Estimating aboveground biomass in interior Alaska with Landsat data and field measurements

被引:79
|
作者
Ji, Lei [1 ]
Wylie, Bruce K. [2 ]
Nossov, Dana R. [3 ]
Peterson, Birgit [1 ]
Waldrop, Mark P. [4 ]
McFarland, Jack W. [4 ]
Rover, Jennifer [2 ]
Hollingsworth, Teresa N. [5 ]
机构
[1] US Geol Survey, ASRC Res & Technol Solut, Earth Resources Observat & Sci EROS Ctr, Sioux Falls, SD 57198 USA
[2] US Geol Survey, EROS Ctr, Sioux Falls, SD 57198 USA
[3] Univ Alaska Fairbanks, Boreal Ecol Cooperat Res Unit, Fairbanks, AK 99775 USA
[4] US Geol Survey, Menlo Pk, CA 94025 USA
[5] US Forest Serv, Boreal Ecol Cooperat Res Unit, Pacific NW Res Stn, USDA, Fairbanks, AK 99775 USA
来源
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION | 2012年 / 18卷
关键词
Aboveground biomass; Spectral vegetation index; Landsat; Lidar; Alaska; Yukon Flats ecoregion; WATER INDEX NDWI; NORTHERN ALASKA; FOREST BIOMASS; BOREAL FORESTS; ETM+ DATA; VEGETATION; TM; PERFORMANCE; FIRE; PRODUCTIVITY;
D O I
10.1016/j.jag.2012.03.019
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field measurements. Tree, shrub, and herbaceous AGB data in both live and dead forms were collected in summers and autumns of 2009 and 2010. Using the Landsat-derived spectral variables and the field AGB data, we generated a regression model and applied this model to map AGB for the ecoregion. A 3-fold cross-validation indicated that the AGB estimates had a mean absolute error of 21.8 Mg/ha and a mean bias error of 5.2 Mg/ha. Additionally, we validated the mapping results using an airborne lidar dataset acquired for a portion of the ecoregion. We found a significant relationship between the lidar-derived canopy height and the Landsat-derived AGB (R-2 = 0.40). The AGB map showed that 90% of the ecoregion had AGB values ranging from 10 Mg/ha to 134 Mg/ha. Vegetation types and fires were the primary factors controlling the spatial AGB patterns in this ecoregion. Published by Elsevier B.V.
引用
收藏
页码:451 / 461
页数:11
相关论文
共 50 条
  • [11] EVALUATING LANDSAT THEMATIC MAPPER DERIVED VEGETATION INDEXES FOR ESTIMATING ABOVEGROUND BIOMASS ON SEMIARID RANGELANDS
    ANDERSON, GL
    HANSON, JD
    HAAS, RH
    REMOTE SENSING OF ENVIRONMENT, 1993, 45 (02) : 165 - 175
  • [12] Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data
    Jialong Zhang
    Chi Lu
    Hui Xu
    Guangxing Wang
    Journal of Forestry Research, 2019, 30 : 1689 - 1706
  • [13] Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data
    Jialong Zhang
    Chi Lu
    Hui Xu
    Guangxing Wang
    Journal of Forestry Research, 2019, (05) : 1689 - 1706
  • [14] Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data
    Zhang, Jialong
    Lu, Chi
    Xu, Hui
    Wang, Guangxing
    JOURNAL OF FORESTRY RESEARCH, 2019, 30 (05) : 1689 - 1706
  • [15] Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data
    Jialong Zhang
    Chi Lu
    Hui Xu
    Guangxing Wang
    JournalofForestryResearch, 2019, 30 (05) : 1689 - 1706
  • [16] Estimating aboveground woody biomass change in Kalahari woodland: combining field, radar, and optical data sets
    Wingate, Vladimir R.
    Phinn, Stuart R.
    Kuhn, Nikolaus
    Scarth, Peter
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018, 39 (02) : 577 - 606
  • [17] Estimating aboveground forest carbon density using Landsat 8 and field-based data: a comparison of modelling approaches
    Li, Chao
    Li, Mingyang
    Li, Yingchang
    Qian, Pei
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2020, 41 (11) : 4269 - 4292
  • [18] Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations
    Babcock, Chad
    Finley, Andrew O.
    Andersen, Hans-Erik
    Pattison, Robert
    Cook, Bruce D.
    Morton, Douglas C.
    Alonzo, Michael
    Nelson, Ross
    Gregoire, Timothy
    Ene, Liviu
    Gobaldten, Terje
    Naesset, Erik
    REMOTE SENSING OF ENVIRONMENT, 2018, 212 : 212 - 230
  • [19] Estimating the Aboveground Biomass of Robinia pseudoacacia Based on UAV LiDAR Data
    Cheng, Jiaqi
    Zhang, Xuexia
    Zhang, Jianjun
    Zhang, Yanni
    Hu, Yawei
    Zhao, Jiongchang
    Li, Yang
    FORESTS, 2024, 15 (03):
  • [20] Estimating the Aboveground Biomass of Bornean Forest
    Budiharta, Sugeng
    Slik, Ferry
    Raes, Niels
    Meijaard, Erik
    Erskine, Peter D.
    Wilson, Kerrie A.
    BIOTROPICA, 2014, 46 (05) : 507 - 511