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Mapping rubber plantations in Xishuangbanna, southwest China based on the re-normalization of two Landsat-based vegetation-moisture indices and meteorological data

文献类型：期刊论文

作者Xiao, Chiwei1,2; Li, Peng1,2; Feng, Zhiming1,2,3; Lin, Yumei1,2; You, Zhen1,2,3; Yang, Yanzhao1,2,3
刊名GEOCARTO INTERNATIONAL
出版日期2019-11-20
页码15
关键词Rubber plantations phenology Landsat Normalized Difference Vegetation Index Normalized Difference Moisture Index Xishuangbanna
ISSN号1010-6049
DOI10.1080/10106049.2019.1687592
通讯作者Feng, Zhiming(fengzm@igsnrr.ac.cn)
英文摘要Information on where rubber plantations are located and when they were established is essential for understanding changes in the regional carbon cycle, biodiversity, hydrology and ecosystem services. Here, we proposed a simple and modified phenology-based method to map rubber plantations and evaluate the effectiveness of this method in Xishuangbanna in southwest China, the second largest area of natural rubber cultivation. Our phenological algorithm is supported by local meteorological data and involves the re-normalization of two Landsat-8 Operational Land Imager-derived vegetation moisture indices (i.e. the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI)). We then applied air temperature data (daily records from 1981 to 2015) and periodic in situ observations of rubber plantations (weekly records from 2017 to 2018) to determine the phenological stages of rubber tree growth with the goal of selecting the most effective Landsat images. The re-normalization algorithm was able to highlight the temporal differences in the vegetation canopy and moisture content of rubber plantations, because rubber trees in Xishuangbanna display unique defoliation-foliation signals during the dry season. The resultant map of rubber plantations showed a high overall accuracy of 92.3% and a Kappa coefficient of 0.846. The developed phenological re-normalization method with meteorological data greatly enriches remote sensing-based approaches for mapping rubber plantations.
WOS关键词RICE PLANTING AREA ; TIME-SERIES DATA ; INTEGRATING PALSAR ; TROPICAL FORESTS ; STAND AGES ; PHENOLOGY ; OLI ; ALGORITHM ; TEMPERATURE ; DYNAMICS
资助项目China Postdoctoral Science Foundation[2019M660777] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20010203] ; National Natural Science Foundation of China[41971242] ; Program for BINGWEI Excellent Young Talents of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[2018RC201]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000497632700001
出版者TAYLOR & FRANCIS LTD
资助机构China Postdoctoral Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; Program for BINGWEI Excellent Young Talents of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/130306]  
专题中国科学院地理科学与资源研究所
通讯作者Feng, Zhiming
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
3.Minist Nat Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xiao, Chiwei,Li, Peng,Feng, Zhiming,et al. Mapping rubber plantations in Xishuangbanna, southwest China based on the re-normalization of two Landsat-based vegetation-moisture indices and meteorological data[J]. GEOCARTO INTERNATIONAL,2019:15.
APA Xiao, Chiwei,Li, Peng,Feng, Zhiming,Lin, Yumei,You, Zhen,&Yang, Yanzhao.(2019).Mapping rubber plantations in Xishuangbanna, southwest China based on the re-normalization of two Landsat-based vegetation-moisture indices and meteorological data.GEOCARTO INTERNATIONAL,15.
MLA Xiao, Chiwei,et al."Mapping rubber plantations in Xishuangbanna, southwest China based on the re-normalization of two Landsat-based vegetation-moisture indices and meteorological data".GEOCARTO INTERNATIONAL (2019):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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