LAND COVER CHARACTERIZATION USING MULTITEMPORAL RED, NEAR‐IR, AND THERMAL‐IR DATA FROM NOAA/AVHRR

A simple land cover classification scheme is proposed based on energy absorption and exchange properties of various land cover types, observable from remote sensing. Seasonal trajectories of the Normalized Difference Vegetation Index (NDVI) and surface temperature (Ts), routinely available from NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer), are used to characterize different land cover types into four groups: water limited (shrub, grass), energy limited (wetlands, boreal forests, snow, ice, and water), atmospherically coupled (aerodynamically rough canopies, forests), and atmospherically decoupled (aerodynamically smooth canopies, crops). Further separation is achieved using growing-season average NDVI for shrub and grass, seasonal NDVI amplitude for deciduous vs. evergreen, and near-infrared (NIR) reflectance for broadleaf vs. needleleaf vegetation. The methodology using threshold-based rules is completely remote sensing based; classification rules ...

[1]  O. T Denmead,et al.  Comparative micrometeorology of a wheat field and a forest of Pinus radiata , 1969 .

[2]  G. Szeicz,et al.  Aerodynamic and surface factors in evaporation , 1969 .

[3]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

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

[5]  J. C. Price,et al.  Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer , 1984 .

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

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

[8]  Samuel N. Goward,et al.  Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape , 1985 .

[9]  A. Henderson‐sellers,et al.  A global archive of land cover and soils data for use in general circulation climate models , 1985 .

[10]  S. Sader.  Analysis of effective radiant temperatures in a Pacific Northwest forest using thermal infrared multispectral scanner data , 1986 .

[11]  Samuel N. Goward,et al.  Simulated Relationships Between Spectral Reflectance, Thermal Emissions, and Evapotranspiration of a Soybean Canopy , 1986 .

[12]  K. G. McNaughton,et al.  Stomatal Control of Transpiration: Scaling Up from Leaf to Region , 1986 .

[13]  F. Woodward.  Climate and plant distribution , 1987 .

[14]  Ramakrishna R. Nemani,et al.  Testing a theoretical climate-soil-leaf area hydrologic equilibrium of forests using satellite data and ecosystem simulation , 1989 .

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

[16]  F. Achard,et al.  Analysis of vegetation seasonal evolution and mapping of forest cover in West Africa with the use of NOAA AVHRR HRPT data. , 1990 .

[17]  J. C. Price.  Using spatial context in satellite data to infer regional scale evapotranspiration , 1990 .

[18]  S. Running,et al.  The seasonality of AVHRR data of temperate coniferous forests - Relationship with leaf area index , 1990 .

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

[20]  R. Myneni,et al.  Radiative transfer in three dimensional leaf canopies , 1990 .

[21]  M. D. Schwartz,et al.  Spring phenology : nature's experiment to detect the effect of Green-up on surface maximum temperatures , 1990 .

[22]  B. Choudhury.  Multispectral satellite data in the context of land surface heat balance , 1991 .

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

[24]  J. Luvall,et al.  Thermal remote sensing methods in landscape ecology , 1991 .

[25]  C. Justice,et al.  Global land cover classification by remote sensing: present capabilities and future possibilities , 1991 .

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

[27]  B. Choudhury,et al.  Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study , 1992 .

[28]  Scott J. Goetz,et al.  Biophysical, morphological, canopy optical property, and productivity data from the Superior National Forest , 1992 .

[29]  C. Field,et al.  A reanalysis using improved leaf models and a new canopy integration scheme , 1992 .

[30]  J. Eidenshink.  The 1990 conterminous U. S. AVHRR data set , 1992 .

[31]  W. Cramer,et al.  A global biome model based on plant physiology and dominance, soil properties and climate , 1992 .

[32]  S. Running,et al.  8 – Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models , 1993 .

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

[34]  Steven W. Running,et al.  Community type differentiation using NOAA/AVHRR data within a sagebrush-steppe ecosystem , 1993 .

[35]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

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

[37]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

[38]  C. Justice,et al.  Development of vegetation and soil indices for MODIS-EOS , 1994 .

[39]  Richard H. Waring,et al.  Ecological Remote Sensing at OTTER: Satellite Macroscale Observations , 1994 .

[40]  S. Kalluri,et al.  The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring , 1994 .

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

[42]  R. Neilson.  A Model for Predicting Continental‐Scale Vegetation Distribution and Water Balance , 1995 .