Combining Remote Sensing and Forest Ecosystem Modeling: An Example Using the Regional HydroEcological Simulation System (RHESSys)

Images from airborne or satellite-based remote sensing systems are the only data available for regional and global productivity studies that do not require interpolation or extrapolation. Four categories of image use are identified: image classification, model initialization, model input and model verification. Model initialization using vegetation indices derived from images is discussed using a regional modeling framework, the Regional HydroEcological Simulation System (RHESSys). In this chapter, we illustrate RHESSys’ sensitivity to soil moisture and the interrelationships between the soil data theme and the vegetation and climate data themes. Improving image transfer functions can increase the quality of vegetation estimates; however, ancillary data (such as topography and soil data) are also needed at appropriate levels of accuracy and precision. An example simulation is provided, which uses vegetation data from two watersheds in western Montana. Results demonstrate the model’s sensitivity to soil data in a wet, dry climate, and indicate the importance of considering the data collection process as an integrated effort guided by modeling requirements and model sensitivity. Additional consideration must be made for validation and collection of independent data for these purposes.

[1]  G. Campbell,et al.  An Introduction to Environmental Biophysics , 1977 .

[2]  Mary E. Martin,et al.  Estimating Forest Canopy Characteristics as Inputs for Models of Forest Carbon Exchange by High Spectral Resolution Remote Sensing , 1997 .

[3]  R. Leuning,et al.  A model of canopy photosynthesis and water use incorporating a mechanistic formulation of leaf CO2 exchange , 1992 .

[4]  Jesslyn F. Brown,et al.  Development of a land-cover characteristics database for the conterminous U.S. , 1991 .

[5]  B. J. Garnier,et al.  A Method of Calculating the Direct Shortwave Radiation Income of Slopes , 1968 .

[6]  P. Curran,et al.  Assessing Leaf Area and Canopy Biochemistry of Florida Pine Plantations Using Remote Sensing , 1997 .

[7]  C. Wessman,et al.  Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems , 1988, Nature.

[8]  Steven W. Running,et al.  A vegetation classification logic-based on remote-sensing for use in global biogeochemical models , 1994 .

[9]  M. G. Ryan,et al.  Effects of Climate Change on Plant Respiration. , 1991, Ecological applications : a publication of the Ecological Society of America.

[10]  W. Parton,et al.  Analysis of factors controlling soil organic matter levels in Great Plains grasslands , 1987 .

[11]  Vincent M. Caruso,et al.  Digital elevation models , 1983 .

[12]  S. Running,et al.  Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data , 1989 .

[13]  S. Running,et al.  A general model of forest ecosystem processes for regional applications I. Hydrologic balance, canopy gas exchange and primary production processes , 1988 .

[14]  Wout Verhoef,et al.  A new forest light interaction model in support of forest monitoring , 1992 .

[15]  Steven W. Running,et al.  Comparing site quality indices and productivity in ponderosa pine stands of western Montana , 1988 .

[16]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[17]  A-Xing Zhu,et al.  A Knowledge-Based Approach to Data Integration for Soil Mapping , 1994 .

[18]  P. Sellers,et al.  Testing the Simple Biosphere model (SiB) using point micrometeorological and biophysical data , 1987 .

[19]  Ramakrishna R. Nemani,et al.  Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation , 1989 .

[20]  L. Band.  Topographic Partition of Watersheds with Digital Elevation Models , 1986 .

[21]  Harold A. Mooney,et al.  Remote Sensing of Biosphere Functioning , 1990 .

[22]  M. Goodchild,et al.  Environmental Modeling with GIS , 1994 .

[23]  P. E. O'connell,et al.  An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system , 1986 .

[24]  W. Rawls,et al.  Estimating generalized soil-water characteristics from texture , 1986 .

[25]  David C. Schneider,et al.  Quantitative Ecology: Spatial and Temporal Scaling , 1994 .

[26]  S. Running.  Microclimate control of forest productivity: Analysis by computer simulation of annual photosynthesis/ transpiration balance in different environments , 1984 .

[27]  C. Field,et al.  Scaling Physiological Processes: Leaf to Globe , 1995 .

[28]  Thomas J. Schmugge,et al.  Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields , 1990 .

[29]  Ramakrishna R. Nemani,et al.  MTCLIM: a mountain microclimate simulation model , 1989 .

[30]  S. Running,et al.  Water Stress Response After Thinning Pinus contorta Stands in Montana , 1986, Forestry sciences.

[31]  G. Campbell,et al.  On the relationship between incoming solar radiation and daily maximum and minimum temperature , 1984 .

[32]  S. Running,et al.  Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed leaf area index estimates , 1993 .

[33]  Henry L. Gholz,et al.  The Use of Remote Sensing in the Modeling of Forest Productivity , 1997, Forestry Sciences.

[34]  M. Wigmosta,et al.  A distributed hydrology-vegetation model for complex terrain , 1994 .

[35]  S. Running,et al.  Developing Satellite-derived Estimates of Surface Moisture Status , 1993 .

[36]  R. Dean Graetz,et al.  Remote Sensing of Terrestrial Ecosystem Structure: An Ecologist’s Pragmatic View , 1990 .

[37]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model , 1986 .

[38]  Jennifer L. Dungan,et al.  Forest ecosystem processes at the watershed scale: basis for distributed simulation , 1991 .

[39]  S. Running,et al.  FOREST-BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. , 1991, Tree physiology.

[40]  Ramakrishna R. Nemani,et al.  Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis , 1987 .

[41]  John R. G. Townshend,et al.  Global data sets for land applications from the Advanced Very High Resolution Radiometer: an introduction , 1994 .

[42]  Gordon B. Bonan,et al.  Atmosphere-biosphere exchange of carbon dioxide in boreal forests , 1991 .

[43]  Lawrence E. Band,et al.  Effect of land surface representation on forest water and carbon budgets , 1993 .

[44]  R. Waring,et al.  Evergreen Coniferous Forests of the Pacific Northwest , 1979, Science.

[45]  N. Goel,et al.  Evaluation of a Canopy Reflectance Model for LAI Estimation through Its Inversion , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[46]  C. Rosenzweig,et al.  Determination of vegetated fraction of surface from satellite measurements , 1987 .

[47]  R. Shepanek,et al.  Environmental Information Management and Analysis: Ecosystem to Global Scales , 1994 .

[48]  S. Running,et al.  Forest ecosystem processes at the watershed scale: incorporating hillslope hydrology , 1993 .