Multitemporal analysis of satellite imagery: implementation of a land evolution model in ALCS (ATSR LAND CLASSIFICATION SYSTEM)

In this report it was designed an innovative satellite-based monitoring approach applied on the Iraqi Marshlands to survey the extent and distribution of marshland re-flooding and assess the development of wetland vegetation cover. The study, conducted in collaboration with MEEO Srl , makes use of images collected from the sensor (A)ATSR onboard ESA ENVISAT Satellite to collect data at multi-temporal scales and an analysis was adopted to observe the evolution of marshland re-flooding. The methodology uses a multi-temporal pixel-based approach based on classification maps produced by the classification tool SOIL MAPPER ®. The catalogue of the classification maps is available as web service through the Service Support Environment Portal (SSE, supported by ESA). The inundation of the Iraqi marshlands, which has been continuous since April 2003, is characterized by a high degree of variability, ad-hoc interventions and uncertainty. Given the security constraints and vastness of the Iraqi marshlands, as well as cost-effectiveness considerations, satellite remote sensing was the only viable tool to observe the changes taking place on a continuous basis. The proposed system (ALCS – AATSR LAND CLASSIFICATION SYSTEM) avoids the direct use of the (A)ATSR images and foresees the application of LULCC evolution models directly to „stock‟ of classified maps. This approach is made possible by the availability of a 13 year classified image database, conceived and implemented in the CARD project (http://earth.esa.int/rtd/Projects/#CARD).The approach here presented evolves toward an innovative, efficient and fast method to exploit the potentiality of multi-temporal LULCC analysis of (A)ATSR images. The two main objectives of this work are both linked to a sort of assessment: the first is to assessing the ability of modeling with the web-application ALCS using image-based AATSR classified with SOIL MAPPER ® and the second is to evaluate the magnitude, the character and the extension of wetland rehabilitation.

Land classification ....

Mimeographed.

Land classification and utilization tour, New York state college of agriculture, Ithaca, New York ... Connecticut Hill ....

Mimeographed.

A preliminary report on developments in land classification methods /

Mode of access: Internet.

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

Using a cartographic modeling language to manipulate spectral satellite imagery

Land related information about the Earth's surface is commonIJ found in two forms: (1) map infornlation and (2) satellite image da ta. Satellite imagery provides a good visual picture of what is on the ground but complex image processing is required to interpret features in an image scene. Increasingly, methods are being sought to integrate the knowledge embodied in mop information into the interpretation task, or, alternatively, to bypass interpretation and perform biophysical modeling directly on derived data sources. A cartographic modeling language, as a generic map analysis package, is suggested as a means to integrate geographical knowledge and imagery in a process-oriented view of the Earth. Specialized cartographic models may be developed by users, which incorporate mapping information in performing land classification. In addition, a cartographic modeling language may be enhanced with operators suited to processing remotely sensed imagery. We demonstrate the usefulness of a cartographic modeling language for pre-processing satellite imagery, and define two nerv cartographic operators that evaluate image neighborhoods as post-processing operations to interpret thematic map values. The language and operators are demonstrated with an example image classification task.

Autoecología de los hayedos catalanes

Este estudio está basado en el muestreo de campo y posterior análisis de 24 parcelas de hayedo seleccionadas mediante una estratificación de su área de distribución basada en la clasificación CLATERES de la Ecorregión Catalano-Aragonesa. En cada parcela se han evaluado 3 parámetros fisiográficos, 15 climáticos y 18 edáficos, a partir de los cuales se han establecido sus valores paramétricos centrales y marginales que permiten definir los hábitats fisiográfico, climático y edáfico de las masas de Fagus sylvatica L. en Cataluña. Los hayedos catalanes se presentan sobre substratos litológicos muy diversos (plutonitas, vulcanitas, metamorfitas y sedimentitas, tanto ácidas como básicas), con texturas predominantes francas, franco-arenosas o franco-limosas. Los suelos, según FAO, son mayoritariamente cambisoles. A pesar de que la capacidad de retención de agua de sus suelos es escasa, la sequía fisiológica es reducida. Los humus predominantemente pertenecen a los tipos mull forestal y mull cálcico. Además, se presentan una serie de parámetros selvícolas ( Densidad de pies y densidad de chirpiales, Area basimétrica, Altura Total dominante, Índices de Hart-Becking, Índice de Calidad de Estación y Edad de la masa) que al correlacionarlos con los ecológicos nos ha permitido comprobar que los mejores hayedos se encuentran en las localizaciones más térmicas, en las que incluso se podría producir sequía fisiológica si no fuera por que existen suficientes precipitaciones estivales.

Geotecnologias aplicadas à estimativa de perdas de solo por erosão hídrica na sub-bacia do Ribeirão das Perobas, Município de Santa Cruz do Rio Pardo-SP

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

El territorio en el sistema agroalimentario: el tramo medio del valle del Duero 1900-2015

La lectura histórica del territorio en relación con el sistema agroalimentario aporta elementos claves para reconstruir el sistema territorial, aprovechando la oportunidad que ofrece un renovado interés por la alimentación local y sostenible. El análisis histórico transdisciplinar incorpora variables espaciales, económicas, energéticas, urbanísticas, agronómicas y nutricionales y se centra en el tramo medio del valle del Duero (Castilla y León, España). Se trata de un territorio tradicionalmente agrícola, donde un producto de la tierra -el vino- es motor de innovación y ha transformado paisajes y estructuras. Aún así, se enfrenta a un desarrollo desigual e ilustra las contradicciones del mundo rural en un contexto alimentario globalizado. El análisis de la región desde 1900 permite constatar la relación entre la organización del territorio, el sistema agroalimentario, y cada una de las etapas nutricionales: a) la superación de la desnutrición está asociada a una agricultura familiar y al territorio de proximidad, que persiste en la zona hasta 1950; b) el modelo de consumo de masas y sobrealimentación, se basa en una agricultura industrializada y un territorio polarizado ligado al desarrollismo, que se extiende hasta 1985; c) finalmente, el modelo de consumo segmentado se apoya en una agricultura terciarizada y un territorio de enclaves en un contexto de globalización, que dura hasta nuestros días. En la última fase aparecen nuevos modelos alternativos de reconstrucción territorial con sistemas emergentes que reconectan campo y ciudad, consumo y producción desde sistemas de alimentación sostenible. Conviven dos tendencias: una hacia la jerarquización y el productivismo tecnificado y otra hacia la multifuncionalidad y la recampesinización que se reapropia de las innovaciones técnicas. La adaptación a las condiciones locales y aprovechar los recursos endógenos son elementos clave de sostenibilidad ambiental y social. Incorporar la alimentación en la planificación urbana y territorial desde una perspectiva agroecológica reduciría la insostenibilidad del sistema alimentario. Las propuestas de ordenación han de tener en cuenta la tipología de municipios, sus interrelaciones, las características agrológicas y productivas, la relación del muncipcon los núcleos de referencia y con las poblaciones que concentran las necesidades de alimentación. Se debe considerar asimismo la disponiblidad de infraestructuras, de equipamientos y de capital humano y relacional para fijar cadena de valor local. La ordenación urbanística cuenta ya con mecanismos como la clasificación del suelo, la regulación de usos y el diseño de redes de equipamientos que inciden sobre la autonomía de los sistema de alimentación locales y permiten fomentar la biodiversidad y las variedades locales. Son mecanismos insuficientemente aprovechados. Una adecuada utilización de los instrumentos de ordenación existentes, junto con el desarrollo de otros nuevos mejorarían de forma significativa la resiliencia de los sistemas agroalimentarios locales. ABSTRACT The historical review of the relationship between territory and agrifood system provides key lessons to help rebuild the territorial fabric, seizing the opportunity offered by a renewed interest in local and sustainable food. The historical transdisciplinary analysis spans spatial, economic, energy, agronomic and nutritional variables, focuses on the middle reaches of the Douro valley (Castilla y Leon, Spain). This a traditionally agricultural region, which has managed to turn a land product – the wine– into an engine of innovation which has transformed landscapes and structures. Even so, it faces the challenges of uneven development and illustrates the contradictions of the rural world in a globalized context. After the analysis of the evolution of the region since 1900, it can be concluded that the territory has been organized over time according to three models of food system that are in turn linked to different nutritional stages: a) the nutritional stage of overcoming malnutrition is related to family agriculture and a territory of proximity, which persists in the studied area until 1950; b) the model of mass consumption and overeating, was built on an industrialized agriculture and a polarized territory with unhindered development, which runs until 1985; c) and, finally, the model of consumer segmentation associated with terciarized agriculture and enclave territories in the context of globalization, which lasts until present time. During this last stage new alternative models of small-scale territorial reconstruction appear, linked to emerging systems that, based on sustainable food systems, reconnect city and countryside, consumption and production. Actually two trends coexist: one towards hierarchisation and tech-based productivism, and another one towards multifunctionality and peasantization that reappropriates technical innovations. The adaptation to local conditions taking advantage of local resources is a key element of environmental and social sustainability. Integrating food into urban and regional planning from an agroecological perspective would help reduce the current unsustainability of the food system. Planning proposals for municipalities need to consider their typology, agrological characteristics, productive capacity, links to other municipalities, proximity to reference nodes and population concentrations with food demands that need to be met. Availability of infrastructure, facilities, as well as human and relational capital to establish and reinforce local value chains is another aspect to consider in planning proposals. Spatial and urban planning are already equipped with mechanisms, such as land classification and the design of facilities’ networks, that affect the autonomy and stability of local food systems and can support biodiversity and adoption of local varieties. We are, however, missing opportunities. An adequate use of existing planning tools and the development of new ones could significantly improve the resilience of local agrifood systems.

Using GIS and Remote Sensing Technologies to Identify Environmental Variables of Malaria Vector Breeding Sites in Western Kenya

This study used Landsat 8 satellite imagery to identify environmental variables of households with malaria vector breeding sites in a malaria endemic rural district in Western Kenya. Understanding the influence of environmental variables on the distribution of malaria has been critical in the strengthening of malaria control programs. Using remote sensing and GIS technologies, this study performed a land classification, NDVI, Tasseled Cap Wetness Index, and derived land surface temperature values of the study area and examined the significance of each variable in predicting the probability of a household with a mosquito breeding site with and without larvae. The findings of this study revealed that households with any potential breeding sites were characterized by higher moisture, higher vegetation density (NDVI) and in urban areas or roads. The results of this study also confirmed that land surface temperature was significant in explaining the presence of active mosquito breeding sites (P< 0.000). The present study showed that freely available Landsat 8 imagery has limited use in deriving environmental characteristics of malaria vector habitats at the scale of the Bungoma East District in Western Kenya.

The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems

Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic.

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.

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.