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In this paper we propose a method for characterizing UHIs by using a non-parametric model. Landsat 5 and 7 images obtained over the city of Indianapolis were used for analysis of UHI over space and time. The images were subjected to kernel convolution implementing Gaussian bi-variate function for non-parametric characterization of the UHI. The convoluted images were then analyzed, for pattern over space and over time. It was found that the spread of the UHI in Indianapolis increased from 13.90 km to 21.63 km in the west-east direction and from 10.98 km to 15.90 km in the north-south direction from 1985 to 2000. The changes in the UHI from 1995 to 2000 were evident in all cardinal directions. The model aided in successfully characterizing UHI in terms of its location, spread, and the rate of increase, facilitating a clearer image of UHI through space and time. [ABSTRACT FROM AUTHOR]

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10-100 t ha-1 for woodlands, 1-100 t ha-1 for shrub sites, and 0.5-10 t ha-1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r 2 = 0.72 when using a one-step approach (i.e. using all points) and 0.78 when using a two-step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two-step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two-step approach over the one-step approach, however, is not significant for the validation dataset, suggesting that the one-step approach is as good as the two-step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canada's north have been incorporated into Canada's 2006 UNFCCC report. [ABSTRACT FROM AUTHOR]

Understanding spatial patterns is a critical and under-explored aspect of remote sensing. This paper describes how multifractal theory can be applied to characterize these heterogeneous patterns in remotely sensed data as well as to determine the operational scale. An example based on the characterization of ulexite distribution on the world's largest salt flat (10 000 km²), located in Bolivia, using a binarized Landsat Thematic Mapper (TM) 4/7 ratio image, is used to describe the step-by-step procedure. Distribution was well characterized by the multifractal parameters and expressed through the f-α, τ-q and D-q relationships. Moments from (q=-2 to 5 showed a linear trend in scales from approximately 0.007 to 10000km². This implies that the attribute analysed could be measured at different scales, within defined boundaries, and up- and down-scaled using the multifractal parameters. In addition, the asymmetry shown by the f-α a spectrum indicates the presence of clusters with high probability of finding ulexite. and large areas where the mineral might be found in small patches. The areas with a high probability of finding ulexite were mapped to guide any future field survey. Using the maximum entropy concept, the operational scale to determine the mineral was obtained at 1062 m. [ABSTRACT FROM AUTHOR]

The temperature–vegetation index method (TVX method, also called contextual method) for the area-wide mapping of instantaneous air temperature is adopted for use with Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The method requires multispectral data consisting of bands in the red, near-infrared and thermal spectral regions, and no additional data. The approach is complemented with an iterative filtering routine for eliminating outliers and an interpolation algorithm for filling data gaps. The adopted method is applied to a multi-temporal dataset of nine ETM+ scenes, covering large parts of north-eastern Germany including the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site. Thus, for the first time the TVX method is applied to fine spatial resolution data and a central European region. The satellite-derived air temperatures (60 m spatial resolution) are compared with in situ measurements, showing an average error of about 3 K (root mean square, RMS), whereas the mean error in land surface temperature (LST) estimation is about 2 K. The results compare well with the in situ values throughout all seasons. The accuracy of about 3 K is in line with previously reported results for the TVX method (employing medium spatial resolution data) as well as for physically based approaches (ecosystem- or energy-balance models). Only remote sensing models incorporating in situ air temperature (as training data for neural networks or in multiple regression analysis) are reported to perform better in terms of RMS deviations. In the past, overestimation of air temperature by the TVX method was repeatedly observed. It is shown that the remote sensing approach tends to under- or overestimate the in situ air temperatures, depending on the in situ measurement heights. In conjunction with the attempt to assign the satellite-derived air temperature to a certain height above ground, the possibility of a simple correction for reference height is investigated. Over- and underestimations larger than 2 K seem to reflect existing differences in temperature rather than calculation errors. Furthermore, the dependence of the derived air temperature spatial pattern on different moving window sizes is shown. Possible sources of errors and limitations of the approach are discussed in detail. [ABSTRACT FROM AUTHOR]

In recent years, the areas damaged by the East Asia migratory locust plagues (Locusta migratoria manilensis) have enlarged geographically in China. Some outbreaks in parts of the Dagang region, in the east coast of China, have caused drastic reductions in the yield of agricultural reeds and have created a severe threat to local and regional agriculture. Currently, the monitoring system for this species depends on field observations conducted by sparsely distributed observing stations established in the 1950s. This observation network was. in the past, successfully operated to identify major locust outbreaks. However, due lo the expansion of infestation areas and the increase of locust density, spatial information technology was needed to establish a satellite sensor-based monitoring system for the East Asia migratory locust plagues. This Letter outlines the preliminary experimental work conducted in 2001 for a feasibility study in establishing a sensor-based locust monitoring system. In this experiment, sample statistics of biomass and leaf area index (LAI) from the affected and non-affected areas by locusts were calculated, and the spectra of field reed samples from both areas were also evaluated together with synchronous Landsat Enhanced Thematic Mapper (ETM+) multi-spectral data. The study showed that satellite sensors may be used operationally to monitor locust damage. [ABSTRACT FROM AUTHOR]