Remote sensing of forest biomass dynamics using Landsat-derived disturbance and recovery history and lidar data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/p8418q47d

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  • Improved monitoring of forest biomass is needed to quantify natural and anthropogenic effects on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, fine spatial grain, and long history of earth observations provide a unique opportunity for measuring biophysical properties of vegetation across large areas and long time scales. However, like other multi-spectral data, the relationship between single-date reflectance and forest biomass weakens under certain canopy conditions. Because the structure and composition of a forest stand at any point in time is linked to the stand's disturbance history, one potential means of enhancing Landsat's spectral relationships with biomass is by including information on vegetation trends prior to the date for which estimates are desired. The purpose of this research was to develop and assess a method that links field data, airborne lidar, and Landsat-derived disturbance and recovery history for mapping of forest biomass and biomass change. Our study area is located in eastern Oregon (US), an area dominated by mixed conifer and single species forests. In Chapter 2, we test and demonstrate the utility of Landsat-derived disturbance and recovery metrics to predict current forest structure (live and dead biomass, basal area, and stand height) for 51 field plots, and compare the results with estimates from airborne lidar and single-date Landsat imagery. To characterize the complex nature of long-term (insect, growth) and short-term (fire, harvest) vegetation changes found in this area, we use annual Landsat time series between 1972 and 2010. This required integrating Landsat data from MSS (1972-1992) and TM/ETM+ (1982-present) sensors. In Chapter 2, we describe a method to bridge spectral differences between Landsat sensors, and therefore extent Landsat time-series analyses back to 1972. In Chapter 3, we extend and automate our approach and develop maps of current (2009) and historic (1993-2009) live forest biomass. We use lidar data for model training and evaluate the results with forest inventory data. We further conduct a sensitivity analysis to determine the effects of forest structure, time-series length, terrain and sampling design on model predictions. Our research showed that including disturbance and recovery trends in empirical models significantly improved predictions of forest biomass, and that the approach can be applied across a larger landscape and across time for estimating biomass change.
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