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Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms

文献类型：期刊论文

作者Wang, Ming1; Liu, Zhengjia2,3; Baig, Muhammad Hasan Ali4; Wang, Yongsheng2,3; Li, Yurui2,3; Chen, Yuanyan1
刊名LAND USE POLICY
出版日期2019-11-01
卷号88页码:11
关键词Crop phenology Sentinel-2 images Machine learning approach Sugarcane mapping Land use
ISSN号0264-8377
DOI10.1016/j.landusepol.2019.104190
通讯作者Liu, Zhengjia(liuzj@igsnrr.ac.cn)
英文摘要Sugarcane is an important type of cash crop and plays a crucial role in global sugar production. Clarifying the magnitude of sugarcane planting will likely provide very evident supports for local land use management and policy-making. However, sugarcane growth environment in complex landscapes with frequent rainy weather conditions poses many challenges for its rapid mapping. This study thus tried and used 10-m Sentinel-2 images as well as crop phenology information to map sugarcane in Longzhou county of China in 2018. To minimize the influences of cloudy and rainy conditions, this study firstly fused all available images in each phenology stage to obtain cloud-free remote sensing images of three phenology stage (seedling, elongation and harvest) with the help of Google Earth Engine platform. Then, the study used the fused images to compute the normalized difference vegetation index (NDVI) of each stage. A three-band NDVI dataset along with 4000 training samples and 2000 random validation samples was finally used for sugarcane mapping. To assess the robustness of the threeband NDVI dataset with phenological characteristics for sugarcane mapping, this study employed five classifiers based on machine learning algorithms, including two support vector machine classifiers (Polynomial-SVM and RBF-SVM), a random forest classifier (RF), an artificial neural network classifier (ANN) and a decision tree classifier (CART-DT). Results showed that except for ANN classifier, Polynomial-SVM, RBF-SVM, RF and CART-DT classifiers displayed high accuracy sugarcane resultant maps with producer's and user's accuracies of greater than 91%. The ANN classifier tended to overestimate area of sugarcane and underestimate area of forests. Overall performances of five classifiers suggest Polynomial-SVM has the best potential to improve sugarcane mapping at the regional scale. Also, this study observed that most sugarcane (more than 75% of entire study area) tends to grow in flat regions with slope of less than 10 degrees. This study emphasizes the importance of considering phenology in rapid sugarcane mapping, and suggests the potential of fine-resolution Sentinel-2 images and machine learning approaches in high-accuracy land use management and decision-making.
WOS关键词SUPPORT VECTOR MACHINES ; LAND-USE ; RANDOM FOREST ; TIME-SERIES ; SPECIES CLASSIFICATION ; POVERTY ALLEVIATION ; NEURAL-NETWORKS ; MODIS ; ACCURACY ; POLICY
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23070302] ; National Natural Science Foundation of China[41601582] ; National Natural Science Foundation of China[41971218] ; National Key Research and Development Program of China[2017YFC0504701] ; Start-up Research Program of IGSNRR
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000494886800070
出版者ELSEVIER SCI LTD
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; Start-up Research Program of IGSNRR
源URL[http://ir.igsnrr.ac.cn/handle/311030/131808]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Zhengjia
作者单位1.Guangxi Normal Univ, Coll Comp Sci & Informat Technol, Guilin 541004, Guangxi, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
4.PMAS Arid Agr Univ, IGEO, Rawalpindi, Pakistan
推荐引用方式
GB/T 7714		 Wang, Ming,Liu, Zhengjia,Baig, Muhammad Hasan Ali,et al. Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms[J]. LAND USE POLICY,2019,88:11.
APA Wang, Ming,Liu, Zhengjia,Baig, Muhammad Hasan Ali,Wang, Yongsheng,Li, Yurui,&Chen, Yuanyan.(2019).Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms.LAND USE POLICY,88,11.
MLA Wang, Ming,et al."Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms".LAND USE POLICY 88(2019):11.

入库方式： OAI收割

来源：地理科学与资源研究所

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