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Displaying 626 - 630 of 661Land use and land cover changes over a century (1914–2007) in the Neyyar River Basin, Kerala: a remote sensing and GIS approach
Land use and land cover change, perhaps the most significant anthropogenic disturbance to the environment, mainly due to rapid urbanization/industrialization and large scale agricultural activities. In this paper, an attempt has been made to appraise land use/land cover changes over a century (1914–2007) in the Neyyar River Basin (L=56 km; Area = 483.4 km²) in southern Kerala – a biodiversity hot spot in Peninsular India.
Water Right Prices in the Rio Grande: Analysis and Policy Implications
Climate change, water supply limits, growing environmental values of water and worldwide population growth continue to raise the scarcity of water. These challenges have intensified the transfer of water from farms to cities. Water right transfers are an important international institution to stretch water supplies. In North America's Rio Grande Basin water right transfers are an especially important institution for meeting the growth in urban demands.
Earthquake-induced landslide mapping in the western Himalayas using medium resolution ASTER imagery
On 8 October 2005, a devastating earthquake struck northern Pakistan and several parts of Pakistani- and Indian-controlled Kashmir. The severely hit areas lie in close proximity to the most tectonically active region of the western Himalayas. The earthquake destroyed close to 400 000 houses and over 75 000 people lost their lives. The intensity of the earthquake was such that it triggered widespread landslides, which caused considerable destruction of the area's forests, and blocked the mountain roads and rivers.
Impact of reference datasets and autocorrelation on classification accuracy
Reference data and accuracy assessments via error matrices build the foundation for measuring success of classifications. An error matrix is often based on the traditional holdout method that utilizes only one training/test dataset. If the training/test dataset does not fully represent the variability in a population, accuracy may be over – or under – estimated. Furthermore, reference data may be flawed by spatial errors or autocorrelation that may lead to overoptimistic results.
Spatio-temporal errors in land–cover change analysis: implications for accuracy assessment
This research examined the spatial and temporal patterns of error in time-series classified maps as a first step to creating a model to propagate error in post-classification change analysis. Two Landsat images were acquired for Pittsfield Township, MI, USA, classified, and overlaid to produce a map of change. Error variables were created for the classified maps. Hypotheses were proposed describing the spatial and temporal structures of error in the classified maps, and evaluated using geostatistics and point pattern analysis.