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Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China

文献类型：期刊论文

作者Xu, Xiaolin3; Li, Dan3; Liu, Hongxi2; Zhao, Guang1; Cui, Baoshan3; Yi, Yujun3; Yang, Wei3; Du, Jizeng3
刊名REMOTE SENSING
出版日期2024-11-01
卷号16期号:22页码:27
关键词land cover maps inconsistency evaluation accuracy assessment misclassification diagnosis
DOI10.3390/rs16224330
产权排序3
英文摘要Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land-atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 +/- 0.76%, followed by CNLUCC (81.38 +/- 0.87%) and GLC_FCS30 (77.83 +/- 0.80%). Globeland30 had the lowest accuracy (75.24 +/- 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human-machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables.
WOS关键词ACCURACY ASSESSMENT ; RANDOM FOREST ; SAMPLING DESIGNS ; CLASSIFICATION ; PRODUCT ; URBAN ; CONSISTENCY ; SELECTION ; DYNAMICS ; PATTERNS
资助项目National Key Research and Development Program of China ; National Science Fund for Distinguished Young Scholars[52025092] ; National Natural Science Foundation of China[41930970] ; National Natural Science Foundation of China[42475198] ; National Natural Science Foundation of China[52109074] ; [2023YFC3209003] ; [2022YFC3202001] ; [2022YFF1301801]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001366053000001
出版者MDPI
资助机构National Key Research and Development Program of China ; National Science Fund for Distinguished Young Scholars ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/211477]  
专题生态系统网络观测与模拟院重点实验室_外文论文
通讯作者Du, Jizeng
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Beijing Normal Univ Zhuhai, Adv Inst Nat Sci, Zhuhai 519087, Peoples R China
3.Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Cont, Beijing 100875, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Xiaolin,Li, Dan,Liu, Hongxi,et al. Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China[J]. REMOTE SENSING,2024,16(22):27.
APA Xu, Xiaolin.,Li, Dan.,Liu, Hongxi.,Zhao, Guang.,Cui, Baoshan.,...&Du, Jizeng.(2024).Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China.REMOTE SENSING,16(22),27.
MLA Xu, Xiaolin,et al."Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China".REMOTE SENSING 16.22(2024):27.

入库方式： OAI收割

来源：地理科学与资源研究所

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