Small area estimation of forest biomass via a two-stage model for continuous zero-inflated data
Grayson W. White, Josh K. Yamamoto, Dinan H. Elsyad, Julian F. Schmitt, Niels H. Korsgaard, Jie Kate Hu, George C. Gaines, Tracey S. Frescino, Kelly S. McConville
The United States (US) Forest Inventory & Analysis Program (FIA) collects data on and monitors the trends of forests in the US. FIA is increasingly interested in monitoring forest attributes such as biomass at fine geographic and temporal scales, resulting in a need for assessment and development of small area estimation techniques in forest inventory. We implement a small area estimator and parametric bootstrap estimator that account for zero-inflation in biomass data via a two-stage model-based approach and compare its performance to a post-stratified estimator and to the unit- and area-level empirical best linear unbiased prediction (EBLUP) estimators. For estimator comparison, we conduct a simulation study with counties in the US state Nevada as domains based on sampled plot data and remote sensing data products. Results show the zero-inflated estimator has the lowest relative bias and the smallest empirical root mean square error. Moreover, the 95% confidence interval coverages of the zero-inflated estimator and the unit-level EBLUP are more accurate than the other two estimators. To further illustrate the practical utility, we employ a data application across the 2019 measurement year in Nevada. We introduce the R package, saeczi, which efficiently implements the zero-inflated estimator and its mean squared error estimator.
Within-fire estimates of biomass loss across national forests of the US West Coast: inventory-informed inference with uncertainty
Michigan State University（密歇根州立大学）；South Dakota School of Mines & Technology（南达科他矿业理工学院）；USDA Forest Service, Pacific Northwest Research Station（美国农业部林务局，太平洋西北研究站）；Northern Research Station, United States Department of Agriculture Forest Service（美国农业部林务局北方研究站）；USDA Forest Service, Rocky Mountain Research Station（美国农业部林务局，落基山研究站）
Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages
State University of New York College of Environmental Science and Forestry（纽约州立大学环境科学与林业学院）
在大尺度和精细的空间分辨率下估算森林AGB对于温室气体核算、监测和核查工作以减缓气候变化变得越来越重要。机载LiDAR对于包括AGB在内的森林结构的建模属性非常有价值，但大多数LiDAR收集都是在局部或区域尺度上进行的，覆盖不规则，不连续的足迹，导致不同时间点的不同景观片段的拼凑。在这里，作为纽约州（美国）全州森林碳评估的一部分，我们解决了利用LiDAR拼凑AGB地图在景观尺度上的常见障碍，包括训练数据的选择，预测误差的区域或覆盖特定模式的调查，以及与多个尺度的实地调查的地图协议。三个机器学习算法和一个集成模型进行了训练与FIA现场测量，机载激光雷达，地形，气候和地籍地理数据。使用一套严格的地块选择标准，选择了801个FIA地块，并从17个叶片关闭LiDAR覆盖范围（2014-2019）中绘制了同位点云。我们的集成模型被用来产生30米AGB预测表面内的预测定义的适用性区域（98%的激光雷达覆盖范围），并由此产生的AGB地图进行了比较与FIA图级和面积估计在多个尺度的聚合。我们的模型总体准确（% RMSE 22-45%; MAE 11.6-29.4 Mg ha $^{-1}$ ; ME 2.4-6.3 Mg ha $^{-1}$），解释了73-80%的实地观察变异，并产生了与FIA基于设计的估计一致的估计值（89%的估计值在FIA的95% CI内）。我们分享实用的解决方案，以应对使用…
Optimizing GEDI Simulator Configuration for European Temperate Forests
UMR TETIS, INRAE, AgroParisTech, CIRAD, CNRS, Univ Montpellier（TETIS联合研究单位，法国国家农业食品与环境研究院，巴黎农学院，CIRAD，法国国家科研中心，蒙彼利埃大学）；LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay（气候与环境科学实验室/地球系统研究所，法国原子能和替代能源委员会，法国国家科研中心-凡尔赛大学，巴黎萨克雷大学）；Université de Lorraine（洛林大学）；Géodata Paris, IGN, LIF（Géodata Paris，法国国家地理信息研究所，LIF）；Université Gustave Eiffel（古斯塔夫·埃菲尔大学）；School of GeoSciences, University of Edinburgh（爱丁堡大学地球科学学院）；National Centre for Earth Observation, UK（英国国家地球观测中心）；Office National des Forêts RDI（国家林业局研发部）
New allometric models for the USA create a step-change in forest carbon estimation, modeling, and mapping
State University of New York College of Environmental Science and Forestry（纽约州立大学环境科学与林业学院）；USDA Forest Service（美国农业部林业局）；Northern Research Station（北方研究站）
