%0 Dataset
%T Long-term land use/cover dataset of the Mu Us Sandy Land (1980-2025)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/b183b6e1-c7a6-47e8-b1b5-47a87651d6b8
%W NCDC
%R 10.12072/ncdc.nieer.db7325.2026
%A luo zhi jia
%A LIAN Jie
%A zhang lei
%A li yan qing
%A wu ming run
%K Land use and cover;remote sensing interpretation;Landscape fragmentation;Ecologically fragile zone
%X The Maowusu Desert is located in the core area of the "Yellow River Key Ecological Zone" in the national "Three Zones and Four Belts" ecological security strategic pattern, and belongs to the key ecological function zone of windbreak and sand fixation in the western section of the northern agricultural pastoral ecotone. Due to the dual impact of long-term agricultural and animal husbandry development and ecological restoration projects, the surface landscape in this area is highly fragmented, leading to frequent misjudgment or poor applicability of existing large-scale open source land classification products. For this purpose, this dataset is specifically tailored to the complex surface features of sandy areas, aiming to compensate for the limited availability of national scale products in local vulnerable areas and provide a more accurate base map for ecological assessment.This data product is based on a total of 10 Landsat series remote sensing images (5-year time step) from 1980 to 2025, with a spatial resolution of 30 meters. The dataset adopts a first level land use/cover classification system, covering 7 major categories: tree forest, shrub forest, farmland, water body, grassland, bare sand land, and construction land. In terms of production process, a highly reliable ground sample set was first constructed through field investigations, low altitude drone orthophoto, and multi-source open source data fusion; Then, relying on the ENVI platform, multiple supervised classification algorithms are compared and screened, and a subjective and objective collaborative mechanism of confusion matrix quantitative evaluation and expert visual interpretation is introduced; Finally, through manual interactive inspection and land boundary correction, a full sequence spatial distribution product is generated. After verification, the average Kappa coefficient of the entire sequence dataset is 0.71, with an average overall accuracy of 80.36%, which can fully meet the research n