LiDAR-Based Classification of Sagebrush Community Types

被引:24
|
作者
Sankey, Temuulen Tsagaan [1 ]
Bond, Pamela [1 ]
机构
[1] Idaho State Univ, Boise Ctr Aerosp Lab, Boise, ID 83712 USA
基金
美国海洋和大气管理局;
关键词
active sensors; laser data; rangeland classification; vegetation height; RANGELAND VEGETATION; THEMATIC MAPPER; BIG SAGEBRUSH; RECOVERY; LANDSAT; STEPPE; IMAGERY; HEIGHT; FUSION; IDAHO;
D O I
10.2111/REM-D-10-00019.1
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Sagebrush (Artemisia spp.) communities constitute the largest temperate semidesert in North America and provide important rangelands for livestock and habitat for wildlife. Remote sensing methods might provide an efficient method to monitor sagebrush communities. This study used airborne LiDAR and field data to measure vegetation heights in five different community types at the Reynolds Creek Experimental Watershed, southwestern Idaho: herbaceous-dominated, low sagebrush (Artemisia arbuscula) -dominated, big sagebrush (Artemisia tridentata spp.) -dominated, bitterbrush (Purshia tridentata) -dominated, and other vegetation community types. The objectives were 1) to quantify the correlation between field-measured and airborne LiDAR-derived shrub heights, and 2) to determine if airborne LiDAR-derived mean vegetation heights can be used to classify the five community types. The dominant vegetation type and vegetation heights were measured in 3 X 3 m field plots. The LiDAR point cloud data were converted into a raster format to generate a maximum vegetation height map in 3-m raster cells. The regression relationship between field-based and airborne LiDAR-derived shrub heights was significant (R-2 = 0.77; P value < 0.001). An analysis of variance test with all pairwise post hoc comparisons indicated that LiDAR-derived vegetation heights were significantly different among all vegetation community types (all P values < 0.01), except for herbaceous-dominated communities compared to low sagebrush-dominated communities. Although LiDAR measurements consistently underestimated vegetation heights in all community types, shrub heights at some locations were overestimated due to adjacent taller vegetation. We recommend for future studies a smaller rasterized pixel size that is consistent with the target vegetation canopy diameter.
引用
收藏
页码:92 / 98
页数:7
相关论文
共 50 条
  • [41] LiDAR-Based Dense Pedestrian Detection and Tracking
    Wang, Wenguang
    Chang, Xiyuan
    Yang, Jihuang
    Xu, Gaofei
    APPLIED SCIENCES-BASEL, 2022, 12 (04):
  • [42] On Onboard LiDAR-Based Flying Object Detection
    Vrba, Matous
    Walter, Viktor
    Pritzl, Vaclav
    Pliska, Michal
    Baca, Tomas
    Spurny, Vojtech
    Hert, Daniel
    Saska, Martin
    IEEE TRANSACTIONS ON ROBOTICS, 2025, 41 : 593 - 611
  • [43] HistoGrid: Robust LiDAR-based Traffic Monitoring
    Buerkle, Cornelius
    Oboril, Fabian
    Zayed, Omar
    Scholl, Kay-Ulrich
    2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC, 2023, : 209 - 214
  • [44] Lidar-based lane marker detection and mapping
    Kammel, Soren
    Pitzer, Benjamin
    2008 IEEE INTELLIGENT VEHICLES SYMPOSIUM, VOLS 1-3, 2008, : 1162 - 1167
  • [45] OverlapNet: Loop Closing for LiDAR-based SLAM
    Chen, Xieyuanli
    Laebe, Thomas
    Milioto, Andres
    Roehling, Timo
    Vysotska, Olga
    Haag, Alexandre
    Behley, Jens
    Stachniss, Cyrill
    ROBOTICS: SCIENCE AND SYSTEMS XVI, 2020,
  • [46] Paper: Classification of Grass and Forb Species on Riverdike Using UAV LiDAR-Based Structural Indices
    Miura, Naoko
    Koyanagi, Tomoyo F.
    Yamada, Susumu
    Yokota, Shigehiro
    INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY, 2021, 15 (03) : 268 - 273
  • [47] LiDAR-Based Snowfall Level Classification for Safe Autonomous Driving in Terrestrial, Maritime, and Aerial Environments
    Park, Ji-il
    Jo, Seunghyeon
    Seo, Hyung-Tae
    Park, Jihyuk
    SENSORS, 2024, 24 (17)
  • [48] Assessing lidar-based classification schemes for polar stratospheric clouds based on 16 years of measurements at Esrange, Sweden
    Achtert, P.
    Tesche, M.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2014, 119 (03) : 1386 - 1405
  • [49] Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey
    Lolli, Simone
    REMOTE SENSING, 2023, 15 (17)
  • [50] SOIL CHARACTERISTICS OF MOUNTAINOUS NORTHEASTERN NEVADA SAGEBRUSH COMMUNITY TYPES
    JENSEN, ME
    GREAT BASIN NATURALIST, 1989, 49 (04): : 469 - 481