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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 115-126     DOI: 10.6046/zrzyyg.2021064
Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images
SHI Feifei1,2,3,4,5(), GAO Xiaohong1,3,4,6(), XIAO Jianshe2,5, LI Hongda1,3,4, LI Runxiang1,3,4, ZHANG Hao1,3,4
1. School of Geographical Sciences, Qinghai Normal University, Xining 810008, China
2. Institute of Qinghai Meteorological Science Research, Xining 810008, China
3. Key Laboratory of Physical Geography and Environmental Process of Qinghai Province, Xining 810008, China
4. Ministry of Education Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China
5. Key Laboratory of Disaster Prevention and Mitigation of Qinghai Province, Xining 810008, China
6. Academy of Plateau Science and Sustainability, Xining 810008, China
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It is significant for the market management and regulation of local government to accurately extract wolfberry planting areas in the Qaidam Basin using remote sensing technology. Taking the Nuomuhong Farm, a typical wolfberry planting area, as an example, this study selected Landsat8 OLI and GF-1 WFV images to construct the time-series NDVI/EVI data of the crop growth period. Then, this study employed four novel ensemble learning classifiers (i.e., LightGBM, GBDT, XGBoost, and RF) and two widely used machine learning classifiers (SVM and MLPC) to classify wolfberry planting areas. The results show that: ① Relatively high classification accuracy were obtained using LightGBM (90.4%), GBDT (90.4%), XGBoost (89.31%), and RF (86.96%). Most especially, LightGBM-EVI yielded the highest overall classification accuracy (91.67%), with a Kappa coefficient of 0.90; ② Enhanced vegetation index (EVI) is more sensitive in the middle-late stage of the wolfberry growth period. For the same classifier, better mapping effects of wolfberry planting areas can be obtained when time-series EVI data were used; ③ Data redundancy can be further reduced while obtaining high classification accuracy by determining the optimal temporal features of NDVI/EVI classification using the feature importance scores of the GBDT, XGBoost, and RF classifiers.

Keywords crop classification      wolfberry      NDVI/EVI time series      ensemble learning     
ZTFLH:  TP79S5  
Corresponding Authors: GAO Xiaohong     E-mail:;
Issue Date: 14 March 2022
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Feifei SHI
Xiaohong GAO
Jianshe XIAO
Hongda LI
Runxiang LI
Cite this article:   
Feifei SHI,Xiaohong GAO,Jianshe XIAO, et al. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
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Fig.1  Distribution map of wolfberry planting area
数据类型 传感
影像质量 数据用途
2017-04-25 Landsat8 OLI 30 良(5%云量) 构建时序多源遥感数据
2017-05-11 Landsat8 OLI 30 优(无云)
2017-05-27 Landsat8 OLI 30 良(8%云量)
2017-06-28 Landsat8 OLI 30 优(无云)
2017-07-14 Landsat8 OLI 30 优(无云)
2017-08-02 GF-1 WFV 16 优(无云)
2017-08-14 GF-1 WFV 16 优(无云)
2017-09-27 GF-1 WFV 16 优(无云)
2017-11-03 Landsat8 OLI 30 优(无云)
Tab.1  List of image data
Fig.2  Distribution of sample points in the study area
Fig.3  Phenology of wolfberry crops
Fig.4  NDVI and EVI curve
Fig.5  NDVI and EVI indices increase and decrease
Fig.6  Classification results based on NDVI time series data
Fig.7  Classification results based on EVI time series data
Fig.8  Producer accuracy and user accuracy heatmap
Fig.9  Overall classification accuracy and Kappa coefficient Radar map
Fig.10  Comparison of overall classification accuracy and Kappa coefficient of models before and after the selection of temporal features
Fig.11  Statistics of time-phase selection times of NDVI and EVI
Fig.12  Comparison of NDVI and EVI indices point density of GF-1 and Landsat8 data before and after preprocessing
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