Extracting tea plantations in southern hilly and mountainous region based on mesoscale spectrum and temporal phenological features
Chao MA1,2,Fei YANG1(),Xuecheng WANG1,2
1.Institute of Geographic Sciences and Natural Resources Research, CAS, State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China 2.University of Chinese Academy of Sciences, Beijing 100049, China
The extraction of the spatial distribution of tea plantations in hilly areas of southern China is of great importance for economic development and ecological environment protection in southern China. Therefore, a method of tea plantation based on mesoscale spectrum and temporal phenology characteristics is proposed. The study used MODIS enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) data products to select the optimal time window for Landsat images. The preliminary classification results were extracted using the object-oriented method and the decision tree classification model. For extracting the distribution of tea plantation, different vegetation phenology parameters were obtained by using MODIS-EVI vegetation timing data. Verification results showed that the overall classification accuracy reached 85.71% and the Kappa coefficient reached 0.83, with the accuracy of tea plantation producers reaching 83.72% and the user precision reaching 90.00%. The extraction results are close to the open statistics of tea plantation area in Zhangzhou City and Anxi County. The results show that this method can obtain high tea plantation extraction accuracy and the classification results can provide some reference and guidance for the economic development of southern China and the government departments' regulation of the tea plantation.
马超,杨飞,王学成. 基于中尺度光谱和时序物候特征提取南方丘陵山区茶园[J]. 国土资源遥感, 2019, 31(1): 141-148.
Chao MA,Fei YANG,Xuecheng WANG. Extracting tea plantations in southern hilly and mountainous region based on mesoscale spectrum and temporal phenological features. Remote Sensing for Land & Resources, 2019, 31(1): 141-148.
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