Satellite based synthetic aperture radar and optical spatial-temporal information as aid for operational and environmental mine monitoring

A sustainable society is a society that satisfies its resource requirements without endangering the sustainability of these resources. The mineral endowment on the African continent is estimated to be the first or second largest of world reserves. Therefore, it is recognised that the African continent still heavily depends on mineral exports as a key contributor to the gross domestic product (GDP) of various countries. These mining activities, however, do introduce primary and secondary environmental degradation factors. They attract communities to these mining areas, light and heavy industrial establishments occur, giving rise to artisanal activities. This study focussed on satellite RS products as an aid to a mine’s operations and the monitoring of its environment. Effective operational mine management and control ensures a more sustainable and profitable lifecycle for mines. Satellite based RS holds the potential to observe the mine and its surrounding areas at high temporal intervals, different spectral wavelengths and spatial resolutions. The combination of SAR and optical information creates a spatial platform to observe and measure the mine’s operations and the behaviour of specific land cover and land use classes over time and contributes to a better understanding of the mining activities and their influence on the environment within a specific geographical area. This study will introduce an integrated methodology to collect, process and analyse spatial information over a specific targeted mine. This methodology utilises a medium resolution land cover base map, derived from Landsat 8, to understand the predominant land cover types of the surrounding area. Using very high resolution mono- and stereoscopic satellite imagery provides a finer scale analysis and identifies changes in features at a smaller scale. Combining these technologies with the synthetic aperture radar (SAR) applications for precise measurement of surface subsidence or upliftment becomes a spatial toolbox for mine management. This study examines a combination of satellite remote sensing products guided by a systematic workflow methodology to integrate spatial results as an aid for mining operations and environmental monitoring. Some of the results that can be highlighted is the successful land cover classification using the Landsat 8 satellite. The land cover that dominated the Kolomela mine area was the “SHRUBLAND/GRASS” class with a 94% coverage and “MINE” class of 2.6%. Sishen mine had a similar dominated land cover characteristic with a “SHRUBLAND/GRASS” class of 90% and “MINE” class of 4.8%. The Pléiades time-series classification analysis was done using three scenes each acquired at a different time interval. The Sishen and Kolomela mine showed especially changes from the bare soil class to the asphalt or mine class. The Pléiades stereoscopic analysis provided volumetric change detection over small, medium, large and recessed areas. Both the Sishen and Kolomela mines demonstrated height profile changes in each selected category. The last category of results focused on the SAR technology to measure within millimetre accuracy the subsidence and upliftment behaviour of surface areas over time. The Royal Bafokeng Platinum tailings pond area was measured using 74 TerraSAR-X scenes. The tailings wall area was confirmed as stable with natural subsidence that occurred in its surrounding area due to seasonal changes of the soil during rainy and dry periods. The Chuquicamata mine as a large open pit copper mine area was analysed using 52 TerraSAR-X scenes. The analysis demonstrated significant vertical surface movement over some of the dumping sites. It is the wish of the researcher that this dissertation and future research scholars will continue to contribute in this scientific field. These contributions can only assist the mining sector to continuously improve its mining operations as well as its monitoring of the primary as well as the secondary environmental impacts to ensure improved sustainability for the next generation.Environmental SciencesM. Sc. (Environmental Science

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Automated detection of rock glaciers using deep learning and object-based image analysis

B Robson was supported by the Meltzer foundation and a University of Bergen grant. S MacDonell was supported by CONICYT-Programa Regional (R16A10003) and the Coquimbo Regional Government via FIC-R(2016)BIP 40000343. D. Hölbling has been supported by the Austrian Science Fund through the project MORPH (Mapping, Monitoring and Modeling the Spatio-Temporal Dynamics of Land Surface Morphology; FWF-P29461-N29). N Schaffer was financed by CONICYT-FONDECYT (3180417) and P Rastner by the ESA Dragon 4 programme (4000121469/17/I-NB).Rock glaciers are an important component of the cryosphere and are one of the most visible manifestations of permafrost. While the significance of rock glacier contribution to streamflow remains uncertain, the contribution is likely to be important for certain parts of the world. High-resolution remote sensing data has permitted the creation of rock glacier inventories for large regions. However, due to the spectral similarity between rock glaciers and the surrounding material, the creation of such inventories is typically conducted based on manual interpretation, which is both time consuming and subjective. Here, we present a novel method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow based on freely available Sentinel-2 optical imagery (10 m spatial resolution), Sentinel-1 interferometric coherence data, and a digital elevation model (DEM). CNNs identify recurring patterns and textures and produce a prediction raster, or heatmap where each pixel indicates the probability that it belongs to a certain class (i.e. rock glacier) or not. By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics. We analysed two distinct catchments, the La Laguna catchment in the Chilean semi-arid Andes and the Poiqu catchment in the central Himalaya. In total, our method mapped 108 of the 120 rock glaciers across both catchments with a mean overestimation of 28%. Individual rock glacier polygons howevercontained false positives that are texturally similar, such as debris-flows, avalanche deposits, or fluvial material causing the user's accuracy to be moderate (63.9–68.9%) even if the producer's accuracy was higher (75.0–75.4%). We repeated our method on very-high-resolution Pléiades satellite imagery and a corresponding DEM (at 2 m resolution) for a subset of the Poiqu catchment to ascertain what difference image resolution makes. We found that working at a higher spatial resolution has little influence on the producer's accuracy (an increase of 1.0%), however the rock glaciers delineated were mapped with a greater user's accuracy (increase by 9.1% to 72.0%). By running all the processing within an object-based environment it was possible to both generate the deep learning heatmap and perform post-processing through image segmentation and object reshaping. Given the difficulties in differentiating rock glaciers using image spectra, deep learning combined with OBIA offers a promising method for automating the process of mapping rock glaciers over regional scales and lead to a reduction in the workload required in creating inventories.Publisher PDFPeer reviewe

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Uncertainties in Digital Elevation Models: Evaluation and Effects on Landform and Soil Type Classification

Digital elevation models (DEMs) are a widely used source for the digital representation of the Earth's surface in a wide range of scientific, industrial and military applications. Since many processes on Earth are influenced by the shape of the relief, a variety of different applications rely on accurate information about the topography. For instance, DEMs are used for the prediction of geohazards, climate modelling, or planning-relevant issues, such as the identification of suitable locations for renewable energies. Nowadays, DEMs can be acquired with a high geometric resolution and over large areas using various remote sensing techniques, such as photogrammetry, RADAR, or laser scanning (LiDAR). However, they are subject to uncertainties and may contain erroneous representations of the terrain. The quality and accuracy of the topographic representation in the DEM is crucial, as the use of an inaccurate dataset can negatively affect further results, such as the underestimation of landslide hazards due to a too flat representation of relief in the elevation model. Therefore, it is important for users to gain more knowledge about the accuracy of a terrain model to better assess the negative consequences of DEM uncertainties on further analysis results of a certain research application. A proper assessment of whether the purchase or acquisition of a highly accurate DEM is necessary or the use of an already existing and freely available DEM is sufficient to achieve accurate results is of great qualitative and economic importance. In this context, the first part of this thesis focuses on extending knowledge about the behaviour and presence of uncertainties in DEMs concerning terrain and land cover. Thus, the first two studies of this dissertation provide a comprehensive vertical accuracy analysis of twelve DEMs acquired from space with spatial resolutions ranging from 5 m to 90 m. The accuracy of these DEMs was investigated in two different regions of the world that are substantially different in terms of relief and land cover. The first study was conducted in the hyperarid Chilean Atacama Desert in northern Chile, with very sparse land cover and high elevation differences. The second case study was conducted in a mid-latitude region, the Rur catchment in the western part of Germany. This area has a predominantly flat to hilly terrain with relatively diverse and dense vegetation and land cover. The DEMs in both studies were evaluated with particular attention to the influence of relief and land cover on vertical accuracy. The change of error due to changing slope and land cover was quantified to determine an average loss of accuracy as a function of slope for each DEM. Additionally, these values were used to derive relief-adjusted error values for different land cover classes. The second part of this dissertation addresses the consequences that different spatial resolutions and accuracies in DEMs have on specific applications. These implications were examined in two exemplary case studies. In a geomorphometric case study, several DEMs were used to classify landforms by different approaches. The results were subsequently compared and the accuracy of the classification results with different DEMs was analysed. The second case study is settled within the field of digital soil mapping. Various soil types were predicted with machine learning algorithms (random forest and artificial neural networks) using numerous relief parameters derived from DEMs of different spatial resolutions. Subsequently, the influence of high and low resolution DEMs with the respectively derived land surface parameters on the prediction results was evaluated. The results on the vertical accuracy show that uncertainties in DEMs can have diverse reasons. Besides the spatial resolution, the acquisition technique and the degree of improvements made to the dataset significantly impact the occurrence of errors in a DEM. Furthermore, the relief and physical objects on the surface play a major role for uncertainties in DEMs. Overall, the results in steeper areas show that the loss of vertical accuracy is two to three times higher for a 90 m DEM than for DEMs of higher spatial resolutions. While very high resolution DEMs of 12 m spatial resolution or higher only lose about 1 m accuracy per 10° increase in slope steepness, 30 m DEMs lose about 2 m on average, and 90 m DEMs lose more than 3 m up to 6 m accuracy. However, the results also show significant differences for DEMs of identical spatial resolution depending on relief and land cover. With regard to different land cover classes, it can be stated that mid-latitude forested and water areas cause uncertainties in DEMs of about 6 m on average. Other tested land cover classes produced minor errors of about 1 – 2 m on average. The results of the second part of this contribution prove that a careful selection of an appropriate DEM is more crucial for certain applications than for others. The choice of different DEMs greatly impacted the landform classification results. Results from medium resolution DEMs (30 m) achieved up to 30 % lower overall accuracies than results from high resolution DEMs with a spatial resolution of 5 m. In contrast to the landform classification results, the predicted soil types in the second case study showed only minor accuracy differences of less than 2 % between the usage of a spatial high resolution DEM (15 m) and a low resolution 90 m DEM. Finally, the results of these two case studies were compared and discussed with other results from the literature in other application areas. A summary and assessment of the current state of knowledge about the impact of a particular chosen terrain model on the results of different applications was made. In summary, the vertical accuracy measures obtained for each DEM are a first attempt to determine individual error values for each DEM that can be interpreted independently of relief and land cover and can be better applied to other regions. This may help users in the future to better estimate the accuracy of a tested DEM in a particular landscape. The consequences of elevation model selection on further results are highly dependent on the topic of the study and the study area's level of detail. The current state of knowledge on the impact of uncertainties in DEMs on various applications could be established. However, the results of this work can be seen as a first step and more work is needed in the future to extend the knowledge of the effects of DEM uncertainties on further topics that have not been investigated to date

Estimating Solar Energy Production in Urban Areas for Electric Vehicles

Cities have a high potential for solar energy from PVs installed on buildings\u27 rooftops. There is an increased demand for solar energy in cities to reduce the negative effect of climate change. The thesis investigates solar energy potential in urban areas. It tries to determine how to detect and identify available rooftop areas, how to calculate suitable ones after excluding the effects of the shade, and the estimated energy generated from PVs. Geographic Information Sciences (GIS) and Remote Sensing (RS) are used in solar city planning. The goal of this research is to assess available and suitable rooftops areas using different GIS and RS techniques for installing PVs and estimating solar energy production for a sample of six compounds in New Cairo, and explore how to map urban areas on the city scale. In this research, the study area is the new Cairo city which has a high potential for harvesting solar energy, buildings in each compound have the same height, which does not cast shade on other buildings affecting PV efficiency. When applying GIS and RS techniques in New Cairo city, it is found that environmental factors - such as bare soil - affect the accuracy of the result, which reached 67% on the city scale. Researching more minor scales, such as compounds, required Very High Resolution (VHR) satellite images with a spatial resolution of up to 0.5 meter. The RS techniques applied in this research included supervised classification, and feature extraction, on Pleiades-1b VHR. On the compound scale, the accuracy assessment for the samples ranged between 74.6% and 96.875%. Estimating the PV energy production requires solar data; which was collected using a weather station and a pyrometer at the American University in Cairo, which is typical of the neighboring compounds in the new Cairo region. It took three years to collect the solar incidence data. The Hay- Devis, Klucher, and Reindl (HDKR) model is then employed to extrapolate the solar radiation measured on horizontal surfaces β =0°, to that on tilted surfaces with inclination angles β =10°, 20°, 30° and 45°. The calculated (with help of GIS and Solar radiation models) net rooftop area available for capturing solar radiation was determined for sample New Cairo compounds . The available rooftop areas were subject to the restriction that all the PVs would be coplanar, none of the PVs would protrude outside the rooftop boundaries, and no shading of PVs would occur at any time of the year; moreover typical other rooftop occupied areas, and actual dimensions of typical roof top PVs were taken into consideration. From those calculations, both the realistic total annual Electrical energy produced by the PVs and their daily monthly energy produced are deduced. The former is relevant if the PVs are tied to a grid, whereas the other is more relevant if it is not; optimization is different for both. Results were extended to estimate the total number of cars that may be driven off PV converted solar radiation per home, for different scenarios

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Assessing the potential of sentinel-1 and sentinel-2 satellite imagery for shoreline classification in support of oil spill preparedness and response

Coastal zones are critical ecosystems that provide important habitat for marine animals, fish, shellfish, birds, and many other species. However, there is a risk of mineral oil impacting in these areas due to human activities offshore. Shoreline classification is the first step to establishing response contingency plans in case of an oil spill. This study estimates the potential of using open-access, high-resolution Sentinel-1 and Sentinel-2 imagery for the mapping of shoreline types in support of oil spill preparedness and response activities. The two classification maps, depicting shoreline and coastal land cover, were produced using an advanced object-based Random Forest (RF) algorithm. Various features extracted from multi-source data, including spectral, texture, ratio, polarimetric features, and digital elevation model (DEM), were analyzed to identify the most valuable features for discrimination between different shoreline types. Multiple classification scenarios with the most useful features were then assessed and compared to find the best classification model. The developed algorithm achieved accuracies of 87.10% and 84.75% of coastal land cover and shoreline maps. These results demonstrated the high potential of using freely available Sentinel-1 and -2 satellite data for coastal mapping

Métodos para estimativa do índice de área foliar em um fragmento de floresta ombrófila mista montana no estado do Paraná

Orientadora : Profª. Drª. Ana Paula Dalla CorteCoorientador : Prof. Dr. Carlos Roberto SanquettaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Engenharia Florestal. Defesa: Curitiba, 19/02/2016Inclui referências : f. 102-122Área de concentração : Manejo florestalResumo: O Índice de Área Foliar (IAF) é considerado como parte da estrutura mais sensível da floresta, pois as folhas do dossel regulam alguns processos fundamentais da produtividade florestal. A modelagem do IAF é considerada crítica e pouco acurada, principalmente no que se refere a florestas nativas. Diante disso, o presente estudo teve como objetivo testar diferentes métodos para estimativa do IAF por meio de detecção remota, em um fragmento de Floresta Ombrófila Mista, em São João do Triunfo, no Estado do Paraná. A área de estudo possui 4 parcelas permanentes, de modo que, foram distribuídas 81 unidades amostrais por parcela e 33 abrangendo a área total. A estimativa do IAF pelo método terrestre foi gerada automaticamente pelo sensor óptico CI-110, considerada como verdade de campo. O IAF estimado pelo método orbital foi gerado a partir de imagens do sensor Pléiades, baseado na relação empírica entre os índices de vegetação. Para o IAF obtido pelo método orbital foram testados os modelos propostos por SEBAL (2002) e Duchemin et al. (2006), e foram analisados os índices de vegetação NDVI, SR, SAVI e EVI. O IAF obtido a campo apresentou uma grande variabilidade, com valores entre 6,01 e 8,01, fator que está relacionado ao grande número de espécies existentes no local. Os índices de vegetação NDVI e SR não foram capazes de apresentar a variabilidade das espécies florestais para a estimativa do IAF, e os índices SAVI e EVI foram os que geraram melhores resultados, possibilitando a estimativa do IAF. O EVI apresentou correlação linear com o IAF obtido a campo variando de 0,54 a 0,77, e o SAVI entre 0,55 e 0,78. O valor médio do IAF gerado pelo modelo de Duchemin et al. (2006) foi bastante homogêneo, onde as parcelas variaram de 4,11 a 4,34, porém, não obteve correlação linear com os dados obtidos a campo. O modelo proposto por SEBAL (2002) apresenta uma maior variação entre as parcelas, exibindo valores entre 2,65 a 3,14, com r variando de 0,60 a 0,72, tendo forte correlação com os dados de IAF obtidos a campo. Logo, foram gerados modelos de regressão para estimativa do IAF baseados nos índices de vegetação SAVI e EVI, de modo que, todas as equações ajustadas foram consideradas satisfatórias, com R² maior do que 0,6 e Syx(%) de 4%. Os modelos gerados apresentaram resultados satisfatórios, o que demonstra a aplicabilidade desta metodologia. Palavras-chave: Floresta com Araucária; Fotografias Hemisféricas; Sensoriamento Remoto.Abstract: The Leaf Area Index (LAI) is considered as part of the most sensitive structure of the forest, because the leaves of the canopy regulate some fundamental processes of the forest productivity. The modeling of LAI is considered critical and little accurate, mainly with regard to native forests. Therefore, the present study aimed to test different methods for estimate of LAI by means of remote sending, in a Subtropical Ombrophilous Forest fragment, in São João do Triunfo, in Paraná state. The area of study has 4 permanent plots, so, 81 sampling units were distributed by plots and 33 covering the total area. The estimate of LAI by terrestrial method was generated automatically by the optical sensor CI-110, considered as truth field. The LAI estimated by orbital method was generated from images of Pléiades sensor, based on the empirical relation among the vegetation indexes. For the LAI obtained by orbital method it was tested the models proposed by SEBAL (2002) and Duchemin et al. (2006), and it was analyzed the indexes of vegetation NDVI, SR, SAVI and EVI. The LAI obtained by field presented a great variability, with values between 6.01 and 8.01, factor that is towed to the great number of existing species in the local. The indexes of vegetation NDVI and SR were not capable of presenting the variability of the forest species for estimate of LAI, and the indexes SAVI and EVI were the ones that generated better results, allowing the estimate of LAI. The EVI presented linear correlation with the LAI obtained by field varying from 0.54 to 0.77, and the SAVI between 0.55 and 0.78. The average value of LAI generated by the model of Duchemin et al. (2006) was fairly homogeneous, where the plots vary from 4.11 to 4.34; however, it was not obtained linear correlation with the data obtained by field. The model proposed by SEBAL (2002) presents a greater variation between the plots, displaying values between 2.65 and 3.14, with r varying from 0.60 to 0.72, and it has strong correlation with the LAI data obtained by field. Thus, it was generated models of regression for estimate of LAI based on the indexes of vegetation SAVI and EVI, in such a way that all the adjusted equations were considered satisfying, with R² greater than 0.6 and Syx of 4%. The generated models presented satisfying results, which demonstrates the applicability of this methodology. Keywords: Araucaria forest; Hemispherical Photographs; Remote Sensing

Caractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale

Very High spatial Resolution (VHR) images like Pléiades imagery (50 cm panchromatic, 2m multispectral) allows a detailed description of forest structure (tree distribution and size) at stand level, by exploiting the spatial relationship between tree structure and image texture when the pixel size is smaller than tree dimensions. This information meets the expected strong need for spatial inventory of forest resources at the stand level and its changes due to forest management, land use or catastrophic events. The aim is twofold : (1) assess the VHR satellite images potential to estimate the main variables of forest structure from the image texture: crown diameter, stem diameter, height, density or tree spacing, (2) on these bases, a pixel-based image classification of forest structure is processed in order to produce the finest possible spatial information. The main developments concern parameter optimization, variable selection, multivariate regression modelling and ensemble-based classification (Random Forests). They are tested and evaluated on the Landes maritime pine forest with three Pléiades images and a Quickbird image acquired under different conditions (season, sun angle, view angle). The method is generic. The robustness of the proposed method to image acquisition parameters is evaluated. Results show that fine variations of texture characteristics related to those of forest structure are clearly identifiable. Performances in terms of forest variable estimation (RMSE): ~1,1m for crown diameter, ~3m for tree height and ~0,9m for tree spacing, as well as forest structure mapping (~82% Overall accuracy for the classification of the five main forest structure classes) are satisfactory from an operational perspective. Their application to multi- annual images will assess their ability to detect and map forest changes such as clear cut, urban sprawl or storm damages.Les images à très haute résolution spatiale (THR) telles que les images Pléiades (50 cm en Panchromatique, 2m en multispectral) rendent possible une description fine de la structure forestière (distribution et dimensions des arbres) à l'échelle du peuplement, en exploitant la relation entre la structure spatiale des arbres et la texture d'image quand la taille du pixel est inférieure à la dimension des arbres. Cette attente répond au besoin d'inventaire spatialisé de la ressource forestière à l'échelle du peuplement et de ses changements dus à la gestion forestière, à l'aménagement du territoire ou aux événements catastrophiques. L'objectif est double: (1) évaluer le potentiel de la texture d'images THR pour estimer les principales variables de structure forestière (diamètre des couronnes, diamètre du tronc, hauteur, densité ou espacement des arbres) à l'échelle du peuplement; (2) sur ces bases, classer les données image, au niveau pixel, par types de structure forestière afin de produire l'information spatialisée la plus fine possible. Les principaux développements portent sur l'automatisation du paramètrage, la sélection de variables, la modélisation par régression multivariable et une approche de classification par classifieurs d'ensemble (Forêts Aléatoires ou Random Forests). Ils sont testés et évalués sur deux sites de la forêt landaise de pin maritime à partir de trois images Pléiades et une Quickbird, acquises dans diverses conditions (saison, position du soleil, angles de visée). La méthodologie proposée est générique. La robustesse aux conditions d'acquisition des images est évaluée. Les résultats montrent que des variations fines de texture caractéristiques de celles de la structure forestière sont bien identifiables. Les performances en terme d'estimation des variables forestières (RMSE) : ~1.1 m pour le diamètre des couronnes, ~3 m pour la hauteur des arbres ou encore ~0.9 m pour leur espacement, ainsi qu'en cartographie des structures forestières (~82 % de taux de bonne classification pour la reconnaissance des 5 classes principales de la structure forestière) sont satisfaisantes d'un point de vue opérationnel. L'application à des images multi-annuelles permettra d'évaluer leur capacité à détecter et cartographier des changements tels que coupe forestière, mitage urbain ou encore dégâts de tempête