Mapping spatial and temporal distribution information of plantations in Guangxi from 2000 to 2020

Abstract

Plantations are formed entirely by artificial planting which are different from natural forests. The rapid expansion of plantation forestry has brought about a series of ecological and environmental problems. Timely and accurate information on the distribution of plantation resources and continuous monitoring of the dynamic changes in plantations are of great significance. However, plantations have similar spectral and texture characteristics with natural forests. In addition, cloud and rain greatly affected the image quality of large area mapping. Here, we tested the possibility of applying Continuous Change Detection and Classification to distinguish plantations from natural forests and described the spatiotemporal dynamic changes of plantations. We adopted the Continuous Change Detection and Classification algorithm and used all available Landsat images from 2000 to 2020 to map annual plantation forest distribution in Guangxi Zhuang Autonomous Region, China and analyzed their spatial and temporal dynamic changes. The overall accuracy of the plantation extraction is 88.77%. Plantations in Guangxi increased significantly in the past 20 years, from 2.37 × 106 ha to 5.11 × 106 ha. Guangxi is expanding new plantation land every year, with the largest expansion area in 2009 of about 2.58 × 105 ha. Over the past 20 years, plantations in Guangxi have clearly shown a tendency to expand from the southeast to the northwest, transformed from natural forests and farmland. 30% of plantations have experienced at least one logging-and-replanting rotation event. Logging rotation events more intensively occur in areas with dense plantation forests. Our study proves that using fitting coefficients from Continuous Change Detection and Classification algorithm is effective to extract plantations and mitigating the adverse effects of clouds and rain on optical images in a large scale, which provides a fast and effective method for long-time and large-area plantation identification and spatiotemporal distribution information extraction, and strong data support and decision reference for plantation investigation, monitoring and management.