The diploma theses focused on the topic of removing spectral aberration of sensor. The anomaly is being called spectral smile effect, or spectral curvature. Usually are with this artifact affected hyperspectral sensors, whose sensing is based on principle corresponding to pushbroom scanner. The defect occurs in the data as brightness gradient in the cross-track direction to the sensor flight and affects the object's spectral characteristics shape. Removing is important particularly in case of applying atmospheric correction algorithm, which are being degraded with presence of this defect. The work contents description of current methods used for detecting and eliminating this anomaly. The work is closer focused on the methods removing the defect based on own measured data without use of calibration targets. There is being examined sensitivity of the methods on the scene perception character, meaning heterogeneity of the scene and other outside influence. One of the methods is deeply processed resulting in increasing quality of the method for removing the defect.

Master of Science / Department of Geography / Douglas Goodin / Hyperspectral data has been widely used for estimates of canopy biochemical content over the past decades. Most of these studies were conducted in forests or crops with relatively uniform canopies. Feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures remains uncertain. Spectral data at the canopy level, with mixed background noise, canopy biochemical and biophysical properties create more problems in spectral analysis than that at the leaf level. Complications of heterogeneous canopies make biochemical retrieval through remote sensing even more difficult due to more uneven spatial distribution of biochemical constituents. The objective of my research was to map canopy nitrogen content in tallgrass prairie with mixed canopies by means of hyperspectral data from in-situ and airborne measurements. Research efforts were divided into three steps: (1) the green leaf area index (LAI) retrieval, given LAI is an important parameter in scaling nitrogen content from leaves to canopies; (2) canopy nitrogen modeling from analysis of in-situ hyperspectral data; and (3) canopy nitrogen mapping based on aerial hyperspectral imagery. Research results revealed that a fine chlorophyll absorption feature in the green-yellow region at wavelengths of 562 – 600 nm was sensitive to canopy nitrogen status. Specific spectral features from the normalized spectral data by the first derivative or continuum removal in this narrow spectral region could be selected by multivariate regression for nitrogen modeling. The optimal nitrogen models with high predictive accuracy measured as low values of root-mean-square error (RMSE) were applied to the aerial hyperspectral imagery for canopy nitrogen mapping during the growth seasons from May to September. These maps would be of great value in studies on the interactions between canopy vegetation quality and grazing patterns of large herbivores in tallgrass prairie.

Fungal infection and mycotoxin contamination in agricultural products are a serious food safety issue. The detection of fungal infection and mycotoxin contamination in food products should be in a rapid way. A Near-infrared (NIR) hyperspectral imaging system was used to detect fungal infection in 2013 crop year canola, wheat, and barley at different periods after inoculation and different concentration levels of ochratoxin A in wheat and barley. Artificially fungal infected (Fungi: Aspergillus glaucus, Penicillium spp.) kernels of canola, wheat and barley, were subjected to single kernel imaging after 2, 4, 6, 8, and 10 weeks post inoculation in the NIR region from 1000 to 1600 nm at 61 evenly distributed wavelengths at 10 nm intervals. The acquired image data were in the three-dimensional hypercube forms, and these were transformed into two-dimensional data. The two-dimensional data were subjected to principal component analysis to identify significant wavelengths based on the highest principal component factor loadings. Wavelengths 1100, 1130, 1250, and 1300 nm were identified as significant for detection of fungal infection in canola kernels, wavelengths 1280, 1300, and 1350 nm were identified as significant for detection of fungal infection in wheat kernels, and wavelengths 1260, 1310, and 1360 nm were identified as significant for detection of fungal infection in barley kernels. The linear, quadratic and Mahalanobis statistical discriminant classifiers differentiated healthy canola kernels with > 95% and fungal infected canola kernels with > 90% classification accuracy. All the three classifiers discriminated healthy wheat and barley kernels with > 90% and fungal infected wheat and barley kernels with > 80% classification accuracy. The wavelengths 1300, 1350, and 1480 nm were identified as significant for detection of ochratoxin A contaminated wheat kernels, and wavelengths 1310, 1360, 1480 nm were identified as significant for detection of ochratoxin A contaminated barley kernels. All the three statistical classifiers differentiated healthy wheat and barley kernels and ochratoxin A contaminated wheat and barley kernels with a classification accuracy of 100%. The classifiers were able to discriminate between different durations of fungal infections in canola, wheat, and barley kernels with classification accuracy of more than 80% at initial periods (2 weeks) of fungal infection and 100% at the later periods of fungal infection. Different concentration levels of ochratoxin A contamination in wheat and barley kernels were discriminated with a classification accuracy of > 98% at ochratoxin A concentration level of ≤ 72 ppb in wheat kernels and ≤ 140 ppb in barley kernels and with 100% classification accuracy at higher concentration levels. / May 2016

L'étude des sols argileux fait l'objet de nombreux travaux motivés par leur rôle dans les processus d'érosion, les catastrophes naturelles et l'agriculture de précision. La caractérisation du contenu en argiles gonflantes du sol est aussi nécessaire pour évaluer la traficabilité d’une région ou le risque de retrait-gonflement des sols, responsable d’engendrer des dégâts sur le bâti. En effet, les argiles gonflantes sont des smectites qu'il faut différencier des autres types d'argiles telles que l'illite ou la kaolinite, en milieu tempéré. Les techniques traditionnelles pour réaliser la cartographie des minéraux argileux des sols sont en général couteuses, financièrement et en temps et sont basées sur des campagnes terrain intensives simultanément à des acquisitions photographiques afin de spatialiser l'information qui reste qualitative. La télédétection hyperspectrale est une technique potentiellement intéressante pour obtenir des cartes d'argile plus précises et à moindre coût. Néanmoins, elle est limitée par le fait que (i) les minéraux sont mélangés de manière intime dans les sols avec d’autres composants; mais aussi que (ii) à l’échelle aéroportée, le signal réfléchi au sein d’un pixel (résolution spatiale de l’ordre du mètre) comprend de la végétation en plus du sol nu. Ces phénomènes de mélange, aux échelles microscopique et macroscopique, rendent difficile l’estimation du contenu en argiles minéralogiques. Le développement des drones ainsi que leur possibilité d'embarquer de nouvelles caméras hyperspectrales couvrant l'ensemble du spectre [0,4 - 2,5 µm] avec une haute résolution spatiale (environ 10 cm) et un signal à bruit élevé ouvrent la voie à un inventaire plus précis des argiles.L'objectif de cette thèse est de montrer l'intérêt de l'utilisation de méthodes de démélange sur des données hyperspectrales à très haute résolution spatiale pour estimer le contenu en minéraux argileux du sol, et plus précisément des argiles responsables du retrait-gonflement, les smectites. Dans un premier temps, les méthodes existantes de détection, de caractérisation des différents types d'argile et d'estimation de leur abondance sont présentées. Les potentialités des méthodes de démélange existantes dans la littérature pour l’estimation du contenu en minéraux argileux des sols sont mises en avant. Dans un second temps, les méthodes de démélange sont utilisées sur une base de données d’images hyperspectrale acquises en laboratoire de mélanges contrôlés minéraux contenant des argiles (montmorillonite, illite, kaolinite) et d’autres minéraux présents dans les sols (quartz, calcite). Comme les minéraux sont mélangés de manière intime, des méthodes de démélange, linéaires et non-linéaires sont décrites et comparées. Néanmoins, les algorithmes non-linéaires ont des performances similaires aux algorithmes linéaires. De plus, l’effet de la variabilité des données sur la précision de l’estimation des abondances a pu être réduit en utilisant des prétraitements spectraux. Dans une dernière étape, la comparaison des méthodes de démélange sont étendues à des mesures en environnement extérieur. Cette analyse repose sur une campagne de mesure en extérieur réalisée pour la mesure d'images hyperspectrales acquises depuis une nacelle (12 m de hauteur environ, 1,5 cm de résolution spatiale), et l’acquisition d’échantillons prélevés et analysés par DRX (données quantitatives des abondances des minéraux) pour validation. Cette dernière phase permet d'analyser l'impact d'un sol naturel (composé d'un mélange minéralogique, des matières carbonées telles que la cellulose, et ayant une rugosité de surface…) sur les méthodes de démélange. Les performances obtenues (moins de 15% RMSE sur l’estimation de la montmorillonite) permettent d’ouvrir des perspectives quant à l’application de ces méthodes sur des capteurs embarqués par drone, pour la cartographie de la traficabilité et de l’aléa de retrait-gonflement des sols. / Clayey soils are studied because of the importance of soils in erosion processes, natural disasters and precision agriculture. Mapping of clay mineralogy is essential for surveying and predicting trafficability and ground instability hazards, such as shrink-swelling, in order to cope with damages caused by expansive soils on infrastructures. Clay minerals in temperate zone soils are mainly divided in smectites, which highly contribute to soil swelling, illite and kaolinite. Geotechnical engineering practice for clayey soils mapping are expensive and time-consuming. Indeed, it is based on field and extensive laboratory studies. In addition, spatial distribution of clay is assessed using aerial photographs and low-scale geological maps. Thereby, small heterogeneities in geological features are rarely detected, and spatial information remains qualitative. Hyperspectral remote sensing could be an alternative to conventional methods for clay mapping. However, this method is limited by two facts: (i) soils are an intimate mixture of minerals, and (ii) vegetation is mixed with bare soil within airborne sensors pixels (meter range of spatial resolution). Those mixtures (at microscopic and macroscopic scales) mask clays specific spectral signatures and limit clay mineral quantification. Recent development in UAV offers new possibilities for carrying hyperspectral cameras in the reflective domain [0.4 – 2.5 µm], and obtaining data with higher SNR and resolution (10 cm). These advances open new perspectives for accurate and less expensive clay maps. This PhD thesis aims to present the potentiality of clay mapping in soils using very-high spatial resolution hyperspectral data, and more specifically, to estimate swelling clay minerals (smectite) abundances. First, existing methods of detection, and abundance estimation of clay minerals are presented. Second, unmixing methods are used on a database of hyperspectral images of controlled mixtures with different abundances of clay minerals, (illite, montmorillonite and kaolinite) and other minerals existing in soils (quartz, calcite). Due to the intimate nature of mixtures, linear and non-linear unmixing methods are described and compared. However, linear and nonlinear algorithms exhibit similar performances. Moreover, the accuracy of estimation of abundances of mineral clay increased using spectral preprocessings. Regarding the third step, field measurements are used to assess clay unmixing methods. This study is based on an outdoor experiment which acquired hyperspectral images from a bucket truck (12 m elevation, 1.5 cm ground sampling distance), and a sampling collection analyzed by XRD (quantitative analysis of mineral abundances) for validation. This step analyzed effects of a natural soil, (with organic matter, a larger diversity of mineralogical components and with surface roughness) on unmixing methods tested with laboratory data. Obtained performances (less than 15% RMSE for montmorillonite estimation) allow perspectives to apply these methods on data obtained with UAV sensors, for trafficability or expansive soils mapping purposes.

L’imagerie hyperspectrale est maintenant très développée dans les applications de télédétection. Il y a principalement deux manières de construire les imageurs associés : la première méthode utilise un réseau et une fente, et l’image spectrale est acquise ligne par la ligne le long de la trajectoire du porteur. La seconde est basée sur le principe de la spectrométrie par transformée de Fourier (TF). Certains des systèmes utilisés sont construits de manière à enregistrer l’interférogramme de chaque point de la scène suivant le déplacement dans le champ. Le spectre de la lumière venant d’un point de la scène est alors calculé par la transformée de Fourier de son interférogramme. Les imageurs classiques basés sur des réseaux sont plus simples à réaliser et les données qu’ils fournissent sont souvent plus faciles à interpréter. Cependant, les spectro-imageurs par TF fournissent un meilleur rapport signal sur bruit si la source principale de bruit vient du détecteur.Dans la première partie de cette thèse, nous étudions l’influence de différents types de bruit sur les architectures classiques et TF afin d’identifier les conditions dans lesquelles ces dernières présentent un avantage. Nous étudions en particulier l’influence des bruits de détecteur, de photons, des fluctuations de gain et d’offset du détecteur et des propriétés de corrélation spatiale des fluctuations d’intensité du spectre mesuré. Dans la seconde partie, nous présentions la conception, la réalisation et les premiers résultats d’un imageur basé sur un interféromètre de Michelson à dièdres statique nommé DéSIIR (Démonstrateur de Spectro-Imagerie Infrarouge). Les premiers résultats montrent, qu’en mode spectromètre simultané, DéSIIR permet la restitution du spectre avec les spécifications requises dans le cadre des applications recherchées, c'est-à-dire détecter avec une résolution d environ 25 cm-1 un object de quelques degrés plus chaud que le fond de la scène et présentant une signature spectrale entre 3 et 5 juin. En mode spectromètre imageur, après recalage des images, il est possible de reconstruire le spectre de chaque point de la scène observée. / Hyperspectral imaging is now very important in remote sensing applications. There are two main ways to build such imagers : the first one uses a grating and a slit, and the spectral image is acquired line by line along the track of the carrier. The second way is to use the principle of Fourier transform (FT) spectrometry. Some of these systems are built in such a way that they record the interferogram of each point of the scene as it moves through the field of view. The spectrum of the light coming from a particular point is then calculated by the Fourier transform of its interferogram. Classical gratting-based spectral imagers are easier to build and the data they provide a better signal to noise ratio if the main source of noise comes from the detector.In the first part of this thesis, we study the influence of various types of noise on the classic and TF-based architectures to identify the conditions in which these last ones present an advantage. We study particularly the influence of detector noise, photons noise, detector gain and offset fluctuations and spatial correlation properties of the intensity fluctuations. In the second part, we present the conception, the realization and the first results of an imager bases on a Michelson interferometer with dihedrons named DéSIIR (“Démonstrateur de Spectro Imagerie Infrarouge”). The first results show that, in simultaneous spectrometer mode, DéSIIR allows the reconstruction of the spectrum with respect to the specific requirements, which are to be able to detect an objet of some degrees warmer than the background of the scene observed with a resolution of about 25 cm -1. In imager mode, this reconstruction is performed for each point of the scene.

Segundo a Agência Nacional de Transportes Terrestres (ANTT) em seu Anuário Estatístico dos Transportes Terrestres AETT (2008), o Brasil em todo o seu território possui 211.678 quilômetros de rodovias pavimentadas. O valor de serventia do pavimento diminui com o passar do tempo por dois fatores principais: o tráfego e as intempéries (BERNUCCI et al., 2008). Monitorar a condição de uso de toda a extensão das rodovias brasileiras é tarefa dispendiosa e demorada. A investigação de novas técnicas que permitam o levantamento da condição dos pavimentos de forma ágil e automática é parte da pesquisa deste trabalho. Nos últimos anos, um número crescente de imagens de alta resolução espacial tem surgido no mercado mundial com o aparecimento dos novos satélites e sensores aeroembarcados de sensoriamento remoto. Da mesma forma, imagens multiespectrais e até mesmo hiperespectrais estão sendo disponibilizadas comercialmente e para pesquisa científica. Neste trabalho são utilizadas imagens hiperespectrais de sensor digital aeroembarcado. Uma metodologia para identificação automática dos pavimentos asfaltados e classificação das principais ocorrências dos defeitos do asfalto foi desenvolvida. A primeira etapa da metodologia é a identificação do asfalto na imagem, utilizando uma classificação híbrida baseada inicialmente em pixel e depois refinada por objetos foi possível a extração da informação de asfalto das imagens disponíveis. A segunda etapa da metodologia é a identificação e classificação das ocorrências dos principais defeitos nos pavimentos flexíveis que são observáveis nas imagens de alta resolução espacial. Esta etapa faz uso intensivo das novas técnicas de classificação de imagens baseadas em objetos. O resultado final é a geração de índices da condição do pavimento, a partir das imagens, que possam ser comparados com os indicadores da qualidade da superfície do pavimento já normatizados pelos órgãos competentes no país. / According to Statistical Survey of Land Transportation AETT (2008) of National Agency of Land Transportation (ANTT), Brazil has in its territory 211,678 kilometers of paved roads. The pavement Present Serviceability Ratio (PSR) value decreases over time by two main factors: traffic and weather (BERNUCCI et al., 2008). Monitor the condition of use of all Brazilian roads is expensive and time consuming task. The investigation of new techniques that allow a quick and automatic survey of pavement condition is part of this research. In recent years, an increasing number of images with high spatial resolution has emerged on the world market with the advent of new remote sensing satellites and airborne sensors. Similarly, multispectral and even hyperspectral imagery are become available commercially and for scientific research nowadays. Hyperspectral images from digital airborne sensor have been used in this work. A new methodology for automatic identification of asphalted pavement and also for classification of the main defects of the asphalt has been developed. The first step of the methodology is the identification of the asphalt in the image, using hybrid classification based on pixel initially and after improved by objects. Using this approach was feasible to extract asphalt information from the available images. The second step of the methodology is the identification and classification of the main defects of flexible pavement surface that are observable in high spatial resolution imagery. This step makes intensive use of new techniques for classification of images based on objects. The goal, is the generation of pavement surface condition index from the images that can be compared with quality index of pavement surface that are already regulated by the regulatory agency in the country.

A utilização de dados provenientes do sensoriamento remoto é alternativa para otimizar a utilização de insumos, dentre eles o nitrogênio. O presente trabalho teve como objetivo verificar a possibilidade de uso de um sensor hiperespectral em dossel na cultura da cana-de-açúcar, verificando sua capacidade em discriminar a resposta da cultura as diferentes doses de nitrogênio e estimar o teor foliar de nitrogênio, em três áreas experimentais. O trabalho foi dividido em três capítulos: O primeiro capitulo utiliza os dados hiperespectrais somente da variedade SP 81-3250, única comum em todas as áreas, de todas as datas de coleta das três áreas experimentais para verificar o potencial dos dados em diferenciar as doses de nitrogênio aplicado (0, 50, 100 e 150 kg.ha-1) e qual melhor época. Os dados espectrais foram avaliados pela estatística multivariada da análise discriminante, em que os centroides das diferentes doses foram submetidos a análise de variância. Os resultados obtidos foram que os meses de dezembro, janeiro e fevereiro discriminou todas as doses nas três áreas, o mesmo não ocorreu no mês de agosto. As bandas que apresentaram maiores significância foram na região do verde, red-edge e infravermelho próximo. No segundo capitulo foi avaliado a sensibilidade dos dados hiperespectrais em estimar a biomassa do ponteiro da cana-de-açúcar. Para isso foi utilizado somente os dados de Piracicaba. A análise espectral foi realizada aos 137, 169 e 193 Dias Após o Corte (DAC) e a avaliação biométrica foi realizada aos 345 DAC. Durante o corte de dois metros de linha, realizado manualmente. A biomassa do ponteiro foi submetida ao teste de Shapiro-Wilk, análise de variância pelo Teste F e as médias quando significativas, comparadas pelo Teste de Tukey. Posteriormente foi realizada a análise de correlação de Pearson da biomassa do ponteiro e cada comprimento de onda. Análise mostrou que existe correlação positiva entre a biomassa do ponteiro e a reflectância do dossel aos 137 DAC e 169 DAC, porém aos 193 DAC não houve nenhum comprimento de onda com correlação significativa. O comprimento de onda de 685 nm aos 137 DAC obteve a maior correlação, de 0,33. No terceiro capitulo teve por objetivo selecionar variáveis a partir de dados hiperespectrais de dossel da cana-de-açúcar para geração de modelos para predição do Teor Foliar de Nitrogênio. Para isso foi utilizado os dados das três áreas experimentais, que receberam doses de 0, 50, 100 e 150 kg.ha-1 de nitrogênio. Para redução da dimensionalidade dos dados foi utilizada a metodologia sparse Partial Least Square (sPLS), posteriormente foi feito a combinação linear das variáveis selecionadas, por meio de Regressão Linear Múltipla por Stepwise (SMLR). O modelo geral teve valores de R² ajustado e RMSE respectivamente de 0,50 e 1,67 g kg-1. Os modelos gerados para Piracicaba, Jaú e Santa Maria obtiveram R² ajustado, respectivamente, de 0,31, 0,53 e 0,54. Sensores hiperespectrais de dossel podem ser utilizados para predição do TFN e monitoramento de aplicação de nitrogênio em cana-de-açúcar. / The use of data from remote sensing is an alternative to optimize the use of agricultural inputs, including nitrogen. The present study aimed to verify the possibility of using a hyperspectral sensor in sugarcane canopy, verifying its ability to discriminate crop response to different rates of nitrogen and estimating leaf nitrogen content in three experimental areas. The work is divided in three chapters: The first chapter uses hyperspectral data of the variety SP 81-3250, which is the only one present in all the areas for all dates of collection in three of experimental areas, to check the potential of the data and the best time to differentiate between rates of nitrogen (0, 50, 100 and 150 kg.ha-1). Spectral data were evaluated by multivariate discriminant analysis, wherein the centroids of the rates were submitted to an Analysis of Variance. The results showed that the all doses in three areas of study were discriminated for the months of December, January and February, but the same thing hasn\'t happened in the month of August. The bands that showed statistically significant power difference were found in the green, red, and near-infrared edge spectral regions. In the second chapter, the sensitivity of hyperspectral data was evaluated to estimate the sugarcane biomass (pointes) for the data from Piracicaba. Spectral analysis was performed at 137, 169 and 193 Days After Harvest (DAH) and evaluation of sugarcane yield was performed 345 DAH. Biomass was analyzed using The Shapiro-Wilk test of normality, F test (analysis of variance), respectively, and when significant, compared by the Tukey test. Biomass (pointer) and each wavelength were analyzed by Pearson\'s correlation analysis. The results showed that there is a positive correlation between biomass (pointer) and the canopy reflectance to 137 DAH and 169 DAH, however there was no wavelength with a significant correlation to 193 DAH. The best power relationship was obtained at 685 nm, at 137 days. The third chapter aimed to select variables from hyperspectral data of sugarcane canopy to generate models for prediction of Foliar Nitrogen Content, for three experimental areas that received nitrogen rates (0, 50, 100 and 150 kg.ha-1). Sparse Partial Least Square (sPLS) was used to reduce the dimensionality of the data. Subsequently, the linear combination of selected variables was done through Stepwise Multiple Linear Regression (SMLR). The RMSE and adjusted R-squared statistics were 0.50 and 1.67 g.kg-1, respectively. The models to Piracicaba, Jaú and Santa Maria presented adjusted R-squared 0.31, 0.53, and 0.54, respectively. Hyperspectral sensors for canopy can be used for prediction of the TFN and monitoring of nitrogen application in sugarcane.

Les objectifs de cette thèse sont la validation d’une méthodologie de détermination de la température de surface (LST) et de l’émissivité de surface (LES) à partir des radiances hyperspectrales du capteur IASI à bord du satellite METOP. Il s’agit de montrer la possibilité d’extraire ces deux paramètres d’un signal hyperspectral IRT télédétecté dans une approche physique. Le domaine spectral d'étude s'étend de 750 à 1250 cm-1 (8 à 13,3 μm) et la résolution spectrale est de l'ordre du 0,25 cm-1, inscrivant ainsi ce travail dans le giron de la radiométrie à très haute résolution spectrale infrarouge. Après une étude des méthodes de séparation existantes, la méthode SpSm (Spectral Smothness), est validée. Une étude de sensibilité aux erreurs aux bruits atmosphérique et instrumental est menée. La méthode SpSm est appliquée aux données IASI en conditions réelles pour l’année 2008 dans une zone spatiale couvrant l’Europe et le Nord d’ Afrique. Les résultats sont validés d’une part avec les produits MODIS et SEVIRI, et d’autre part avec les paramètres température et émissivité obtenus à partir des radiances SEVIRI et l’algorithme TISI. / This thesis focuses on the validation of a methodology for determining the surface temperature (LST) and surface emissivity (LES) from hyperspectral radiances from the IASI sensor on board of the European satellite METOP. We show that it is possible to extract these two parameters from a remotely sensed TIR signal using a physical approach. The spectral range under study extends from 750 to 1250 cm-1 (8 to 13.3 μm) and the spectral resolution is 0.25 cm-1, placing this work in the context of very high spectral resolution infrared radiometry. After studying the existing methods of separation, the SpSm method (Spectral Smothness), is validated. A study of sensitivity to atmospheric and instrumental noise is conducted. The SpSm method is applied to the IASI data in real conditions in 2008 in a spatial area that covers Europeand North Africa. The results are validated on one hand with the MODIS and SEVIRI products, and on the otherhand with temperatures and emissivities obtained from the SEVIRI radiances and the TISI algorithm.

La température de surface terrestre (LST) est un paramètre important dans les systèmes climatiques. Les données infrarouge thermique (TIR) contiennent un nombre d'information de la surface terrestre et de l'atmosphère sont des sources de l'information important pour estimer la LST à l'aide de télédétection. / Land surface temperature (LST) is an important parameter in climate systems. Hyperspectral thermal infrared (TIR) data, containing large information about the surface and the atmosphere, is an important source of information for retrieving LST by remote-sensing.

Airborne multi-sensor imaging is increasingly used to examine vegetation properties. The advantage of using multiple types of sensor is that each detects a different feature of the vegetation, so that collectively they provide a detailed understanding of the ecological pattern. Specifically, Light Detection And Ranging (LiDAR) devices produce detailed point clouds of where laser pulses have been backscattered from surfaces, giving information on vegetation structure; hyperspectral sensors measure reflectances within narrow wavebands, providing spectrally detailed information about the optical properties of targets; while aerial photographs provide high spatial-resolution imagery so that they can provide more feature details which cannot be identified from hyperspectral or LiDAR intensity images. Using a combination of these sensors, effective techniques can be developed for mapping species and inferring leaf physiological processes at ITC-level. Although multi-sensor approaches have revolutionised ecological research, their application in mapping individual tree crowns is limited by two major technical issues: (a) Multi-sensor imaging requires all images taken from different sensors to be co-aligned, but different sensor characteristics result in scale, rotation or translation mismatches between the images, making correction a pre-requisite of individual tree crown mapping; (b) reconstructing individual tree crowns from unstructured raw data space requires an accurate tree delineation algorithm. This thesis develops a schematic way to resolve these technical issues using the-state-of-the-art computer vision algorithms. A variational method, called NGF-Curv, was developed to co-align hyperspectral imagery, LiDAR and aerial photographs. NGF-Curv algorithm can deal with very complex topographic and lens distortions efficiently, thus improving the accuracy of co-alignment compared to established image registration methods for airborne data. A graph cut method, named MCNCP-RNC was developed to reconstruct individual tree crowns from fully integrated multi-sensor imagery. MCNCP-RNC is not influenced by interpolation artefacts because it detects trees in 3D, and it detects individual tree crowns using both hyperspectral imagery and LiDAR. Based on these algorithms, we developed a new workflow to detect species at pixel and ITC levels in a temperate deciduous forest in the UK. In addition, we modified the workflow to monitor physiological responses of two oak species with respect to environmental gradients in a Mediterranean woodland in Spain. The results show that our scheme can detect individual tree crowns, find species and monitor physiological responses of canopy leaves.