蒙古高原雅氏落叶松尺蠖灾害遥感监测与暴发预测研究

Jas's larch inchworm is a kind of special forest pest in the Mongolian Plateau, in recent years, this insect disaster has frequently occurred in the northern Mongolian plateau, resulting in the death of a large area of larch in Mongolia and bringing serious damage to the forest ecosystem. The Pest-prone area is only a hundred kilometers distance to the Chinese border, which has been threatened to the security of forest ecological barriers in northern China. Chairman Xi Jinping has repeatedly emphasized the construction of the "Belt and Road" initiative in Mongolia and China, strengthen ecological security protection and create an ecological safety barrier. Therefore, it is imperative to carry out the remote sensing monitoring and outbreak forecasting research on the Jas's larch inchworm disaster of Mongolian Plateau. Based on this, this project intends to carry out discrimination at early occurrence of jas's larch inchworm，monitoring of jas's larch inchworm severity degree，and outbreaks prediction of jas's larch inchworm based on remote sensing.It is a new way of forest pest remote sensing warning that based on hyperspectral remote sensing recognition model of forest physiological factors for discriminating early occurrence of jas's larch inchworm in this project.It is a progress technology of forest pest remote sensing monitoring that based on multi-source remote sensing inversion model of insect density,leaf loss rate, and tree canopy color for monitoring the severity of disasters in this project.It is a new thought to effectively predict forest pest outbreaks using constructing a pest outbreak prediction model based on multi-source data.The above research will provide basic data and technical reserves for the eco-environmental protection big data service platform of "Belt and Road" for China and Mongolian cooperation, and will provide a demonstration for national forest pest monitoring and forecasting, which is of great strategic significance.

雅氏落叶松尺蠖是蒙古高原独有森林害虫。近年该虫在蒙古高原北部频发，蒙古国森林生态系统遭到严重破坏。害虫多发区离中国边境只有百余公里，已对我国北方森林生态屏障安全有重大威胁。习近平主席强调中蒙绿色“一带一路”建设，加大生态安全保护力度，打造生态安全屏障。因此开展蒙古高原雅氏落叶松尺蠖灾害遥感监测与预测研究势在必行。本申请利用遥感技术拟开展雅氏落叶松尺蠖发生早期识别、灾害严重度监测以及暴发预测等研究。本申请建立林木生理因子高光谱遥感反演模型，识别害虫发生早期，是森林虫害遥感预警技术的新途径；构建基于多源遥感的虫口密度、失叶率和林木冠层颜色反演模型，监测灾害严重度，是森林虫害遥感监测技术的发展；建立基于多源数据的害虫暴发预测模型，是对森林害虫暴发预测的新思路。上述研究将为中蒙合作建设“一带一路”生态环保大数据服务平台提供基础数据和技术储备，为国家森林虫害监测与预测提供示范，具有重大战略意义。

雅氏落叶松尺蠖（Erannis jacobsoni Djak.）为蒙古高原独有森林害虫，暴发时大量啃食西伯利亚落叶松针叶致使林木死亡，对蒙古国生态系统造成严重威胁，且害虫多发地距中国边境不足百余公里，将对我国北方“绿色屏障”造成巨大威胁。应尽早掌握雅氏落叶松尺蠖发生早期、该虫暴发和灾害严重度，为林业部门提供决策依据。基于此，本研究基于实测数据、卫星遥感数据、无人机遥感数据、气象数据和生境信息，获取林木冠层高光谱反射率、卫星图像和无人机图像、生化组分（如叶绿素含量、水分含量）和表观参量（如失叶率、冠层颜色）等，开展了发生早期高光谱遥感识别、灾害严重度监测和暴发预测研究。通过高光谱特征和林木生化组分结合，采用数据挖掘和机器学习算法，建立雅氏落叶松尺蠖发生早期高光谱遥感识别模型（基于叶绿素含量\含水率\光合速率的高光谱早期识别模型）。利用地面高光谱数据、无人机和卫星遥感数据，结合林木外观特征和内部生化组分，构建基于多源遥感数据的虫害严重度监测模型（不同严重度虫口密度\冠层失叶率\生化组分估测模型、冠层颜色判别模型、无人机多光谱不同严重度识别模型、卫星遥感不同严重度监测模型），并对其进行应用示范与检验。同时通过害虫历史灾情、生物学特征、生境信息等相关数据，从生物地理学视角分析害虫的暴发成因，在此基础上，选取敏感特征作为环境变量，借助生态位理论模型和GIS空间分析方法，构建虫害暴发预测模型，实现了雅氏落叶松尺蠖潜在发生地理分布。.本研究成果不仅拓展了森林虫害早期遥感诊断方法，而且发展了森林虫害遥感监测技术与方法，还实现了基于多源数据协同融合的害虫暴发预测的有效方法。形成了虫害早期识别、虫害严重度监测、虫害暴发区预测等的一套较为完整的森林虫害遥感监测预测体系，获得了宝贵的基础数据和成果。可见，本研究为筑牢我国北方重要森林生态安全屏障和绿色“一带一路”建设能够提供技术和信息储备，具有重要科学意义和实际应用价值。