Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series

Increasing awareness of the issue of deforestation and degradation in the tropics has resulted in efforts to monitor forest resources in tropical countries. Advances in satellite-based remote sensing and ground-based technologies have allowed for monitoring of forests with high spatial, temporal and thematic detail. Despite these advances, there is a need to engage communities in monitoring activities and include these stakeholders in national forest monitoring systems. In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.随着人们对热带地区森林砍伐与退化问题的认知不断提升，针对热带国家森林资源的监测工作应运而生。星载遥感与地面技术的进步，使得森林监测能够具备高空间、高时间与高专题细节精度。尽管取得了这些进展，仍需推动社区参与监测活动，并将此类利益相关者纳入国家森林监测体系。本研究针对埃塞俄比亚西南部一处非洲山地林区，分析了当地林业专家在3年内收集的活动数据（森林砍伐与森林退化情况），以及对应的陆地卫星时间序列（Landsat Time Series, LTS）数据。当地专家收集的数据包含森林变化属性、地理定位信息，以及通过集成GPS与拍照功能的移动设备记录的影像证据。此外，本研究基于所有光谱波段的可用数据，结合时间序列轨迹分析生成的一系列额外指数与时间指标，构建了陆地卫星时间序列数据集。我们以当地专家数据与陆地卫星时间序列光谱-时间指标作为模型协变量，训练随机森林模型，以此预测森林的砍伐、退化或保持稳定状态。最终模型对森林砍伐与退化的整体袋外（out of bag, OOB）误差估计为29%，其中森林砍伐类别的误差为26%，退化类别为31%。通过将当地专家数据划分为对应本地监测活动的训练阶段与业务应用阶段，我们发现随着当地专家数据样本量的增加，森林变化模型的性能得到提升。最后，我们利用重要性最高的光谱波段生成了森林砍伐与退化的分布地图。本研究的成果是首批结合本地专家森林变化数据与高密度陆地卫星时间序列数据的研究之一，证实了两类连续数据流的互补价值。我们的研究结果验证了两类数据集的实用性，并为依托多源数据流构建集成化森林监测体系提供了重要基础。