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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (6) : 241-250     DOI: 10.6046/zrzyyg.2024355
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Forest disturbances in the South China hilly and mountainous belt based on long time-series Landsat data
PEI Du1(), YUAN Wubin2, LI Hengkai1()
1. Jiangxi University of Science and Technology, School of Civil and Surveying & Mapping Engineering, Ganzhou 341000, China
2. Jiangxi Provincial Natural Resources Development Center, Nanchang 330025, China
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Abstract  

The South China hilly and mountainous belt is one of the “three regions and four belts” involved in China's major ecosystem conservation and restoration program. This belt hosts the largest and most well-preserved middle subtropical forest ecosystem at the same latitudes globally, playing a crucial role in ensuring the ecological security in South and Southwest China. Based on the Google Earth Engine (GEE) platform, this study conducted preliminary monitoring of disturbances in this belt using the LandTrendr algorithm and the Jeffries-Matusita (JM) distance. It further applied the random forest algorithm to relevant disturbance outputs, enabling the monitoring and analysis of forest disturbances in this belt from 1985 to 2022. The results indicate that the total forest disturbance area in this belt reached 38 564.62 km2 during the study period. Specifically, the disturbance areas of four ecological restoration projects decreased in the following order: Wuyi Mountains forests (12 040.27 km2), Nanling Mountains forests (11 820.79 km2), Hunan and Guangxi karst areas (8 228.97 km2), and mining areas (6 474.59 km2). Based on the 38-year forest loss dataset, this study analyzed the spatiotemporal variations in forest disturbances within this belt, revealing significant spatiotemporal forest disturbances. Spatially, forest disturbances were characterized by distinct geographic clustering. Temporally, the forest loss areas under four ecological restoration projects experienced several stages of change. Despite similar critical transition points and interannual variation patterns, differences in forest resources, climate, and economic conditions led to variations in the areas and trends of forest loss. Besides, the implementation of forestry policies somewhat influenced the forest loss trend. Overall, this study provides a scientific basis and decision-making reference for the management of forest ecosystems within this belt.
Keywords forest disturbance      South China hilly and mountainous belt      LandTrendr algorithm      spatiotemporal distribution      Google Earth Engine (GEE)     
ZTFLH:  S771.8  
  TP79  
Issue Date: 31 December 2025
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Du PEI
Wubin YUAN
Hengkai LI
Cite this article:   
Du PEI,Wubin YUAN,Hengkai LI. Forest disturbances in the South China hilly and mountainous belt based on long time-series Landsat data[J]. Remote Sensing for Natural Resources, 2025, 37(6): 241-250.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024355     OR     https://www.gtzyyg.com/EN/Y2025/V37/I6/241
Fig.1  Landsat image time series distribution chart
参数 描述 取值
maxSegments 最大分段数 8
SpikeThreshold 如果相邻时间点指数值差异百分比小于该值,则被认为是异常值 0.9
vertxCountOvershoot 在初始阶段的潜在节点回归中可以超过的节点数 0
preventOneYearRecovery 是否阻止一年后恢复的情况 是
recoveryThreshold 如果某个分割段的恢复率大于该值的倒数,那么这个分割段将会被移除 0.5
pvalThreshold 回归分析中F检验的p值,超过该值的话,则认为该像元没有发生变化 0.05
bestModelProportion 简单模型的选择规则,如果超过该值,则被选中 0.75
minObservationsNeeded 拟合中需要的最少观测数 6
Tab.1  Parameters used in LandTrendr
指数 公式
归一化植被指数
(normalized difference vegetation index,NDVI)		 $NDVI=\frac{(NIR-RED)}{(NIR+RED)}$
增强型植被指数
(vegetation enhancement index,EVI)		 $EVI=\frac{(NIR-RED)\times 2.5}{(NIR+6\times RED-7.5\times BLUE+1)}$
归一化燃烧指数
(normalized burning index,NBR)		 $NBR=\frac{(NIR-SWIR2)}{(NIR+SWIR2)}$
缨帽变换亮度指数
(TCT conversion brightness index,TCB)		 $\begin{array}{l}TCW=0.204\mathrm{?}3BLUE+0.415\mathrm{?}8GREEN+0.552\mathrm{?}4RED+0.574\mathrm{?}1NIR+\\ 0.312\mathrm{?}4SWIR1+0.230\mathrm{?}3SWIR2\end{array}$
缨帽变换湿度指数
(TCT conversion humidity index,TCW)		 $\begin{array}{l}TCW=0.031\mathrm{?}5BLUE+0.202\mathrm{?}1GREEN+0.310\mathrm{?}2RED+0.159\mathrm{?}4NIR-\\ 0.680\mathrm{?}6SWIR1-0.610\mathrm{?}9SWIR2\end{array}$
缨帽变换绿度指数
(TCT conversion greenness index,TCG)		 $\begin{array}{l}TCG=-0.160\mathrm{?}3BLUE-0.281\mathrm{?}9GREEN-0.493\mathrm{?}4RED+0.794\mathrm{?}0NIR-\\ 0.000\mathrm{?}2SWIR1-0.144\mathrm{?}6SWIR2\end{array}$
Tab.2  Calculation formula for spectral indices
Fig.2  J-M distance heatmap
Fig.3  Disturbance result comparison
Fig.4  Annual forest disturbance variation in four ecological restoration projects (1985—2022)
Fig.5  Annual forest disturbance variation in 4 ecological restoration projects (1985—2022)
Fig.6  Timeline of major forestry policies and annual changes in forest loss area
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