Search

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural and repeatable phenomenon definable at specified time and area. In addition, social and economic issues can be affected by drought. Information such as intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, therefore lessen the risks associated with drought episodes. Lack of long-term meteorological data for many parts of the country is one of the most important problems for drought monitoring in Iran. One of the useful ways for gathering information about soil and vegetation conditions is using satellite-based imagery. In this study, remotely sensed image data were applied in order to forecast and model the drought. To this end, SPI (standardized precipitation index) drought indicator was used to represent the drought and its intensity in different time spans (1, 3, 6, 9, 12, and 24 months). Some vegetation indices (VIs) including normalized difference vegetation index, temperature condition index, vegetation condition index, and normalized difference vegetation index deviation were extracted using Advanced Very High Resolution Radiometer sensor imagery. These indices were plugged into the model to calculate the SPI. A unique Support Vector Machine classifier improved for all types of the SPI by applying various remotely sensed VIs. The best vegetation index for each kind of SPI was determined. In this framework, meteorological stations were clustered based on their land cover extracted from satellite-based indices before insertion to the model. [ABSTRACT FROM PUBLISHER]

A unified image fusion approach suitable for all employed imaging sources becomes necessary, with the development of space-borne imaging sensors. In this work, two issues were tested to improve the spectral quality of fused images and also investigating class-based fusion results. First, a Generalized Intensity-Hue-Saturation (GIHS), Brovey Transform (BT) and SFIM approach was implemented. This kind of algorithm has shown computational advantages among other fusion methods like wavelet and can be extended to different number of bands as in literature discussed. The used IHS-BT-SFIM algorithm incorporates IHS, IHS-BT, BT, BT-SFIM and SFIM methods by two adjustable parameters. Second, a method was proposed to add edge information in previous GIHS_BT_SFIM and edge enhancement by panchromatic image. Fused image was not improved significantly by panchromatic data addition. Third, anEdge Adaptive GIHS_BT_SFIMwas proposed to enforce fidelity away from the edges. Using MS image off edges has shown spectral improvement in some fusion methods. Fourth, a class-based fusion was tested, which tests different coefficients for each method due to its class. The best parameters for vegetated areas werek1 = 0.6,k2 = 0.8, and for urban region, they werek1 = 0.4,k2 = 0.4. Results might be applicable in generalisation of various fusion methods. [ABSTRACT FROM AUTHOR]