Full Hierarchic Versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data

Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84

Historical Land use/Land cover classification and its change detection mapping using Different Remotely Sensed Data from LANDSAT (MSS, TM and ETM+) and Terra (ASTER) sensors: a case study of the Euphrates River Basin in Syria with focus on agricultural irrigation projects

This thesis deals spatially and regionally with the natural boundaries of the Euphrates River Basin (ERB) in Syria. Scientifically, the research covers the application of remote sensing science (optical remote sensing: LANDSAT-MSS, TM, and ETM+; and TERRA: ASTER); and methodologically, in Land Use/Land Cover (LULC) classification and mapping, automatically and/or semi-automatically; in LULC-change detection; and finally in the mapping of historical irrigation and agricultural projects for the extraction of differing crop types and the estimation of their areas. With regard to time, the work is based on the years 1975, 1987, 2005 and 2007. Initially, preprocessing of the satellite data (geometric- and radiometric- processing, image enhancement, best bands composite selection, transformation, mosaicing and finally subsetting) was carried out. Then, the Land Use/Land Cover Classification System (LCCS) of the Food and Agriculture Organization (FAO) was chosen. The following steps were followed in LULC- classification and change detection mapping: visual interpretation in addition to digital image processing techniques; pixel-based classification methods; unsupervised classification: ISODATA-method; and supervised classification and multistage supervised approaches using the algorithms: Maximum Likelihood Classifier (MLC), Neural Network classifier (NN) and Support Vector Machines (SVM). These were trialed on a test area to determine the optimized classification approach/algorithm for application on the whole study area (ERB) based on the available imagery. Pre- and post- classification change detection methods (comparison approaches) were used to detect changes in land use/land cover-classes (for the years 1975, 1987 and 2007) in the study area. The remote sensing methods show a high potential in mapping historical and present land use/land cover classes and its changes over time. Significant results are also possible for agricultural crop classification in relatively large regional areas (the ERB in Syria is almost 50,335 km²). Change trends in the study area and period was characterized by land-intensive agricultural expansion. The rapid, more labor- and capital- intensive growth in the agricultural sector was enabled by the introduction of fertilizer, improved access to rural roads and markets, and the expansion of the government irrigation projects. Irrigated areas increased 148 % in the past 32 years from 249,681 ha in 1975 to 596,612 ha in 2007

Aplicación de Sistemas de Información Geográfica y Teledetección en Exploración Minera

La exploración minera moderna se apoya tanto en métodos para medir variables de interés prospectivo, como en técnicas de procesamiento e interpretación de datos experimentales. La teledetección, entre los primeros, y los sistemas de información geográfica(SIG), entre las segundas,desempeñan en la actualidad un papel creciente en este y otros campos de las ciencias de la tierra, ya que aportan ventajas que pueden redundar en la reducción de los costes de producción. Las técnicas de teledetección pueden permitir la detección y caracterización de zonas de interés metalogenético facilitando la selección de objetivos de exploración. Por otro lado, los SIG permiten integrar y analizar espacialmente grandes volúmenes de datos de distintas fuentes de manera conjunta, rápida y economía. En este trabajo se desarrolla un metodología integrada para la cartograf¿¿ia de potencialidad minera. Esta metodología integra técnicas de teledetección, de análisis espacial, tanto estadístico como no estadístico, y de modelización espacial con SIG. La investigación se realiza en un área de estudio situada en los distritos mineros de Rodalquilar y San José, al suroeste de la Peninsula Ibérica, dentro de la provincia de Almería. En la zona se lozalizan numerosas explotaciones abandonadas de depósitos epitermales de oro y sulfuros polimetálicos. Esta circunstancia, junto con su clima semiárido y escasez de vegetación, la convierten en un área propicia para la realización de estudios de tratamiento, integración y optimización de información y de teledetección. El análisis de imágenes de teledetección se enfocó hacia la identificación y caracterización de zonas de potencial interés minero. El tratamiento realizano sobre imágenes Landsat TM y SPOT HRV permitió derivar diferentes cartografías geológicas y de alteraciones. Este tratamiento incluyó todas las etapas clásicas de un estudio de teledetección.Tesis Univ. Granada