Obtaining quantitative information about the recovery of fire affected ecosystems is of utmost importance from the management and decision-making point of view. Nowadays the concern about natural environment protection and recovery is much greater than in the past. However, the resources and tools available for its management are still not sufficient. Thus, attention and precision are needed when decisions must be taken. Quantitative estimates on how the vegetation is recovering after a fire can be of help for evaluating the necessity of human intervention on the fire-affected ecosystem, and their importance will grow as the problem of forest fires, climate change and desertification increase. This thesis presents a comparison of methods to extract quantitative estimates of vegetation cover regrowth in burned areas with remote sensing data. In order to eliminate possible sources of error, a thorough pre-processing was carried out, including a careful geometric correction (reaching RMSE lower than 0.3 pixels), a topographic correction by means of a constrained Minneart model and a combination of absolute and relative atmospheric correction methods. Pseudo Invariant Features (PIF) were identified either by visual inspection methods or by a new automated selection method based in temporal Principal Component Analysis (PCA), which has been called multi-Temporal n-Dimensional Principal Component Analysis (mT-nD-PCA). This automated method demonstrated its capability in selecting accurate and objective PIFs within the satellite images. Spectral Mixture Analysis (SMA) was compared against quantitative vegetation indices which are based on well known traditional vegetation indices like Normalised Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI). Accuracy assessment was performed by regressing vegetation cover results obtained with each method, against field data gathered during the field work campaigns. Results obtained showed how vegetation cover fractions obtained with the NDVI based quantitative index were the most accurate, being superior to the rest of the techniques applied, including SMA
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