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The extent, frequency and intensity of forest fires in Mediterranean regions have become an important problem in recent decades. Nowadays, remote sensing is an essential tool for the planning and management of the land at different scales. In the field of forest fires remote sensing images have been used in many different types of studies and currently applied to detect burned areas by means of images, providing quickly, easily and affordable the limits of burned areas immediately during or after the fire season. The importance of these products lies in the possibility to obtain perimeter, area and damage level caused by wildfires. The objective of this study was the evaluation of multi-scale remotely sensed images and various mapping methods for the identification and estimation of burned areas. The area of the study was situated in Galicia, a region of Spain punished year after year by important wildfires. By employing 7 images before, during and after the occurrence of forest fires, and working with different methods it was possible the collection of several products and results. The satellite imagery used was Landsat TM5 and MODIS, and the methods carried out were mainly spectral indices such as Normalized Burnt Ratio (NBR), Short Wave InfraRed Index (SWIR), Burnt Area Index (BAI), Burnt Area Index for MODIS (BAIM) and supervised classifications. Based on a wide literature review there were selected as suitable techniques for assess, localize and quantify burned areas. The work was separated in two sections, being differenced monotemporal and multitemporal analyses, depending on the images involved in each part. The results showed that which indices can distinguish burned areas with the high precision. There were found common problems of all indices as the classification of burned areas in shaded regions as unburned areas. Landsat images proved to be the most accurate images to perform studies with burned areas due to its high spatial resolution comparing with MODIS images. As a final products were obtained with precision the total burned area, the perimeter, the localization and the burn severity of the regions affected by wildfires. The data obtained could be used to create a database of burned areas, or based in the repetitive patterns, as useful information in order to prevent future forest fires.

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A novel dual-mode microwave scatterometer system has been designed and fabricated for remote sensing. This dissertation describes the sensor and presents unique C- and Ku-band ocean surface radar backscatter measurements obtained with it during flights on NASA C-130 and P-3 aircraft. Anisotropic C-band normalized radar cross section measurements obtained for a limited range of ocean surface windspeeds with a spinning antenna are presented. These measurements are potentially free of errors that corrupt similar measurements made with fixed-azimuth airborne scatterometers during "circle-flights". Also presented, for the first time, are open-ocean observations of the electromagnetic (EM) bias at C- and Ku-bands.

A better understanding of ecosystem processes requires accurate estimates of forest biomass and structure on global scales. Recently, there have been demonstrations of the ability of remote sensing instruments, such as radar and lidar, for the estimation of forest parameters from spaceborne platforms in a consistent manner. These advances can be exploited for global forest biomass accounting and structure characterization, leading to a better understanding of the global carbon cycle. The popular techniques for estimation of forest parameters from radar instruments in particular, use backscatter intensity, interferometry and polarimetric interferometry. This dissertation analyzes the accuracy of biomass and structure estimates over temperate forests of the North-Eastern United States. An empirical approach is adopted, relying on ground truth data collected during field campaigns over the Harvard and Howland Forests in 2009. The accuracy of field biomass estimates, including the impact of the diameter-biomass allometry is characterized for the field sites. Full waveform lidar data from two LVIS field campaigns of 2009 over the Harvard and Howland forests is analyzed to assess the accuracy of various lidar-biomass relationships. Radar data from NASA JPL's UAVSAR is analyzed to assess the accuracy of the backscatter-biomass relationships with a theoretical radar error model. The relationship between field biomass and InSAR heights is explored using SRTM elevation and LVIS derived ground topography. Temporal decorrelation, a major factor affecting the accuracy of repeat-pass InSAR observations of forests is analyzed using the SIR-C single-day repeat data from 1994. Finally, PolInSAR inversion of heights over the Harvard and Howland forests is explored using UAVSAR repeat-pass data from the 2009 campaign. These heights are compared with LVIS height estimates and the impact of temporal decorrelation is assessed.

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Calibration of the Defense Meteorological Space Program's (DMSP) Special Sensor Microwave/Imager (SSM/I) and construction of algorithms used to retrieve environmental parameters for the raw SSM/I measurements of brightness temperature, TA, are presented. Retrieval algorithm work includes validation and repair of the DMSP ocean surface wind speed algorithm which was developed at Environmental Research & Technology Inc. (ERT). The ERT algorithm is based on the "D-matrix" approach which seeks a linear relationship between measured SSM/I brightness temperatures and environmental parameters. D-matrix performance was validated by comparing algorithm derived wind speeds with near-simultaneous and co-located measurements made by off-shore ocean buoys maintained by the National Oceanic and Atmospheric Administration. A revised D-matrix algorithm satisfied the DMSP accuracy requirement of 2 m/s for wind speed predictions in the range of 3 m/s to 25 m/s. Explanation of the process by which the SSM/I is able to measure ocean-surface winds is given and is based on the theory of microwave radiative transfer. The explanation concludes with construction of a nonlinear, iterative algorithm which is able to retrieve ocean surface wind speed, integrated atmospheric water vapor and integrated cloud liquid water from the raw SSM/I data. Instrument calibration issues include brightness temperature accuracy and precision, antenna beamwidth measurements, antenna pattern correction, and geolocation of the SSM/I measurements.

The ability of radars to detect and classify targets has been greatly enhanced by maturation of the theory and practice of radar polarimetry. Although a substantial body of polarimetric measurements of natural surfaces has been accumulated over the last several decades at microwave frequencies, researchers have only recently begun to establish a similar data base at millimeter wavelengths. As an extension of this work, we have developed a fully polarimetric 225 GHz radar capable of remotely characterizing the average Mueller matrix of natural surfaces at ranges up to 1 km. We describe two incoherent measurement techniques, one which uses four transmit polarizations to uniquely specify the Mueller matrix, and a second using five or more transmit polarizations that reduces measurement errors. Documentation of the radar design is presented along with a description of the various procedures used to calibrate the receiver and transmitter subsystems. This radar was used to study the polarimetric behavior of a variety of natural surfaces, including decidous and coniferous trees, grass and sand. The ability to discriminate between planophil trees (horizontally oriented leaves) and erectophil trees (vertically oriented leaves) based on the fraction of polarized power is established for near grazing incidence. Dry sand was found to be the least polarized of all targets studied, bud became much more highly polarized when wet. These measurements show a substantial sensitivity to fine-scale surface features, and suggest the application of such a radar to class of remote sensing problems not addressed by other sensors.

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