A theoretical analysis of the effect of forest structure on synthetic aperture radar backscatter and the remote sensing of biomass

Stand level forest canopy structure as measured by the size, density, and distribution of the stems, branches, and leaves may have a strong effect on SAR backscatter. This study used the Michigan Microwave Canopy Scattering model (MIMICS) and forest canopy biometric data from tropical and subtropical broadleaf forests to simulate a series of forest stands having equivalent above ground biomass while still exhibiting substantial structural differences. The model stands were made to represent a wide range of basic structural differences found in Earth’s broadleaf forests. The radar response to these structural differences is shown for the NASNJPL AIRSAR P-, L-, and C-band quadpol. configuration at incidence angles of 20°, 40°, and 60°. Results for three sets of equal-biomass-foreststands were generated: five structural types having a total above ground biomass of 5 kg/m2 (50 tonha), three structural types at 15 kg/m2 (150 tonsha), and three structural types at 30 kg/m2 (300 tonsha). Simplified model input were used to reduce the number of geometric variables tested across the range of structural types. The potential effect that extreme structural differences might have on the biomass signal and saturation threshold was assessed. Results indicate that structure can have a considerable effect on the SAR return for forests with equivalent above ground biomass. Differences in backscatter of up to 18 dB were predicted for some bands and polarizations. A forest canopy structural descriptor derived from the vegetation surface area to volume ratio (SAN), which is a measure of stuctural consolidation, appears to explain the differences in backscatter. In many vegetation stands structural consolidation is directly related to an increase in biomass during the "thinning phase" of forest succession. This structural effect may explain the good relationship between SAR backscatter and biomass in these cases.

[1]  Kamal Sarabandi,et al.  Michigan microwave canopy scattering model , 1990 .

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

[3]  K. Yoda,et al.  Self-thinning in overcrowded pure stands under cultivated and natural conditions (Intraspecific competition among higher plants. XI) , 1963 .

[4]  E. Tanner,et al.  STUDIES ON THE BIOMASS AND PRODUCTIVITY IN A SERIES OF MONTANE RAIN FORESTS IN JAMAICA , 1980 .

[5]  Robin M. Reich,et al.  Estimating splash pine biomass using radar backscatter , 1991, IEEE Trans. Geosci. Remote. Sens..

[6]  A Method for Estimating Crown Weight in Eucalyptus, and Some Implications of Relationships between Crown Weight and Stem Diameter , 1966 .

[7]  JoBea Way,et al.  The evolution of synthetic aperture radar systems and their progression to the EOS SAR , 1991, IEEE Trans. Geosci. Remote. Sens..

[8]  M. Craig Dobson,et al.  The relationship between aboveground biomass and radar backscatter as observed on airborne SAR imagery , 1991 .

[9]  P. Attiwill.  The Loss of Elements from Decomposing Litter , 1968 .

[10]  M. Cannell,et al.  Dry Matter Production and Partition in Relation to Yield of Tea , 1981, Experimental Agriculture.

[11]  C. Uhl,et al.  Abandoned pastures in eastern Amazonia. I. Patterns of plant succession , 1988 .

[12]  Fawwaz T. Ulaby,et al.  Using Mimics To Model L-band Multiangle and Multitemporal Backscatter From A Walnut Orchard , 1990 .

[13]  J. White,et al.  CORRELATED CHANGES IN PLANT SIZE AND NUMBER IN PLANT POPULATIONS , 1970 .

[14]  Guoqing Sun,et al.  Simulation of L-band and HH microwave backscattering from coniferous forest stands: a comparison with SIR-B data , 1988 .

[15]  G. Dimmock,et al.  Nutrient distribution in karri (Eucalyptus diversicolor F. muell.) ecosystems in southwest western Australia , 1979 .

[16]  Mark Westoby,et al.  The Place of the Self-Thinning Rule in Population Dynamics , 1981, The American Naturalist.

[17]  Kyle C. McDonald.  Modeling microwave backscatter from tree canopies. , 1991 .

[18]  A. K. Fung,et al.  A scatter model for vegetation up to Ku-band , 1984 .

[19]  P. J. Edwards,et al.  World Forest Biomass and Primary Production Data. , 1983 .

[20]  Thuy Le Toan,et al.  Dependence of radar backscatter on coniferous forest biomass , 1992, IEEE Trans. Geosci. Remote. Sens..

[21]  Roger H. Lang,et al.  Electromagnetic Backscattering from a Layer of Vegetation: A Discrete Approach , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[22]  John Richards,et al.  L-Band Radar Backscatter Modeling of Forest Stands , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Kamal Sarabandi,et al.  Preliminary analysis of ERS-1 SAR for forest ecosystem studies , 1992, IEEE Trans. Geosci. Remote. Sens..

[24]  E. Gorham,et al.  Shoot height, weight and standing crop in relation to density of monospecific plant stands , 1979, Nature.

[25]  Eric S. Kasischke,et al.  Connecting forest ecosystem and microwave backscatter models , 1990 .

[26]  Shih-tseng Wu,et al.  Potential Application of Multipolarization SAR for Pine-Plantation Biomass Estimation , 1987, IEEE Transactions on Geoscience and Remote Sensing.