Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

Ling Wu,Zhaoliang Li,Biyao Zhang,Yibo Tang,Meiling Liu,Boyuan Liu,Yuanyuan Meng,Lihong Zhu,Boliang Xu,Xiangnan Liu

doi:10.3390/rs12020341

Abstract

Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an ‘anytime’ forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability.

Highlights

Dramatic changes have occurred in many wetland ecosystems in the previous decades and have been attributed to the influence of climatic changes and human activities [1]
The change detection with an overall accuracy of 87.8% indicated that the changes related to forest were well captured by Breaks For Additive Seasonal and Trend (BFAST) algorithm
The accuracy of the timing of change detection was further assessed for 253 reference pixels in which the change was identified with the Landsat time series data or high spatial resolution images acquired from Google Earth and correctly detected by BFAST (Table 6)

Summary

Introduction

Dramatic changes have occurred in many wetland ecosystems in the previous decades and have been attributed to the influence of climatic changes and human activities [1]. A large number of Populus plantations have accelerated the desiccation of wetland [4], resulting in a decrease of the wetland area [5,6,7]. This situation leads to the death of wetland vegetation [8]; induces biodiversity loss [7]; and influences the community composition [4], structure [7] and function [4] of the wetland ecosystem. Accurately detecting the land cover changes related to forests and monitoring spatiotemporal dynamics of forests in the Dongting Lake region are critical for protecting wetland ecosystems

Objectives

Results

Discussion

Conclusion