Publications

Kang, YW; Chen, ZH; Li, LX; Zhang, Q (2023). Construction of multidimensional features to identify tea plantations using multisource remote sensing data: A case study of Hangzhou city, China. ECOLOGICAL INFORMATICS, 77, 102185.

Abstract
China is the world's largest tea producer and trader. From 1995 to 2020, the plantation area and tea production increased by approximately 200 and 400%, respectively. Therefore, we must accurately identify the distribution of tea plantations to optimize production and promote sustainable development. However, most studies on the identification of tea plantations rely on local hyperspectral data processing, which have limitations related to a small scale, significant time consumption, and difficulty in integrating multisource images and multidimensional features to achieve the accuracy and optimization of the results. Based on the Google Earth Engine (GEE) platform, we integrated the spectral, synthetic aperture radar (SAR), terrain, and textural features to extract the seasonal phenological index using specific phenological information on tea plantations. Then, the random forest (RF) algorithm was applied to evaluate the importance of each feature in the initial feature set, followed by screening of the optimal feature combination and verification of the Jeffries-Matusita (JM) distance. Finally, the optimal feature combination was used as the input variable for the RF classification algorithm to identify the distribution of tea plantations in Hangzhou, China. The following outcomes were achieved: (1) compared to the processing of local hyperspectral data, the identification of tea plantations using the GEE had notable advantages, such as high automation, convenient algorithm adjustment, and rapid iteration of the results. (2) The user and producer accuracies of the classification of tea plantations using SAR and terrain and textural features improved from 89.3 and 85.9% to 95.8 and 91.7%, respectively, compared to only using spectral information. (3) The high-contribution feature combination identified by the importance analysis using the RF algorithm increased the JM distance to a high degree of separability of approximately 2, which is highly effective for the optimization of classification features. (4) In addition to the visible and near-infrared bands commonly used in spectral information, short-wave infrared and vegetation red-edge bands can play important roles in identifying tea plantations. (5) The importance of the same spectral or SAR information varied for different seasonal phenological indicators, indicating that the addition of phenological information was beneficial for the identification of tea plantations.

DOI:
10.1016/j.ecoinf.2023.102185

ISSN:
1878-0512