Construction and application of a degradation assessment system for the Three-North Shelterbelt Forest based on sliding window and trend analysis.

The Three-North Shelterbelt Forest Program is the largest ecological project in the world and plays a vital role in improving the environmental quality in northern China. However, degradation has occurred in some areas of this shelterbelt. There are several shortfalls in the literature about the degradation assessment of the Three-North Shelterbelt Forest, including the difficulty of assessment indicators in comprehensively characterizing the growth state of the Three-North Shelterbelt Forest and the insufficient ability of assessment methods to capture short-term fluctuations and interannual variations in growth state. The kernel normalized difference vegetation index (kNDVI) comprehensively characterizes stand structure, photosynthetic capacity, and health status. We used it as the assessment indicator to construct a degradation determination standard and assessed the degradation status of the Three-North Shelterbelt Forest by integrating time-series trend analysis and sliding window analysis. The results showed that the total degraded area reached 83100 km<sup>2</sup>, accounting for 16.0% of the total area of the Three-North Shelterbelt Forest. The mild, moderate, and severe degradation accounted for 11.2%, 4.0%, and 0.8% of the total area, respectively. The overall accuracy of remote sensing validation reached 86.3%, and the overall accuracy of field survey validation reached 92.4%. By constructing a degradation assessment system for the Three-North Shelterbelt Forest and identifying its degradation distribution pattern, this study could provide scientific support for the subsequent protection and restoration of the Three-North Shelterbelt Forest.

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Publication Details

Journal
PubMed
Published
2025-10-01
DOI
https://doi.org/10.13287/j.1001-9332.202510.005
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Construction and application of a degradation assessment system for the Three-North Shelterbelt Forest based on sliding window and trend analysis.

Chenguang Zhao, Zhisen Zhang, Jiazheng Wang, Chao Guan et al.
PubMed
Remote Sensing in Agriculture
article

Construction and application of a degradation assessment system for the Three-North Shelterbelt Forest based on sliding window and trend analysis.

Chenguang Zhao, Zhisen Zhang, Jiazheng Wang, Chao Guan, Changming Zhao
article en

Abstract

The Three-North Shelterbelt Forest Program is the largest ecological project in the world and plays a vital role in improving the environmental quality in northern China. However, degradation has occurred in some areas of this shelterbelt. There are several shortfalls in the literature about the degradation assessment of the Three-North Shelterbelt Forest, including the difficulty of assessment indicators in comprehensively characterizing the growth state of the Three-North Shelterbelt Forest and the insufficient ability of assessment methods to capture short-term fluctuations and interannual variations in growth state. The kernel normalized difference vegetation index (kNDVI) comprehensively characterizes stand structure, photosynthetic capacity, and health status. We used it as the assessment indicator to construct a degradation determination standard and assessed the degradation status of the Three-North Shelterbelt Forest by integrating time-series trend analysis and sliding window analysis. The results showed that the total degraded area reached 83100 km<sup>2</sup>, accounting for 16.0% of the total area of the Three-North Shelterbelt Forest. The mild, moderate, and severe degradation accounted for 11.2%, 4.0%, and 0.8% of the total area, respectively. The overall accuracy of remote sensing validation reached 86.3%, and the overall accuracy of field survey validation reached 92.4%. By constructing a degradation assessment system for the Three-North Shelterbelt Forest and identifying its degradation distribution pattern, this study could provide scientific support for the subsequent protection and restoration of the Three-North Shelterbelt Forest.

PubMedVol. 36(10)
Lanzhou University (CN)
Openalex Percentile: Top 68%
Remote Sensing in Agriculture
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