气候变化领域集成服务门户(CCPortal): Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar

CCPortal

DOI	10.1016/j.rse.2020.111841
	Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar
	Alonzo M.; Dial R.J.; Schulz B.K.; Andersen H.-E.; Lewis-Clark E.; Cook B.D.; Morton D.C.
发表日期	2020
ISSN	00344257
卷号	245
英文摘要	Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R2 = 0.81, RMSE = 1.09 kg m−2) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R2 = 0.74, RMSE = 1.26 kg m−2), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts. m−2) to the G-LiHT SfM point cloud (500–2000 pts. m−2) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass. © 2020 Elsevier Inc.
英文关键词	Alaska; Biomass; Boreal forest; G-LiHT; Shrub; Structure-from-motion; UAV
语种	英语
scopus关键词	Antennas; Arctic vehicles; Forestry; Mapping; NASA; Optical radar; Pixels; Regression analysis; Testing; Uncertainty analysis; Unmanned aerial vehicles (UAV); Above ground biomass; Akaike's information criterions; Beyond line of sight; High resolution imagery; Lidar , hyperspectral; Operational forest inventories; Small-footprint lidars; Structure from motion; Biomass; airborne sensing; biomass; deciduous tree; landscape change; lidar; photogrammetry; precision; remotely operated vehicle; shrub; shrubland; spatial resolution; Alaska; United States; Alnus; Salix
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179304
作者单位	American University, Washington, DC, United States; Alaska Pacific University, Anchorage, AK, United States; USDA Forest Service PNW, Seattle, WA, United States; NASA Goddard Space Flight Center, Greenbelt, MD, United States; USDA Forest Service PNW, Anchorage, AK, United States
推荐引用方式 GB/T 7714	Alonzo M.,Dial R.J.,Schulz B.K.,et al. Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar[J],2020,245.
APA	Alonzo M..,Dial R.J..,Schulz B.K..,Andersen H.-E..,Lewis-Clark E..,...&Morton D.C..(2020).Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar.Remote Sensing of Environment,245.
MLA	Alonzo M.,et al."Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar".Remote Sensing of Environment 245(2020).