LARGE-SCALE ESTABLISHMENT OF AMMOPHILA-ARENARIA AND QUANTITATIVE ASSESSMENT BY REMOTE-SENSING

被引:0
|
作者
VANDERPUTTEN, WH
KLOOSTERMAN, EH
机构
关键词
AMMOPHILA-ARENARIA; SAND DUNES; EROSION CONTROL; LARGE-SCALE ESTABLISHMENT; QUANTITATIVE ASSESSMENT; REMOTE SENSING;
D O I
暂无
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
New methods for the establishment of Ammophila arenaria (marram grass), i.e. sowing seeds and disk-harrowing rhizomes, were compared with the traditional method (planting bundles of culms). A large-scale experiment was undertaken on 120 ha of fortified foredune ridge and approximately 40 percent of this area was evaluated by airborne remote sensing (false colour photography). Above-ground biomass production and percentage cover of one- and two-year-old stands were determined by combining field data with colour density measurements on the images. A linear empirical relationship could be established between colour density ratio on the photographs and above-ground biomass, as well as percentage cover. This relationship depended on the age of the plantation. Planting method or relief did not influence this relationship. After the first growing season, planted culms had produced less biomass and percentage cover than stands established via the new methods. In the second growing season the traditional method was the most productive. During the first growing season more than 90 percent of the total area had been stabilized successfully by the new vegetation. Therefore, the alternative methods could be as effective for sand stabilization as the traditional method of planting. Effectiveness of the temporary sand stabilization after sowing and the origin of the culms and rhizomes used for planting affected productivity of the new stands more than the method of establishment. Therefore, if applied properly A. arenaria can be established at a practical level from seeds or rhizomes, instead of from culms. It is also shown that remote sensing could be used to assess above-ground biomass, percentage cover and heterogeneity of foredune vegetation.
引用
收藏
页码:1181 / 1194
页数:14
相关论文
共 50 条
  • [31] IDENTIFYING EVERY BUILDING'S FUNCTION IN LARGE-SCALE URBAN AREAS WITH MULTI-MODALITY REMOTE-SENSING DATA
    Li, Zhuohong
    He, Wei
    L, Jiepan
    Zhang, Hongyan
    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024, 2024, : 310 - 314
  • [32] The establishment of the assessment system of large-scale medical equipment allocation
    Lu, Xiaoqing
    Tian, Ruyu
    Cui, Ying
    Journal of Chemical and Pharmaceutical Research, 2013, 5 (11) : 549 - 555
  • [33] ASSESSMENT OF INSECT DAMAGE TO VEGETATION BY REMOTE-SENSING
    KLEMAS, V
    JOURNAL OF THE NEW YORK ENTOMOLOGICAL SOCIETY, 1980, 88 (01): : 52 - 53
  • [34] USE OF REMOTE-SENSING IN ABANDONED WELL ASSESSMENT
    JORDAN, JD
    SHIH, SF
    TRANSACTIONS OF THE ASAE, 1988, 31 (05): : 1416 - 1422
  • [35] Policy assessment of the impacts of remote-sensing technology
    Hitchings, S
    SPACE POLICY, 2003, 19 (02) : 119 - 125
  • [36] SYMPOSIUM ON REMOTE-SENSING FOR VEGETATION DAMAGE ASSESSMENT
    MURTHA, PA
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 1978, 44 (09): : 1139 - 1145
  • [37] A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data
    Bartalev, S. A.
    Egorov, V. A.
    Loupian, E. A.
    Khvostikov, S. A.
    REMOTE SENSING LETTERS, 2014, 5 (01) : 55 - 64
  • [38] REMOTE-SENSING-BASED CONDITION ASSESSMENT FOR NONEQUILIBRIUM RANGELANDS UNDER LARGE-SCALE COMMERCIAL GRAZING
    PICKUP, G
    BASTIN, GN
    CHEWINGS, VH
    ECOLOGICAL APPLICATIONS, 1994, 4 (03) : 497 - 517
  • [39] Large-scale assessment of date palm plantations based on UAV remote sensing and multiscale vision transformer
    Gibril, Mohamed Barakat A.
    Shafri, Helmi Zulhaidi Mohd
    Shanableh, Abdallah
    Al-Ruzouq, Rami
    Hashim, Shaiful Jahari bin
    Wayayok, Aimrun
    Sachit, Mourtadha Sarhan
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2024, 34
  • [40] RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
    Zhou, Xiaohua
    Wang, Xuezhi
    Zhou, Yuanchun
    Lin, Qinghui
    Zhao, Jianghua
    Meng, Xianghai
    REMOTE SENSING, 2021, 13 (09)