Name
Title | ||
|---|---|---|
Comparison of Aviris and AISA Airborne Hyperspectral Sensing for Above-Ground Forest Carbon Mapping |
Abstract | ||
|---|---|---|
2 of 0.90. AISA with its higher spectral and spatial resolution offers the potential to separate ground cover (salal) from the forest overstory. Salal, a broad-leafed bush, is the dominant understory in the wetter portions of our GVWD test site. Salal can have high reflectance of 80% at 800 nm. In the drier portions of the site, the understory is dark soil and litter. To accurately map forest biomass, it is important to identify the understory and stratify the forests by understory type. This was done to create separate relationships for mapping biomass by sensor and by forest units. GVWD test site has extensive ground data plots. These plots were used to calibrate the biomass relationships. For the AISA and AVIRIS comparisons, spectral relationships were established between the AISA (adjusted to AVIRIS spectral and spatial resolution) and AVIRIS sensors for selected calibration targets. This between- sensor calibration placed the AISA data on the same calibration basis as the AVIRIS data. The calibrated reflectance data were used to generate forest species classifications, endmember fractions, and biomass estimates for the test site. Average classification accuracies exceeded 89% in mapping major forest species (2). These products were used to create a map of above-ground carbon for the forested portion of the GVWD test site. Earlier, above-ground carbon mapping was done with multi-temporal LANDSAT data for a test site in Alberta near the town of Hinton (3), (4). Above-ground carbon mapping from remote sensing gave more accurate estimates than traditional estimates from inventory summaries for individual map sheets. Particularly critical was accurate determination of biomass. For the GVWD site, tree heights derived from LIDAR data, and allometric equations were used to provide independent ground estimates of biomass. These estimates were compared to the hyperspectral estimates of biomass. The resulting above-ground carbon maps were compared to estimates obtained from carbon budget models (5). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IGARSS.2008.4778944 | IGARSS |
Keywords | Field | DocType |
we demonstrated that biomass obtained from aviris hyperspectral data agreed with ground measurements of biomass with an r,working on our test site in hoquiam,washington that contained similar forest species to gvwd,species,calibration,lidar,laser radar,hyperspectral sensors,biomass,forest,noise,hyperspectral imaging,sustainable development,spatial resolution,vegetation,testing,atmospheric boundary layer,remote sensing,hyperspectral,c,carbon | Biomass,Vegetation,Computer science,Hydrology,Remote sensing,Tree allometry,Watershed,Hyperspectral imaging,Lidar,Test site,Reflectivity | Conference |
Citations | PageRank | References |
3 | 0.59 | 5 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David G. Goodenough | 1 | 7 | 1.83 |
| K. Olaf Niemann | 2 | 23 | 7.09 |
| a dyk | 3 | 85 | 18.72 |
| Geordie Hobart | 4 | 31 | 6.44 |
| Piper Gordon | 5 | 4 | 2.01 |
| Matthew Loisel | 6 | 3 | 0.59 |
| Hao Chen | 7 | 87 | 16.52 |