Remote Sensing Detection of Forest Changes in the South Ridge Corridor and an Attribution Analysis

Nan Wu; Linghui Huang; Meng Zhang; Yaqing Dou; Kehan Mo; Junang Liu

doi:10.3390/f16020205

journal article Open Access Jan 23, 2025

Remote Sensing Detection of Forest Changes in the South Ridge Corridor and an Attribution Analysis

Nan Wu

Linghui Huang Meng Zhang

Yaqing Dou

Kehan Mo Junang Liu

Forests Vol. 16 No. 2 pp. 205 · MDPI AG

View at Publisher Save 10.3390/f16020205

Abstract

As the largest mountain range in Southern China, the natural vegetation of Nanling plays an irreplaceable role in maintaining the stability of the ecosystem and exerting its functions. The forested area of the Nanling Corridor encompasses 168,633 km2, with a forest coverage rate exceeding 60% of all cities together. Long-term analysis of the temporal and spatial evolution of this forest and the disturbance factors in this region is of great importance for realizing the “dual carbon” goals, sustainable forest management, and protecting biodiversity. In this study, remote sensing images from a Landsat time series with a resolution of 30 m were obtained from the GEE (Google Earth Engine) cloud processing platform, and forest disturbance data were obtained using the LandTrendr algorithm. Using a machine learning random forest algorithm, the forest disturbance status and disturbance factors were explored from 2001 to 2020. The results show that the estimated disturbed forest area from 2001 to 2020 was 11,904.3 km2, accounting for 7.06% of the total area of the 11 cities in the Nanling Corridor, and the average annual disturbed area was 595.22 km2. From 2001 to 2016, the overall disturbed area increased, reaching a peak value of 1553.36 km2 in 2008, with a low value of 37.71 km2 in 2002. After 2016, the disturbed area showed a downward trend. In this study, an attribution analysis of forest disturbance factors was carried out. The results showed that the overall accuracy of forest disturbance factor attribution was as high as 82.48%, and the Kappa coefficient was 0.70. Among the disturbance factors, deforestation factors accounted for 58.45% of the total area of forest disturbance, followed by fire factors (28.69%) and building or road factors (12.85%). The regional distribution of each factor also had significant characteristics, and the Cutdown factors were mostly distributed in the lower elevations of the mountain margin, with most of them distributed in sheets. The fire factors were spatially distributed in the center of the mountains, and their distribution was loose. Building or road factors were mostly distributed in clusters or lines. These research results are expected to provide technical and data support for the study of the large-scale spatiotemporal evolution of forests and its driving mechanisms.

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Details

Published: Jan 23, 2025
Vol/Issue: 16(2)
Pages: 205
License: View

Authors

N

Nan Wu

Key Laboratory of National Forestry and Grassland Administration on Control of Artificial Forest Diseases and Pests in South China, Central South University of Forestry and Technology, Changsha 410004, China; Hunan Linkeda Agroforestry Technical Service Co. Ltd., Changsha 410004, China

L

Linghui Huang

Key Laboratory of National Forestry and Grassland Administration on Control of Artificial Forest Diseases and Pests in South China, Central South University of Forestry and Technology, Changsha 410004, China; Hunan Linkeda Agroforestry Technical Service Co. Ltd., Changsha 410004, China

M

Meng Zhang

Key Laboratory of National Forestry and Grassland Administration on Control of Artificial Forest Diseases and Pests in South China, Central South University of Forestry and Technology, Changsha 410004, China

Y

Yaqing Dou

Key Laboratory of National Forestry and Grassland Administration on Control of Artificial Forest Diseases and Pests in South China, Central South University of Forestry and Technology, Changsha 410004, China

K

Kehan Mo

College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China

J

Junang Liu

Key Laboratory of National Forestry and Grassland Administration on Control of Artificial Forest Diseases and Pests in South China, Central South University of Forestry and Technology, Changsha 410004, China

Funding

National Key R&D Program of the 14th Five-Year Plan Award: 2021YFD1400904

Cite This Article

Nan Wu, Linghui Huang, Meng Zhang, et al. (2025). Remote Sensing Detection of Forest Changes in the South Ridge Corridor and an Attribution Analysis. Forests, 16(2), 205. https://doi.org/10.3390/f16020205

Remote Sensing Detection of Forest Changes in the South Ridge Corridor and an Attribution Analysis

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