Name
Abstract | ||
|---|---|---|
Hyperspectral sensing of forest chemistry can provide indicators of forest health. Foliar pigments are directly involved with the photosynthetic process and, therefore, are intimately tied to vegetation vigor. AISA and AVIRIS hyperspectral datasets were acquired over the Greater Victoria Watershed District test site in 2006 and 2002, respectively. AISA was calibrated to AVIRIS to facilitate sensor comparison. The data were used to generate a forest species classification, endmember fractions and chemistry for test plots. The hyperspectral products were used to separate ground cover (Salal) from the forest overstory and chemistry was estimated for both layers. Classification accuracies exceeded 89% in mapping major forest species. AVIRIS predicted chemistry agreed with measured chemistry (R2: 0.98). Incorporating an understory stratification step was anticipated to increase the accuracy of chemistry estimates; however, R2 values were unchanged. While plot data suggested AISA chemistry prediction performed well, significant bidirectional reflectance effects were evident; this effect was absent in the AVIRIS data. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IGARSS.2009.5416937 | Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009 |
Keywords | Field | DocType |
forestry,photosynthesis,vegetation mapping,AD 2002,AD 2006,AISA hyperspectral dataset,AVIRIS hyperspectral dataset,Greater Victoria Watershed District,bidirectional reflectance effects,chemistry mapping,classification accuracy,foliar pigments,forest chemistry hyperspectral sensing,forest health,forest overstory,forest species classification,ground cover,photosynthetic process,sensor comparison,Classification,chemistry,imaging spectroscopy,spectral un-mixing | Endmember,Vegetation,Understory,Computer science,Remote sensing,Chemistry,Watershed,Hyperspectral imaging,Test site,Reflectivity | Conference |
Volume | ISSN | ISBN |
1 | 2153-6996 | 978-1-4244-3395-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David G. Goodenough | 1 | 0 | 0.34 |
| K. Olaf Niemann | 2 | 23 | 7.09 |
| Geoffrey S. Quinn | 3 | 0 | 1.69 |
| Piper Gordon | 4 | 4 | 2.01 |
| Ashley Gross | 5 | 0 | 0.68 |
| Tian Han | 6 | 18 | 1.71 |
| Geordie Hobart | 7 | 31 | 6.44 |
| Hao Chen | 8 | 45 | 7.54 |
| a dyk | 9 | 85 | 18.72 |