Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

编号 030027904

推送时间 20210222

研究领域 森林经理 

年份 2021 

类型 期刊 

语种 英语

标题 Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

来源期刊 REMOTE SENSING

期 第279期

发表时间 20200113

关键词 LIDAR;  mixed-effect models;  calibration;  point-cloud;  raster;  semiparametric models;  biomass;  forest fuels; 

摘要 Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

服务人员 付贺龙

PDF文件 浏览全文