Linking a spatially-explicit model of acacias to GIS and remotely-sensed data

被引:0
|
作者
Kerstin Wiegand
Heike Schmidt
Florian Jeltsch
David Ward
机构
[1] UFZ Centre for Environmental Research Leipzig-Halle,Department of Ecological Modelling
[2] Princeton University,Ecology and Evolutionary Biology, Guyot Hall
[3] Ben Gurion University of the Negev,The Remote Sensing Laboratory, Blaustein Institute for Desert Research
[4] University of Potsdam,Plant Ecology and Nature Conservation, Institute for Biochemistry and Biology
[5] Ben Gurion University of the Negev,Mitrani Department for Desert Ecology and Ramon Science Center, Blaustein Institute for Desert Research
来源
Folia Geobotanica | 2000年 / 35卷
关键词
Landscape related models; NDVI; Simulation model; Spatially-explicit; Wadi morphology;
D O I
暂无
中图分类号
学科分类号
摘要
Spatially-explicit and landscape-related simulation models are increasingly used in ecology, but are often criticized because their parameterization has high data requirements. A frequently suggested approach to overcome this difficulty is the linkage of spatially-explicit or landscape-related models with GIS (geographic information system) and remote-sensing technology. GIS can provide data on relevant landscape features, such as topography, and satellite images can be used to identify spatial vegetation distribution. In this paper, we use these techniques for simple, cost-inexpensive (in both time and money) parameterization based on readily-available GIS and remotely-sensed data.
引用
收藏
页码:211 / 230
页数:19
相关论文
共 50 条
  • [1] Linking a spatially-explicit model of acacias to GIS and remotely-sensed data
    Wiegand, K
    Schmidt, H
    Jeltsch, F
    Ward, D
    [J]. FOLIA GEOBOTANICA, 2000, 35 (02) : 211 - 230
  • [2] Linking a spatially-explicit model of acacias to GIS and remotely-sensed data
    Wiegand, K
    Schmidt, H
    Jeltsch, F
    Ward, D
    [J]. PLANT INTERACTIONS, DISPERSAL AND COMMUNITY STRUCTURE, 2000, 16 : 111 - 130
  • [3] Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data
    Shang, Chen
    Wulder, Michael A.
    Coops, Nicholas C.
    White, Joanne C.
    Hermosilla, Txomin
    [J]. CANADIAN JOURNAL OF REMOTE SENSING, 2020, 46 (03) : 313 - 329
  • [4] Modelling Spatially-Distributed Soil Erosion through Remotely-Sensed Data and GIS
    Aiello, Antonello
    Adamo, Maria
    Canora, Filomena
    [J]. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2014, PT IV, 2014, 8582 : 372 - +
  • [5] Incorporating remotely-sensed snow albedo into a spatially-distributed snowmelt model
    Molotch, NP
    Painter, TH
    Bales, RC
    Dozier, J
    [J]. GEOPHYSICAL RESEARCH LETTERS, 2004, 31 (03) : L035011 - 4
  • [6] REMOTELY-SENSED DATA - TECHNOLOGY, MANAGEMENT AND MARKETS
    不详
    [J]. SPACE POLICY, 1995, 11 (01) : 71 - 72
  • [7] Remotely-sensed slowing down in spatially patterned dryland ecosystems
    Veldhuis, Michiel P.
    Martinez-Garcia, Ricardo
    Deblauwe, Vincent
    Dakos, Vasilis
    [J]. ECOGRAPHY, 2022, 2022 (10)
  • [8] Estimation of spatially distributed surface energy fluxes using remotely-sensed data for agricultural fields
    Melesse, AM
    Nangia, V
    [J]. HYDROLOGICAL PROCESSES, 2005, 19 (14) : 2653 - 2670
  • [9] MINIMUM-VOLUME TRANSFORMS FOR REMOTELY-SENSED DATA
    CRAIG, MD
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1994, 32 (03): : 542 - 552
  • [10] REMOTELY-SENSED DATA FOR NATURAL-RESOURCE MODELS
    RITCHIE, JC
    ENGMAN, ET
    [J]. ENVIRONMENTAL CONSERVATION, 1986, 13 (03) : 203 - 210