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Abstract：
Despite increasing the number of studies for mapping remote sensing insect-induced forest infestations, applying novel approaches for mapping and identifying its triggers are still developing. This study was accomplished to test the performance of Geographic Object-Based Image Analysis (GEOBIA) TreeNet for discerning insect-infested forests induced by defoliators from healthy forests using Landsat 8 OLI and ancillary data in the broadleaved mixed Hyrcanian forests. Moreover, it has studied mutual associations between the intensity of forest defoliation and the severity of forest fires under TerraClimate-derived climate hazards by analyzing panel data models within the TreeNet-derived insect-infested forest objects. The TreeNet optimal performance was obtained after building 333 trees with a sensitivity of 93.7% for detecting insect-infested objects with the contribution of the top 22 influential variables from 95 input object features. Accordingly, top image-derived features were the mean of the second principal component (PC2), the mean of the red channel derived from the gray-level co-occurrence matrix (GLCM), and the mean values of the normalized difference water index (NDWI) and the global environment monitoring index (GEMI). However, tree species type has been considered as the second rank for discriminating forest-infested objects from non-forest-infested objects. The panel data models using random effects indicated that the intensity of maximum temperatures of the current and previous years, the drought and soil-moisture deficiency of the current year, and the severity of forest fires of the previous year could significantly trigger the insect outbreaks. However, maximum temperatures were the only significant triggers of forest fires. This research proposes testing the combination of object features of Landsat 8 OLI with other data for monitoring near-real-time defoliation and pathogens in forests.
摘要：
尽管对遥感昆虫诱发的森林侵扰的制图研究越来越多，应用新的方法来映射和识别其触发因素仍在发展中。这项研究是为了测试基于地理对象的图像分析（GEOBIA）TreeNet的性能，该性能用于使用Landsat8OLI和阔叶混合海因森林中的辅助数据来辨别由健康森林中的脱叶者诱导的昆虫侵扰森林。此外，它通过分析TreeNet衍生的昆虫出没的森林对象中的面板数据模型，研究了TerraClimate衍生的气候灾害下森林落叶强度与森林火灾严重程度之间的相互关联。TreeNet的最佳性能是在构建333棵树后获得的，其灵敏度为93.7％，用于检测昆虫侵染的物体，其中包括来自95个输入物体特征的前22个有影响的变量。因此，顶部图像衍生特征是第二主成分(PC2)的平均值，从灰度共生矩阵(GLCM)导出的红色通道的平均值，以及归一化差异水指数（NDWI）和全球环境监测指数（GEMI）的平均值。然而,树种类型被认为是区分森林侵扰对象和非森林侵扰对象的第二等级。使用随机效应的面板数据模型表明，当前和前几年的最高温度强度，今年的干旱和土壤水分不足，前一年森林火灾的严重程度可能会引发昆虫的爆发。然而,最高温度是森林火灾的唯一重要触发因素。这项研究建议测试Landsat8OLI的对象特征与其他数据的组合，以监测森林中的近实时落叶和病原体。
