Remote Sensing of Terrestrial Ecosystem Structure: An Ecologist’s Pragmatic View

This chapter reviews the scientific concepts involved in the application of remote sensing technology to current and future problems in terrestrial ecology. The approach is pragmatic, being decisively user oriented, and is based on the proposition that currently available technology far exceeds the scientific capability of interpreting and applying it. For most terrestrial ecological problems of current and future concern, data types and volumes are not immediately limiting. Rather it is the understanding of the ecological significance of what has already been acquired that fetters the wider, more constructive use of remote sensing technology.

[1]  G. Asrar,et al.  Light Interception and Leaf Area Estimates from Measurements of Grass Canopy Reflectance , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[2]  C. J. Tucker,et al.  Spectral assessment of soybean leaf area and leaf biomass , 1980 .

[3]  J. Townshend,et al.  African Land-Cover Classification Using Satellite Data , 1985, Science.

[4]  K. Ranson,et al.  Scene Shadow Effects on Multispectral Response , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Gwynn H. Suits,et al.  Procedures for using signals from one sensor as substitutes for signals of another , 1988 .

[6]  Steven W. Running,et al.  Global primary production from terrestrial vegetation: Estimates integrating satellite remote sensing and computer simulation technology , 1986 .

[7]  Kevin P. Gallo,et al.  Differences in visible and near-IR responses, and derived vegetation indices, for the NOAA-9 and NOAA-10 AVHRRs: a case study , 1988 .

[8]  C. Justice,et al.  Analysis of the phenology of global vegetation using meteorological satellite data , 1985 .

[9]  Paul J. Curran,et al.  Simple size for ground and remotely sensed data , 1986 .

[10]  B. Foran,et al.  Detection of yearly cover change with Landsat MSS on pastoral landscapes in Central Australia , 1987 .

[11]  R. Pech,et al.  The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data , 1988 .

[12]  Stephen J. Walsh,et al.  Impact of environmental variables on spectral signatures acquired by the LANDSAT thematic mapper , 1986 .

[13]  D. Vidal-Madjar,et al.  Evaluation of a Surface/Vegetation Parameterization Using Satellite Measurements of Surface Temperature , 1986 .

[14]  P. J. Curran,et al.  Estimating the green leaf area index of grassland with airborne multispectral scanner data , 1987 .

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

[16]  H. Delcourt,et al.  Dynamic plant ecology: the spectrum of vegetational change in space and time , 1982 .

[17]  Jack F. Paris,et al.  Characterization of vegetation with combined Thematic Mapper (TM) and Shuttle Imaging Radar (SIR-B) image data , 1987 .

[18]  R. Specht,et al.  Foliage projective cover and standing biomass , 1981 .

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

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

[21]  Ray Jackson,et al.  Development of Agrometeorological Crop Model Inputs from Remotely Sensed Information , 1986, IEEE Transactions on Geoscience and Remote Sensing.

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

[23]  J. Walker,et al.  Australian Soil and Land Survey Field Handbook , 1984 .

[24]  R. Pech,et al.  Reflectance modelling and the derivation of vegetation indices for an Australian semi-arid shrubland , 1986 .

[25]  George M. Woodwell,et al.  The role of terrestrial vegetation in the global carbon cycle : measurement by remote sensing , 1985 .

[26]  H. D. Williamson Evaluation of middle and thermal infrared radiance in indices used to estimate GLAI , 1988 .

[27]  P. J. Curran,et al.  Radiometric leaf area index , 1988 .

[28]  C. Woodcock,et al.  The use of variograms in remote sensing: I , 1988 .

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

[30]  Alfred T. C. Chang,et al.  Estimating surface soil moisture from satellite microwave measurements and a satellite derived vegetation index , 1988 .

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

[32]  Michael Spanner,et al.  Analysis of Forest Structure Using Thematic Mapper Simulator Data , 1986, IEEE Transactions on Geoscience and Remote Sensing.

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

[34]  Ross Nelson,et al.  Estimating forest biomass and volume using airborne laser data , 1988 .

[35]  Gwynn H. Suits,et al.  The prospects for detecting spectral shifts due to satellite sensor aging , 1988 .

[36]  A. Strahler,et al.  Geometric-Optical Modeling of a Conifer Forest Canopy , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[37]  C. L. Wiegand,et al.  Spectral components analysis Rationale, and results for three crops , 1987 .

[38]  C. Woodcock,et al.  The use of variograms in remote sensing. I - Scene models and simulated images. II - Real digital images , 1988 .

[39]  J. Malingreau Global vegetation dynamics - Satellite observations over Asia , 1986 .

[40]  Gautam D. Badhwar,et al.  Satellite-derived leaf-area-index and vegetation maps as input to global carbon cycle models-a hierarchical approach , 1986 .

[41]  Susan L. Ustin,et al.  Discriminating semiarid vegetation using airborne imaging spectrometer data: a preliminary assessment , 1987 .

[42]  J. K. Aase,et al.  Radiometric reflectance measurements of northern Great Plains rangeland and crested wheatgrass pastures. , 1987 .

[43]  Alan H. Strahler,et al.  On the nature of models in remote sensing , 1986 .

[44]  B. Holben,et al.  Red and near-infrared sensor response to off-nadiir viewing , 1984 .

[45]  Christopher O. Justice,et al.  Monitoring the grasslands of the Sahel 1984-1985 , 1986 .

[46]  R. R. Lamacraft,et al.  Calibration of LANDSAT data for sparsely vegetated semi-arid rangelands , 1986 .

[47]  P. Vujaković,et al.  Monitoring extensive ‘buffer zones’ in Africa: An application for satellite imagery , 1987 .

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

[49]  A. Strahler Stratification of natural vegetation for forest and rangeland inventory using Landsat digital imagery and collateral data , 1981 .

[50]  H. V. Gils,et al.  Vegetation structure in reconnaissance and semi-detailed vegetation surveys , 1984 .

[51]  G. Pickup,et al.  Relationship of aircraft radiometric measurements to bare ground on semi-desert landscapes in central Australia. , 1984 .

[52]  M. Matson,et al.  Fire detection using data from the NOAA-N satellites , 1987 .

[53]  Geoffrey J. McLachlan,et al.  Mixture models : inference and applications to clustering , 1989 .

[54]  E. Matthews Global Vegetation and Land Use: New High-Resolution Data Bases for Climate Studies , 1983 .

[55]  Christopher O. Justice,et al.  Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983 , 1986 .

[56]  B. Holben,et al.  Satellite detection of tropical burning in Brazil , 1987 .

[57]  Gautam Badhwar,et al.  Signature-Extendable Technology: Global Space-Based Crop Recognition , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[58]  R. Pech,et al.  Reflectance modeling of semiarid woodlands , 1987 .

[59]  Bhaskar J. Choudhury,et al.  Relative sensitivity of normalized difference vegetation Index (NDVI) and microwave polarization difference Index (MPDI) for vegetation and desertification monitoring , 1988 .

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

[61]  J. Walker,et al.  Interpretation of vegetation structure in Landsat MSS imagery: a case study in disturbed semi-arid eucalypt woodlands. Part 1. Field data analysis , 1986 .

[62]  J. F. O'Callaghan,et al.  The application of Landsat image data to rangeland assessment and monitoring: the development and demonstration of a land image-based resource information system (LIBRIS) , 1986 .

[63]  D. F. Wanjura,et al.  Vegetative and optical characteristics of four-row crop canopies , 1988 .

[64]  J. Millard,et al.  Spectral Radiance Estimates of Leaf Area and Leaf Phytomass of Small Grains and Native Vegetation , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[65]  Gautam Badhwar,et al.  Spectral Characterization of Biophysical Characterstics in a Boreal Forest: Relationship between Thematic Mapper Band Reflectance and Leaf Area Index for Aspen , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[66]  C. Woodcock,et al.  Autocorrelation and regularization in digital images. I. Basic theory , 1988 .

[67]  J. Franklin,et al.  Coniferous Forest Classification and Inventory Using Landsat and Digital Terrain Data , 1986, IEEE Transactions on Geoscience and Remote Sensing.

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

[69]  H. Musick,et al.  Temporal change of Landsat MSS albedo estimates in arid rangeland , 1986 .

[70]  C. Justice,et al.  Analysis of the dynamics of African vegetation using the normalized difference vegetation index , 1986 .

[71]  G. Pickup,et al.  The use of spectral and spatial variability to monitor cover change on inert landscapes , 1987 .

[72]  Ned Horning,et al.  Determining the rate of forest conversion in Mato Grosso, Brazil, using Landsat MSS and AVHRR data , 1987 .

[73]  M. F. Gross,et al.  Continental scale variability in vegetation reflectance and its relationship to canopy morphology , 1988 .

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

[75]  C. J. Tucker,et al.  Relationship between atmospheric CO2 variations and a satellite-derived vegetation index , 1986, Nature.

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