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Uncovering Forest and Cropland Change with High-Resolution Data in a Biodiversity Hotspot, Madagascar

Domain:

geospatialenvironment and energy

Record type:

softwarepaper
Creator:
Rakotoarimanana Zy HarifidyOHTRakotoarimanana Zy Misa Harivelo
Publisher:
Zenodo
Host:avatar

This repository contains Python scripts used for land cover classification, reclassification, spatial agreement mapping, and accuracy assessment of multiple land cover datasets. The analysis compares historical and future spatial patterns of forest, cropland, and other classes across datasets using bar plots, agreement maps, and statistical accuracy metrics.

Key features include:
- Visualization of original and reclassified land cover maps.
- Calculation of land cover class percentages and pixel counts.
- Agreement analysis across datasets (forest and cropland).
- Confusion matrix and class-specific accuracy (producer's and user's accuracy).
- Export of spatial maps (GeoTIFF) and statistical summaries (CSV).

This code was developed for assessing the consistency and reliability of land cover products in Ankarafantsika National Park and Betsiboka basin, Madagascar, , and is adaptable for other geospatial applications.

Visit

doi.org

Tasks

computer visionimage classification

Tags

forest and cropland assessment, high-resolution LULC datasets, remote sensing, Google Earth Engine, Betsiboka basin, Ankarafantsika National Park

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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