Wildfires across northern Australia are a growing problem with more than 2.5 million hectares being burnt each year. Accordingly, remote sensing has been used as a tool to routinely monitor and map fire histories. In northern Western Australia, the Department of Land Information Satellite Remote Sensing Services (DLI SRSS) has been responsible for providing and interpreting NOAA-AVHRR (National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer) data. SRSS staff utilise this data to automatically map hotspots on a daily basis, and manually map fire affected areas (FAA) every nine days. This information is then passed on to land managers to enhance their ability to manage the effects of fire and assess its impact over time. The aim of this study was to develop an algorithm for the near real-time automatic mapping of FAA in the Kimberley and Pilbara as an alternative to the currently used semimanual approach. Daily measures of temperature, surface reflectance and vegetation indices from twenty nine NOAA-16 (2001) passes were investigated. It was firstly necessary to apply atmospheric and BRDF corrections to the raw reflectance data to account for the variation caused by changing viewing and illumination geometry over a cycle. Findings from the four case studies indicate that case studies 1 and 2 exhibited a typical fire response (visible and near-infrared channels and vegetation indices decreased), whereas 3 and 4 displayed an atypical response (visible channel increased while the near-infrared channel and vegetation indices decreased). Alternative vegetation indices such as GEMI, GEMI3 and VI3 outperformed NDVI in some cases. Likewise atmospheric and BRDF corrected NDVI provided better performance in separating burnt and unburnt classes. The difficulties in quantifying FAA due to temporal and spatial variation result from numerous factors including vegetation type, fire intensity, rate of ash and charcoal dispersal due to wind and rain, background soil influence and rate of revegetation. In this study two different spectral responses were recorded, indicating the need to set at least two sets of thresholds in an automated or semi-automated classification algorithm. It also highlighted the necessity of atmospheric and BRDF corrections. It is therefore recommended that future research apply atmospheric and BRDF corrections at the pre-processing stage prior to analysis when utilising a temporal series of NOAAAVHRR data. Secondly, it is necessary to investigate additional FAA within the four biogeographic regions to enable thresholds to be set in order to develop an algorithm. This algorithm must take into account the variation in a fire’s spectral response which may result from fire intensity, vegetation type, background soil influence or climatic factors.

The role of remote sensing data for monitoring different parameters in the study of ecosystems has been increasing. Particularly the development of different indices has played a great role to study the properties of vegetation and vegetation dynamics in large countries. In addition to this, satellite rainfall estimate data has been used to study the pattern of precipitation in areas where station rain-gauge data is not available. The Normalized Difference Vegetation Index (NDVI) and rainfall estimates data from the National Oceanic and Atmospheric Administration (NOAA) satellites were used to investigate the spatio-tempotal pattern of precipitation and the response of vegetation to precipitation in Ethiopia from 1996 to 2008. The patterns were studied in different land cover classes using data from the Global Land Cover Network (GLCN). The spatial patternof NDVI and precipitation showed that vegetation responded directly to precipitation. The seasonal patterns showed that there was between 0 to 3 months lag between precipitationand vegetation. However it was not possible to draw conclusion regarding the annual trendsof precipitation and NDVI because of the nature of the NDVI data, which was produced using the 10 day maximum composite values.

Lakes are key indicators of environmental change and major repositories of biodiversity and ecosystem services. However, studies of lake response to drivers of change at a pan-European scale are exceptionally rare. The need for such studies has been given renewed impetus by concerns over climate change and because of international policyrelated schemes, such as the EU Water Framework Directive that has made it legal requirement to repeatedly assess and monitor the ecological status of European lakes toward their effective management and sustainable use. This has introduced the need for methods that can be widely applied across large spatial and temporal scales and produce comparable results. Remote sensing is a promising method for providing such information, but the spatial transferability and temporal repeatability of methods and relationships observed remains untested. In this project, an extensive dataset of field measurements was compiled covering temperature, chlorophyll a and Secchi disk depth in 23 European lakes spanning the last 30 years. The characteristics of these lake systems were explored and similarities in their ecological behavior identified, thus providing the basis for their grouping. Then the potential of remote sensing for estimating and monitoring lake water quality at wide spatial and temporal scales was assessed and thus the long remote sensing archive at the NEODAAS DSRS was fundamental for the purposes of this project. Using NOAA AVHRR, Terra/Aqua MODIS and field data from lakes that represented three main lake groups, the spatial and temporal reliability of 26 existing water quality estimation algorithms was assessed. Following this, the best performing algorithms were applied to all study sites and the effect of scale and spatial resolution upon reliable estimation of key water quality parameters was evaluated. It was demonstrated that the NOAA AVHRR and Terra/Aqua MODIS were both capable of producing highly accurate (R2 > 0.9) lake surface temperature estimates in lakes with variable characteristics and a variety of thermal spatial features, and longterm patterns within the study sites could be studied with NOAA AVHRR data despite the relatively coarse spatial resolution of the sensor. Restricting factors to the latter were the size and shape of lakes and the frequency of cloud cover. By contrast, the development of a universal Terra/Aqua MODIS algorithm for the estimation of chlorophyll a and Secchi disk depth in variable lakes was more challenging due to the optical complexity of Case II waters. Terra/Aqua MODIS data showed a potential, but the use of a different technique (e.g. multivariate regression or neural networks) and/or a different sensor (e.g. Envisat MERIS) could potentially improve the predictive accuracy of the algorithms.

L'important increment de la capacitat de càlcul computacional que s'ha donat en els últims anys ha fet que els models numèrics meteorològics hagin pogut assolir resolucions de treball molt fines. Ara bé, aquest increment en la resolució, per si sol, no és suficient per millorar-ne les prestacions. Cal introduir algorismes més sofisticats per afinar en la simulació de la dinàmica atmosfèrica, i també cal millorar la caracterització física de la superfície. En aquest sentit, els valors climàtics globals que es fan servir actualment no siguin prou precisos i calguin valors característics de cada regió. Malgrat que a través de la teledetecció no es pot fer una mesura real dels diferents paràmetres de la superfície terrestre, les imatges de satèl·lit són el millor recurs per estimar-los. La periodicitat en l'adquisició d'imatges d'una mateixa zona fa que es puguin fer estimacions estacionals o mensuals, podent així fer un seguiment de la variació d'aquests paràmetres al llarg del temps.En aquest treball s'ha establert una metodologia operacional per a l'obtenció periòdica de paràmetres de superfície, a partir d'imatges del sensor satel·litari NOAA/AVHRR, per Catalunya i les regions que l'envolten. La revisió bibliogràfica de les diverses metodologies existents ha permès seleccionar les més idònies per aquesta zona. A nivell dels resultats, s'ha obtingut una sèrie mensual de diferents paràmetres, que cobreix els mesos de març a octubre de l'any 2000. Aquests paràmetres de superfície són els següents:· Albedo· Emissivitat· Índex de vegetació NDVI· Temperatura de superfície diürna i nocturna.· Inèrcia tèrmica Els resultats obtinguts amb dades de satèl·lit són espacials, són imatges que ens donen valors quantitatius per a cada cel·la de la superfície estudiada. A l'hora de fer-ne un tractament estadístic, aquestes imatges es sintetitzen a través de mapes d'usos del sòl, obtenint resultats per a les categories dels mapes. Com tota simplificació, aquesta comporta una pèrdua d'informació; i en aquest punt cal ser força crític a l'hora de triar una classificació d'usos del sòl de la regió de treball. Aquest aspecte és un dels punts on s'ha volgut aprofundir en la discussió de resultats. S'ha treballat amb dos mapes d'usos del sòl, el del USGS (Servei Geològic dels EEUU) i el CORINE (Agència Europea del Medi Ambient). El primer és d'abast global, té 24 categories i és el que fa servir el model meteorològic MM5 per la caracterització de la superfície en les simulacions. El segon és més actual i cobreix gran part d'Europa. El major nombre de categories (44), i la metodologia emprada en l'elaboració, pensada per els usos del sòl que dominen el nostre continent, fan que aquest mapa sigui més adequat per tal de caracteritzar els usos del sòl de Catalunya i els seus voltants.Els paràmetres geofísics de superfície obtinguts s'han fet servir per inicialitzar un model meteorològic de mesoescala, amb la finalitat de millorar els pronòstics. Les simulacions s'han fet amb el model de mesoescala MM5 (PSU/NCAR). Els canvis introduïts en els paràmetres de superfície, a través del canvi del mapa d'usos del sòl i de la introducció dels valors calculats amb AVHRR per l'any 2000, han estat prou importants com per afectar els resultats de les simulacions. Cal destacar les diferències en el balanç hídric, que provoquen simulacions diferents de les masses nuvoloses i el patrons de precipitació. Aquestes diferències en la nuvolositat també modifiquen el balanç radiatiu, que alhora afecta l'evolució diària de la temperatura a nivells baixos. A nivell meteorològic, les variacions en el desenvolupament de núvols en situacions de domini mesoescalar és important per a la correcta simulació de desenvolupament de tempestes locals durant èpoques estivals. Finalment, també s'han observat variacions significatives en el camp de vent de superfície, aspecte important quan s'utilitza el model MM5 per a la simulació de la dispersió de contaminants atmosfèrics.Els resultats obtinguts per l'any 2000 no es poden considerar climàticament significatius. En aquest sentit, una de les vies de continuïtat d' aquest treball és l'obtenció de resultats per una sèrie més llarga de dades, que permeti conèixer millor les característiques geofísiques de la superfície estudiada i millorar les simulacions meteorològiques. / Mesoscale models, with grid resolution higher than synoptic models, and with advanced physical parameterizations, have been an important tool for meteorological research over the past twenty years. Important improvements on mesoscale models have occurred in the last decade. The availability of high-performance workstations at affordable prices; the sharing of mesoscale models within the community; and finally the real-time accessibility of forecast data from the operational runs; have allowed using mesoscale models for real-time numerical weather prediction (NWP) at high resolutions (~1 km).As mesoscale models continue to increase in spatial resolution, correctly treating the land surface processes is becoming increasingly important for the model to be able to capture local mesoscale circulations induced by land surface forcing. Mesoscale models are incorporating progressively advanced land surface modules in order to properly initialize the state of the ground.Physical model improvements should be complemented with more accurate surface properties initiation data. The present work is focused in this point. An operational methodology has been developed, in order to calculate, from satellite imagery, the surface properties for Catalonia, in the NE of Spain. Satellite observations constitute the only available means for global or regional repetitive monitoring of the surface properties at homogeneous resolution.Prior to calculations, a bibliographical research has been done, in order to choose the most adequate methodology according to the remote sensing data available and the studied region. Monthly mean surface parameters have been calculated for the working region from an AVHRR data set of year 2000. Besides the resulting images, surface parameters have also been calculated for the land-use categories in the region. Calculated parameters are:· Albedo· Emissivity · Normalized Difference vegetation Index (NDVI)· Surface temperature· Thermal inertiaIn order to test the obtained parameters, two simulations have been done with the MM5 mesoscale model. The Fifth-Generation NCAR / Penn State Mesoscale Model (MM5) is a limited-area, non-hydrostatic, terrain-following sigma-coordinate model designed to simulate or predict mesoscale and regional-scale atmospheric circulation.A first simulation, using MM5 default values, has been compared with a second simulation where the local physical parameters have been introduced. Besides the change of the surface parameters, the default MM5 land-use map has also been changed, using a more recent land-use map of the region. Results have shown that differences in surface parameters basically rely on thermal inertia. Besides, the land-use maps comparison had shown important differences between classifications that also affect the final composition of surface parameters that get into the model. Modifications on the second simulation have been sufficiently significant to produce variations in the performance of the model. The cloud development differs basically in the location and dimensions of the clouds, that drives to a different superficial radiative budget affecting the evolution of air temperature at low levels. The different results in cumulus simulation produced important differences in the surface wind field and the updrafts. The changes introduced are sufficiently significant to obtain also slight variations in the pattern of accumulated precipitation for the simulated period. Comparisons with ground measurements of wind and temperature have been done in the test regions. Similar errors are obtained with the two land-use maps and physical parameters, without a clear improvement in the performance of the meteorological model.The simulations done in this work contributes evidence to the high influence of surface scheme applications of mesoscale models at high spatial resolution. In the context of dialogue between remote sensing scientists and numerical climate modelers, it is expected that more research should be done to investigate the sensibility of the mesoscale models to improvements in the surface properties characterization.

Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2005. / Land degradation in Lesotho is undermining the finite resource on which people depend for survival. Use of satellite imagery has been recommended for monitoring land degradation because remotely sensed data enable monitoring of large areas at more frequent intervals than intensive ground based research. Various techniques have been developed for land cover change detection. In the present study, vegetation changes were identified by image differencing, which involved finding the difference between the earlier date NDVI image and the later date image. NDVI images are among products that are generated from the NOAA AVHRR sensor to provide information about the quantity of biomass on the earth’s surface. The resulting NDVI change data showed land areas that had experienced vegetation loss, which were identified as potentially degraded. The change data were combined with other data sets to determine how potentially degraded areas were influenced by different environmental variables and population pressure. These data sets included land cover, ecological zones, elevation, soil and human and livestock populations. By integrating NDVI data with ancillary data, land degradation was attributed to both demographic pressure and biophysical factors. Widespread degradation was detected on the arable parts of the Lowlands where cultivation was intensive and human settlements were extensive. Signs of grassland depletion and forest decline were also evident and were attributed to population expansion, overgrazing and indiscriminate cutting of trees and shrubs for firewood. Extensive biomass decline was also associated more with soils in the lowlands derived from sedimentary rocks than soils of basalt origin that occur mostly in the highlands. Significant degradation was evident on gentle slopes where land uses such as cultivation and expansion of settlements were identified as the main causes of the degradation. There was evidence of greater vegetation depletion on north and east-facing slopes than on other slopes. The depletion was attributed to the fragility of ecosystems resulting from intense solar radiation. The study demonstrated that NOAA AVHRR NDVI images could be used effectively for detecting land cover changes in Lesotho. However, future research could focus on obtaining and using high resolution data for detailed analysis of factors driving land degradation.

The study of solar radiation is a key process for the exploitation of solar energy systems. In most cases, however, the availability of information is insufficient and not up-to-date. In addition, the interpolation of ground-level measurement stations data does not allow the observation of microclimatic aspects of solar radiation (Zelenka at al., 1999). Therefore, a statistical model and NOAA-AVHRR satellite images were selected for the determination of solar radiation maps for the entire Iberian Peninsula. The statistical methodology based on Diabate et al. (1989) and Flores (2002) assures the accuracy of the process and the NOAA-AVHRR images provide a fine high resolution of 1 km2, which is needed because of the complexity of the area of study. The period of study covers five years (1998-2002), which assures the study of the year-on-year variation of solar radiation. The statistical model was calibrated with data from 21 ground-level measurement stations all around the Iberian Peninsula. These data were filtered with the aim of eliminating all erroneous registers, which a crucial subject in the method. The satellite images were geometrically calibrated and corrected. Furthermore, a methodology for the detection of clouds was applied in order to obtain the surface albedo (Laine et al., 1999). The evaluation of results, with 7 independient ground-level measurement stations, leads to a MBE of −3.8% and a RMSE of 24.4% for the daily data and a MBE of −1.1% and a RMSE of 15.9% for the hourly data. Maps of hourly and daily solar radiation for the years of study and the average tendency were determined. The Iberian Peninsula receives a mínimum of 4.2 MJm−2 d−1 and a maximum of 26 MJm−2 d−1 of dailly solar radiation, whith a mean of 15.1 MJm−2 d−1. The year-to-year variability of solar radiation ranges between 14.9 MJm−2 d−1 for year 2002 to 17.3 MJm−2 d−1 for year 2000. The results clearly show the usefulness of this work when obtaining solar radiation maps with a high temporal (hourly and daily maps) and spatial resolution (1 km2). / El estudio de la radiación solar es un proceso clave para el aprovechamiento de la energía solar. En la mayoría de los casos, sin embargo, la disponibilidad de información es insuficiente y desactualizada. Adicionalmente, la interpolación de superficie obtenida a partir de estaciones de medición en superficie no permite la observación de los aspectos microclimáticos de la radiación solar (Zelenka et al., 1999). Por esto, se ha elegido un modelo estadístico e imágenes del satélite NOAA-AVHRR para la determinación de mapas de radiación solar para toda la Península Ibérica. El método estadístico basado en Diabaté et al. (1989) y Flores (2002) asegura la exactitud del proceso y las imágenes NOAA-AVHRR una alta resolución espacial del orden de 1 km2, lo cual es necesario dada la complejidad del área de estudio. El período de estudio es de 5 años (1998-2002), lo que permite estudiar la variación interanual de la radiación solar. El modelo estadístico fue calibrado con 21 estaciones de medición en superficie distribuidas en toda la superficie de España. Los datos de las estaciones de medición son acuciosamente filtrados con el propósito de eliminar todos los registros erróneos, aspecto crucial del método. Las imágenes son calibradas y corregidas geométricamente, además se les realiza un proceso de detección de nubes con el fin de obtener el albedo superficial (Laine et al., 1999). La evaluación de los resultados, con datos de 7 estaciones de medición en superficie independientes, permite obtener un MBE de −3.8% y un RMSE de 24.2% para los dato diarios y un MBE de −1.1% y un RMSE de 15.9% para los datos horarios. Se obtienen mapas de radiación solar horarios puntuales y diarios para todos los años del período de estudio, además se determina la tendencia media de los mismos. Los resultados muestran que la Península Ibérica recibe un mínimo de 4.2 MJm−2 d−1 y un máximo de 26 MJm−2 d−1, con una radiación solar media de 15.1 MJm−2 d−1. La variabilidad interanual de la radiación solar queda expresada con valores que van desde 14.9 MJm−2 d−1 para el año 2002 hasta 17.3 MJm−2 d−1 para el año 2000. Los resultados demuestran claramente la utilidad del trabajo en la obtención de mapas de radiación solar horarios y diarios con una alta resolución espacial (1 km2).