Global environmental change leads to the variation in the relative coverage of dead trees, liana-infested and non-liana-infested trees in many tropical forests. Increase in the coverage of lianas had adverse effects on forested ecosystems such as decreasing tree growth rates and increasing tree mortality. This paper proposes a classification framework that integrates unmanned aerial vehicle systems (UAVs)-derived multi-spectral images and a Deep self-encoding network (DSEN) with the goal of monitoring and quantifying the relative coverage of dead trees, liana-infested, and non-liana-infested trees at high spatial scales. Today's UAVs-derived multi-spectral images provide the much necessary high resolution/quality data to monitor ecosystem-level processes at low cost and on demand. On the other hand, DSEN, a state-of-the-art classification approach that uses multiple layers to exploit abstract, invariant features from input data, has been proved to have the ability to acquire excellent results. This new classification framework, implemented at a tropical Dry Forest site in Costa Rica, provided accurate estimations of the relative coverage of dead trees, liana-infested trees, non-liana-infested trees, and non-forests. The approach opens the door to start exploring linkages between a booming UAVS industry and machine learning/Deep learning classifiers.

全球环境变化导致许多热带森林中死木，藤本植物和非藤本植物的相对覆盖率发生变化。藤本植物的覆盖范围增加对森林生态系统产生不利影响，例如降低树木生长速度和增加树木死亡率。本文提出了一个分类框架，该框架将源自无人机系统（UAV）的多光谱图像与一个深度自编码网络（DSEN）集成在一起，目的是监视和量化死树，藤本植物和在高空间尺度上不受藤本植物侵染的树木。如今，衍生自无人机的多光谱图像提供了非常必要的高分辨率/质量数据，从而可以低成本，按需监视生态系统级过程。另一方面，DSEN 事实证明，使用多层技术从输入数据中提取抽象不变特征的最新分类方法具有获取出色结果的能力。这个新的分类框架在哥斯达黎加的热带干旱森林站点实施，提供了对死树，藤本植物感染的树木，非藤本植物感染的树木和非森林的相对覆盖率的准确估计。该方法为开始探索蓬勃发展的UAVS行业与机器学习/深度学习分类器之间的联系打开了大门。提供了对死树，藤本植物感染的树木，非藤本植物感染的树木和非森林的相对覆盖率的准确估计。该方法为开始探索蓬勃发展的UAVS行业与机器学习/深度学习分类器之间的联系打开了大门。提供了对死树，藤本植物感染的树木，非藤本植物感染的树木和非森林的相对覆盖率的准确估计。该方法为开始探索蓬勃发展的UAVS行业与机器学习/深度学习分类器之间的联系打开了大门。