimann et al (2017).</p> <p>The map is in Goode Homolosine projection.</p>
Tropical deforestation drivers 2008-2019, 1km, IIASA - accompanying file to https://doi.org/10.3389/fcosc.2022.830248
<p>The zip file contains a map to support the publication 'A Continental Assessment of the Drivers of Tropical Deforestation With a Focus on Protected Areas', available at <a href="https://doi.org/10.3389/fcosc.2022.830248">https://doi.org/10.3389/fcosc.2022.830248</a>. </p> <p>This is a first version of the drivers of forest loss map, It is an allocation of crowdsourced data on predominant drivers of forest loss (data available here <a href="https://doi.org/10.1038/s41597-022-01227-3">https://doi.org/10.1038/s41597-022-01227-3</a>) to the Hansen et al (2013) dataset of global tree loss (v 1.7), set to a resolution of 1x1 km.</p> <p>Furthermore, the classes 'Other subsistence agriculture' and 'Shifting cultivation' are parts of the 'Subsistence agriculture' class on the original crowdsourced data, separated by extracting shifting agriculture according to Heinimann et al (2017).</p> <p>The map is in Goode Homolosine projection.</p>
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