Monitoring the Vegetation Dynamics in the Dongting Lake Wetland from 2000 to 2019 Using the BEAST Algorithm Based on Dense Landsat Time Series
Source abstract
Abstract
The dynamic monitoring and analysis of wetland vegetation play important roles in revealing the change, restoration and reconstruction of the ecosystem environment. The increasing availability of high spatial-temporal resolution remote sensing data provides an unprecedented opportunity for wetland dynamic monitoring and change detection. Using the reconstructed dense monthly Landsat time series, this study focuses on the continuous monitoring of vegetation dynamics in Dongting Lake wetland, south China, in the last two decades (2000–2019) by using the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) method. Firstly, the flexible spatiotemporal data fusion (FSDAF) model is applied to blend Landsat and moderate-resolution imaging spectroradiometer (MODIS) images on the basis of the input image pair selection strategy named “cross-fusion” to generate the monthly time-series normalized difference vegetation index (NDVI) with the spatial resolution of 30 m. Then, the abrupt changes, trend, and seasonality of the vegetation in the study area as well as the uncertainties of change detection are estimated by the BEAST method. Results show that there is a close relationship between the ground true data and the estimated changepoints. A high overall accuracy (OA) of 87.37% and Kappa coefficient of 0.85 were achieved by the proposed framework. Additionally, the temporal validation got the interval intersection of 86.57% and the absolute difference of mean interval length of 6.8 days. All of the results demonstrate that the vegetation changes in the Dongting Lake wetland varied spatially and temporally in the last two decades, because of extreme weathers and anthropogenic factors. The presented approach can accurately identify the vegetation changes and time of disturbance in both the spatial and temporal domains, and also can retrieve the evolution process of wetland vegetation under the influence of climate changes and human activities. Therefore, it can be used to reveal potential causes of the degradation and recovery of wetland vegetation in subtropical areas.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing in Agriculture · Land Use and Ecosystem Services · Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China
College of Forestry, Central South University of Forestry & Technology, Changsha 410004, China
Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China
Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China
Funding
Forestry Remote Sensing Application System based on GF satellites (Phase 2) · Postgraduate Scientific Research Innovation Project of Hunan Province · Scientific Innovation Fund for Post-graduates of Central South University of Forestry and Technology
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