DOI: 10.57760/sciencedb.j00001.01842
title: Pakistan Desertification Distribution Dataset, 1990-2020
title: 1990-2020年巴基斯坦荒漠化分布数据集

description:  Desertification is one of the most serious ecological and environmental problems in arid and semi-arid regions worldwide. Pakistan, located in South Asia, has long faced challenges such as desertification and its resulting consequences, including declining agricultural yields, water scarcity, food security crises, and loss of biodiversity. The lack of baseline and change data on desertification in this region directly impacts the sustainable development of the China-Pakistan Economic Corridor. This study selected five desertification indicators: fractional vegetation cover (FVC), normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), surface albedo, and topsoil granularity index (TGSI). Through comparative evaluation, the optimal machine learning model (gradient boosting decision tree) was obtained. Based on Google Earth Engine and using Landsat remote sensing images, a dataset of Pakistan's desertification distribution with a 30 m spatial resolution was obtained for every five years from 1990 to 2020. The quality and accuracy of this dataset were verified using high-resolution Google Earth images, resulting in an overall accuracy of 98.41% and a Kappa coefficient of 0.97. This dataset objectively reflects the spatial distribution of different degrees of desertification in Pakistan and can provide detailed and reliable data support for delineating and restoring key areas for desertification control, which is of great significance for decision-making regarding the green and sustainable development of the China-Pakistan Economic Corridor.
description:  Desertification is one of the most serious ecological and environmental problems in arid and semi-arid regions worldwide. Pakistan, located in South Asia, has long faced challenges such as desertification and its resulting consequences, including declining agricultural yields, water scarcity, food security crises, and loss of biodiversity. The lack of baseline and change data on desertification in this region directly impacts the sustainable development of the China-Pakistan Economic Corridor. This study selected five desertification indicators: fractional vegetation cover (FVC), normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), surface albedo, and topsoil granularity index (TGSI). Through comparative evaluation, the optimal machine learning model (gradient boosting decision tree) was obtained. Based on Google Earth Engine and using Landsat remote sensing images, a dataset of Pakistan's desertification distribution with a 30 m spatial resolution was obtained for every five years from 1990 to 2020. The quality and accuracy of this dataset were verified using high-resolution Google Earth images, resulting in an overall accuracy of 98.41% and a Kappa coefficient of 0.97. This dataset objectively reflects the spatial distribution of different degrees of desertification in Pakistan and can provide detailed and reliable data support for delineating and restoring key areas for desertification control, which is of great significance for decision-making regarding the green and sustainable development of the China-Pakistan Economic Corridor.