Estimating Sagebrush Biomass Using Terrestrial Laser Scanning

被引:19
作者
Olsoy, Peter J. [1 ]
Glenn, Nancy F. [1 ]
Clark, Patrick E. [2 ]
机构
[1] Idaho State Univ, Boise Ctr Aerosp Lab, Boise, ID 83702 USA
[2] USDA ARS, Northwest Watershed Res Ctr, Boise, ID 83712 USA
基金
美国海洋和大气管理局; 美国国家科学基金会;
关键词
Artemisia tridentata; monitoring; sagebrush steppe; terrestrial LiDAR; voxel volume; SAGE-GROUSE; IDAHO;
D O I
10.2111/REM-D-12-00186.1
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
The presence of sagebrush (Artemisia tridentata) in rangelands has declined due to the invasion of annual grasses such as cheatgrass (Bromus tectorum) and the feedback between these flammable grasses and wildfire frequency. Monitoring the change and distribution of suitable habitat and fuel loads is an important aspect of sagebrush management, particularly under future climate conditions. Assessments of sagebrush biomass are used to monitor habitat for critical wildlife species, determine fire risk, and quantify carbon storage. Field techniques such as destructive and point-intercept sampling have been used to determine sagebrush biomass, but both of these techniques can be expensive and time consuming to implement. Light detection and ranging techniques, including airborne laser scanning and terrestrial laser scanning (TLS) have potential for rapidly assessing biomass in sagebrush steppe. This study used TLS to estimate biomass of 29 sagebrush plants in Reynolds Creek Experimental Watershed, Idaho. Biomass was estimated using TLS-derived volume, then compared with destructive samples to assess the estimation accuracy. This accuracy level was then contrasted with the estimates obtained using point-intercept sampling of the same plants. The TLS approach (R-2=0.90) was slightly better for predicting total biomass than point-intercept sampling (R-2=0.85). Prediction of green biomass, or production, was more accurate using TLS-derived volume (R-2=0.86) than point-intercept sampling (R-2=0.65). This study explores a promising new method to repeatedly monitor sagebrush biomass across extensive landscapes. Future work should focus on making this method independent of sensor type, scan distance, scan number, and study area.
引用
收藏
页码:224 / 228
页数:5
相关论文
共 28 条
[1]  
[Anonymous], 2013, WEB SOIL SURV
[2]  
[Anonymous], T N AM WILDLIFE NATU
[3]   Digital outcrop models: Applications of terrestrial scanning lidar technology in stratigraphic modeling [J].
Bellian, JA ;
Kerans, C ;
Jennette, DC .
JOURNAL OF SEDIMENTARY RESEARCH, 2005, 75 (02) :166-176
[4]  
Bonham C.D., 1989, Measurements for terrestrial vegetation
[5]   Invasive grass reduces aboveground carbon stocks in shrublands of the Western US [J].
Bradley, Bethany A. ;
Houghtonw, R. A. ;
Mustard, John F. ;
Hamburg, Steven P. .
GLOBAL CHANGE BIOLOGY, 2006, 12 (10) :1815-1822
[6]  
Clark DA, 2001, ECOL APPL, V11, P356, DOI 10.1890/1051-0761(2001)011[0356:MNPPIF]2.0.CO
[7]  
2
[8]   Point Sampling to Stratify Biomass Variability in Sagebrush Steppe Vegetation [J].
Clark, Patrick E. ;
Hardegree, Stuart P. ;
Moffet, Corey A. ;
Pierson, Fredrick B. .
RANGELAND ECOLOGY & MANAGEMENT, 2008, 61 (06) :614-622
[9]  
Connelly JW, 2000, WILDLIFE SOC B, V28, P967
[10]   Long-term climate database, Reynolds Creek Experimental Watershed, Idaho, United States [J].
Hanson, CL ;
Marks, D ;
Van Vactor, SS .
WATER RESOURCES RESEARCH, 2001, 37 (11) :2839-2841