Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

Abstract

China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations.

Forest Age Mapping Using Landsat Time-Series Stacks Data Based on Forest Disturbance and Empirical Relationships between Age and Height

“…Xian et al [61] showed that forest change losses in Guangdong Province between 1980 and 2015 were mainly influenced by economically oriented factors, although deforestation driven by economic factors was limited by forest management policies. In addition, the "Guidance on Strengthening the Management of Collective Forest Resources and Prohibiting Indiscriminate Logging in the South" was promulgated in 1987 to curb the uncontrolled logging resulting from the "three determinations" policy; however, forest disturbance in Guangdong Province has remained high over recent decades [38]. Since mature and overmature forests are almost completely logged, consequently, the remaining disturbed forests also tend to be younger.…”

“…Subsequently, we calculated the annual maximum of the time series of normalized burn ratio (NBR) based on Landsat data, which served as input for the LandTrendr algorithm. NBR is highly sensitive to forest disturbance and restoration [38,51,52], with higher NBR values typically indicating healthy and dense vegetation, and can be calculated using Equation (1).…”

“…Since the United States Geological Survey (USGS) has made Landsat data freely accessible, there has been rapid development of change-detection algorithms for forest disturbance based on long-term Landsat time-series stacks (LTSS), such as Vegetation Change Tracker (VCT) [35], Breaks For Additive Season and Trend Monitor (BFAST) [36], and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm [37]. LandTrendr is considered to be one of the best algorithms for detecting forest disturbances due to the effectiveness of the spectral temporal segmentation algorithm in detecting both short-term severe disturbances and long-term slow vegetation change events [37,38]. LTSS provide an ideal choice of data for the LandTrendr algorithm, not only because of the medium spatial resolution and global coverage advantages of LTSS, but also because its decades-long time span supports long-term forest change detection [39].…”

Monitoring Vegetation Changes and Disturbances in Gunung Merbabu National Park Using Landtrendr Algorithm and Landsat Images

“…There are several possibilities for this to happen. Another effect is a bad image due to the influence of clouds at the research location [Shen et al, 2022] or no significant changes within a certain period.…”

Monitoring forest gain and loss based on LandTrendr algorithm and Landsat images in KTH Pati social forestry area, Indonesia

“…The dynamic change of vegetation can be monitored by observing the spectral trajectory of an individual pixel over time (Yang et al, 2018). Taking advantage of prolonged Landsat data acquisition, Kennedy et (Shen et al, 2022;Yin et al, 2022). This study aimed to identify the trends of forest change using the LandTrendr approach, taking a case study in a social forestry area in Pati, Indonesia.…”