天山东部雪岭云杉林小蠹灾害的长时序遥感监测及其时空分布特征

Long-term time series remote sensing monitoring and spatiotemporal distribution characteristics of bark beetle outbreaks in Picea schrenkiana forests in the eastern Tianshan Mountains

摘要

【目的】新疆天山东部部分雪岭云杉林遭受小蠹危害并暴发成灾，严重威胁区域森林生态系统的稳定与功能。本研究利用长时序 Sentinel-2 卫星影像识别小蠹受害林分，分析其时空分布特征及与地形因子的关系，以期为小蠹灾害监测与科学防控提供技术支撑。【方法】以新疆天山东部国有林管理局板房沟分局雪岭云杉林为研究区，利用 2016—2025 年长时序 Sentinel-2 影像，结合地面调查和 GF-2/7 亚米级分辨率卫星影像，采用随机森林算法构建雪岭云杉林小蠹受害林分分类模型，并分析光谱反射率、植被指数（VIs）及植被指数时序异常值（ΔVIs）等变量的重要性。结合小蠹危害特性绘制了小蠹灾害时空动态分布图，并统计分析探讨地形因子与灾害空间分布的关系。【结果】小蠹受害林分分类模型的总体精度（OA）达到 92.04%，Kappa 系数为 0.84，表明模型能够准确识别受害林分。变量重要性分析表明，ΔVIs 的整体贡献高于单时相光谱反射率和 VIs，其中归一化植被指数时序异常值（ΔNDVI）是识别受害林分的最重要特征。基于分类结果绘制的小蠹灾害时空动态分布图与 GF-2/7 卫星影像解译结果具有较高一致性。研究区小蠹灾害主要分布于 1 600 ~ 2 000 m 的较低海拔区域，不同海拔区域的灾害分布存在一定年际变化。【结论】基于 Sentinel-2 时序数据构建的随机森林模型能够有效识别雪岭云杉林小蠹受害林分，并实现小蠹灾害时空动态分布制图。研究区小蠹灾害分布具有明显的海拔差异和年际变化。研究结果可为天山雪岭云杉林小蠹受害林分的区域遥感监测及精准防控提供技术支撑。

Abstract

[Objective] Bark beetle outbreaks in spruce forests in the eastern Tianshan Mountains of Xinjiang pose a serious threat to the stability and ecological functions of the regional forest ecosystems. This study aimed to identify bark beetle-infested spruce stands using time series of Sentinel-2 satellite imagery, analyze their spatiotemporal distribution characteristics and relationships with topographic factors, and provide technical support for bark beetle monitoring and scientific management. [Method] The Picea schrenkiana forests in the Banfanggou Branch of the Eastern Tianshan State-owned Forest Bureau were selected as the study area. Time series of Sentinel-2 images acquired from 2016 to 2025, together with field survey data and GF-2/7 sub-meter-resolution satellite imagery, were used to develop a random forest classification model for identifying bark beetle-infested spruce stands. The importance of spectral reflectance, vegetation indices (VIs), and temporal anomaly variables of vegetation indices (ΔVIs) was evaluated. Based on the biological characteristics of bark beetle infestation, spatiotemporal distribution maps of bark beetle damage were generated. Statistical analyses were further conducted to investigate the relationships between bark beetle distribution and topographic factors. [Result] The random forest classification model achieved an overall accuracy (OA) of 92.04% with a Kappa coefficient of 0.84, demonstrating high accuracy in identifying bark beetle-infested stands. Variable importance analysis showed that ΔVIs contributed more to the classification than single-date spectral reflectance and VIs, with the temporal anomaly of the normalized difference vegetation index (ΔNDVI) identified as the most important feature. The spatiotemporal distribution maps derived from the classification results were highly consistent with visual interpretation of GF-2/7 satellite imagery. Bark beetle damage in the study area was primarily concentrated in low-elevation areas (1 600–2 000 m), with some interannual variation in its distribution across different elevation ranges. [Conclusion] The random forest model based on Sentinel-2 time-series data effectively identified bark beetle-infested spruce stands and enabled spatiotemporal mapping of bark beetle damage. The spatial distribution of bark beetle damage in the study area exhibited distinct differences across elevation gradients and considerable interannual variation. These findings provide technical support for regional remote sensing monitoring and precision control of bark beetle-infested spruce forests in the Tianshan Mountains.

关键词

雪岭云杉 / 小蠹灾害 / Sentinel-2 时序遥感影像 / 地形因子

Key words

Picea schrenkiana / bark beetle infestation / time series of Sentinel-2 imagery / topographic factors

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基金资助

新疆维吾尔自治区重点研发计划项目课题(2026B02001-1)