Forest ecosystem simulation modelling: the role of remote sensing

In recent years forest ecosystems have come under increasing pressure from environmental changes such as global warming and the impacts of pollution. Recent research has indicated that computer-simulation models driven by remotely sensed estimates of key variables may be used to assess the spatial impact of global environment changes on forest processes. This article begins with a discussion of key issues related to driving such models with remotely sensed estimates of these key variables. The article then outlines an investigation that examined whether a general ecosystem simulation model (FOREST-BGC), driven by remotely sensed and meteorological data, could be used to estimate forest processes for a Sitka spruce (Picea sitchensis) plantation in mid-Wales.

[1]  Mary E. Martin,et al.  Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data , 1998 .

[2]  Wallace M. Porter,et al.  The airborne visible/infrared imaging spectrometer (AVIRIS) , 1993 .

[3]  Paul J. Curran,et al.  Factors affecting the remotely sensed response of coniferous forest plantations , 1993 .

[4]  J. Aber,et al.  A strategy for the regional analysis of the effects of physical and chemical climate change on biogeochemical cycles in northeastern (U.S.) forests , 1993 .

[5]  Christopher O. Justice,et al.  Spatial variability of images and the monitoring of changes in the Normalized Difference Vegetation Index , 1995 .

[6]  W. Cohen,et al.  Semivariograms of digital imagery for analysis of conifer canopy structure. , 1990 .

[7]  D. H. Card,et al.  Remote sensing of forest canopy and leaf biochemical contents , 1988 .

[8]  A. Gitelson,et al.  Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .

[9]  John R. Miller,et al.  Correlating leaf area index of Ponderosa pine with hyperspectral casi data , 1992 .

[10]  C. Justice,et al.  Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations , 1988 .

[11]  Peter M. Atkinson,et al.  Geostatistics and remote sensing , 1998 .

[12]  H. Eva,et al.  Remote Sensing of Biomass Burning in Tropical Regions: Sampling Issues and Multisensor Approach , 1998 .

[13]  S. Running,et al.  Relationship of thematic mapper simulator data to leaf area index , 1987 .

[14]  Carol A. Wessman,et al.  Remote Sensing and the Estimation of Ecosystem Parameters and Functions , 1994 .

[15]  Steven E. Franklin,et al.  Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production , 1997 .

[16]  D. Schimel,et al.  Terrestrial biogeochemical cycles: Global estimates with remote sensing , 1995 .

[17]  Ramakrishna R. Nemani,et al.  A remote sensing based vegetation classification logic for global land cover analysis , 1995 .

[18]  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 .

[19]  Bernard Pinty,et al.  Evaluation of the performance of various vegetation indices to retrieve vegetation cover from AVHRR data , 1994 .

[20]  Stella W. Todd,et al.  Responses of spectral indices to variations in vegetation cover and soil background , 1998 .

[21]  P. Gong,et al.  Remote Sensing of Seasonal Leaf Area Index Across the Oregon Transect , 1994 .

[22]  J. Randerson,et al.  Global net primary production: Combining ecology and remote sensing , 1995 .

[23]  Adrian Luckman,et al.  A study of the relationship between radar backscatter and regenerating tropical forest biomass for spaceborne SAR instruments , 1997 .

[24]  Edwin T. Engman,et al.  Status of remote sensing algorithms for estimation of land surface state parameters , 1995 .

[25]  P. Sellers.  Canopy reflectance, photosynthesis and transpiration , 1985 .

[26]  Samuel N. Goward,et al.  Comparison of North and South American biomes from AVHRR observations , 1987 .

[27]  P. J. Curran,et al.  The importance of measurement error for certain procedures in remote sensing at optical wavelengths , 1986 .

[28]  R. Webster.  Is regression what you really want , 1989 .

[29]  Giles M. Foody,et al.  Coupling remotely sensed data to an ecosystem simulation model - an example involving a coniferous plantation in upland Wales , 1996 .

[30]  Steven W. Running,et al.  Numerical Terradynamic Simulation Group 4-1993 A Physiology-Based Gap Model of Forest Dynamics , 2017 .

[31]  Peter M. Atkinson,et al.  Choosing an appropriate spatial resolution for remote sensing investigations , 1997 .

[32]  P. Millard,et al.  Storage and internal cycling of nitrogen in relation to seasonal growth of Sitka spruce. , 1992, Tree physiology.

[33]  M. Coutts,et al.  TOLERANCE OF TREE ROOTS TO WATERLOGGING , 1978 .

[34]  Ruth D. Yanai,et al.  Modeling changes in red spruce carbon balance and allocation in response to interacting ozone and nutrient stresses. , 1991, Tree physiology.

[35]  Peter M. Atkinson,et al.  Defining an optimal size of support for remote sensing investigations , 1995, IEEE Trans. Geosci. Remote. Sens..

[36]  A. Marshak,et al.  Inversion of Monte Carlo Model for Estimating Vegetation Canopy Parameters , 1990 .

[37]  A. Lang.  Simplified estimate of leaf area index from transmittance of the sun's beam , 1987 .

[38]  O. Vetaas.  Micro‐site effects of trees and shrubs in dry savannas , 1992 .

[39]  S. Goetz.  Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site , 1997 .

[40]  V. Meentemeyer,et al.  Macroclimate and Lignin Control of Litter Decomposition Rates , 1978 .

[41]  Ann Henderson-Sellers,et al.  Sensitivity of the biosphere-atmosphere transfer scheme (BATS) to the inclusion of variable soil characteristics , 1987 .

[42]  Alan H. Strahler,et al.  Invertible canopy reflectance modeling of vegetation structure in semiarid woodland , 1988 .

[43]  A. Hutsinpiller.  Discrimination of hydrothermal alteration mineral assemblages at Virginia City, Nevada, using the airborne imaging spectrometer , 1988 .

[44]  P. J. Curran,et al.  Airborne MSS data to estimate GLAI , 1987 .

[45]  A. Dalcher,et al.  A Simple Biosphere Model (SIB) for Use within General Circulation Models , 1986 .

[46]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[47]  Paul J. Curran,et al.  Remote sensing the biochemical composition of a slash pine canopy , 1997, IEEE Trans. Geosci. Remote. Sens..

[48]  J. Muller,et al.  Terrestrial remote sensing science and algorithms planned for EOS/MODIS , 1994 .

[49]  Steven W. Running,et al.  Incorporating intertree competition into an ecosystem model , 1995 .

[50]  P. G. Jarvis,et al.  Evaluating progress toward closed forest models based on fluxes of carbon, water and nutrients. , 1991, Tree physiology.

[51]  S. Running,et al.  What does Remote Sensing Do for Ecology , 1991 .

[52]  J. Clevers.  The Derivation of a Simplified Reflectance Model for the Estimation of Leaf Area Index , 1988 .

[53]  Paul J. Curran,et al.  The signal-to-noise ratio (SNR) required for the estimation of foliar biochemical concentrations , 1996 .

[54]  Jennifer L. Dungan,et al.  Seasonal LAI in slash pine estimated with landsat TM , 1992 .

[55]  E. Ridao,et al.  Estimating fAPAR from nine vegetation indices for irrigated and nonirrigated faba bean and semileafless pea canopies , 1998 .

[56]  Guoqing Sun,et al.  Mapping biomass of a northern forest using multifrequency SAR data , 1994, IEEE Trans. Geosci. Remote. Sens..

[57]  G. Bonan.  Land-Atmosphere interactions for climate system Models: coupling biophysical, biogeochemical, and ecosystem dynamical processes , 1995 .

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

[59]  Steven W. Running,et al.  Numerical Terradynamic Simulation Group 5-1994 Testing Forest-BGC Ecosystem Process Simulations Across a Climatic Gradient in Oregon , 2018 .

[60]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

[61]  P. Sellers.  Remote sensing of the land surface for studies of global change , 1993 .

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

[63]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[64]  P. Atkinson,et al.  Cokriging with airborne MSS imagery , 1994 .

[65]  C. Tucker,et al.  Invertibility of a 1-D discrete ordinates canopy reflectance model , 1994 .

[66]  R. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995 .

[67]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[68]  T. M. Lillesand,et al.  Estimating the leaf area index of North Central Wisconsin forests using the landsat thematic mapper , 1997 .

[69]  R. Lawrence,et al.  Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape : Mount St. Helens, Washington , 1998 .

[70]  S. Running,et al.  Numerical Terradynamic Simulation Group 12-1988 Rapid Estimation of Coniferous Forest Leaf Area Index Using a Portable Integrating Radiometer , 2018 .

[71]  P. Curran,et al.  The accuracy of ground data used in remote-sensing investigations , 1985 .

[72]  S. Ustin,et al.  Special Feature: The Future of Remote Sensing in Ecological Studies , 1991 .

[73]  S. Running,et al.  Simulated dry matter yields for aspen and spruce stands in the North American boreal forest , 1992 .

[74]  T. Lin,et al.  The Lambertian assumption and Landsat data. , 1980 .

[75]  N. Goel,et al.  Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation , 1994 .

[76]  Ruiliang Pu,et al.  Seasonal Patterns and Remote Spectral Estimation of Canopy Chemistry Across the Oregon Transect , 1994 .

[77]  Alexander F. H. Goetz,et al.  Terrestrial imaging spectrometry - Current status, future trends , 1993 .

[78]  Philip Lewis,et al.  On the information content of multiple view angle (MVA) images , 1997 .

[79]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .

[80]  Ramakrishna R. Nemani,et al.  Implementation of a hierarchical global vegetation classification in ecosystem function models , 1996 .

[81]  Thuy Le Toan,et al.  Relating forest biomass to SAR data , 1992, IEEE Trans. Geosci. Remote. Sens..

[82]  S. Running.  Estimating Terrestrial Primary Productivity by Combining Remote Sensing and Ecosystem Simulation , 1990 .

[83]  C. Perry,et al.  Functional equivalence of spectral vegetation indices , 1984 .

[84]  Stephan J. Maas,et al.  Use of remotely-sensed information in agricultural crop growth models , 1988 .

[85]  Diane E. Wickland,et al.  Mission to Planet Earth: The Ecological Perspective , 1991 .

[86]  J. Woolley.  Reflectance and transmittance of light by leaves. , 1971, Plant physiology.

[87]  Jean-Philippe Gastellu-Etchegorry,et al.  An assessment of canopy chemistry with AVIRIS ― a case study in the Landes Forest, South-west France , 1995 .

[88]  Darrel L. Williams,et al.  Landsat and Earth Systems Science : Development of terrestrial monitoring , 1997 .

[89]  Susan L. Ustin,et al.  Opportunities for Using the EOS Imaging Spectrometers and Synthetic Aperture Radar in Ecological Models , 1991, Ecology.

[90]  H. Shugart.  A Theory of Forest Dynamics , 1984 .

[91]  Paul J. Curran,et al.  The Estimation of Foliar Biochemical Content of a Slash Pine Canopy from AVIRIS Imagery , 1995 .

[92]  R. M. Hoffer,et al.  Biomass estimation on grazed and ungrazed rangelands using spectral indices , 1998 .

[93]  H. Rahman,et al.  Coupled surface‐atmosphere reflectance (CSAR) model: 1. Model description and inversion on synthetic data , 1993 .

[94]  P. Curran,et al.  LIBERTY—Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra , 1998 .

[95]  J. Dungan.  Spatial prediction of vegetation quantities using ground and image data , 1998 .

[96]  C. Wessman.  Evaluation of canopy biochemistry , 1990 .

[97]  F. M. Danson,et al.  Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .

[98]  G. Asrar,et al.  Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1 , 1984 .

[99]  P. Jarvis,et al.  Photosynthesis in Sitka Spruce (Picea sitchensis) (Bong.) Carr.). I. General Characteristics , 1971 .

[100]  Gordon B. Bonan,et al.  Importance of leaf area index and forest type when estimating photosynthesis in boreal forests , 1993 .

[101]  N. Goel.  Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data , 1988 .

[102]  H. Gausman,et al.  Optical parameters of leaves of 30 plant species. , 1973, Plant physiology.

[103]  J. Marshall,et al.  Comparison of Methods of Estimating Leaf‐Area Index In Old‐Growth Douglas‐Fir , 1986 .

[104]  Blaine L. Blad,et al.  Evaluation of spectral reflectance models to estimate corn leaf area while minimizing the influence of soil background effects , 1986 .

[105]  Kenneth J. Ranson,et al.  Forest biomass from combined ecosystem and radar backscatter modeling , 1997 .

[106]  Fabio Maselli,et al.  Integration of High and Low Resolution NDVI Data for Monitoring Vegetation in Mediterranean Environments , 1998 .

[107]  Ramakrishna R. Nemani,et al.  Sensitivity of a general circulation model to global changes in leaf area index , 1996 .

[108]  P. Sellers.  Canopy reflectance, photosynthesis, and transpiration. II. the role of biophysics in the linearity of their interdependence , 1987 .

[109]  Robert Frouin,et al.  Upscale integration of normalized difference vegetation index: the problem of spatial heterogeneity , 1992, IEEE Trans. Geosci. Remote. Sens..

[110]  Raymond J. Soffer,et al.  Radiometric Calibration of the Compact Airborne Spectrographic Imager (CASI) , 1992 .

[111]  Bernard Pinty,et al.  Designing optimal spectral indexes for remote sensing applications , 1996, IEEE Trans. Geosci. Remote. Sens..

[112]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[113]  Steven W. Running,et al.  A biophysical soil–site model for estimating potential productivity of forested landscapes , 1996 .

[114]  G. Bonham-Carter.  Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data , 1988 .

[115]  Kenneth W. Brodlie,et al.  GKS; the standard for computer graphics , 1985 .

[116]  F. M. Danson,et al.  RED-EDGE RESPONSE TO FOREST LEAF-AREA INDEX (VOL 16, PG 183, 1995) , 1995 .

[117]  Steven W. Running,et al.  Testing scale dependent assumptions in regional ecosystem simulations , 1994 .

[118]  Keith Beven,et al.  Catchment geomorphology and the dynamics of runoff contributing areas , 1983 .

[119]  J. Dungan,et al.  Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. , 1990, Tree physiology.