Remote sensing data has great potential for wildfire detection and monitoring with enhanced spatial resolution and temporal coverage. Earth Observation satellites have been employed to systematically monitor fire activity over large regions in two ways: (i) to detect the location of actively burning spots (during the fire event), and (ii) to map the spatial extent of the burned scars (during or after the event). Active fire detection plays an important role in wildfire early warning systems. The open-access of Sentinel-2 multispectral data at 20-m resolution offers an opportunity to evaluate its complementary role to the coarse indication in the hotspots provided by MODIS-like polar-orbiting and GOES-like geostationary systems. In addition, accurate and timely mapping of burned areas is needed for damage assessment. Recent advances in deep learning (DL) provides the researcher with automatic, accurate, and bias-free large-scale mapping options for burned area mapping using uni-temporal multispectral imagery. Therefore, the objective of this thesis is to evaluate multispectral remote sensing data (in particular Sentinel-2) for wildfire detection, including active fire detection using a multi-criteria approach and burned area detection using DL models.        For active fire detection, a multi-criteria approach based on the reflectance of B4, B11, and B12 of Sentinel-2 MSI data is developed for several representative fire-prone biomes to extract unambiguous active fire pixels. The adaptive thresholds for each biome are statistically determined from 11 million Sentinel-2 observations samples acquired over summertime (June 2019 to September 2019) across 14 regions or countries. The primary criterion is derived from 3 sigma prediction interval of OLS regression of observation samples for each biome. More specific criteria based on B11 and B12 are further introduced to reduce the omission errors (OE) and commission errors (CE).        The multi-criteria approach proves to be effective in cool smoldering fire detection in study areas with tropical &amp; subtropical grasslands, savannas &amp; shrublands using the primary criterion. At the same time, additional criteria that thresholds the reflectance of B11 and B12 can effectively decrease the CE caused by extremely bright flames around the hot cores in testing sites with Mediterranean forests, woodlands &amp; scrub. The other criterion based on reflectance ratio between B12 and B11 also avoids the effects of CE caused by hot soil pixels in sites with tropical &amp; subtropical moist broadleaf forests. Overall, the validation performance over testing patches reveals that CE and OE can be kept at a low level  (0.14 and 0.04) as an acceptable trade-off. This multi-criteria algorithm is suitable for rapid active fire detection based on uni-temporal imagery without the requirement of multi-temporal data. Medium-resolution multispectral data can be used as a complementary choice to the coarse resolution images for their ability to detect small burning areas and to detect active fires more accurately.        For burned area mapping, this thesis aims to expound on the capability of deep DL models for automatically mapping burned areas from uni-temporal multispectral imagery. Various burned area detection algorithms have been developed using Sentinel-2 and/or Landsat data, but most of the studies require a pre-fire image, dense time-series data, or an empirical threshold. In this thesis, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast- SCNN, and DeepLabv3+ are applied to Sentinel-2 imagery and Landsat-8 imagery over three testing sites in two local climate zones. In addition, three popular machine learning (ML) algorithms (LightGBM, KNN, and random forests) and NBR thresholding techniques (empirical and OTSU-based) are used in the same study areas for comparison.        The validation results show that DL algorithms outperform the machine learning (ML) methods in two of the three cases with the compact burned scars,  while ML methods seem to be more suitable for mapping dispersed scar in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrate that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. With the uni-temporal image, DL-based methods have the potential to be used for the next Earth observation satellite with onboard data processing and limited storage for previous scenes.    In the future study, DL models will be explored to detect active fire from multi-resolution remote sensing data. The existing problem of unbalanced labeled data can be resolved via advanced DL architecture, the suitable configuration on the training dataset, and improved loss function. To further explore the damage caused by wildfire, future work will focus on the burn severity assessment based on DL models through multi-class semantic segmentation. In addition, the translation between optical and SAR imagery based on Generative Adversarial Network (GAN) model could be explored to improve burned area mapping in different weather conditions. / Fjärranalysdata har stor potential för upptäckt och övervakning av skogsbränder med förbättrad rumslig upplösning och tidsmässig täckning. Jordobservationssatelliter har använts för att systematiskt övervaka brandaktivitet över stora regioner på två sätt: (i) för att upptäcka placeringen av aktivt brinnande fläckar (under brandhändelsen) och (ii) för att kartlägga den brända ärrens rumsliga omfattning ( under eller efter evenemanget). Aktiv branddetektering spelar en viktig roll i system för tidig varning för skogsbränder. Den öppna tillgången till Sentinel-2 multispektral data vid 20 m upplösning ger en möjlighet att utvärdera dess kompletterande roll i förhållande till den grova indikationen i hotspots som tillhandahålls av MODIS-liknande polaromloppsbanesystem och GOES-liknande geostationära system. Dessutom krävs en korrekt och snabb kartläggning av brända områden för skadebedömning. Senaste framstegen inom deep learning (DL) ger forskaren automatiska, exakta och förspänningsfria storskaliga kartläggningsalternativ för kartläggning av bränt område med unitemporal multispektral bild. Därför är syftet med denna avhandling att utvärdera multispektral fjärranalysdata (särskilt Sentinel- 2) för att upptäcka skogsbränder, inklusive aktiv branddetektering med hjälp av ett multikriterietillvägagångssätt och detektering av bränt område med DL-modeller. För aktiv branddetektering utvecklas en multikriteriemetod baserad på reflektionen av B4, B11 och B12 i Stentinel-2 MSI data för flera representativa brandbenägna biom för att få fram otvetydiga pixlar för aktiv brand. De adaptiva tröskelvärdena för varje biom bestäms statistiskt från 11 miljoner Sentinel-2 observationsprover som förvärvats under sommaren (juni 2019 till september 2019) i 14 regioner eller länder. Det primära kriteriet härleds från 3-sigma-prediktionsintervallet för OLS-regression av observationsprover för varje biom. Mer specifika kriterier baserade på B11 och B12 införs vidare för att minska utelämningsfel (OE) och kommissionsfel (CE). Det multikriteriella tillvägagångssättet visar sig vara effektivt när det gäller upptäckt av svala pyrande bränder i undersökningsområden med tropiska och subtropiska gräsmarker, savanner och buskmarker med hjälp av det primära kriteriet. Samtidigt kan ytterligare kriterier som tröskelvärden för reflektionen av B11 och B12 effektivt minska det fel som orsakas av extremt ljusa lågor runt de heta kärnorna i testområden med skogar, skogsmarker och buskage i Medelhavsområdet. Det andra kriteriet som bygger på förhållandet mellan B12 och B11:s reflektionsgrad undviker också effekterna av CE som orsakas av heta markpixlar i områden med tropiska och subtropiska fuktiga lövskogar. Sammantaget visar valideringsresultatet för testområden att CE och OE kan hållas på en låg nivå (0,14 och 0,04) som en godtagbar kompromiss. Algoritmen med flera kriterier lämpar sig för snabb aktiv branddetektering baserad på unika tidsmässiga bilder utan krav på tidsmässiga data. Multispektrala data med medelhög upplösning kan användas som ett kompletterande val till bilder med kursupplösning på grund av deras förmåga att upptäcka små brinnande områden och att upptäcka aktiva bränder mer exakt. När det gäller kartläggning av brända områden syftar denna avhandling till att förklara hur djupa DL-modeller kan användas för att automatiskt kartlägga brända områden från multispektrala bilder i ett tidsintervall. Olika algoritmer för upptäckt av brända områden har utvecklats med hjälp av Sentinel-2 och/eller Landsat-data, men de flesta av studierna kräver att man har en förebränning. bild före branden, täta tidsseriedata eller ett empiriskt tröskelvärde. I den här avhandlingen tillämpas flera arkitekturer för semantiska segmenteringsnätverk, dvs. U-Net, HRNet, Fast- SCNN och DeepLabv3+, på Sentinel- 2 bilder och Landsat-8 bilder över tre testplatser i två lokala klimatzoner. Dessutom används tre populära algoritmer för maskininlärning (ML) (Light- GBM, KNN och slumpmässiga skogar) och NBR-tröskelvärden (empiriska och OTSU-baserade) i samma undersökningsområden för jämförelse. Valideringsresultaten visar att DL-algoritmerna överträffar maskininlärningsmetoderna (ML) i två av de tre fallen med kompakta brända ärr, medan ML-metoderna verkar vara mer lämpliga för kartläggning av spridda ärr i boreala skogar. Med hjälp av Sentinel-2 bilder uppvisar U-Net och HRNet jämförelsevis identiska prestanda med högre kappa (omkring 0,9) i en heterogen brandplats i Medelhavet i Grekland; Fast-SCNN presterar bättre än andra med kappa över 0,79 i en kompakt boreal skogsbrand med varierande brännskadegrad i Sverige. Vid direkt överföring av de tränade modellerna till motsvarande Landsat-8-data dominerar HRNet dessutom på de tre testplatserna bland DL-modellerna och kan bevara den höga noggrannheten. Resultaten visade att DL-modeller kan utnyttja kontextuell information fullt ut och fånga rumsliga detaljer i flera skalor från brandkänsliga spektralband för att kartlägga brända områden. Med den unika tidsmässiga bilden har DL-baserade metoder potential att användas för nästa jordobservationssatellit med databehandling ombord och begränsad lagring av tidigare scener. I den framtida studien kommer DL-modeller att undersökas för att upptäcka aktiva bränder från fjärranalysdata med flera upplösningar. Det befintliga problemet med obalanserade märkta data kan lösas med hjälp av en avancerad DL-arkitektur, lämplig konfiguration av träningsdatasetet och förbättrad förlustfunktion. För att ytterligare utforska de skador som orsakas av skogsbränder kommer det framtida arbetet att fokusera på bedömningen av brännskadornas allvarlighetsgrad baserat på DL-modeller genom semantisk segmentering av flera klasser. Dessutom kan översättningen mellan optiska bilder och SAR-bilder baserad på en GAN-modell (Generative Adversarial Network) undersökas för att förbättra kartläggningen av brända områden under olika väderförhållanden. / <p>QC 20210525</p>

A research report submitted to the Faculty of Science, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Science Johannesburg, August 2015 / The central Kalahari is likely to become hotter and drier as a result of climate change in the region. These changes may result in behavioural changes in Gemsbok due to temperature induced stress, in spite of physiological and behavioural adaptations, and are likely to manifest in a preference for particular landscape patch classes. Recent Landsat 8 satellite imagery and classification analysis were used to map landscape patch classes in a heterogeneous landscape in the central Kalahari. The classification map of the research area identified 6 classes of landscape patches used by Gemsbok. Eight collared Gemsbok were tracked by satellite to monitor their movement in their respective home ranges over a period of 9 months. Gemsbok locations were plotted on to the classification map, and location frequency distributions were produced for each landscape patch class. Gemsbok home ranges were calculated using minimum convex polygon geometry, and the available patch class areas within each home range were analysed against the usage patterns of each animal. The analysis showed less preference for shade producing classes and more preference for open classes. Exploration of the role of temperature in landscape patch selection showed that temperature is a weak predictor of patch class, that critical temperature thresholds have not yet been reached, and that Gemsbok preference for pans is more likely related to seasonally available forage and reduced predation risk in a “landscape of fear” (Laundré, Hernández, & Altendorf, 2001).

Les eaux superficielles sont nécessaires à toute forme de vie en tant que parties intégrantes de tout processus de vie sur Terre. Quantifier les eaux de surface et suivre leurs variations est primordial en raison du lien direct qui existe entre les variables hydrologiques et le changement climatique. La télédétection par satellite, de l’hydrologie continental offre l’opportunité unique d’étudier, depuis l’espace, les processus hydrologiques à différentes échelles (régionale et globale). Dans cette thèse, différentes techniques ont été développées afin d’étudier les variations des eaux superficielles ainsi que d’autres variables hydrologiques, au niveau du bassin inférieur du Mékong (entre le Vietnam et le Cambodge) et ce en utilisant plusieurs estimations satellitaires différentes. Cette thèse s’articule autour de quatre points principaux. Premièrement, l’utilisation d’observations satellitaires dans le visible et dans l’infrarouge (MODIS) est étudiée et comparée afin d’évaluer les eaux de surface au niveau du bassin inférieur du Mékong. Quatre méthodes de classification ont été utilisées afin de différencier les types de surface (inondés ou pas) dans le bassin. Les différentes méthodes ont donné des cartes d’eaux de surface aux résultats semblables en terme de dynamique saisonnière. La classification la plus adaptée aux régions tropicales a été ensuite choisie pour produire une carte des eaux de surface à la résolution de 500 m entre janvier 2001 et aujourd’hui. La comparaison des séries temporelles issues de cette carte et de celles issues du produit de référence MODIS donne une forte corrélation temporelle (> 95%) pour la période 2001-2007. Deuxièmement, l’utilisation des observations issues du satellite SAR Sentinel-1 est examinée à des fins identiques. L’imagerie satellitaire optique est ici remplacée i par les images SAR qui grâce aux longueurs d’ondes utilisées dans le micro-ondes, permettent de « voir » à travers les nuages. Un jeu d’images Landsat-8-sans-nuage est alors utilisé pour entraîner un Réseau de Neurones (RN) afin de restituer des cartes d’eaux de surface par l’utilisation d’un seuillage sur les sorties du modèle RN. Les cartes sont à la résolution spatiale de 30 m et disponibles depuis janvier 2015. Comparées aux cartes de référence Landsat-8-sans-nuage, les sorties de modèles RN montre une très grande corrélation (90%) ainsi qu’une détection "vraie" à 90%. Les cartes restituées d’eaux de surface utilisant la technologie SAR sont enfin comparées aux cartes d’inondation issues de données topographiques. Les résultats montrent une fois encore une très grande consistance entres les deux cartes avec 98% des pixels considérés comme inondés dans cartes SAR se trouvant dans les régions de très grande probabilité d’inondation selon la topographie (>60%). Troisièmement, la variation volumique des eaux de surface est calculée comme le produit de l’étendue de la surface avec la hauteur d’eau. Ces deux variables sont validées à l’aide d’autres produits hydrologiques et montrent de bons résultats. La hauteur d’eau superficielle est linéairement interpolée aux régions non inondées afin de produire des cartes mensuelles à la résolution spatiale de 500 m. La hauteur d’eau est ensuite analysée pour estimer les variations volumiques. Ces résultats montrent une très bonne corrélation avec la variation volumique induite par la mesure du contenu en eau du satellite GRACE (95%) ainsi qu’avec la variation des mesures in situ de débit des rivières. Finalement, deux produits globaux et multi-satellites d’eaux superficielles sont comparés à l’échelle régionale et globale sur la période 1993-2007: GIEMS et SWAMPS. Lorsqu’elles existent, les données auxiliaires sont utilisées afin de renforcer l’analyse. Les deux produits montrent une dynamique similaire, mais 50% des pixels inondés dans SWAMPS se trouvent le long des côtes. / Surface water is essential for all forms of life since it is involved in almost all processes of life on Earth. Quantifying and monitoring surface water and its variations are important because of the strong connections between surface water, other hydrological components (groundwater and soil moisture, for example), and the changing climate system. Satellite remote sensing of land surface hydrology has shown great potential in studying hydrology from space at regional and global scales. In this thesis, different techniques using several types of satellite estimates have been made to study the variation of surface water, as well as other hydrological components in the lower Mekong basin (located in Vietnam and Cambodia) over the last two decades. This thesis focuses on four aspects. First, the use of visible/infrared MODIS/Terra satellite observations to monitor surface water in the lower Mekong basin is investigated. Four different classification methods are applied, and their results of surface water maps show similar seasonality and dynamics. The most suitable classification method, that is specially designed for tropical regions, is chosen to produce regular surface water maps of the region at 500 m spatial resolution, from January 2001 to present time. Compared to reference data, the MODIS-derived surface water time series show the same amplitude, and very high temporal correlation for the 2001-2007 period (> 95%). Second, the use of SAR Sentinel-1 satellite observations for the same objective is studied. Optical satellite data are replaced by SAR satellite data to benefit the ability of their microwave wavelengths to pass through clouds. Free-cloud Landsat-8 satellite imagery are set as targets to train and optimize a Neural Network (NN). Predicted surface water maps (30 m spatial resolution) are built for the studied region from January 2015 to present time, by applying a threshold (0.85) to the output of the NN. Compared to reference free-cloud Landsat-8 surface water maps, results derived from the NN show high spatial correlation (_90%), as well as true positive detection of water pixels (_90%). Predicted SAR surface water maps are also compared to floodability maps derived from topography data, and results show high consistency between the two independent maps with 98% of SAR-derived water pixels located in areas with a high probability of inundation (>60%). Third, the surface water volume variation is calculated as the product of the surface water extent and the surface water height. The two components are validated with other hydrological products, and results show good consistencies. The surface water height are linearly interpolated over inundated areas to build monthly maps at 500 m spatial resolution, then are used to calculate changes in the surface water volume. Results show high correlations when compared to variation of the total land surface water volume derived from GRACE data (95%), and variation of the in situ discharge estimates (96%). Fourth, two monthly global multi-satellite surface water products (GIEMS & SWAMPS) are compared together over the 1993-2007 period at regional and global scales. Ancillary data are used to support the analyses when available. Similar temporal dynamics of global surface water are observed when compared GIEMS and SWAMPS, but _50% of the SWAMPS inundated surfaces are located along the coast line. Over the Amazon and Orinoco basins, GIEMS and SWAMPS have very high water surface time series correlations (95% and 99%, respectively), but SWAMPS maximum water extent is just a half of what observed from GIEMS and SAR estimates. SWAMPS fails to capture surface water dynamics over the Niger basin since its surface water seasonality is out of phase with both GIEMS- and MODIS-derived water extent estimates, as well as with in situ river discharge data.

Master of Arts / Department of Geography / Jida Wang / Monitoring salt pans is important especially for agricultural management in arid or semi-arid regions because salt pans can negatively affect human life, wildlife, and ecology. Some of the harmful impacts of salt pans are accelerated desertification, cropland loss, economic downturn, wildlife loss, and forced migration of humans and animals due to salt storms. Spectral salt pan indices based upon remotely sensed data (using spectral properties of Landsat-8 imagery) suggested in previous studies vary by location. In other words, the spectral configuration of a salt index for a given location may not be readily applicable to another location due to spatial heterogeneity of salt components across the continental surface. Using Landsat-8 OLI imagery and climate data sets, this study aims to develop a mapping framework which can effectively extract salt pans and salt playas under various spectral conditions in different geographic locations. Based on training samples selected in eight major salt pans/playas in North America, Central Asia, Africa, and Australia, the mapping framework was designed to include the following steps: i) a conservative salt index to highlight potential salt-covered regions, ii) a calibrated support vector machine (SVM) to extract high-salinity areas in the mask regions, and iii) a posterior quality assurance/ quality control (QA/QC) with assistance of auxiliary datasets (e.g., surface slope and land covers) to eliminate commission errors and refine the extracted saltpan areas. The developed mapping framework was validated in the arid endorheic regions across the western United States, with a total area of 699 thousand square kilometers. Both qualitative and quantitative assessments of the results show reliability of the developed framework. The overall accuracy of the extracted salt pans prior to QA/QC is 97%. The final product after QA/QC achieves an overall accuracy of 99.95% and a Kappa statistic of 0.99.According to the results of salt pans areas and endorheic basins areas, it can be concluded that two aforementioned variables of this study are positively correlated to each other, and 1.10 percent of the entire case study area is covered by salt pans. The accuracy of the results suggests a potential that the mapping framework, together with the collected training sample and algorithms, may be applicable to identify salt pan and salt playa regions across the Earth’s land surface.

Indiana University-Purdue University Indianapolis (IUPUI) / Monitoring lake quality remotely offers an economically feasible approach as opposed to in-situ field data collection. Researchers have demonstrated that lake clarity can be successfully monitored through the analysis of remote sensing. Evaluating satellite imagery, as a means of water quality detection, offers a practical way to assess lake clarity across large areas, enabling researchers to conduct comparisons on a large spatial scale. Landsat data offers free access to frequent and recurring satellite images. This allows researchers the ability to make temporal comparisons regarding lake water quality. Lake water quality is related to turbidity which is associated with clarity. Lake clarity is a strong indicator of lake health and overall water quality. The possibility of detecting and monitoring lake clarity using Landsat8 mean brightness values is discussed in this report. Lake clarity is analyzed in three different reservoirs for this study; Brookeville, Geist, and Eagle Creek. In-situ measurements obtained from Brookeville Reservoir were used to calibrate reflectance from Landsat 8’s Operational Land Imager (OLI) satellite. Results indicated a correlation between turbidity and brightness values, which are highly correlated in algal dominated lakes.

Information on habitat preferences is critical for the successful conservation of endangered species. For many species, especially those living in remote areas, we currently lack this information. Time and financial resources to analyze habitat use are limited. We aimed to develop a method to describe habitat preferences based on a combination of bird surveys with remotely sensed fine-scale land cover maps. We created a blended multiband remote sensing product from SPOT 6 and Landsat 8 data with a high spatial resolution. We surveyed populations of three bird species (Yellow-breasted Bunting Emberiza aureola, Ochre-rumped Bunting Emberiza yessoensis, and Black-faced Bunting Emberiza spodocephala) at a study site in the Russian Far East using hierarchical distance sampling, a survey method that allows to correct for varying detection probability. Combining the bird survey data and land cover variables from the remote sensing product allowed us to model population density as a function of environmental variables. We found that even small-scale land cover characteristics were predictable using remote sensing data with sufficient accuracy. The overall classification accuracy with pansharpened SPOT 6 data alone amounted to 71.3%. Higher accuracies were reached via the additional integration of SWIR bands (overall accuracy = 73.21%), especially for complex small-scale land cover types such as shrubby areas. This helped to reach a high accuracy in the habitat models. Abundances of the three studied bird species were closely linked to the proportion of wetland, willow shrubs, and habitat heterogeneity. Habitat requirements and population sizes of species of interest are valuable information for stakeholders and decision-makers to maximize the potential success of habitat management measures.

Une estimation des quantités de carbone organique dissous dans les millions de lacs boréaux est nécessaire pour améliorer notre connaissance du cycle global du carbone. Les teneurs en carbone organique dissous sont corrélées avec les quantités de matière organique dissoute colorée qui est visible depuis l’espace. Cependant, les capteurs actuels offrent une radiométrie et une résolution spatiale qui sont limitées par rapport à la taille et l’opacité des lacs boréaux. Landsat 8, lancé en février 2013, offrira une radiométrie et une résolution spatiale améliorées, et produira une couverture à grande échelle des régions boréales. Les limnologistes ont accumulé des années de campagnes de terrain dans les régions boréales pour lesquelles une image Landsat 8 sera disponible. Pourtant, la possibilité de combiner des données de terrain existantes avec une image satellite récente n'a pas encore été évaluée. En outre, les différentes stratégies envisageables pour sélectionner et combiner des mesures répétées au cours du temps, sur le terrain et depuis le satellite, n'ont pas été évaluées. Cette étude présente les possibilités et les limites d’utiliser des données de terrain existantes avec des images satellites récentes pour développer des modèles de prédiction du carbone organique dissous. Les méthodes se basent sur des données de terrain recueillies au Québec dans 53 lacs boréaux et 10 images satellites acquises par le capteur prototype de Landsat 8. Les délais entre les campagnes de terrain et les images satellites varient de 1 mois à 6 ans. Le modèle de prédiction obtenu se compare favorablement avec un modèle basé sur des campagnes de terrain synchronisées avec les images satellite. L’ajout de mesures répétées sur le terrain, sur le satellite, et les corrections atmosphériques des images, n’améliorent pas la qualité du modèle de prédiction. Deux images d’application montrent des distributions différentes de teneurs en carbone organique dissous et de volumes, mais les quantités de carbone organique dissous par surface de paysage restent de même ordre pour les deux sites. Des travaux additionnels pour intégrer les sédiments dans l’estimation sont nécessaires pour améliorer le bilan du carbone des régions boréales. / A remote sensing approach to estimate carbon stocks in the millions of boreal lakes is highly desirable to improve our understanding of carbon cycles. Lakes carbon content is often correlated to colored dissolved organic matter (CDOM) content, which is visible from space. Meanwhile, current sensors offer limited radiometry and spatial resolution in regard to boreal lakes opacity and size. Landsat 8, launched in February 2013, offers improved radiometry and spatial resolution, and will provide large-scale coverage of boreal regions. Limnologists gathered years of field campaigns in the boreal regions for which a clear Landsat 8 image will be available. Yet the possibility to combine legacy field data with new satellite imagery has not been assessed yet. Furthermore, the different strategies to select and combine timely repeated lakes measurements in the field and on the satellite have not been assessed either. In this study, we address the opportunities and limits to combine legacy field data with new satellite imagery to develop CDOM predictive models. Methods are based on field data from Quebec collected in 53 boreal lakes and 10 satellite images acquired with the prototype of Landsat 8. Delays between field campaigns and satellite overpasses varied from 1 month to 6 years. Results show that a CDOM predictive model based on existing field data compares favorably with models based on carefully coordinated field campaigns. The quality of the model does not improve by adding repeat measurements in the field and on the satellite, or by using atmospherically corrected images. Two images from different sites show different distributions of lakes dissolved organic carbon concentrations and volumes, but the total dissolved organic carbon storage per landscape unit in the two sites are in the same range. Additional work to link satellite data to lakes sediments carbon content is needed to refine the global carbon budget in the boreal regions.

Une estimation des quantités de carbone organique dissous dans les millions de lacs boréaux est nécessaire pour améliorer notre connaissance du cycle global du carbone. Les teneurs en carbone organique dissous sont corrélées avec les quantités de matière organique dissoute colorée qui est visible depuis l’espace. Cependant, les capteurs actuels offrent une radiométrie et une résolution spatiale qui sont limitées par rapport à la taille et l’opacité des lacs boréaux. Landsat 8, lancé en février 2013, offrira une radiométrie et une résolution spatiale améliorées, et produira une couverture à grande échelle des régions boréales. Les limnologistes ont accumulé des années de campagnes de terrain dans les régions boréales pour lesquelles une image Landsat 8 sera disponible. Pourtant, la possibilité de combiner des données de terrain existantes avec une image satellite récente n'a pas encore été évaluée. En outre, les différentes stratégies envisageables pour sélectionner et combiner des mesures répétées au cours du temps, sur le terrain et depuis le satellite, n'ont pas été évaluées. Cette étude présente les possibilités et les limites d’utiliser des données de terrain existantes avec des images satellites récentes pour développer des modèles de prédiction du carbone organique dissous. Les méthodes se basent sur des données de terrain recueillies au Québec dans 53 lacs boréaux et 10 images satellites acquises par le capteur prototype de Landsat 8. Les délais entre les campagnes de terrain et les images satellites varient de 1 mois à 6 ans. Le modèle de prédiction obtenu se compare favorablement avec un modèle basé sur des campagnes de terrain synchronisées avec les images satellite. L’ajout de mesures répétées sur le terrain, sur le satellite, et les corrections atmosphériques des images, n’améliorent pas la qualité du modèle de prédiction. Deux images d’application montrent des distributions différentes de teneurs en carbone organique dissous et de volumes, mais les quantités de carbone organique dissous par surface de paysage restent de même ordre pour les deux sites. Des travaux additionnels pour intégrer les sédiments dans l’estimation sont nécessaires pour améliorer le bilan du carbone des régions boréales. / A remote sensing approach to estimate carbon stocks in the millions of boreal lakes is highly desirable to improve our understanding of carbon cycles. Lakes carbon content is often correlated to colored dissolved organic matter (CDOM) content, which is visible from space. Meanwhile, current sensors offer limited radiometry and spatial resolution in regard to boreal lakes opacity and size. Landsat 8, launched in February 2013, offers improved radiometry and spatial resolution, and will provide large-scale coverage of boreal regions. Limnologists gathered years of field campaigns in the boreal regions for which a clear Landsat 8 image will be available. Yet the possibility to combine legacy field data with new satellite imagery has not been assessed yet. Furthermore, the different strategies to select and combine timely repeated lakes measurements in the field and on the satellite have not been assessed either. In this study, we address the opportunities and limits to combine legacy field data with new satellite imagery to develop CDOM predictive models. Methods are based on field data from Quebec collected in 53 boreal lakes and 10 satellite images acquired with the prototype of Landsat 8. Delays between field campaigns and satellite overpasses varied from 1 month to 6 years. Results show that a CDOM predictive model based on existing field data compares favorably with models based on carefully coordinated field campaigns. The quality of the model does not improve by adding repeat measurements in the field and on the satellite, or by using atmospherically corrected images. Two images from different sites show different distributions of lakes dissolved organic carbon concentrations and volumes, but the total dissolved organic carbon storage per landscape unit in the two sites are in the same range. Additional work to link satellite data to lakes sediments carbon content is needed to refine the global carbon budget in the boreal regions.

Le nerprun bourdaine (Rhamnus frangula L.) est une espèce exotique qui envahit plusieurs régions du sud du Québec, et plus particulièrement la région administrative de l'Estrie. Actuellement, on connaît encore peu l'écologie de l'espèce dans le contexte québécois et il n’existe pas de portrait d’ensemble de sa distribution dans les forêts tempérées de cette région. Dans ce contexte, le premier objectif du projet était de cartographier par télédétection la distribution du nerprun bourdaine dans deux secteurs de l'Estrie. Un second objectif était d'évaluer les variables environnementales déterminantes pour expliquer le recouvrement de nerprun bourdaine. La phénologie du nerprun bourdaine diffère de celle de la plupart des espèces indigènes arborescentes puisque ses feuilles tombent plus tard en automne. Cette caractéristique a permis de cartographier, par démixage spectral, la probabilité d'occurrence du nerprun bourdaine grâce à une série temporelle d'images du capteur OLI de Landsat 8. Le recouvrement du nerprun bourdaine a été calculé dans 119 placettes sur le terrain. La cartographie résultante a montré un accord de 69% avec les données terrain. Une image SPOT-7, dont la résolution spatiale est plus fine, a ensuite été utilisée, mais n’a pas permis d'améliorer la cartographie, puisque la date d’acquisition de l’image n’était pas optimale dû à un manque de disponibilité. Concernant le second objectif de la recherche, la variable la plus significative pour expliquer la présence de nerprun bourdaine était la densité du peuplement, ce qui suggère que l’ouverture de la couverture forestière pourrait favoriser l’envahissement. Néanmoins, les résultats tendent à démontrer que le nerprun bourdaine est une espèce «généraliste» qui s’adapte bien à plusieurs conditions environnementales. / Glossy buckthorn (Rhamnus frangula L.) is an exotic species invading many areas in southern of Quebec, particularly in the Eastern Townships. Currently, we do not know very much about the species ecology and no thorough study of its distribution in temperate forest has been performed. Therefore, the first objective of the project was to map the spatial distribution of glossy buckthorn in two areas of the Eastern Townships, using remote sensing techniques. The second objective was to evaluate the environmental variables, or predictors, best explaining the presence of glossy buckthorn. The phenology of glossy buckthorn differs from most of the indigenous tree species found in this area because its leaves fall later in autumn. This characteristic allowed to map, using spectral unmixing, the probability of occurrence of glossy buckthorn, with temporal Landsat 8 (OLI) imagery data series. Glossy buckthorn coverage was calculated on 119 plots on the field. The resulting maps showed an agreement of 69% with field data. A SPOT-7 image, which has a finer resolution than Landsat 8 (OLI), was then used but it did not improve the quality of the map, since its acquisition date was not optimal, due to a lack of availability. Concerning the second objective of the research, the best variable explaining the presence of glossy buckthorn was stand density, which leads to believe that forest cover openings could ease the establishment of buckthorn. However, the results tend to show that glossy buckthorn is a generalist species, easily adapting to various environmental conditions.

Temperature characteristics of surface using remote sensing methods Abstract The aim of this thesis is to design a methodology for refining the land surface temperature values obtained from Landsat 8 satellite data in areas with diverse land cover. The research section describes factors influencing the radiation of the Earth's surface. Also mentioned are current methods used for processing infrared thermal data and calculate land surface temperature. The practical part describes satellite and airborne data used in the analytical and verification process. All parts of the applied method leading to the subpixel value of the land surface temperature are described in detail in the method part. The results are then compared with airborne verification data with better spatial resolution and with currently used methods. Finally, the pros and cons of this method and its possible improvement in the future are mentioned. Key words: land surface temperature, land surface emissivity, satellite data, Landsat 8, airborne data, subpixel method, Czech Republic