30 m祁连山地区1990 - 2020年5年土地覆被图(QMA_LC30)

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Aixia Yang, Bo Zhong, Xuelei Wang, Aiping Feng, Longfei Hu, Kai Ao, QiuPing Zhai, Shanlong Wu, Bolin Du, Junjun Wu
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引用次数: 0

摘要

祁连山地区是中国重要的生态屏障和战略水资源涵养区。近年来,社会对QMA内的环境问题的关注日益增加,强调需要准确和连续的土地覆盖图来支持生态监测、分析和预测。本文介绍了QMA_LC30数据集,该数据集包括9个土地覆盖类别,时间跨度为1990 - 2020年,每5年更新一次。该数据集主要利用30 m Landsat系列数据,具有以下特点:1)高精度,通过地理划分和分层分类决策树方法实现,并辅以视觉解释。2)鲁棒一致性,采用基于基准图的变更检测方法。QMA_LC30数据集经过严格的精度验证,所有7期土地覆盖图的总体精度均超过0.92。与GlobeLand30、ESA WorldCover、ESRI 2020 Land Cover、FROM_GLC30和GLC_FCS30相比,QMA_LC30与遥感影像的一致性最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
30 m 5-yearly land cover maps of Qilian Mountain Area (QMA_LC30) from 1990 to 2020.

The Qilian Mountain Area (QMA) serves as a crucial ecological barrier and strategic water conservation zone in China. Recent years have seen heightened social attention to environmental issues within the QMA, underscoring the need for accurate and continuous land cover maps to support ecological monitoring, analysis, and forecasting. This paper presents the QMA_LC30 dataset, which includes 9 land cover categories and spans the period from 1990 to 2020, with updates every 5 years. The dataset primarily utilizes 30 m Landsat series data and features: 1) High precision, achieved through a geographical division and hierarchical classification decision tree approach, complemented by visual interpretation. 2) Robust consistency, ensured by a change detection method based on a benchmark map. The QMA_LC30 dataset undergoes rigorous accuracy validation, achieving an overall accuracy of over 0.92 for all 7 periods of land cover maps. Compared to GlobeLand30, ESA WorldCover, ESRI 2020 Land Cover, FROM_GLC30, and GLC_FCS30, QMA_LC30 demonstrates the highest consistency with remote sensing images.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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