#forestcover — Public Fediverse posts

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
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https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
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"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

Larger Forest Patches Have Greater Per-Area Productivity
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https://doi.org/10.1038/s41559-026-03075-5 <-- shared paper
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https://doi.org/10.1038/s41586-025-08688-7 <-- shared related paper
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https://doi.org/10.1641/0006-3568(2002)052[0411:FFOTCU]2.0.CO;2 <-- shared related paper
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#GIS #spatial #mapping #spatialanalysis #habitat #ecosystems #fragmented #forests #vegetation #carbonstorage #trees #logging #forestry #naturalresources #canopy #soil #moisture #foresthealth #metrics #productivity #carboncapture #contiguous #USA #CONUS #environment #sustainability #model #modeling #AI #machinelearning #geoai #carbonsequestration #forestcover

Populus Tremuloides [Aka Aspen] As A Natural Fire Barrier In Canada’s Boreal Forest Under A Changing Climate
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https://doi.org/10.1016/j.foreco.2026.123671 <-- shared paper 🔗
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#GIS #spatial #mapping #flammable #barrier #extremeweather #fireweather #fireactivity #wildfire #bushfire #fire #firebreak #FireKnowledge #FireEcology #AspenTrees #Canada #spatialanalysis #spatiotemporal #forest #risk #hazard #damage #climatechange #warming #tree #mixed #diversity #species #perimeter #ecozones #ecology #geostatistics #forest #forestcover #burn #severity #stand #burnarea #management #aspen #populustremuloides #Fireactivity #Burnseverity #Fireweather #Deciduous #Phenology
#NRCANScience #NRCAN #NaturalResourcesCanada

Predictability Of Earth’s Greenness [incl. spatial]
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https://doi.org/10.1016/j.isprsjprs.2026.02.038 <-- shared paper
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https://phys.org/news/2026-02-global-greening-earth-green-shifting.html <-- shared technical article
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https://www.vox.com/down-to-earth/2024/2/7/24057308/earth-global-greening-climate-change-carbon <-- shared technical media article
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https://doi.org/10.1038/s41893-019-0220-7 <-- shared (2019) paper
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#Earth #greenness #deeplearning #AI #usecase #productivity #vegetation #foliage #greening #forecasting #geomorphometry #vegetationindex #NDVI #GIS #spatial #mapping #earthobservation #remotesensing #spatialanalysis #spatiotemporal #satellite #global #landuse #landcover #management #biomass #agriculture #crops #environment #ecosystems #forestcover #sustainability #foodsecurity #soilmoisture #treecover #temperature #parameters #forest #forested #landmanagement #agronomic #irrigation #fertilisation #croprotation #pestcontrol #attentionmodel #metrics

Map Reveals How America's Forests Have Changed Over Time
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https://www.newsweek.com/america-forests-change-over-time-2041291 <-- shared 2025 media article
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https://doi.org/10.5194/essd-15-1005-2023 <-- shared 2023 paper
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https://doi.org/10.5281/zenodo.7055086 <-- shared study datasets
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#GIS #spatial #mapping #remotesensing #spatialanalysis #spatiotemporal #copernicus #CONUS #USA #forest #model #modeling #landuse #landcover #change #forestland #agriculture #urban #development #cities #industry #vegetation #America #humanimpacts #settlement #decline #cropland #pasture #grassland #grazing #reforestation #deforestation #urbanization #conservation #ecosystems #logging #mining #railroad #infrastructure #forestcover #NLDI #farmland #colonial #LULC #census #climate #hydrology #biogeochemical

Combining Remote Sensing With Local Knowledge Is Vital For Understanding Forest Change In West Africa
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https://doi.org/10.1038/s41598-025-23133-5 <-- shared paper
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#GIS #spatial #mapping #remotesensing #earthobservation #tropical #forest #Africa #satellite #vegetation #ontheground #localobservations #WestAfrica #community #crowdsourcing #Landsat #spatialanalysis #household #surveys #localknowledge #integration #forestcover #deforestration #regrowth #convergence #limitations #imagery #spatiotemporal #Togo #Benin #Nigeria #Cameroon

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
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https://doi.org/10.1016/j.jag.2022.102716 <-- shared 2022 paper
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“HIGHLIGHTS:
• [They] evaluated the impacts of topographic correction on forest mapping in the mountains.
• The enhanced C-correction and the physical model reduced topographic effects.
• The corrected Landsat imagery time series resulted in higher accuracy.
• Terrain information improved classification but not as much as topographic correction.
• [They] recommend using topographic correction for forest cover mapping..."
#GIS #spatial #AtmosphericCorrection #IlluminationCondition #LandCover #ModelComparison #TimeSeries #TopographicCorrection #remotesensing #comparasion #topographic #correction #NDVI #forest #vegetation #model #modeling #spatialanalyis #accuracy #forestcover #Russia #Georgia #CaucasusMountains #spatiotemporal #landsat #elevation #DEM

A detailed report on the methodologies in this paper on the #decisiontree classifiers for mapping #forestcover & change was also published by Trix Estomata in 2018: https://bit.ly/Estomata_2018

Mari Trix Estomata & Klaus Schmitt (2019) used #decisiontree classification & unbiased area estimation to map #forestcover extent & change in the #Philippines using #ALOS #PALSAR mosaic data in support of #REDD+ initiatives. #LoLManuscriptMonday http://bit.ly/Estomata_2019

Cheers to Estomata & Schmitt’s affiliation & supporting organizations for this #LoLManuscriptMonday feature: GIZ Philippines, the K&C Initiative of JAXA & the Forest Management Bureau of DENR Philippines #EOChat #GISChat 🛰️🌳🥳

How Nepal Regenerated Its Forests
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https://earthobservatory.nasa.gov/images/150937/how-nepal-regenerated-its-forests <-- shared @nasa post
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#GIS #spatial #mapping #remotesensing #forestry #regeneration #Nepal #vegetation #spatialanalysis #change #improvement #earthobservation #planning #management #governmentpolicy #forest #trees #forestcover #landcover #landmanagement #environmental #community #people #development #projects #administration #villages #communitydevelopment #communityleaders #communityengagement #communitymanagement
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