Title :
Forest parameter retrieval with JPL Airsar P-, L- and C-band data: a plot level analysis for slash pine stands in Georgia
Author :
Kellndorfer, Josef M. ; Dobson, M. Craig ; Clutter, Mike ; Vona, John ; Triplett, Greg
Author_Institution :
Radiat. Lab., Michigan Univ., Ann Arbor, MI, USA
Abstract :
During an EOCAP-SAR project, Airsar P-, L- and C-Band data were used to test the capability of SAR to predict biometric parameters which are frequently used by timber managers as inputs to growth, harvest and yield models. The test site was a commercially managed area in Jesup, south-east Georgia, with stands owned by The Timber Company (TTC). Field campaigns provided an extensive plot-level georeferenced ground data set of four slash pine stands which was used to determine the correlation with the Airsar data. Additionally, 18 stands were surveyed and stand level summaries of biometric variables height, basal area, and volume were obtained. A statistical model was used to test all possible scenarios of polarimetric and frequency combinations. The best models were chosen to develop inversion models for basal area. These model results were applied to stands which are owned by a different timber company (Rayonier), and which were located in the Airsar strip. Within the TTC land holdings, R2 correlation coefficients for volume prediction from SAR were 0.85. The correlation between predicted basal area and stand age on 65 Rayonier stands resulted in the an adjusted R2 of 0.86.
Keywords :
airborne radar; remote sensing by radar; synthetic aperture radar; vegetation mapping; Airsar data; C-band data; EOCAP-SAR project; Georgia; Jesup; L-band data; P-band data; USA; basal area; biometric parameters; biometric variables; correlation coefficients; forestry; multi-frequency multi-polarization data; plot-level georeferenced ground data; polarimetry; slash pine stands; stand age; timber growth; timber harvest; timber management; timber yield models; volume prediction; Biometrics; Computer science; Forestry; Frequency; Information retrieval; Laboratories; Predictive models; Project management; Resource management; Testing;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International
Print_ISBN :
0-7803-7536-X
DOI :
10.1109/IGARSS.2002.1026446