IGSNRR OpenIR
Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest
Liu, Qingsheng1,2; Song, Hongwei3; Liu, Gaohuan1; Huang, Chong1; Li, He1
2019-05-02
Source PublicationREMOTE SENSING
Volume11Issue:10Pages:24
Corresponding AuthorLiu, Qingsheng(liuqs@lreis.ac.cn)
AbstractHigh-resolution satellite imagery enables decametric-scale quasi-circular vegetation patch (QVP) mapping, which greatly aids the monitoring of vegetation restoration projects and the development of theories in pattern evolution and maintenance research. This study analyzed the potential of employing five seasonal fused 5 m spatial resolution CBERS-04 satellite images to map QVPs in the Yellow River Delta, China, using the Random Forest (RF) classifier. The classification accuracies corresponding to individual and multi-season combined images were compared to understand the seasonal effect and the importance of optimal image timing and acquisition frequency for QVP mapping. For classification based on single season imagery, the early spring March imagery, with an overall accuracy (OA) of 98.1%, was proven to be more adequate than the other four individual seasonal images. The early spring (March) and winter (December) combined dataset produced the most accurate QVP detection results, with a precision rate of 66.3%, a recall rate of 43.9%, and an F measure of 0.528. For larger study areas, the gain in accuracy should be balanced against the increase in processing time and space when including the derived spectral indices in the RF classification model. Future research should focus on applying higher resolution imagery to QVP mapping.
KeywordCBERS-04 multi-seasonal images quasi-circular vegetation patch random forest Yellow River Delta
DOI10.3390/rs11101216
WOS KeywordHIGH-SPATIAL-RESOLUTION ; TREE SPECIES CLASSIFICATION ; LAND-COVER CLASSIFICATION ; REMOTELY-SENSED IMAGERY ; TIME-SERIES ; WORLDVIEW-2 IMAGERY ; GREEN LAI ; SPOT 5 ; CROP ; SOIL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41671422] ; National Natural Science Foundation of China[41661144030] ; National Natural Science Foundation of China[4151144012] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; Innovation Project of LREIS[O88RA20CYA] ; Innovation Project of LREIS[08R8A010YA] ; National Mountain Flood Disaster Investigation Project[SHZH-IWHR-57]
Funding OrganizationNational Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Innovation Project of LREIS ; National Mountain Flood Disaster Investigation Project
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000480524800074
PublisherMDPI
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68859
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLiu, Qingsheng
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
3.Henan Aero Geophys Survey & Remote Sensing Ctr, Zhengzhou 450053, Henan, Peoples R China
Recommended Citation
GB/T 7714
Liu, Qingsheng,Song, Hongwei,Liu, Gaohuan,et al. Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest[J]. REMOTE SENSING,2019,11(10):24.
APA Liu, Qingsheng,Song, Hongwei,Liu, Gaohuan,Huang, Chong,&Li, He.(2019).Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest.REMOTE SENSING,11(10),24.
MLA Liu, Qingsheng,et al."Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest".REMOTE SENSING 11.10(2019):24.
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