Extraction of Non-forest Trees for Biomass Assessment Based on Airborne and Terrestrial LiDAR Data

被引:0
|
作者
Rentsch, Matthias [1 ]
Krismann, Alfons [2 ]
Krzystek, Peter [1 ]
机构
[1] Munich Univ Appl Sci, Dept Geoinformat, Karlstr 6, D-80333 Munich, Germany
[2] Univ Hohenheim, Inst Landscape & Plant Ecol, D-70593 Stuttgart, Germany
来源
关键词
LiDAR; Vegetation; Correlation; Point Cloud; Segmentation; Three-dimensional; VOLUME;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The main goal of the federal funded project 'LiDAR based biomass assessment' is the nationwide investigation of the biomass potential coming from wood cuttings of non-forest trees. In this context, first and last pulse airborne laserscanning (F+L) data serve as preferred database. First of all, mandatory field calibrations are performed for pre-defined grove types. For this purpose, selected reference groves are captured by full-waveform airborne laserscanning (FWF) and terrestrial laserscanning (TLS) data in different foliage conditions. The paper is reporting about two methods for the biomass assessment of non-forest trees. The first method covers the determination of volume-to-biomass conversion factors which relate the reference above-ground biomass (AGB) estimated from allometric functions with the laserscanning derived vegetation volume. The second method is focused on a 3D Normalized Cut segmentation adopted for non-forest trees and the follow-on biomass calculation based on segmentation-derived tree features.
引用
收藏
页码:121 / +
页数:3
相关论文
共 50 条
  • [21] Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data
    Jiang, Xiandie
    Li, Guiying
    Lu, Dengsheng
    Chen, Erxue
    Wei, Xinliang
    REMOTE SENSING, 2020, 12 (07)
  • [22] Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR
    Stovall, Atticus E. L.
    Vorster, Anthony G.
    Anderson, Ryan S.
    Evangelista, Paul H.
    Shugart, Herman H.
    REMOTE SENSING OF ENVIRONMENT, 2017, 200 : 31 - 42
  • [23] Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data
    Olagoke, Adewole
    Proisy, Christophe
    Feret, Jean-Baptiste
    Blanchard, Elodie
    Fromard, Francois
    Mehlig, Ulf
    de Menezes, Moirah Machado
    dos Santos, Valdenira Ferreira
    Berger, Uta
    TREES-STRUCTURE AND FUNCTION, 2016, 30 (03): : 935 - 947
  • [24] Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data
    Adewole Olagoke
    Christophe Proisy
    Jean-Baptiste Féret
    Elodie Blanchard
    François Fromard
    Ulf Mehlig
    Moirah Machado de Menezes
    Valdenira Ferreira dos Santos
    Uta Berger
    Trees, 2016, 30 : 935 - 947
  • [25] Extraction of forest density based on airborne LiDAR and mean shift algorithms
    Chen, Wei
    Yang, Minhua
    Hong, Yifeng
    Li, Fei
    Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 2015, 50 (06): : 1156 - 1163
  • [26] Estimation of Forest Carbon Storage Based on Airborne LiDAR Data
    Liu, Chong
    Shao, Zhenfeng
    MECHANICAL ENGINEERING AND INTELLIGENT SYSTEMS, PTS 1 AND 2, 2012, 195-196 : 1314 - 1320
  • [27] Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data
    Bahadur, K. C. Yam
    Liu, Qijing
    Saud, Pradip
    Gaire, Damodar
    Adhikari, Hari
    LAND, 2024, 13 (02)
  • [28] Multilevel Extraction of Vegetation Type Based on Airborne LiDAR Data
    Chang, Lexin
    Zhang, Ziyi
    Li, Yuxuan
    Mao, Xuegang
    CANADIAN JOURNAL OF REMOTE SENSING, 2020, 46 (06) : 681 - 694
  • [29] Estimating biomass of individual pine trees using airborne lidar
    Popescu, Sorin C.
    BIOMASS & BIOENERGY, 2007, 31 (09): : 646 - 655
  • [30] Forest biomass estimation from airborne LiDAR data using machine learning approaches
    Gleason, Colin J.
    Im, Jungho
    REMOTE SENSING OF ENVIRONMENT, 2012, 125 : 80 - 91