4.7 Article

Mapping the Spatial Distribution of Tea Plantations Using High-Spatiotemporal-Resolution Imagery in Northern Zhejiang, China

期刊

FORESTS
卷 10, 期 10, 页码 -

出版社

MDPI
DOI: 10.3390/f10100856

关键词

tea plantation; phenological period; high spatiotemporal resolution; VEN mu S; Sentinel-2

类别

资金

  1. Zhejiang Provincial Natural Science Foundation [LQ19D010010]
  2. Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX170819]

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Tea plantations are widely distributed in the southern provinces of China and have expanded rapidly in recent years due to their high economic value. This expansion has caused ecological problems such as soil erosion, and it is therefore urgent to clarify the spatial distribution and area of tea plantations. In this study, we developed a simple method to accurately map tea plantations based on their unique phenological characteristics observed from VEN mu S high-spatiotemporal-resolution multispectral imagery. The normalized difference vegetation index (NDVI) and red-green ratio index (RGRI) of time series were calculated using 40 VEN mu S images taken in 2018 to evaluate the phenology of tea plantations. The unique phenological period of tea plantations in northern Zhejiang is from April to May, with obvious deep pruning, which is very different from the phenological period of other vegetation. During this period, the RGRI values of tea plantations were much higher than those of other vegetation such as broadleaf forest and bamboo forest. Therefore, it is possible to identify tea plantations from the vegetation in images acquired during their phenological period. This method was applied to tea plantation mapping in northern Zhejiang. The NDVI value of the winter image was used to extract a vegetation coverage map, and spatial intersection analysis combined with maps of tea plantation phenological information was performed to obtain a tea plantation distribution map. The resulting tea plantation map had a high accuracy, with a 94% producer accuracy and 95.9% user accuracy. The method was also applied to Sentinel-2 images at the regional scale, and the obtained tea plantation distribution map had an accuracy of 88.7%, indicating the good applicability of the method.

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