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Displaying 216 - 220 of 661Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images
Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected.
Spatial–temporal patterns of urban anthropogenic heat discharge in Fuzhou, China, observed from sensible heat flux using Landsat TM/ETM+ data
The urban heat island (UHI) effect is the phenomenon of increased surface temperatures in urban environments compared to their surroundings. It is linked to decreased vegetation cover, high proportions of artificial impervious surfaces, and high proportions of anthropogenic heat discharge. We evaluated the surface heat balance to clarify the contribution of anthropogenic heat discharges into the urban thermal environment.
Feature extraction for high-resolution imagery based on human visual perception
Feature extraction is highly important for classification of remote-sensing (RS) images. However, extraction of comprehensive spatial features from high-resolution imagery is still challenging, leading to many misclassifications in various applications. To address the problem, a shape-adaptive neighbourhood (SAN) technique is presented based on human visual perception. The SAN technique is an adaptive feature-extraction method that not only considers spectral feature information but also the spatial neighbourhood as well as the shape of features.
Robust prediction of time-integrated NDVI
The time-integrated normalized difference vegetation index (iNDVI) provides key remote-sensing-derived information on the interactions between vegetation growth, climatic and soil conditions, and land use. Using a time-series of Landsat imagery obtained for Queensland, Australia, it has been demonstrated how robust geostatistics can be used to predict iNDVI. This approach is novel because it explicitly quantifies the uncertainty of prediction and uses Winsorizing, a data-censoring method, to minimize the distorting effects of outliers.
novel application of satellite radar data: measuring carbon sequestration and detecting degradation in a community forestry project in Mozambique
Background: It is essential that systems for measuring changes in carbon stocks for Reducing Emissions from Deforestation and Forest Degradation (REDD) projects are accurate, reliable and low cost.