Winsemius Sara, Babcock Chad, Kane Van R, Bormann Kat J, Safford Hugh D, Jin Yufang
Department of Land, Air and Water Resources, University of California, Davis, CA, 95616, USA.
Department of Forest Resources, University of Minnesota, St. Paul, MN, 55108, USA.
Carbon Balance Manag. 2024 Dec 20;19(1):41. doi: 10.1186/s13021-024-00286-w.
Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass.
We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data.
By applying a relatively new Bayesian geostatistical modeling method to a novel forest type, our study produced the most accurate and precise aboveground biomass estimates to date for Sierra Nevada subalpine forests at 30 m pixel and management unit scales. Our estimates of total aboveground biomass within the management units had low uncertainty and can be used effectively in carbon accounting and carbon trading markets.
了解气候变化对森林地上生物量的影响是土地管理者的首要任务。高海拔亚高山森林提供许多重要的生态系统服务，包括碳固存，并且易受气候变化影响，气候变化已经改变了森林结构和干扰状况。尽管大型区域研究利用卫星数据推进了地上生物量制图，通常采用一种用现有实地数据进行广泛校准或训练的通用方法，但尚不清楚这些模型在亚高山等不太常见且高度异质的森林类型中的效果如何。使用能在多个尺度上对不确定性进行建模的方法监测生物量，对于确保保留生物量与输入变量之间的局部关系至关重要。由于激光雷达的森林结构指标与生物量高度相关，因此在绘制地上生物量时，这些指标与实地数据一起特别有价值。
我们利用贝叶斯地质统计模型，结合实地清查数据收集，从航空激光雷达数据中估计了加利福尼亚内华达山脉亚高山森林中活树和死树的地上木质生物量以及30米分辨率下的不确定性。十折交叉验证对我们的亚高山特定模型进行了出色的模型校准（94.7%的实测样地生物量在预测的95%可信区间内）。与基于陆地卫星光学图像的两个常用区域估计值相比，我们估计值的均方根误差、相对标准误差和偏差显著更低，这表明了针对亚高山森林进行局部建模的好处。我们绘制了内华达山脉四个管理单元的地上生物量图，发现这些管理单元的生物量密度各不相同，范围从92.4至199.2 Mg/ha，突出了高质量的局部实地和遥感数据的重要性。
通过将一种相对较新的贝叶斯地质统计建模方法应用于一种新型森林类型，我们的研究在30米像素和管理单元尺度上，为内华达山脉亚高山森林生成了迄今为止最准确、最精确的地上生物量估计值。我们对管理单元内地上生物量总量的估计不确定性较低，可有效地用于碳核算和碳交易市场。
