Mapping large area tea plantations using progressive random forest and Google Earth Engine

• Published in: Journal of applied remote sensing 2022-04, Vol.16 (2), p.024509-024509
• Main Authors: Qu, Le’an, Li, Manchun, Chen, Zhenjie, Liu, Wangbing, Zhi, Junjun, Zhang, Lechun
• Format: Article
• Language: English
• ISSN: 1931-3195; 1931-3195
• DOI: 10.1117/1.JRS.16.024509

A timely and accurate understanding of the spatial distribution of tea plantations is beneficial for agricultural management and regional sustainable development. However, obtaining detailed distribution data on large-area tea plantations remains challenging owing to limitations in computational capabilities, training data, and workflow design. Utilizing the Google Earth Engine, which provides a catalog of multisource data in a cloud-based environment, we developed a methodology to generate a highly accurate tea plantation map, with a 10-m resolution, for Anhui Province, China, by integrating a random forest model with a progressive model. Our major contribution lies in this hybrid approach, which comprises two major components: (1) an optimal classification band combination derived from Sentinel-2 products and the digital elevation model filtered by the J-M distance model and (2) a progressive random forest method introduced for tea plantation classification. The experimental results show that our proposed workflow achieved an average classification accuracy of 89.27% for the entire Anhui Province. In addition, this approach is semiautomatic and can effectively reduce the labor required during the generation of training data compared with traditional classification approaches. These findings demonstrate the potential of integrating machine learning and progressive models to produce high-precision remote sensing classification maps.