Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Mapping of Taiga Forest units using AIRSAR data and/or optical data, and retrieval of forest parameters
A maximum a posteriori Bayesian classifier is used to perform a supervised classification of multifrequency, polarimetric, airborne, SAR observations of boreal forests from the Bonanza Creek Experimental Forest, near Fairbanks, Alaska, into six categories: 1) white spruce; 2) black spruce; 3) balsam poplar; 4) alder; 5) treeless areas; and 6) open water. Tree classification accuracy is highest (86%) using L- and C-band fully polarimetric combined on a date where the forest just recovered from river flooding. The SAR map compares favorably with a vegetation map obtained from digitized aerial infra-red photos. C-band frequency and HV-polarization are, respectively, the most useful frequency and polarization for mapping tree types using SAR. Combination of multi-date SAR observations does not improve classification accuracy, and SAR data acquired on different dates, under different environmental conditions, yield classification accuracies 16% to 41% lower. Single-frequency, single-polarization, SAR data show limited mapping capability. Multispectral SPOT observations of the same area on a single date yield a classification accuracy of 78%. Combining optical and SAR data is useful for identifying tree species, independent of ground truth verification, using biomass estimates from SAR, at L-band HV-polarization, NDVI from SPOT red and infra-red radiances, and an unsupervised segmentation map of the SAR data.<>
Mapping of Taiga Forest units using AIRSAR data and/or optical data, and retrieval of forest parameters
A maximum a posteriori Bayesian classifier is used to perform a supervised classification of multifrequency, polarimetric, airborne, SAR observations of boreal forests from the Bonanza Creek Experimental Forest, near Fairbanks, Alaska, into six categories: 1) white spruce; 2) black spruce; 3) balsam poplar; 4) alder; 5) treeless areas; and 6) open water. Tree classification accuracy is highest (86%) using L- and C-band fully polarimetric combined on a date where the forest just recovered from river flooding. The SAR map compares favorably with a vegetation map obtained from digitized aerial infra-red photos. C-band frequency and HV-polarization are, respectively, the most useful frequency and polarization for mapping tree types using SAR. Combination of multi-date SAR observations does not improve classification accuracy, and SAR data acquired on different dates, under different environmental conditions, yield classification accuracies 16% to 41% lower. Single-frequency, single-polarization, SAR data show limited mapping capability. Multispectral SPOT observations of the same area on a single date yield a classification accuracy of 78%. Combining optical and SAR data is useful for identifying tree species, independent of ground truth verification, using biomass estimates from SAR, at L-band HV-polarization, NDVI from SPOT red and infra-red radiances, and an unsupervised segmentation map of the SAR data.<>
Mapping of Taiga Forest units using AIRSAR data and/or optical data, and retrieval of forest parameters
Rignot, E. (Autor:in) / Williams, C. (Autor:in) / Way, J.B. (Autor:in)
01.01.1993
292793 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
An Evaluation of AIRSAR and SIR-C-X-SAR Images for Mapping Northern Forest Attributes in Maine, USA
| Online Contents | 1997
Properties of Alluvial Soils of Taiga Forest under Anthropogenic Salinisation
| DOAJ | 2021
Articles - Vegetation Identification and Biomass Estimation Using AIRSAR Data
| Online Contents | 1999
Forest cover disturbances in the South Taiga of West Siberia
| IOP Institute of Physics | 2011