Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

被引:76
|
作者
Sheridan, Ryan D. [1 ]
Popescu, Sorin C. [1 ]
Gatziolis, Demetrios [2 ]
Morgan, Cristine L. S. [3 ]
Ku, Nian-Wei [1 ]
机构
[1] Texas A&M Univ, Dept Ecosyst Sci & Management, LiDAR Applicat Study Ecosyst Remote Sensing LASER, College Stn, TX 77843 USA
[2] US Forest Serv, Pacific NW Res Stn, Portland, OR 97205 USA
[3] Texas A&M Univ, Dept Soil & Crop Sci, College Stn, TX 77843 USA
关键词
LiDAR; forestry; modeling; Monitoring; inventory; MULTISPECTRAL DATA FUSION; TREE HEIGHT; CANOPY STRUCTURE; CROWN DIAMETER; STAND VOLUME; STEM VOLUME; LASER; ACCURACY; DENSITY; LIGHT;
D O I
10.3390/rs70100229
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation's forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out performed subplot-level and hectare-level models, producing R-2 values of 0.83 and 0.81 for AGBM and gV, utilizing mean height and the 90th height percentile as predictors, respectively. Similar results were found for multiple regression models, where plot-level analysis produced models with R-2 values of 0.87 and 0.88 for AGBM and gV, utilizing multiple height percentile metrics as predictor variables. Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements.
引用
收藏
页码:229 / 255
页数:27
相关论文
共 50 条
  • [1] Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory
    Ferraz, Antonio
    Saatchi, Sassan
    Mallet, Clement
    Jacquemoud, Stephane
    Goncalves, Gil
    Silva, Carlos Alberto
    Soares, Paula
    Tome, Margarida
    Pereira, Luisa
    [J]. REMOTE SENSING, 2016, 8 (08)
  • [2] Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR
    Kankare, Ville
    Vastaranta, Mikko
    Holopainen, Markus
    Raety, Minna
    Yu, Xiaowei
    Hyyppa, Juha
    Hyyppa, Hannu
    Alho, Petteri
    Viitala, Risto
    [J]. REMOTE SENSING, 2013, 5 (05) : 2257 - 2274
  • [3] Modeling Merchantable Wood Volume Using Airborne LiDAR Metrics and Historical Forest Inventory Plots at a Provincial Scale
    Leboeuf, Antoine
    Riopel, Martin
    Munger, Dave
    Fradette, Marie-Soleil
    Begin, Jean
    [J]. FORESTS, 2022, 13 (07):
  • [4] Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data
    Park, Taejin
    Lee, Woo-Kyun
    Lee, Jong-Yeol
    Byun, Woo-Hyuk
    Kwak, Doo-Ahn
    Cui, Guishan
    Kim, Moon-Il
    Jung, Raesun
    Pujiono, Eko
    Oh, Suhyun
    Byun, Jungyeon
    Nam, Kijun
    Cho, Hyun-Kook
    Lee, Jung-Su
    Chung, Dong-Jun
    Kim, Sung-Ho
    [J]. FOREST SCIENCE AND TECHNOLOGY, 2012, 8 (02) : 89 - 98
  • [5] Regional aboveground forest biomass using airborne and spaceborne LiDAR in Quebec
    Boudreau, Jonathan
    Nelson, Ross F.
    Margolis, Hank A.
    Beaudoin, Andre
    Guindon, Luc
    Kimes, Daniel S.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2008, 112 (10) : 3876 - 3890
  • [6] Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data
    Hu, Tianyu
    Su, Yanjun
    Xue, Baolin
    Liu, Jin
    Zhao, Xiaoqian
    Fang, Jingyun
    Guo, Qinghua
    [J]. REMOTE SENSING, 2016, 8 (07)
  • [7] Forest Inventory and Aboveground Biomass Estimation with Terrestrial LiDAR in the Tropical Forest of Malaysia
    Beyene, Solomon M.
    Hussin, Yousif A.
    Kloosterman, Henk E.
    Ismail, Mohd Hasmadi
    [J]. CANADIAN JOURNAL OF REMOTE SENSING, 2020, 46 (02) : 130 - 145
  • [8] TerraSAR-X Stereo Radargrammetry and Airborne Scanning LiDAR Height Metrics in Imputation of Forest Aboveground Biomass and Stem Volume
    Vastaranta, Mikko
    Holopainen, Markus
    Karjalainen, Mika
    Kankare, Ville
    Hyyppa, Juha
    Kaasalainen, Sanna
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (02): : 1197 - 1204
  • [9] Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe
    Brovkina, Olga
    Novotny, Jan
    Cienciala, Emil
    Zemek, Frantisek
    Russ, Radek
    [J]. ECOLOGICAL ENGINEERING, 2017, 100 : 219 - 230
  • [10] Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation
    Luo, Shezhou
    Wang, Cheng
    Xi, Xiaohuan
    Pan, Feifei
    Peng, Dailiang
    Zou, Jie
    Nie, Sheng
    Qin, Haiming
    [J]. ECOLOGICAL INDICATORS, 2017, 73 : 378 - 387