The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in mountainous areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) and ASTER data in Zagros mountains of Iran for 1990, 1998, and 2006. Land-use/cover mapping is achieved thro...

The main aim of this research is to find optimum segmentation parameters for extracting different land cover classes. A relatively new segmentation approach, multiresolution segmentation, is being examined using two data sets (Landsat and IRS). Keywordsmultiresolution segmentation; object-based classification; land-cover classification

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types, the ability to discriminate different land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmo...

Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data is a reliable and consistent source of information on land cover dynamics from the 1970s. This study examines land cover change...

Land cover and vegetation classification systems are generally designed for ecological or land use applications that are independent of remote sensing considerations. As a result, the classes of interest are often poorly separable in the feature space provided by remotely sensed data. In many cases, ancillary data sources can provide useful information to help distinguish between inseparable cl...

Land cover characterization at a regional scale using spaceborne multi-angle remote sensing data is in its infancy. A data mining technique was employed to evaluate the degree to which the accuracy of land cover classification can be increased using multi-spectral, multi-temporal and multi-angle remote sensing data. The study area is around the Jornada Rangeland in New Mexico, USA with shrublan...

The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sens...

The paper studies the applicability of various data mining techniques on aerial remote sensing imagery for automatic land-cover classification. Four techniques are applied, namely the Adaptive Dynamic K-means (ADK), Self Organizing Feature Map (SOFM), Machine Learning Induction Algorithm (C4.5) and Support Vector Machines (SVM). Special attention is drawn to the usefulness of these data mining ...

Very High Resolution (VHR) satellite images offer a great potential for the extraction of landuse and land-cover related information for urban areas. The available techniques are diverse and need to be further examined before operational use is possible. In this paper we applied two pixel-by-pixel classification techniques and the object-oriented image analysis approach (eCognition) for a land-...

Preparation of land cover / land use maps for large areas, based on automatic classification of high-resolution satellite data is the objective of many application programmes, e.g. GSE Land Monitoring Services. The crucial point for this kind of activity is to apply optimal classification approach, which will ensure high class recognition accuracy and classification repeatability. Among differe...