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137 results for “forest protection”
Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests
<p><strong>Data description</strong></p> <p>These datasets were generated for the Geostory "Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests" in the context of the Open Earth Monitor Cyberinfrastructure project.</p> <p>We used open source high-resolution Sentinel-1 satellite data to develop a wall-to-wall map of forest disturbances in the four-year period between the start of 2020 and end of 2023 in Estonia. First results are presented. The methodology is based on RADD-alerts developed for the pan-tropics (Reiche et al. 2021). Three years (2017-2019) of imagery was used as a historical period, and detections were generated for ~4 years (2020-2023). Winter images from November through March were not included as frozen conditions can introduce false detections. This will be addressed in the next version. Disclaimer: Disturbance maps have not been validated.</p> <p>Two additional layers are provided for visualization: a forest baseline layer (<em>forestcover</em>), masking out non-forest disturbance detections, was derived from Copernicus 10m 2018 forest cover density and GLAD 30m 2019 tree removal datasets, and a protected areas layer (<em>natura</em>), which displays the extent of Natura 2000 coverage in Estonia.</p> <p>'.SLD' files are provided for visualization (note: the <em>disturbance</em> .SLC file must be adjusted to contain appropriate time reference fields).</p> <p><strong>Naming Convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For instance:</p> <ul> <li>disturbance_radd_c_10m_s_20200101_20200131_eu_epsg.3035_v20240222.tif</li> </ul> <p>with the following fields:</p> <ul> <li>Generic variable name: <strong>disturbance</strong></li> <li>Variable procedure combination i.e. method standard: <strong>radd</strong></li> <li>Position in the probability distribution / variable type: <strong>c</strong></li> <li>Spatial support: <strong>10m</strong></li> <li>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): <strong>s</strong></li> <li>Time reference begin time (YYYYMMDD): <strong>20200101</strong></li> <li>Time reference end time: <strong>20200131</strong></li> <li>Bounding box (2 letters max): <strong>eu </strong></li> <li>EPSG code: <strong>epsg.3035</strong></li> <li>Version code i.e. creation date: <strong>v20240222</strong></li> </ul> <p><strong>Source Data</strong></p> <p>Disturbance maps:</p> <p>Contains modified Copernicus Sentinel data [2017-2023] and Generated using European Union's EEA-10 Copernicus DEM; https://doi.org/10.5270/ESA-c5d3d65</p> <p>Forest baseline:</p> <p>Generated using European Union's Copernicus Land Monitoring Service information; https://doi.org/10.2909/486f77da-d605-423e-93a9-680760ab6791 and GLAD tree removal; https://doi.org/10.1016/j.rse.2023.113797</p> <p>Natura 2000: </p> <p>Generated using European Environmental Agency's Natura 2000 layers; https://sdi.eea.europa.eu/data/dae737fd-7ee1-4b0a-9eb7-1954eec00c65</p>
Protected planet (protected areas), forests and intact forest landscapes at 100 m, 250 m to 1 km resolution
<p><a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">Protected planet</a> (protected areas; version Oct 2024) and <a href="https://intactforests.org/data.ifl.html">intact forest landscapes</a> (2000, 2013, 2016 and 2020) rasterized to 100 m, 250 m and 1 km resolutions. The aggregated map contains all pixels that are either protected or intacts. To use these resources please refer to original data producers:</p> <ul> <li>Defourny, P., Lamarche, C., Bontemps, S., De Maet, T., Van Bogaert, E., Moreau, I., Brockmann, C., Boettcher, M., Kirches, G., Wevers, J., Santoro, M., Ramoino, F., & Arino, O. (2017). Land Cover Climate Change Initiative - Product User Guide v2. Issue 2.0. <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf">http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf</a></li> <li>Olsson, E., Albrecht, R., & Golden Kroner, R.E. (2021). PADDDtracker Data Release Version 2.1: Technical Notes. Conservation International, Arlington, VA. DOI: 10.5281/zenodo.4749615.</li> <li>Potapov, P., Hansen, M. C., Laestadius L., Turubanova S., Yaroshenko A., Thies C., Smith W., Zhuravleva I., Komarova A., Minnemeyer S., Esipova E. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. <a href="http://advances.sciencemag.org/content/3/1/e1600821">Science Advances, 2017; 3:e1600821</a></li> <li>UNEP-WCMC and IUCN (2024), Protected Planet: The World Database on Protected Areas (WDPA) [Online], October 2024, Cambridge, UK: UNEP-WCMC and IUCN. Available at: <a title="Visit Protected Planet" href="http://protectedplanet.net/" target="_blank" rel="noopener">www.protectedplanet.net</a>.</li> </ul> <p>The time-series of forest areas (<strong>forest.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. Two maps (<strong>forest.cover.sum_esa.cci_p_250m</strong> and <strong>forest.cover.diff_esa.cci_p_250m</strong>) show long term cumulative forest cover and difference in forest cover for 2022 vs 2000.</p> <p>The protected planet areas and intact forest landscapes were rasterized using:</p> <pre><code>## https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA for(j in 0:2){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -where "IUCN_CAT LIKE \'I%\'" /data/CCI_LandCover/WDPA_Oct2024_Public_shp_', j, '/WDPA_Oct2024_Public_shp-polygons.shp WDPA_Oct2024_Public_shp_', j, '_1km.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } s = sds(rast("WDPA_Oct2024_Public_shp_ALL_0_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_1_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_2_1km.tif")) dg.x = app(s, fun=max, na.rm=TRUE, cores = 32) dg.x0 = terra::ifel(is.na(dg.x), 0, dg.x, filename="protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE) ## https://intactforests.org/data.ifl.html for(j in c(2000,2013,2016,2020)){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -l \"ifl_', j, '\" /mnt/lacus/raw/protectedplanet/ifl_', j, '.shp intact.forest_gfw_p_1km_s_', j, '0101_', j, '1231_go_epsg4326_v20241025.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } ## Combination IFL & WPDA b = sds(rast("protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif"), rast("intact.forest_gfw_p_1km_s_20200101_20201231_go_epsg4326_v20241025.tif")) bg.x = app(b, fun=max, na.rm=TRUE, cores = 32) bg.x0 = terra::ifel(is.na(bg.x), 0, bg.x, filename="protected.intact.areas_wdpa.ifl_p_1km_s_2020_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE)</code></pre>
Plate 1 in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India
Plate 1. Habitus of six species of Tabanidae, i.e. A: Chrysops dispar (Fabricius, 1798); B: Atylotus virgo (Wiedemann, 1824); C: Tabanus dorsiger Wiedemann, 1821 (new record from Sonamukhi protected forest area under arid zone of West Bengal); D: Tabanus (Tabanus) rubidus Wiedemann, 1821; E: Tabanus (Tabanus) striatus Fabricius, 1787 and F: Tabanus (Tabanus) tenens Walker, 1850.
Map 1 in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India
Map 1. GIS map showing distribution and richness of family Tabanidae in Sonamukhi protected forest, on the basis of eco-regions in Indo-malayan biome of arid region of west Bengal below and satellite map of all the stations showing study sites of Tabanidae above.
Figure 2. A-C in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India
Figure 2. A-C. Graphs showing log series model of rank abundance of tabanids depicting maximum abundance during pre-monsoon and monsoon of species T. straitus (17, 15) and of species T. striatus (3) and H. javana (3) respectively during post monsoon.
Figure 3 in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India
Figure 3. Species accumulation curves showing sample rarefaction (Mau Tau's) of the tabanids sampled throughout the season.
Burnt forest area and CO2 emissions from fires in Russian forests by fire protection zones in 2010-2020
<p>The dataset is Supplementary Materials for the article ''<em>Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2010-2020</em>'' in the Carbon Balance and Management journal. It contains files with burnt forest area and carbon dioxide emissions from fires data in Russia 2010-2020.<br> Article Supplementary materials are stored in the file Supplementary Tables and contains:</p> <p>- Table 1. Burnt forest area from NIR and MODIS (MCD64A1) in 2010-2020;</p> <p>- Table 2. Burnt forest area in the ground, aviation and the control (no fire protection) zones in 2010-2020 using MCD64A1;</p> <p>- Table 3. Carbon emissions from forest fires from National Inventory Report (NIR) and Copernicus Atmosphere Monitoring Service (CAMS) in 2010-2020</p> <p>Also, there is Supplementary Figure 1 with the Federal Districts of Russia schematic map.</p> <p>There are 22 files in GeoTIFF format for every year: </p> <p>1. Burnt forest area obtained using MODIS product MCD64A1 (250 m pixel, ESRI:102025). Coverage: -4064059.5401764437556267,1967242.6686790268868208 : 3658440.4598235562443733, 6012242.6686790268868208</p> <p>2. CO2 emissions using Copernicus Atmosphere Monitoring System (CAMS) (0.1 degrees, VGS 84). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594<br> <br> In addition, we share Shapefiles:</p> <p>1. Russian borders (necessary to cut Russia from CO2 GeoTIFFs), EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992;</p> <p>2. Forest Fire Protection zoning in 2019: ground zone, aviation zone, the so-called control zone (no fire protection), EPSG:4326. Coverage: 27.4019779002987676,41.3483353426717599 : 173.8255532772949721,72.6575707670955353.</p>
Data from: Occupancy winners in tropical protected forests: a pantropical analysis
<p class="MsoNormal"><span>The structure of forest mammal communities appears surprisingly consistent across the continental tropics<span>, presumably due to convergent evolution in similar environments. W</span>hether such consistency extends to mammal occupancy, despite variation in species characteristics and context, remains unclear. Here we ask whether we can predict occupancy patterns and, if so, whether these relationships are consistent across biogeographic regions. Specifically, we assessed how mammal feeding guild, body mass and ecological specialization relate to occupancy in protected forests across the tropics. We used standardized camera-trap data (</span><span>1,002 camera-trap locations and 2-10 years of data)</span><span> and a hierarchical Bayesian occupancy model. We found that occupancy varied by regions, and </span><span>certain species characteristics</span><span> explained much of this variation. Herbivores consistently had the highest occupancy. However, only in the Neotropics did we detect a significant effect of body mass on occupancy: large mammals had lowest occupancy. Importantly, habitat specialists generally had higher occupancy than generalists, though this was reversed in the Indo-Malayan sites. We conclude that </span><span>habitat specialization is key for understanding variation in mammal occupancy across regions, and that habitat specialists often benefit more from protected areas, than do generalists. </span><span>The contrasting examples seen in the Indo-Malayan region likely reflect distinct anthropogenic pressures.</span></p>
Realizing COP26's declaration on deforestation protects forests at the expense of non-forest land
<p>Data and model source code for the manuscript: "Realizing COP26's declaration on deforestation protects forests at the expense of non-forest land"</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Christopher P.O. Reyer1, Felicitas Beier1,2, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> September 2022</p> <p>See https://github.com/abhimishr/magpie/releases and https://github.com/magpiemodel/magpie/releases/tag/v4.5.0 for further details</p>
Figs. 1–24 in Insect galls of a protected remnant of the Atlantic Forest tableland from Rio de Janeiro State (Brazil)
Figs. 1–24. Insect galls of Guaxindiba. 1. Astronium sp., globose leaf gall; 2. Schinus terebinthifolius, globose stem gall; 3. Xylopia sp., conical leaf gall; 4. Aspidosperma sp., circular leaf gall; 5. Adenocalymma sp., fusiform vein/tendril gall; 6. Amphilophium sp., globose vein gall; 7. Bignonia sp. 1, fusiform vein gall; 8. Bignonia sp. 2, vein swelling; 9. Bignonia sp. 3, vein swelling; 10 and 11. Martinella obovata, 10: globose bud gall; 11: petiole/vein gall; 12 and 13. Pyrostegia sp., 12: conical leaf gall; 13: vein swelling; 14. Bignoniaceae sp. 1, vein swelling; 15. Bignoniaceae sp. 2, tendril/petiole swelling; 16 and 17. Protium heptaphyllum, 16: stem gall; 17: bud gall; 18. Licania sp., globose vein gall; 19. Buchenavia sp., marginal leaf roll; 20. Erythroxylum pauferrense, conical leaf gall; 21. Dodecastigma sp., vein swelling; 22. Manihot sp., cylindrical leaf gall; 23-24. Pachystroma longifolium, 23: marginal leaf roll; 24: conical leaf gall.
Figure 2 in Harvestmen (Arachnida: Opiliones) from the Atlantic Forest of the Fernão Dias Environmental Protection Area, southern Minas Gerais, Brazil
Figure 2. Harvestmen records at the Fernão Dias EPA, Gonçalves municipality, southern Minas Gerais state: A) Ampheres luteus (Giltay, 1928). B) Megapachylus anomalus (Mello-Leitão, 1922). C) Gonyleptes pseudogranulatus (Soares, 1946). D) Munequita sp. / Registros de opiliones de la APA Fernão Dias, municipio de Gonçalves, sur del estado de Minas Gerais: A) Ampheres luteus (Giltay, 1928). B) Megapachylus anomalus (Mello-Leitão, 1922). C) Gonyleptes pseudogranulatus (Soares, 1946). D) Munequita sp.
Figure 1 in Harvestmen (Arachnida: Opiliones) from the Atlantic Forest of the Fernão Dias Environmental Protection Area, southern Minas Gerais, Brazil
Figure 1. Sampling areas for harvestmen (Arachnida) in the Atlantic Forest of the Fernão Dias EPA in the municipality of Gonçalves, southern Minas Gerais state, in mixed and seasonal semideciduous forests. / Áreas de muestreo para opiliones (Arachnida) en la Mata Atlántica de la APA Fernão Dias en el municipio de Gonçalves, sur del estado de Minas Gerais, en bosques semideciduos mixtos y estacionales.
MULTIPLIERS_WP5_Science learning project on Forest use vs. forest protection_UBO_Public data_20241014_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Forest use vs. forest protection</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with OSC members, and student groups (</span><span>Pseudo-/Anonymised)</span></li> </ul>
MULTIPLIERS_WP3/4/5_Science learning project on Forest use vs. forest protection_Public data_UMU_20241025_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Forest use vs. forest protection</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with teachers, OSC members, and students (</span><span>Pseudo-/Anonymised)</span></li> <li><span><span>Summary</span><span> of transcripts from focus group discussions with students (</span><span>Pseudo-/Anonymised)</span></span></li> <li><span><span><span>An indicative set of key messages and exemplary quotes from the teachers and researchers gathered from<span> </span>interviews conducted after the end of Implementation Round 2 and 3.</span></span></span></li> </ul>
Fig. 1 in Representation Of Threatened Vertebrates By A Protected Area System In Southeast Asia: The Importance Of Non-Forest Habitats
Fig. 1. Percentage of habitat use by amphibians, reptiles, birds, mammals and all taxa combined. The habitat types include Evr (evergreen forest), MD (mixed deciduous forest), Dip (dry dipterocarp forest), Mngrv (mangrove forest), Swmp (swamp forest), Bmbo (bamboo forest), Grss (grassland & shrub), Wet (wetland), SltF (salt flat), Strm (freshwater), Wtrf (waterfall), LmCv (limestone cave), and Bch (beach)
Fig. 2 in Representation Of Threatened Vertebrates By A Protected Area System In Southeast Asia: The Importance Of Non-Forest Habitats
Fig. 2. Map showing areas of underrepresented habitats in Thailand still lacking official protection. The light grey areas represent protected areas, while the black areas are underrepresented habitats outside the protected area system.
"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16
Data from: Occupancy winners in tropical protected forests: a pantropical analysis
Open the record for dataset details and reuse information.
Model results of reduced wood harvest and forest protection scenarios using MAgPIE 4.3.5
<p>The files here contain MAgPIE 4.3.5 results of reduced wood harvest and forest protection scenarios.</p> <p>MAgPIE requires <em>GAMS</em> (<a href="https://www.gams.com/">https://www.gams.com/</a>) including licenses for the solvers <em>CONOPT</em> and (optionally) <em>CPLEX</em> for its core calculations. As the model benefits significantly from recent improvements in <em>GAMS</em> and <em>CONOPT4</em> it is recommended to work with the most recent versions of both.<br> <br> The results of the model run here have been cleaned up to avoid bulky uploads. The fulldata.gdx is the technical output of the GAMS optimization and contains all quantities that were used during the optimization in unchanged form. The mif-file is a CSV file of a specific format and is synthetized from the fulldata.gdx by post-processing scripts. It can be read in any text editor or spreadsheet program and is well suited for a brief look at the results and for further analysis.</p>
Figure 4 in Geographic distribution patterns of galling insects in a protected area of Atlantic forest (southeast, Brazil)
Figure 4. The fit of the count part of the "hurdle" model to the relationship between galling species richness and plant genus species richness.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.