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Displaying 261 - 265 of 661Kernel-based extreme learning machine for remote-sensing image classification
This letter evaluates the effectiveness of a new kernel-based extreme learning machine (ELM) algorithm for a land cover classification using both multi- and hyperspectral remote-sensing data. The results are compared with the most widely used algorithms – support vector machines (SVMs). The results are compared in terms of the ease of use (in terms of the number of user-defined parameters), classification accuracy and computation cost.
Reconstruction of the livelihood of resettlers from the Nam Theun 2 hydropower project in Laos
The Nam Theun 2 (NT2) hydropower project displaced 6738 people from 17 villages and 1298 households. This research focuses on four resettlement villages. Household interviews were conducted to learn more about variations in living conditions, traditions and culture in the villages that were relocated independently compared to villages in which relocation had merged older villages together. The case study suggests that most resettlers wanted to remain exclusively with their own village members.
Modelling the potential distribution of Mesalina watsonana (Stoliczka, 1872) (Reptilia: Lacertidae) on the Iranian Plateau
The Persian Long-tailed Desert Lizard, Mesalina watsonana , is one of the most common and most widely distributed lizards on the Iranian Plateau extending from Iran to Pakistan and Afghanistan. The species is frequently encountered in various types of habitats. We collected over 600 distributional records from available literature, museum collections, and our own field work and used bioclimatic and land cover characteristics to develop a model of potential distribution for M. watsonana . According to the model, the most important factors limiting the distribution of M.
efficient method for change detection of soil, vegetation and water in the Northern Gulf of Mexico wetland ecosystem
Mapping and monitoring wetland ecosystems over large geographic areas based on remote sensing is challenging because of the spatial and spectral complexities of the inherent ecosystem dynamics. The main objective of this research was to develop and evaluate a new method for detecting and quantifying wetland changes in the Northern Gulf of Mexico (NGOM) region using multitemporal, multispectral, and multisensor remotely sensed data.
Monitoring of agricultural soil degradation by remote-sensing methods: a review
Agricultural land degradation is a global problem that severely hampers the production of food needed to sustain the growing world population. Mapping of soil degradation by remote sensing is instrumental for understanding the spatial extent and rate of this problem. Methods aimed at detecting soil loss, soil drying, and soil-quality deterioration have been demonstrated in numerous studies utilizing passive and active remote sensors.