Image data sharpening is a widely used method to increase a spatial resolution of images with a higher spectral and lower spatial resolution. In our study we focused on sharpening ASTER image data using a high spatial-resolution panchromatic band of WorldView-2 data. Both datasets were acquired within the framework of a geological mapping project in the southwest Mongolia. Primary remote sensing task was to produce mineral maps for the studied area. ASTER data providing several bands in the short wave infrared (SWIR) spectral region has a great potential for geological/mineral mapping. On the other hand, a spatial resolution is rather coarse for the geological mapping at a 1: 50, 000 scale. ENVI and Erdas Imagine software (SW) were used to test the available Principle Component Analysis sharpening algorithm; however satisfactory spectral mapping results have not been achieved. In the commercial SW, the first component (PC) is used for the sharpening process by default, but the 1st PC usually does not contain the main spectral variability considering mineralogy/geology. Therefore, a new approach using the other principal components for image sharpening was tested and compared with the approach available in the commercial SW. New processing was programmed in ENVI/IDL.

Agricultural productivity patterns are affected by growing season weather risks such as rainfall deficit (meteorological drought), which can develop into persistent soil moisture deficit (agricultural drought), leading to crop yield shortfall or wholesale losses. Weather index-based insurance (WII) is a financial instrument designed to assist smallholder farmers in coping with the impacts of drought through payouts when an index threshold is breached.

Critical for operational WII schemes is the skill of capturing the progression of meterological drought (rainfall deficit) to agricultural drought (crop failure), which is not fully represented in statistical correlations of historical data on rainfall deficit and crop yield. This is a non-trivial task. At the core of designing WII products is the requirement for high-quality data over the long-term and in near-real time to provide spatially explicit and internally consistent information on rainfall. Crop yield data with matching space-time characteristics are virtually non-existent and are prone to errors. Moreover, crops yield shortfalls or losses can result from factors other than rainfall such as soil properties and temperature, as well as evaporation and soil moisture dynamics, as crops differ in their sensitivities to the dynamics of water and energy fluxes during the different phonological stages within the growing season. As these factors are key in determining crop production outcomes, we evaluate new space-time volatility indicators based on land surface and crop process modelling using cotton grown in Zambia as a case study.

The results from our analysis of cotton production in Zambia suggest that combining rainfall and soil moisture information can inform WII applications at both the design and implementation stages. This is achieved through relating area-specific probabilities of rainfall deficit occurrence and severity to crop-specific water requirements and their sensitivities to agricultural drought. We discuss how this approach can be used to inform operational WII applications by capturing the physical dynamics of agro-meteorological risk along the trajectory of meteorological to agricultural drought.

With the development of quantitative remote sensing, regional evapotranspiration modeling based on the feature space has made substantial progresses. Among many remote sensing evapotranspiration models, accurate determination of the theoretical dry/wet lines remains a challenging task. This paper reports the development of a new method, named DDTI (Determination of dry line by Thermal Inertia), which determines the theoretical dry line based on the relationship between thermal inertia and soil moisture. The Simplified Thermal Inertia value estimated in the North China Plain is consistent with the value computed in the laboratory. Two evaluation methods, which are based on the comparison of the location of theoretical dry line and the comparison of Evaporative fraction, were used to assess the performance of the new method DDTI. The location of the theoretical dry line determined by DDTI is higher than the heat energy balance method, which is more reasonable in wet conditions. When compared with the in situ measurement of Evaporative fraction at YuCheng Experimental Station, the ET model based on DDTI reproduces the pixel scale EF with a RMSE(Root Mean Square Error) of 0.071, which is much lower than that based on heat energy balance method with a RMSE of 0.65. Also, the bias between in situ measurements and DDTI method is 0.056，which is ower than that between in situ and the heat energy balance method with a bias of 0.645.

In Chittagong city, landslide phenomena is the most burning issue which causes great problems to the life and properties and it is increasing day by day and becoming one of the main problems of city life. On 11 June 2007, a massive landslide happened in Chittagong City Corporation (CCC) area, a large number of foothill settlements and slums were demolished; more than 90 people died and huge resource destruction took place. It is therefore essential to analyze the landslide susceptibility for CCC area to prepare mitigation strategies as well as assessing the impacts of climate change. To assess community susceptibility of landslide hazard, a landslide susceptibility index map has been prepared using analytical hierarchy process (AHP) model based on geographic information system (GIS) and remote sensing (RS) and its susceptibility is analyzed through community vulnerability assessment tool (CVAT). The major findings of the research are 27% of total CCC area which is susceptible to landslide hazard and whereas 6.5 sq.km areas are found very highly susceptible. The landslide susceptible areas of CCC have also been analyzed in respect of physical, social, economic, environmental and critical facilities and it is found that the overall CCC area is highly susceptible to landslide hazard. So the findings of the research can be utilized to prioritize risk mitigation investments, measures to strengthen the emergency preparedness and response mechanisms for reducing the losses and damages due to future landslide events.

Although in the field of Underwater Acoustic Imaging a large number Automatic Target Detection (ATD) systems have long been developed and their bibliography has been classified, the present study aims to test the potentiality of using common and widely used feature based image classification approaches against small targets detection in SideScan Sonar (SSS) images by combining them with powerful data mining techniques. Conventional Acoustic Classification Systems (ACS) start by extracting numerous texture descriptors from distinct image neighbourhoods throughout the image and forming large Feature Vectors (FVs). In view of the FVs’ high dimensionality, prior to unsupervised classification, a component analysis technique, usually Principal Component Analysis (PCA), is performed to decompose them into a few un-correlated features that explain the majority of the image’s variance. However, small targets belong to subordinary image information and do not contribute significantly to the total information variance of the SSS image. Furthermore targets tend to be independent image characteristics rather than un-correlated ones. In this study, a newly available technique, called Independent Component Analysis (ICA), that decomposes the FVs into independent sources, is tested against its ability to separate SSS images into targets and background and lead to accurate target classification.

The proposed methodological scheme consists of the following stages: 1) windowed feature extraction, 2) ICA  decomposition, 3) selection of certain components that enhance potential targets through a maximum curtosis criterion, 4) decision of the  number of classes that the selected components need to be clustered into so that they are optimally separated in the Euclidean space through validation indices utilization, 5) unsupervised classification and 6) selection of the class or classes that most possibly correspond to areas containing potential targets via a minimum area definition. The above stages are included in the SonarClass Matlab ACS. The classification precision of the proposed system was assessed using a SSS dataset from Igoumenitsa Harbour, Greece, including more than 85 ground truthed man made targets. The classification accuracy of the proposed system was estimated as Pc=tp/(tp+fp), where tp is the number of true positive (expected) and fp the number of false positive (unexpected) predictions, and was compared to the accuracy of following conventional ACS procedures. The method exhibited unquestionable superiority indicating that ICA may worth further attention by ATD system researchers and developers.