IGSNRR OpenIR
Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data
Wang, Zhihui1,2; Yao, Wenyi1; Tang, Qiuhong2; Liu, Liangyun3; Xiao, Peiqing1; Kong, Xiangbing1; Zhang, Pan1; Shi, Fangxin1; Wang, Yuanjian1
2018-11-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:11Pages:23
Corresponding AuthorTang, Qiuhong(tangqh@igsnrr.ac.cn)
AbstractAccurate identification of the spatiotemporal distribution of forest/grassland and cropland is necessary for studying hydro-ecological effects of vegetation change in the Loess Plateau, China. Currently, the accuracy of change detection of land cover using Landsat data in the loess hill and gully areas is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, we propose a method for continuous change detection for two types of land cover, mosaic forest/grassland and cropland, using all available Landsat data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% +/- 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation. Consequently, the accuracy of change detection was improved by reducing temporal false detection and enhancing spatial classification accuracy.
Keywordcontinuous change detection land cover classification Landsat NDVI time series forest/grassland and cropland Loess Plateau
DOI10.3390/rs10111775
WOS KeywordSOIL LOSS EQUATION ; FOREST DISTURBANCE ; GREEN PROGRAM ; COVER ; IMPROVEMENT ; EROSION ; REGION ; CLOUD ; MODEL ; GRAIN
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFC0504500] ; National Natural Science Foundation of China[41701509] ; National Natural Science Foundation of China[51809103] ; National Natural Science Foundation of China[41571276] ; National Natural Science Foundation of China[51509102] ; Special Research Fund of the YRIHR[HKY-JBYW-2017-08] ; Special Research Fund of the YRIHR[HKY-JBYW-2018-06] ; Foundation of development on science and technology, YRIHR[HKF201602] ; CAST[2017QNRC023]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Special Research Fund of the YRIHR ; Foundation of development on science and technology, YRIHR ; CAST
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000451733800106
PublisherMDPI
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/51394
Collection中国科学院地理科学与资源研究所
Corresponding AuthorTang, Qiuhong
Affiliation1.Yellow River Conservancy Commiss, Yellow River Inst Hydraul Res, Zhengzhou 450003, Henan, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China
Recommended Citation
GB/T 7714
Wang, Zhihui,Yao, Wenyi,Tang, Qiuhong,et al. Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data[J]. REMOTE SENSING,2018,10(11):23.
APA Wang, Zhihui.,Yao, Wenyi.,Tang, Qiuhong.,Liu, Liangyun.,Xiao, Peiqing.,...&Wang, Yuanjian.(2018).Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data.REMOTE SENSING,10(11),23.
MLA Wang, Zhihui,et al."Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data".REMOTE SENSING 10.11(2018):23.
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