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应用科学学报 >

2025 , Vol. 43 >Issue 6: 962 - 977

DOI: https://doi.org/10.3969/j.issn.0255-8297.2025.06.006

信号与信息处理

基于CCDC算法的大兴安岭森林扰动监测

  • 牟泓滔 ,
  • 张硕 ,
  • 王淑晴
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  • 中国地质大学 (北京) 土地科学技术学院, 北京 100083

收稿日期: 2023-07-27

  网络出版日期: 2025-12-19

基金资助

中国地质大学（北京）大学生创新创业项目（No. X202211415236）

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Monitoring of Forest Disturbance in the Greater Khingan Mountains Based on CCDC Algorithm

  • MU Hongtao ,
  • ZHANG Shuo ,
  • WANG Shuqing
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  • School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China

Received date: 2023-07-27

  Online published: 2025-12-19

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摘要

大兴安岭是我国北方的关键生态屏障，准确评估其长时序的森林干扰动态，对区域生态监管和“天然林保护工程”成效评估至关重要。然而，传统的双时相遥感变化检测方法难以捕捉大面积森林复杂的年内与年际动态。本研究利用Google Earth Engine (GEE)平台，构建了2000—2021年的Landsat时间序列堆栈。为提升对森林退化、选择性采伐等亚像素级变化的监测敏感性，本研究首先采用光谱混合分析（SMA）对Landsat影像进行逐像元解混，生成了归一化差分分数指数（normalized difference fractional index,NDFI）序列；以NDFI作为连续变化检测与分类（continuous change detection and classification,CCDC）算法的输入，通过对每个像素建立谐波模型拟合变化趋势，并基于统计阈值自动识别时间序列中的突变点，捕捉并细化森林干扰的发生时间与位置。研究结果表明： 1)2000—2021年间，森林干扰总面积为28 958 km2，干扰高发区集中在东北部（呼玛县）和西北部（漠河县）。2)干扰年份呈现明显波动，峰值出现在2002年（4 092 km2）和2013年（4 120 km2）。3)精度验证显示，CCDC算法的总体精度达91%以上，Kappa系数为0.85，与人工解译结果具有高度一致性。本研究实现了大兴安岭地区森林退化的细微干扰监测，可为该地区生态环境监测提供重要的数据支持。

关键词： 森林干扰; 连续变化检测与分类; 植被变化监测; Google Earth Engine

本文引用格式

牟泓滔 , 张硕 , 王淑晴 . 基于CCDC算法的大兴安岭森林扰动监测[J]. 应用科学学报, 2025 , 43(6) : 962 -977 . DOI: 10.3969/j.issn.0255-8297.2025.06.006

Abstract

The Greater Khingan Mountains serve as a crucial ecological barrier in northern China. Accurately assessing its long-term forest disturbance dynamics is vital for regional ecological supervision and evaluating the effectiveness of the “natural forest protection project”. However, traditional bi-temporal remote sensing change detection methods struggle to capture the complex intra-annual and inter-annual dynamics of large-area forests. This study used the Google Earth Engine (GEE) platform to construct a Landsat time-series stack for the period 2000—2021. To improve monitoring sensitivity to sub-pixel changes such as forest degradation and selective logging, this study first adopted spectral mixture analysis (SMA) to perform per-pixel unmixing of Landsat imagery, generating a normalized difference fraction index (NDFI) sequence. By taking NDFI as the input for the continuous change detection and classification (CCDC) algorithm, a harmonic model was established for each pixel to fit its change trend, and breakpoints in the time series were automatically identified based on statistical thresholds to capture and refine the occurrence time and location of forest disturbances. The results indicate that: 1) During 2000—2021, the total area of forest disturbances was 28 958 km2, with disturbance hotspots concentrated in the northeastern part (Huma County) and northwestern part (Mohe County). 2) The annual disturbance area showed significant fluctuations, with peaks in 2002 (4 092 km2) and 2013 (4 120 km2). 3) Accuracy verification shows that the CCDC algorithm has an overall accuracy of over 91% and a Kappa coefficient of 0.85, which is highly consistent with the results of manual interpretation. This study realizes the monitoring of subtle disturbances of forest degradation in the Greater Khingan Mountains region and can provide important data support for the ecological environment monitoring of this region.

Key words： forest disturbance; continuous change detection and classification; monitoring of vegetation change; Google Earth Engine

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