فصلنامه سنجش از دور و GIS ایران
سال دوم شماره 2 (پیاپی 6، تابستان 1389)

Precipitation rate and amount measurements are among the flood warning methods which have been suggested by remote sensing in recent years. Cloud type identification and classification, as basic principles of precipitation estimation methods, are usually performed using visual interpretation of satellite images. In these studies only cloud brightness temperature and albedo are used for cloud classification, while texture and shape of clouds are effective properties in cloud type detection as well. Textures and shapes of clouds are ignored in pixel base classifications. So object-oriented classification technique is a suitable approach. In this technique, in addition to cloud brightnesstemperature and albedo, textures and shapes are the major parameters. Object-orient classification method, despite its benefits, depends on segmentation accuracy. The accuracy of segmentation is scale dependent too. Therefore, optimum segmentation scale is resulting to higher accuracy of object oriented classification. In this study two NOAA/AVHRR images in two consecutive cloudy days in August 2005 are used. In the first step, additional information included brightness temperature of band 3 and 4 and cloud height produced from NOAA/AVHRR images that used in image segmentation;and bi-spectral method has been employed for training region selection. Then the negative impacts of under-segmentation errors on the potential accuracy of object-based classification were quantified by developing a new segmentation accuracy measure. In this step, scale evaluation was performed with quantifying overall effect relative to features and units in 31 scales of segmentation.The results based on a NOAA/AVHRR satellite images were the same and indicate that: (1): cloud segmentation accuracies decrease with increasing segmentation scales; and (2) the negative impacts of under-segmentation errors in cloud segmentation become significantly large at large scales. Hence, the finest scale for cloud segmentation has been defined 50 as in this scale the overall accuracy of classification was 90.5% in cloud object oriented classification.

The trend towards concentration of people in urban areas in developing countries has caused rapid urban expansions. This situation necessitates proper management and planning to avoid profound negative environmental and socioeconomic impacts. Urban planners require temporal and spatial information related to the pattern and extent of the urban growth for better understanding of its process and effects and to then set effective urban management and planning policies. Integration of Geographic Information Systems (GIS) and Remote Sensing (RS) technologies can provide efficient tools for collecting and analyzing the needed spatiotemporal informations. Tehran, the capital of Iran, has witnessed rapid growth since the last two decades. In this research, after investigating and modeling urban expansion in the last two decades, a model has been develop to understand the growth dynamics of this metropolis and to predict its expansion for the next two decades as a base to set urban management policies. In this research, a CA-Markov based urban growth simulation and predicting model has been applied and calibrated with historical data derived from a time series of Landsat satellite imagery captured at 1988, 2000 and 2006. Base on the land use/cover maps obtained from classification of satellite images, urban area has been increased 11% (about 56 km2) during 1988-2006. In order to simulate the urban growth in 2015 and 2025, the CA-Markov model has been utilized; and the result indicates that from 2006 to 2025, the urban areas would increase 3% (about 15 km2). The results of this research represent efficiency of integrated CA-Markov model in monitoring the urban growth process and its pattern in the last years and to predict urban growth for the future.Also, applying a multi-criteria decision making method to characterize urban growth and development parameters is another point of this research.

Landslides are natural disasters that can damage human lives and various properties annually.Unplanned development expansions of cities results in locating inhabited area with high risk of landslides. Consequently, it is essential to generate landslide susceptibility maps for city expansions.The objective of this paper is to propose a method for discovering knowledge (fuzzy membership functions and fuzzy rules) that can be used for predicting landslide locations in Mazandaran province.Landslide phenomenon is a spatial one, and there are numerous sources of uncertainty in spatial data.Therefore, there is a need to integrate a Fuzzy Rule Based Inference System (FRBIS) into a Geographic Information System (GIS) for mapping such events. The first step towards forming such a system is clustering which is exercised by Fuzzy C means algorithm. To determine the optimum number of clusters, two Cluster Validity Indexes (CVI) and three criteria- namely; accuracy, completeness, and consistency-are proposed and then used. By projecting clusters onto perpendicular axes, fuzzy membership functions and fuzzy rules are obtained. At the end, the system is improved by adding experts’ knowledge. The accuracy of the landslide susceptibility map is estimated over 80%. Also, by examining the histograms of the landslide susceptibility maps, it is inferred that 13.5% of the region are presumably faced with high risk of landslides. The results indicated that the landslide susceptibility intensity has the most dependency with three factor maps; lithology, distance to roads and landuse. This is done by calculating the relations between the attained landslide susceptibility zonation and the factor maps.

In order to reduce and deal with the mitigation effects of earthquakes, the reliable and up-to-date information is required. In this paper, the historical earthquakes of Iran, greater than 5 Richter between years 1950 and 2010 were statistically evaluated. It was found that there is no significant trend in the earthquakes’ magnitudes along the North-South and East-West directions. Moreover, the spatial autocorrelation analyses using the distance-based indices (average distance to nearest neighbor and K (d)) revealed that the earthquakes follow the clustering pattern. However variogram, General G and Global Moran’s I indices indicated that earthquakes magnitudes are not autocorrelated.Furthermore, the statistical analyses of the maximum and mean values of the earthquakes’ magnitudes within a one-kilometer pixel illustrated an insignificant autocorrelation. The verification of the chronological behavior of the earthquakes using the cumulative histogram of them based on time and the graph of the largest earthquake’s magnitude annually, indicated that large earthquakes are continual problems. Finally, the cumulative frequency of earthquakes based on the distance to thefault revealed that 90% of the historical earthquakes have occurred within less than 40 km of faults. The obtained results can be used for investigation and extraction of earthquake prone areas, seismic zoning, verification of earthquakes trend and prediction of the future earthquakes.

Remote sensing technology has recently been used in various categories, and is considered as an efficient method in lithological mapping. The ASTER data, in this regard, have been vastly used in mineral and rock enhancement. The objective of this research iscomparing the spectral angle mapping and spectral feature fitting algorithms in enhancing the Neyriz ophiolite lithological units based on the calibrated SWIR and TIR data of ASTER. The Neyriz ophiolite (53° 52' 30"–54° 14' 05" E, and 29° 15' 26" – 29° 40' 22" N) is one of the several large Tethyan ophiolites in a 3000 km abduction belt that was thrust over the edge of the Arabian continent during the Late Cretaceous (Alavi, 1994). Two geological maps-both at scale of 1:100,000- were compiled and published by the Geological Survey of Iran (1994, 1996) for the study area. A generalized geological map and the field photographs of the main lithological units are shown in figures 1 and 2. These evidences were used for comparing the output images to the field criteria. The geological maps were also applied as references for accuracy assessment of output results. Rock units of the study area occur at four geological zones, including: 1)Sanandaj-Sirjan; 2) Tertiary flysch; 3) Ophiolitic zone; and 4) Zone of Pichakan radiolarite, from NE to SW. A total of 50 collected samples were analyzed spectrally in the laboratory of Bowling Green State University, USA, using Analytical Spectral Device (ASD)with spectral range of 0.4–2.5 μm, and Fourier Transform Infrared spectrometry (FTIR) with spectral range of 6-16μm. The high resolution spectra obtained from these instruments were then resample to the ASTER 9 VNIR-SWIR (figure 3) and 5 TIR (figure 4) bands of ASTER in order to determining the diagnostic absorption features of each rock unit being used as an input to surface lithology mapping in SAM and SFF algorithms. As described by Hunt and Salisbury (1970), Burns (1970), Hunt et al. (1974), Adams, (1974), Hunt and Ashley (1979), Hunt and Evarts (1980), King and Ridley (1987), Vander Meer et al. (1997), Vincent (1997) both the fresh and weathered surfaces of igneous rocks show strong absorptions in the visible-near infrared region of the spectrum due to the presence of iron. Serpentines peridotites have multiple absorption bands near 1.4 μm and 2.3 μm, with supplementary broader and weaker features near 1.95 μm and 2.1 μm. These features can be attributed to vibration overtone and combination tones involving OH-stretching modes. Gabbros display broad absorptions typical of ferrous ion, centered near 1.28 and 1.85μm. Diabases show strong features near 2.3μm that could be attributed to Mg-OH vibration in epidotic. Absorption features of the radiolarian charts are dominant near 0.48, 0.9, 2.2and 2.45μm. Because of combination and overtone bands of the CO3 fundamentals occurring in marbles, they display absorption bands near1.87, 1.99, 2.15 and 2.33 μm. A cloud-free day-time ASTER level 1B scene, acquired on 8th of September 2003 and subsets corresponding to the Neyriz ophiolite zone were extracted from them. The Atmospheric and Topographic Correction (ATCOR) andReference Channel (available in ENVI 4.4) models were carried out on the VNIR-SWIR and TIR datasets, respectively. The ophiolite rock units were mapped by using the Spectral Angle Mapping (SAM) and Spectral Feature Fitting (SFF) techniques implemented on thecalibrated datasets using field samples spectra (figure 5 and 6). The output results were validated by the use of field observations and geological map evidences as well as using a confusion matrix and Kappa Coefficient. The overall accuracy and Kappa Coefficientsobtained from SFF and SAM algorithms, based on calibrated SWIR data, are 0.88, 90% and 0.76, 80%, respectively. Comparing the results of these algorithms with geological map and field observations and the results obtained by confusion matrix showed that because of the continuum removal and the resulting normalized spectral features, spectral feature fitting has more accuracy in enhancing lithological units based on the SWIR dataset. This algorithm could enhance lithological unit’s harzburgite-lherzolite, gabbros, marble harzburgite-dunite, database and radiolarite without enhancing the surrounding exposures. However, exposures such as lake and alluvial sediments, screed and agricultural lands were co-enhanced with these rock units while using the SAM algorithm. Results also showed that the spectral features of rock units exposed at the area are dominantly located at the shortwave infrared (SWIR) region, so this dataset could enhance lithological units better than TIR.

Reducing losses in disasters, whether natural or unnatural, requires a suitable management. Geospatial Information System (GIS) with the ability to collect, store, update and retrieve information, could help to make suitable decisions and consequently a good relief management. In this hitech period, the possibility of using new tools in the designing and implementation of such systems has been obtained.Within the framework of this article, a conceptual model for the relief procedure is suggested. Radio Frequency Identification (RFID) with an ability for collecting, maintaining and updating information of injured people, plays major role in developing such system. In this article, the authors describe how to use RFID through triage to improv emergency medical services. As the main contribution, they design injured transporting model by transforming the problem from injured people transportation into an optimization problem. Performance of the model is shown with a simulated problem.

Reducing losses in disasters, whether natural or unnatural, requires a suitable management. Geospatial Information System (GIS) with the ability to collect, store, update and retrieve information, could help to make suitable decisions and consequently a good relief management. In this hitech period, the possibility of using new tools in the designing and implementation of such systems has been obtained.Within the framework of this article, a conceptual model for the relief procedure is suggested. Radio Frequency Identification (RFID) with ability for collecting, maintaining and updating information of injured people, plays major role in developing such system. In this article, the authors describe how to use RFID through triage to improve emergency medical services. As the main contribution, they design injured transporting model by transforming the problem from injured people transportation into an optimization problem. Performance of the model is shown with a simulated problem.