Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Identificação de fendilhamento de pavimentos e propriedades volumétricas das misturas betuminosas utilizando um Georadar

Dissertação de mestrado integrado em Engenharia Civil

Deteção remota na avaliação das alterações da cobertura do solo no Parque Nacional da Peneda-Gerês

Dissertação de mestrado integrado em Engenharia Civil

Da utilização e análise de dados de Geofísica em contextos arqueológicos: o Castro de Sapelos (Boticas) e o Seminário Conciliar de São Pedro e São Paulo (Braga)

Dissertação de mestrado em Arqueologia

Prospecção de ocorrências filonianas do tipo LCT nas formações metassedimentares entre Boticas e Montalegre (N de Portugal)

Dissertação de mestrado em Geologia (área de especialização em Valorização de Recursos Geológicos)

Evaluación de la degradación del suelo en agroecosistemas del centro-sur de Córdoba mediante indicadores e índices. Assessment of soil degradation in agroecosystems of central-southern Córdoba by means of indicators and indices

Las actividades agropecuarias ejercen diferentes presiones sobre los recursos naturales. Esto ha llevado, en algunas áreas, a un deterioro del suelo que provoca un impacto sobre la sustentabilidad en los sistemas agropecuarios. Para evaluar la degradación del suelo se han propuesto listas de indicadores, sin embargo, se carece de una herramienta metodológica robusta, adaptada a las condiciones edafoclimáticas regionales. Además, existe una demanda de productores e instituciones interesados en orientar acciones para preservar el suelo. El objetivo de este proyecto es evaluar la degradación física, química y biológica de los suelos en agroecosistemas del centro-sur de Córdoba. Por ello se propone desarrollar una herramienta metodológica que consiste en un set de indicadores físicos, químicos y biológicos, con valores umbrales, integrados en índices de degradación, que asistan a los agentes tomadores de decisiones y productores, en la toma de decisiones respecto de la degradación del suelo. El área de trabajo será una región agrícola del centro-sur de Córdoba con más de 100 años de agricultura. La metodología comienza con la caracterización del uso del territorio y sistemas de manejo, su clasificación y la obtención de mapas base de usos y manejos, mediante sensores remotos y encuestas. Se seleccionarán sitios de muestreo mediante una metodología semi-dirigida usando un SIG, asegurando un mínimo de un punto de muestreo por unidad de mapeo. Se elegirán sitios de referencia lo más cercano a una condición natural. Los indicadores a evaluar surgen de listas propuestas en trabajos previos del grupo, seleccionados en base a criterios internacionales y a adecuados a suelos de la región. Se usarán indicadores núcleo y complementarios. Para la obtención de umbrales, se usarán por un lado valores provenientes de la bibliografía y por otro, umbrales generados a partir de la distribución estadística del indicador en suelos de referencia. Para estandarizar cada indicador se definirá una función de transformación. Luego serán ponderarán mediante análisis estadísticos mulivariados e integrados en índices de degradación física, química y biológica, y un índice general de degradación. El abordaje concluirá con el desarrollo de dos instrumentos para la toma de decisiones: uno a escala regional, que consistirá en mapas de degradación en base a unidades cartográficas ambientales, de uso del territorio y de sistemas de manejo y otro a escala predial que informará sobre la degradación del suelo de un lote en particular, en comparación con suelos de referencia. Los actores interesados contarán con herramientas robustas para la toma de decisiones respecto de la degradación del suelo tanto a escala regional como local. Agricultural activities exert different pressures on natural resources. In some areas this has led to soil degradation and has an impact on agricultural sustainability. To assess soil degradation a robust methodological tool, adapted to regional soil and climatic conditions, is lacking. In addition, there is a demand from farmers and institutions interested in direct actions to preserve the soil. The objective of this project is to assess physical, chemical and biological soil degradation in agroecosystems of Córdoba. We propose to develop a tool that consists of a set of physical, chemical and biological indicators, with threshold values, integrated in soil degradation indices. The study area is a region with more than 100 years of agriculture. The methodology begins with the characterization of land use and management systems and the obtaining of base maps by means of remote sensing and survey. Sampling sites will be selected through a semi-directed methodology using GIS, ensuring at least one sampling point by mapping unit. Reference sites will be chosen as close to a natural condition. The proposed indicators emerge from previous works of the group, selected based on international standards and appropriate for the local soils. To obtain the thresholds, we will use, by one side, values from the literature, and by the other, values generated from the statistical distribution of the indicator in the reference soils. To standardize indicators transformation functions will be defined. Indicators will be weighted by mans of multivariate analysis and integrated in soil degradation indices. The approach concluded with the development of two instruments for decision making: a regional scale one, consisting in degradation maps based on environmental, land use and management systems mapping units; and an instrument at a plot level which will report on soil degradation of a particular plot compared to reference soils.

Controlled resolution reconstruction of one-dimensional permittivity profiles

Electromagnetic scattering inverse problems, microwave imaging, reconstruction of dielectric media, remote sensing, tomography

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Combining classification techniques to define topo-climatic landscapes

Landscape classification tackles issues related to the representation and analysis of continuous and variable ecological data. In this study, a methodology is created in order to define topo-climatic landscapes (TCL) in the north-west of Catalonia (north-east of the Iberian Peninsula). TCLs relate the ecological behaviour of a landscape in terms of topography, physiognomy and climate, which compound the main drivers of an ecosystem. Selected variables are derived from different sources such as remote sensing and climatic atlas. The proposed methodology combines unsupervised interative cluster classification with a supervised fuzzy classification. As a result, 28 TCLs have been found for the study area which may be differentiated in terms of vegetation physiognomy and vegetation altitudinal range type. Furthermore a hierarchy among TCLs is set, enabling the merging of clusters and allowing for changes of scale. Through the topo-climatic landscape map, managers may identify patches with similar environmental conditions and asses at the same time the uncertainty involved.

Two different 3D biogeochemical models for the NW mediterranean sea: validation with Meris data and intercomparison

Report for the scientific sojourn carried out at the University of New South Wales from February to June the 2007. Two different biogeochemical models are coupled to a three dimensional configuration of the Princeton Ocean Model (POM) for the Northwestern Mediterranean Sea (Ahumada and Cruzado, 2007). The first biogeochemical model (BLANES) is the three-dimensional version of the model described by Bahamon and Cruzado (2003) and computes the nitrogen fluxes through six compartments using semi-empirical descriptions of biological processes. The second biogeochemical model (BIOMEC) is the biomechanical NPZD model described in Baird et al. (2004), which uses a combination of physiological and physical descriptions to quantify the rates of planktonic interactions. Physical descriptions include, for example, the diffusion of nutrients to phytoplankton cells and the encounter rate of predators and prey. The link between physical and biogeochemical processes in both models is expressed by the advection-diffusion of the non-conservative tracers. The similarities in the mathematical formulation of the biogeochemical processes in the two models are exploited to determine the parameter set for the biomechanical model that best fits the parameter set used in the first model. Three years of integration have been carried out for each model to reach the so called perpetual year run for biogeochemical conditions. Outputs from both models are averaged monthly and then compared to remote sensing images obtained from sensor MERIS for chlorophyll.

Testing low-altitude infrared digital photography from a mini-UAV to retrieve information for biological conservation

Satellite remote sensing imagery is used for forestry, conservation and environmental applications, but insufficient spatial resolution, and, in particular, unavailability of images at the precise timing required for a given application, often prevent achieving a fully operational stage. Airborne remote sensing has the advantage of custom-tuned sensors, resolution and timing, but its price prevents using it as a routine technique for the mentioned fields. Some Unmanned Aerial Vehicles might provide a “third way” solution as low-cost techniques for acquiring remotely sensed information, under close control of the end-user, albeit at the expense of lower quality instrumentation and instability. This report evaluates a light remote sensing system based on a remotely-controlled mini-UAV (ATMOS-3) equipped with a color infra-red camera (VEGCAM-1) designed and operated by CATUAV. We conducted a testing mission over a Mediterranean landscape dominated by an evergreen woodland of Aleppo pine (Pinus halepensis) and (Holm) oak (Quercus ilex) in the Montseny National Park (Catalonia, NE Spain). We took advantage of state-of-the-art ortho-rectified digital aerial imagery (acquired by the Institut Cartogràfic de Catalunya over the area during the previous year) and used it as quality reference. In particular, we paid attention to: 1) Operationality of flight and image acquisition according to a previously defined plan; 2) Radiometric and geometric quality of the images; and 3) Operational use of the images in the context of applications. We conclude that the system has achieved an operational stage regarding flight activities, although with meteorological limits set by wind speed and turbulence. Appropriate landing areas can be sometimes limiting also, but the system is able to land on small and relatively rough terrains such as patches of grassland or short matorral, and we have operated the UAV as far as 7 km from the control unit. Radiometric quality is sufficient for interactive analysis, but probably insufficient for automated processing. A forthcoming camera is supposed to greatly improve radiometric quality and consistency. Conventional GPS positioning through time synchronization provides coarse orientation of the images, with no roll information.

Application of a roughness-length representation to parameterize energy loss in 3-D numerical simulations of large rivers

Recent technological advances in remote sensing have enabled investigation of the morphodynamics and hydrodynamics of large rivers. However, measuring topography and flow in these very large rivers is time consuming and thus often constrains the spatial resolution and reach-length scales that can be monitored. Similar constraints exist for computational fluid dynamics (CFD) studies of large rivers, requiring maximization of mesh-or grid-cell dimensions and implying a reduction in the representation of bedform-roughness elements that are of the order of a model grid cell or less, even if they are represented in available topographic data. These ``subgrid'' elements must be parameterized, and this paper applies and considers the impact of roughness-length treatments that include the effect of bed roughness due to ``unmeasured'' topography. CFD predictions were found to be sensitive to the roughness-length specification. Model optimization was based on acoustic Doppler current profiler measurements and estimates of the water surface slope for a variety of roughness lengths. This proved difficult as the metrics used to assess optimal model performance diverged due to the effects of large bedforms that are not well parameterized in roughness-length treatments. However, the general spatial flow patterns are effectively predicted by the model. Changes in roughness length were shown to have a major impact upon flow routing at the channel scale. The results also indicate an absence of secondary flow circulation cells in the reached studied, and suggest simpler two-dimensional models may have great utility in the investigation of flow within large rivers. Citation: Sandbach, S. D. et al. (2012), Application of a roughness-length representation to parameterize energy loss in 3-D numerical simulations of large rivers, Water Resour. Res., 48, W12501, doi: 10.1029/2011WR011284.

Can Remotely Sensed Meteorological Data Significantly Contribute to Reduce Costs of Tsetse Surveys?

A 0.125 degree raster or grid-based Geographic Information System with data on tsetse, trypanosomosis, animal production, agriculture and land use has recently been developed in Togo. This paper addresses the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. A discriminant analysis model is tested using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration and Meteosat platforms. A split sample technique is adopted where a randomly selected part of the field measured data (training set) serves to predict the other part (predicted set). The obtained results are then compared with field measured data per corresponding grid-square. Depending on the size of the training set the percentage of concording predictions varies from 80 to 95 for distribution figures and from 63 to 74 for abundance. These results confirm the potential of satellite data application and multivariate analysis for the prediction, not only of the tsetse distribution, but more importantly of their abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen or substitute one another and thus reduce costs of field surveys.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.