Résumé : La cartographie des bio-régions des océans est d'une importance clé pour permettre une meilleure compréhension des dynamiques des écosystèmes qui y sont présents et permettre une saine gestion de ceux-ci. Les classifications actuelles utilisent les mêmes combinaisons d'attributs soit : la bathymétrie, la température de surface, la concentration en chlorophylle a et certaines luminances normalisées (443 nm, 520 nm 550 nm). L'utilisation de la variabilité de 2e ordre du signal optique de la chlorophylle a a permis de mettre en évidence d'autres attributs globaux, indépendants de la concentration en chlorophylle a, ouvrant la porte à de nouvelles démarches de classification non dirigée des océans en provinces biogéochimiques. L'objectif de l'étude est de développer une méthode de classification dynamique, non dirigée des provinces océaniques en utilisant une combinaison de données satellitaires, soit : les signatures optiques des constituants biochimiques présents dans l'océan et les propriétés physiques des masses d'eau selon une nouvelle approche intégrant à la fois des informations complémentaires et indépendantes de la chlorophylle a. Le but étant d'effectuer la classification des provinces océaniques de l'Atlantique Nord pour la période de disponibilité des données MODIS Aqua (2002-2012) et de déterminer l'évolution spatiale des provinces océaniques et leur succession au fil du temps. L'application de différentes techniques de classification a été réalisée sur deux jeux de données mis en place pour les besoins de l'étude. Les résultats montrent que la méthode K-mean et la méthode DBSCAN ne sont pas appropriées pour classifier de manière dynamique les provinces bio-optiques de l'Atlantique Nord. Une nouvelle méthode de classification : PRODENCAN, a été développée pour combler les lacunes de ces techniques. Les résultats obtenus par cette méthode permettent de confirmer le potentiel d'améliorer la classification océanique par l'utilisation de la variabilité de 2e ordre du signal optique de la chlorophylle a mais n'ont pas permis la création d'un patron de classification dynamique pour l'Atlantique Nord. Ceux-ci permettent de préciser le processus de résolution de ce problème par l'implémentation d'un jeu de données spécifiquement choisi d'un point de vue spatial et temporel. L'analyse dynamique a permis de confirmer le potentiel de l'utilisation de la variabilité de 2e ordre du signale optique de la chlorophylle a combinée à la température de surface de l'eau et de la concentration en chlorophylle a pour mieux définir des régions bio-optiques ayant des signatures phénologiques distinctives.||Abstract : Mapping bioregions of the oceans is of key importance for a better understanding the dynamics of ecosystems in oceans and ensure the adequate management of them. Actual existing classifications use the same combinations of attributes including: bathymetry, sea surface temperature, chlorophyll concentration and certain standard luminance (443 nm, 520 nm 550 nm). The use of second order variability of optical signals from chlorophyll a suggest other possible global attributes, independent of chlorophyll a concentration, opening doors to new approaches in unsupervised classification of oceans biogechimical provinces. The objective of the study is to develop a method of ocean provinces dynamic unsupervised classification, using a combination of satellite data as : optical signatures of biochemical constituents in the ocean and the physical properties of water masses according to a new approach that integrates both information complementary and independent of chlorophyll a. The goal is to perform the classification of oceanic provinces of the North Atlantic for the availability period of MODIS Aqua (2002-2012) and to determine the spatial evolution of oceanic provinces and their succession over time. Different techniques of classification were carried out on two data sets developed for the purposes of the study. The results show that the K-mean and DBSCAN method are not appropriate to perform bio-optical provinces dynamic classification of the North Atlantic. A new method of classification: PRODENCAN was developed to fill the gaps of these techniques. The results obtained by this method can confirm the potential to improve the classification by the use of second order variability of chlorophyll a optical signals but have not yet led to the creation of a dynamic pattern classification for North Atlantic. Nevertheless, they allow to specify the process for solving this problem by implementing a set of specifically training data spatially and temporally chosen. Dynamic analysis has confirmed the potential for the use of second order variability of chlorophyll a optical signals combined with sea surface temperature and the chlorophyll a concentration to better define bio-optical regions with distinctive phenology signatures. [symboles non conformes]

Lake Urmia (LU), which once had been the second largest hypersaline lake in the world, and greatest in the Middle East, has undergone severe environmental changes during recent years that have led to widespread desiccation. These changes have converted the lakebed into a significant Aeolian mineral source, which promotes aerosol plumes that can seriously impact downwind regions. A question remains as to how significant emissions are from LU as compared to others impacting the West and East Azarbaijan provinces encompassing LU. This study uses daily Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2001 and 2015 to show that AOD levels are significantly larger in the latter half of the study period (2008-2015) with AOD values in the West consistently being lower but approaching those of the East with time owing to a combination of increasing emissions from the West province and neighboring areas. While the interannual AOD profile over Azarbaijan resembles that of Iraq owing to transported dust, signatures of the local impact of increasing emissions is evident over the 15-year time period, especially in the months outside of the peak dust season (January, February and October) and on the immediate periphery of LU. Consequently, the spatial profile of AOD over Azarbaijan is not uniform but with distinct hot spot. The onset of the spring AOD ramp-up over Azarbaijan is shown to have started earlier (in February) when comparing 2009-2015 versus earlier years. Correlative analysis confirms that AOD is related to factors promoting dust emissions but also reveals that smoke contributes to AOD over Azarbaijan during the summer months.

Master of Arts / Department of Geography / Douglas G. Goodin / The Chobe River Basin (CRB) is a flood-dependent ecosystem that relies on seasonal floods from the Zambezi and Linyanti Rivers. These flood pulses provide water for the flood recession agriculture in the region, water for the fishing grounds around Lake Liambezi, and nutrients for the vegetation in the CRB. Recent years have shown an increase in the magnitude of flooding, which could have consequences on the region’s biodiversity and the people living in the CRB. The goal of this study is to develop a classification framework based on a training library and time-windows to use in classifying the extent of flooding in the CRB. MODIS MOD09A1 satellite imagery served as the satellite imagery. Bands one through seven were converted into the tasseled cap transformation to serve as the feature selection. The study period, from February to July, is broken down into three time-windows. The time-windows are used because the land covers in the CRB go through significant spectral changes during the study period and the three time-windows seek to improve the classification accuracy. The classification methods include maximum likelihood classifier (MLC), decision trees (DT), and support vector machines (SVMs). The results show that DT and SVMs provide the highest overall accuracy and kappa values over MLC. Classification using the time-window method was statistically significant when comparing kappa values and visually, images classified using the correct training library for a time-window displayed higher agreement with the reference data. Flooding extent was high for 2014 but low in 2015 and 2016, indicating a decreasing trend. DTs provided better inundation maximums compared to SVMs and therefore is the reason that DT are the best classification technique. The results will provide planners with information regarding the extent of flooding in the CRB and where waterborne diseases occur in the region. A new classification technique is also developed for the remote sensing literature.

O arroz irrigado está entre os principais cereais produzidos no mundo. Determinar a evapotranspiração (ET) para as grandes áreas de arroz irrigado é um desafio devido a pouca disponibilidade de dados. Diversos Modelos de ET vêm sendo desenvolvidos com a intenção de monitorar áreas agrícolas, porém poucos estudos experimentais são realizados sobre áreas de arroz irrigado. Este trabalho tem como objetivo geral estimar a ET sobre cultivos de arroz irrigado através de um modelo que utiliza informações meteorológicas in situ (Priestley-Taylor) e outro remoto (MOD16). O Priestley-Taylor (PT) é um modelo de ET que utiliza informações de temperatura do ar e componentes relacionadas ao balanço de energia, juntamente com um parâmetro adimensional α. O modelo MOD16 foi criado para monitorar a ET em grandes áreas, utilizando informações meteorológicas de um banco de dados de reanálise juntamente com dados remotos. Ambos os métodos não apresentam calibração e validação sobre áreas de arroz irrigado no Sul do Brasil. Neste trabalho validamos estes dois modelos a partir de dois sítios experimentais com medidas de ET através da técnica de Eddy Covariance. Os resultados encontrados nesta pesquisa mostraram-se satisfatórios quando comparado o método PT com dados experimentais, recomendando-se a utilização de 1,22 do parâmetro α. A simplificação no método PT realizada a partir das componentes do balanço de energia, com substituição pela variável de radiação global através de uma regressão linear, mostrou-se válida apresentando erros poucos expressivos, e com valor de 1,18 para parâmetro α. Já o modelo de ET MOD16 mostrou-se pouco preciso sobre as áreas de arroz irrigado. A validação de ET MOD16 foi realizada sobre uma área de 3 x 3 km e pixel central, resultando em menor subestimativa do modelo para o pixel central em relação aos dados de Eddy Covariance. Além disso, a correlação foi realizada em função das variáveis ambientais, encontrando maior correlação do dado experimental com as componentes do balando de energia, enquanto o MOD16 apresentou maior correlação com a temperatura do ar. Por fim, sugerem-se melhorias na parametrização da energia disponível no modelo de ET MOD16. Além disso, a aplicação do método simplificado de PT é indicada sobre áreas de arroz irrigado. / Irrigated rice is among principal produced cereals in the world. Determining the evapotranspiration (ET) for large areas of irrigated rice is a challenge task, due to poor data availability. Several ET models have been developed with the intention of monitoring agricultural areas, however few experimental studies are accomplished on areas of irrigated rice. This study aims to estimate ET on irrigated rice crops using a model which employs meteorological information in situ (Priestley-Taylor) and one remote (MOD16). Priestley-Taylor (PT) is a model of ET that uses air temperature and related components to the energy balance as information and a dimensionless parameter α. The MOD16 model was designed to monitor ET in large areas using meteorological information obtained from the reanalysis database together with remote data. Both methods do not present calibration and validation on areas of irrigated rice in Southern Brazil. In this work we validate these two models from two experimental sites with ET measurements employing the Eddy Covariance technique. The results found in this research was satisfactory when compared to the PT method with experimental data. It was suggested 1.22 for the α parameter. The simplification in the PT method performed from the components of the energy balance, with substitution for the global radiation variable using a linear regression. It was validated with few expressive errors, with a value of 1.18 for α parameter. On the other hand, the MOD16 model did not showed good accuracy on the areas of irrigated rice. The validation of ET MOD16 was performed over an area of 3 x 3 km and central pixel, resulting in a small underestimation of the model for the central pixel in relation to Eddy Covariance data. In addition, was performed the correlation in function of the environmental variables, finding a higher correlation of the experimental data with the components of the energy balance, while the MOD16 showed a high correlation with the air temperature. Finally, it was suggested improvements in the parameterization of the available energy in the model of ET MOD16, and to indicate the application of the simplified method of PT on the areas with irrigated rice.

Burn severity was measured within the Mediterranean sclerophyll forests of south-west Western Australia (WA) using remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The region of south-west WA is considered as a high fire prone landscape and is managed by the state government’s Department of Conservation and Land Management (CALM). Prescribed fuel reduction burning is used as a management tool in this region. The measurement of burn severity with remote sensing data focused on monitoring the success and impact of prescribed burning and wildfire in this environment. The high temporal resolution of MODIS with twice daily overpasses in this area was considered highly favourable, as opportunities for prescribed burning are temporally limited by climatic conditions. The Normalised Burn Ratio (NBR) was investigated to measure burn severity in the forested area of south-west WA. This index has its heritage based on data from the Landsat TM/ETM+ sensors (Key and Benson, 1999 [1],[2]) and was transferred from Landsat to MODIS data. The measurement principally addresses the biomass consumption due to fire, whereas the change detected between the pre-fire image and the post-fire image is quantified by the ÄNBR. The NBR and the Normalised Difference Vegetation Index (NDVI) have been applied to MODIS and Landsat TM/ETM+ data. The spectral properties and the index values of the remote sensing data have been analysed within different burnt areas. The influence of atmospheric and BRDF effects on MODIS data has been investigated by comparing uncorrected top of atmosphere reflectance and atmospheric and BRDF corrected reflectance. The definition of burn severity classes has been established in a field trip to the study area. However, heterogeneous fire behaviour and patchy distribution of different vegetation structure made field classification difficult. Ground truth data has been collected in two different types of vegetation structure present in the burnt area. The burn severity measurement of high resolution Landsat data was assessed based on ground truth data. However, field data was not sufficient for rigorous validation of remote sensing data. The NBR index images of both sensors have been calibrated based on training areas in the high resolution Landsat image. The burn severity classifications of both sensors are comparable, which demonstrates the feasibility of a burn severity measurement using moderate spatial resolution 250m MODIS data. The normalisation through index calculation reduced atmospheric and BRDF effects, and thus MODIS top of at-mosphere data has been considered suitable for the burn severity measurement. The NBR could not be uniformly applied, as different structures of vegetation influenced the range of index values. Furthermore, the index was sensitive to variability in moisture content. However, the study concluded that the NBR on MODIS data is a useful measure of burn severity in the forested area of south-west WA.

The use of airborne LiDAR (Light Detection and Ranging) as a direct method to evaluate forest canopy parameters is vital in addressing both forest management and ecological concerns. The overall goal of this study was to develop the use of airborne LiDAR in evaluating canopy parameters such as percent canopy cover (PCC) and leaf area index (LAI) for mixed pine and hardwood forests (primarily loblolly pine, Pinus taeda, forests) of the southeastern United States. More specific objectives were to: (1) Develop scanning LiDAR and multispectral imagery methods to estimate PCC and LAI over both hardwood and coniferous forests; (2) investigate whether a LiDAR and normalized difference vegetation index (NDVI) data fusion through linear regression improve estimates of these forest canopy characteristics; (3) generate maps of PCC and LAI for the study region, and (4) compare local scale LiDAR-derived PCC and regional scale MODIS-based PCC and investigate the relationship. Scanning LiDAR data was used to derive local scale PCC estimates, and TreeVaW, a LiDAR software application, was used to locate individual trees to derive an estimate of plot-level PCC. A canopy height model (CHM) was created from the LiDAR dataset and used to determine tree heights per plot. QuickBird multispectral imagery was used to calculate the NDVI for the study area. LiDAR- and NDVI-derived estimates of plot-level PCC and LAI were compared to field observations for 53 plots over 47 square kilometers. Linear regression analysis resulted in models explaining 84% and 78% of the variability associated with PCC and LAI, respectively. For these models to be of use in future studies, LiDAR point density must be 2.5 m. The relationship between regional scale PCC and local scale PCC was investigated by resizing the local scale LiDAR-derived PCC map to lower resolution levels, then determining a regression model relating MODIS data to the local values of PCC. The results from this comparison showed that MODIS PCC data is not very accurate at local scales. The methods discussed in this paper show great potential for improving the speed and accuracy of ecological studies and forest management.

In this study, we use thermal infrared channels to retrieve the optical thickness and effective particle radius of ice clouds. A physical model is used in conjunction with Atmospheric Infrared Sounder (AIRS) temperature and water vapor profiles to simulate the top-of-atmosphere (TOA) brightness temperatures (BTs) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) for channels located at 8.5, 11.0, and 12.0 µm (1176, 909, and 833 cm-1). The model is initially validated by comparing simulated clear-sky BTs to MODIS-observed clear-sky BTs. We also investigate the effect of introducing a +3 K bias in the temperature profile, a +3 K bias in the surface temperature, and a +20% bias in the water vapor profile in order to test the sensitivity of the model to these inputs. For clear-sky cases, the simulated TOA BTs agree with MODIS to within 2-3 K. The model is then extended to simulate thermal infrared BTs for cloudy skies, and we infer the optical thickness and effective radius of ice clouds by matching MODIS-observed BTs to calculations. The optical thickness retrieval is reasonably consistent with the MODIS Collection 5 operational retrieval for optically thin clouds but tends to retrieve smaller particle sizes than MODIS.

The objectives of dissertation were to examine vegetation and water indices from AVIRIS and MODIS data for monitoring semiarid and upland vegetation communities related with moisture condition and their spatial and temporal dependencies in estimating evapotranspiration (ET). The performance of various water indices, including the normalized difference water index (NDWI) and land surface water index (LSWI), with the chlorophyll-based vegetation indices (VIs), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) was evaluated in 1) investigating sensitivity of vegetation and land surface moisture condition 2) finding optimal indices in detecting seasonal variations in vegetation water status at the landscape level, and 3) their spatial and temporal scale dependency on estimating ET. The analyses were accomplished through field radiometric measurement, airborne-based and satellite data processing accompanied with water flux data.The results of these studies showed vegetation and landscape moisture condition could be identified in VI - WI scatter-plot. LSWI (2100) showed the biggest sensitivity to variation of vegetation and background soil moisture condition as well. Multi-temporal MODIS data analysis was able to show water use characteristic of riparian vegetation and upland vegetation. Results showed water use characteristics of riparian vegetation are relatively insensitive to summer monsoon pulse, while upland vegetation is highly tied to summer monsoon rain. The relationship between water flux measurement from eddy covariance tower and satellite data has shown that MODIS derived EVI and LSWI (2100) have similar merit to estimate ET rate, but better correlation was observed from the relationship between MODIS EVI and ET.Pixel aggregation results using fine resolution AVIRIS data showed moderate resolution spatial scale 250m or 500m, best predicted ET rates over all study areas. Surface fluxes temporally aggregated to weekly or biweekly intervals showed the strongest ET versus EVI relationships. ET measured at flux towers can be scaled over heterogeneous vegetation associations by simple statistical methods that use meteorological data and flux tower data as ground input, and using the MODIS Enhanced Vegetation Index (EVI) as the only source of remote sensing data.

Previously disturbed and regenerating forests make up a significant proportion of the North American land area, and therefore play an important role in the exchanges of heat and trace gases between the terrestrial biosphere and the atmosphere. Assessment of local to global variability in CO2 exchanges by forests requires a combination of CO2 measurements made by eddy covariance (EC), field measurements, remote sensing data, and ecosystem models. The integration of these is problematic because of a mis-match in scale between measurement techniques. Despite the importance of regenerating forests on the global carbon balance, the processes affecting the carbon cycle within these forests is not well understood. Airborne scanning light detection and ranging (lidar) instruments provide new opportunities to examine three-dimensional forest characteristics from the level of individual trees to ecosystems and beyond. Lidar is therefore an effective link between plot measurements, eddy covariance, and low resolution remote sensing pixels. This thesis dissertation presents new science on the use of airborne lidar for evaluating remote sensing products within heterogeneous and previously clearcut ecosystems. The goals of this thesis were to first understand the processes affecting CO2 exchanges within a previously disturbed boreal jack pine chronosequence located in Saskatchewan, Canada and then to apply this understanding to evaluate low resolution remote sensing data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) using airborne lidar. The first objective of this dissertation examined the factors that control light use efficiency (LUE) within the jack pine chronosequence during dry and wet years. The second objective examined the importance of vegetation structure and ground surface elevation on CO2 fluxes within a mature jack pine forest. The third objective developed and tested a simple model of lidar fractional cover and related this to the fraction of photosynthetically active radiation absorbed by the canopy (fPAR). This was then used to evaluate the MODIS fPAR product across the lower part of a watershed. Finally, the fourth objective was to model gross primary production (GPP) from airborne lidar. Lidar estimates of GPP were then compared with those from the EC system at the jack pine chronosequence and with the MODIS GPP (Collection 5) product. / Thesis (Ph.D, Geography) -- Queen's University, 2008-08-22 08:50:51.44

Thesis (M.Sc.). / Written for the Dept. of Geography. Title from title page of PDF (viewed 2008/05/28). Includes bibliographical references.