Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-12, Vol.13 (23), p.4785
• Main Authors: Fu, Hao, Zhao, Wei, Zhan, Qiqi, Yang, Mengjiao, Xiong, Donghong, Yu, Daijun
• Format: Article
• Language: English
• Subjects: Afforestation; Algorithms; Archives & records; artificial forest; Climate change; Correlation coefficient; Correlation coefficients; Decision trees; Desertification; Google Earth Engine; Growing season; Image enhancement; Information retrieval; Landsat; Landsat satellites; Methods; Normalized difference vegetative index; Plant extracts; Planting; planting time; Remote sensing; Rivers; Root-mean-square errors; Satellite imagery; Subspaces; Sustainability management; Thematic Mappers (LANDSAT); Time series; time series analysis; Valleys; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13234785

Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control.