Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, United States of America.
Geography Graduate Group, University of California, Davis, California, United States of America.
PLoS One. 2022 Jul 6;17(7):e0268970. doi: 10.1371/journal.pone.0268970. eCollection 2022.
Studying land use change in protected areas (PAs) located in tropical forests is a major conservation priority due to high conservation value (e.g., species richness and carbon storage) here, coupled with generally high deforestation rates. Land use change researchers use a variety of land cover products to track deforestation trends, including maps they produce themselves and readily available products, such as the Global Forest Change (GFC) dataset. However, all land cover maps should be critically assessed for limitations and biases to accurately communicate and interpret results. In this study, we assess deforestation in PA complexes located in agricultural frontiers in the Amazon Basin. We studied three specific sites: Amboró and Carrasco National Parks in Bolivia, Jamanxim National Forest in Brazil, and Tambopata National Reserve and Bahuaja-Sonene National Park in Peru. Within and in 20km buffer areas around each complex, we generated land cover maps using composites of Landsat imagery and supervised classification, and compared deforestation trends to data from the GFC dataset. We then performed a dissimilarity analysis to explore the discrepancies between the two remote sensing products. Both the GFC and our supervised classification showed that deforestation rates were higher in the 20km buffer than inside the PAs and that Jamanxim National Forest had the highest deforestation rate of the PAs we studied. However, GFC maps showed consistently higher rates of deforestation than our maps. Through a dissimilarity analysis, we found that many of the inconsistencies between these datasets arise from different treatment of mixed pixels or different parameters in map creation (for example, GFC does not detect reforestation after 2012). We found that our maps underestimated deforestation while GFC overestimated deforestation, and that true deforestation rates likely fall between our two estimates. We encourage users to consider limitations and biases when using or interpreting our maps, which we make publicly available, and GFC's maps.
研究位于热带森林中的保护区（PA）的土地利用变化是保护的主要优先事项，因为这里具有很高的保护价值（例如，物种丰富度和碳储存），再加上普遍较高的森林砍伐率。土地利用变化研究人员使用各种土地覆盖产品来跟踪森林砍伐趋势，包括他们自己制作的地图和现成的产品，例如全球森林变化（GFC）数据集。然而，所有的土地覆盖地图都应该受到严格评估，以了解其局限性和偏差，从而准确地传达和解释结果。在本研究中，我们评估了位于亚马逊盆地农业前沿的 PA 复合体中的森林砍伐情况。我们研究了三个特定的地点：玻利维亚的 Amboró 和 Carrasco 国家公园、巴西的 Jamanxim 国家森林以及秘鲁的 Tambopata 国家保护区和 Bahuaja-Sonene 国家公园。在每个复合体内部及其 20 公里缓冲区，我们使用 Landsat 图像和监督分类的组合生成土地覆盖图，并将森林砍伐趋势与 GFC 数据集的数据进行比较。然后，我们进行了不相似性分析，以探讨这两个遥感产品之间的差异。GFC 和我们的监督分类都表明，缓冲区 20 公里内的森林砍伐率高于保护区内，我们研究的保护区中 Jamanxim 国家森林的森林砍伐率最高。然而，GFC 地图显示的森林砍伐率始终高于我们的地图。通过不相似性分析，我们发现这些数据集之间的许多不一致之处源于混合像素的不同处理或地图创建过程中的不同参数（例如，GFC 不会检测到 2012 年后的重新造林）。我们发现，我们的地图低估了森林砍伐，而 GFC 高估了森林砍伐，真实的森林砍伐率可能介于我们的两个估计值之间。我们鼓励用户在使用或解释我们公开提供的地图以及 GFC 的地图时考虑其局限性和偏差。
