Remote sensing provides methods to infer vegetation information over large areas at a variety of spatial and temporal resolutions that is of great use for terrestrial carbon cycle modeling. Understory vegetation and foliage clumping in forests present a challenge for accurate estimates of vegetation structural information. Multi-angle remote sensing was used to derive and refine new information about the vegetation structure for the purpose of improving global leaf area index mapping.
A field experiment with multi-angle, high resolution airborne observations over modified and natural backgrounds (understory, moss, litter, soil) was conducted in 2007 near Sudbury, Ontario to test a methodology for the background reflectivity retrieval. The experiment showed that it is feasible to retrieve the background information, especially over the crucial low to intermediate canopy density range where the effect of the understory vegetation is the largest. The tested methodology was then applied to background reflectivity mapping over conterminous United States, Canada, Mexico, and Caribbean land mass using space-borne Multi-angle Imaging SpectroRadiometer (MISR) data. Important seasonal development of the forest background vegetation was observed across a wide longitudinal and latitudinal span of the study area.
The previous first ever global mapping of the vegetation clumping index with a limited eight-month multi-angular POLDER 1 dataset was expanded by integrating new, complete year-round observations from POLDER 3. A simple topographic compensation function was devised to correct negative bias in the data set cause by topographic effects. The clumping index reductions can reach up to 30% from the topographically non-compensated values, depending on terrain complexity and land cover type. The new global clumping index map is compared with an assembled set of field measurements, covering four continents and diverse biomes.
Finally, inclusion of the new vegetation structural information, including background reflectivity and clumping index, gained from the multi-angle remote sensing was then shown to improve the performance of LAI retrieval algorithms over forests.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/19293 |
| Date | 03 March 2010 |
| Creators | Pisek, Jan |
| Contributors | Chen, Jing Ming |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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