Rangel Pinagé Ekena, Keller Michael, Peck Christopher P, Longo Marcos, Duffy Paul, Csillik Ovidiu
College of Forestry, Oregon State University, Corvallis, OR, 97333, USA.
International Institute of Tropical Forestry, USDA Forest Service, Río Piedras, 00926, Puerto Rico.
Carbon Balance Manag. 2023 Feb 14;18(1):2. doi: 10.1186/s13021-023-00221-5.
Tropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well.
Our classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites.
Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.
热带森林对全球碳预算至关重要，但它们受到森林砍伐以及火灾、选择性采伐和森林破碎化导致的森林退化的威胁。土地覆盖分类和生物量估计方面现有的不确定性阻碍了将碳排放准确归因于特定森林类别。在本研究中，我们使用从PlanetScope图像得出的纹理度量来实施一个概率分类框架，以识别三个亚马逊地区的完整森林、采伐森林和火烧森林。我们还使用机载激光雷达估计这些森林类别的生物量，并仅将激光雷达得出的估计值与考虑森林退化分类后的生物量不确定性进行比较，以比较生物量不确定性。
我们的分类方法总体准确率达到0.86，各个地点的准确率在0.69至0.93之间变化。采伐森林显示出不同的生物量变化，而火烧森林平均碳损失为35%。我们发现，在三个测试地点中的两个地点，将森林退化分类中的不确定性考虑在内会显著增加不确定性并降低平均碳密度估计值。
我们的研究结果表明，将生物量变化归因于森林退化类别需要考虑森林退化分类中的不确定性。通过将超高分辨率图像与激光雷达数据相结合，我们可以将碳储量变化归因于森林退化的特定途径。这种方法还允许量化与采伐和火灾导致的森林退化相关的碳排放不确定性。归因和不确定性量化都为国家温室气体清单提供了关键信息。
