فصلنامه سنجش از دور و GIS ایران
سال ششم شماره 2 (پیاپی 22، تابستان 1393)

In this study, a method for replacing MODIS measured flux densities using CRTM is introduced. For this, the Radiosonde measured temperature profiles in Bandar-abbass synoptic station along with night time flux densities measure by MODIS sensor on board of Aqua platform for the deep water region in the Persian Gulf were used. Then, using standard predictors of OPTRAN version VIII which is the main part of CRTM model, it was tried to model the difference between modeled and MODIS measured radiance values. To evaluate the method, the averaged RMSE were used. The RMSE between CRTM calculated and MODIS measured radiation fluxes was found to be 0.47 . This value was improved to 0.39 using modified CRTM. The equivalent brightness temperature for these fluxes was 6.45 and 5.27 (K) respectively. So using the suggested method in this study, the CRTM calculated radiances fairly approaches the MODIS measured values. It is suggested that this method be used whenever there are high noises, cloud overcast and or any possible malfunctioning of MODIS sensor to replace the missing data.

The purpose of presenting this paper is to determine the snow covered area on Karaj and Latyan basins using MODIS Images, and evaluating Salomonson et al method which is applied in this study. The importance of snow cover_ Such as its impact on radiation budget, water balance and modeling_ has led to several researches. In this research, MODIS data for snow cover mapping and LISSIII_IRS image for accuracy assessment have been used. Up to now, various methods have been applied to compute pixels’ snow fraction. We have used Salomonson etal. method. The method has showed proper accuracy in global scale and doesn’t need priori knowledge of surface characteristics. Also, to increase accuracy, the coefficients of the Salomonson et al. model were modified using regional data. And the results were evaluated in a new region.Accuracy assessment results showed that Salomonson et al. method can calculate snow fraction of MODIS pixels with RMSE of 0.20 pixels. Furthermore, Kappa coefficients and overall accuracy of Salomonson et al. method were 0.84 and 92.12 respectively suggesting proper accuracy of the method. Local accuracy assessment showed that in Iran, river margins with low density tree cover and sparsely scattered orchards, this method has got more errors; therefore, it is important to exclude thisarea. Moreover, it is recommended to use proper masks which allow the narrow rivers to be removed. RMSE of the modified model was 0.258 while, RMSE of Salomonson et al. model was 0.266 at the same area. So, the results showed that modifying the coefficient could improve the result slightly.

In this research, a model for simulating of residential segregation pattern is presented via integration of GIS with agent-based models, and is implemented on real data of an area in north-west of Tehran. For this purpose, at first effective parameters regarding segregation in Tehran that is a mixture of social-economical factors and environment was determined and amount of their effects with AHP method was set. Considering the raster-based approach in modeling, maps related to these parameters was produced in ArcGIS medium. By programming in Netlogo software the mentioned model was developed .In this model, with a primary assumption, people of society were classified in 4 socio-economic groups with specific attributes and behaviors which by their own decisions- micro level actions- affect the segregation pattern- effects in macro level-. The pattern of urban segregation of area was simulated for a period of 20 years between 1986 and 2006. The model was calibrated by changing the important parameters of Schelling model, neighborhood radius and frequency of the same type neighborhoods, and some other parameters as size of pixels, price of parcels, rate of population increase etc. Validation of the proposed model has been done by counting the correct estimated pixels of total pixels. Respectively 62.5 % verifies the accuracy of the models.

Evaluation of snow storage is of high importance in water balance studies and optimum operation of water resources in arid and semi-arid regions like Iran. Particularly in the river basins nearby Zagrous Mountains where surface water flows mainly consist of spring runoffs, stochastic forecasting of the snow storage at the end of the year is necessary. In this study stochastic forecasting of snow in river basins of the Karkheh, Dez, Karun and some parts of the Marun was investigated using the first order Markov Chain model. Snow cover data retrieved from NOAA-AVHRR satellite images between 1989 and 2004 were applied as inputs to the model. Two possible states were defined for each snow cover map including existence (1) and non-existence (0) of snow. Through applying the Markov Chain process, snow cover maps of the study area were predicted for March 2000 to 2004. Results show that stochastic forecasts of snow cover properly consist with satellite derived maximum snow cover maps.So that, not only the area of snow covered lands was successfully estimated, but also the exact location of the snow or dry covers was appropriately predicted in more than 80% of the pixels. The performance of the model was assessed using contingency tables and three measures including: Probability of Detection, False Alarm Ratio and Critical Success Index. Results reveal the promising capability of the first order Markov Chain model to forecast snow covered area.

Morphology analysis which concentrates on spatial relations analysis between neighborhood pixels provides a better image processing compared to analyses which are only based on spectral signature of a single pixel. The proposed method in this paper integrates spectral and spatial information produced from morphology analysis to improve the final result of hyper spectral image classification. For this reason at first, primary components are extracted using limited training samples. Extended morphological profiles are then produced by applying morphological analysis on each extracted features. Afterwards, Final components are extracted by applying a supervised feature selections on a datasets composed of both the spectral and the extended morphological features. The extracted features are introduced into the Support Vector Machine (SVM) algorithm. The final results are then archived by implementing a majority filter as a post-processing step. The proposed method is implemented on aerial hyper spectral images of Rosis sensor taken from urban and semi-urban areas from. The obtained results proved the efficiency of the proposed method where classification accuracies are improved from 98.86 and 82.70 in conventional method to 99.36 and 95.75 in urban and semi-urban areas respectively.

The increasing concentration of greenhouse gases has been identified as a main cause of increase of global mean temperatures since the mid-20th century. The effect of human-induced climate change could be unprecedented and far-reaching. Carbon sequestration into trees and forests is an effective and inexpensive way for mitigating the CO2 level in the atmosphere. Hence, accurate measurement of biomass will be of great importance to global carbon cycle and climate change. This study performed a wavelet-based forest aboveground biomass estimation approach in a temperate deciduous forest, Kheyroud Kenar forest in north part of Iran. Wavelet analysis, specifically two-dimensional discrete wavelet transform (DWT) was applied to ALOS PALSAR images to obtain wavelet coefficients (WCs), which were correlated with forest inventory data using multiple linear regression analysis to investigate the relationship. The results indicate that Db wavelet coefficients correlate better with field biomass data than other parameters. For the first level of the decomposition, the correlation coefficient is 0.5 while for second level, the overall R value increased up to 0.75. This study demonstrates that wavelet-based biomass estimation could be a very promising approach for providing better biomass estimation; however, further research is needed for identifying robust wavelet coefficients and optimizing procedures.

Markov chain is a model that use for prediction future conditions based on the rates of past change. The method is based on probability that a given piece of land will change from one cover to another. These probabilities are generated from past changes and then applied to predict future change. The main objective of this paper is to evaluate the validation of Markov model in vegetation cover crown percentage change simulation. In this study, TM images sensor for 1989, 1998 and LISS III image sensor for 2006 from Landsat and IRS satellite from Mouthe wild life refuge were used to provide maps of vegetation cover percentage. In order to prediction of vegetation cover in 2006, Markov chain model were used by vegetation map of 1989 and 1998, during 9 years. Validation of all maps was evaluated. Finally the map for 2006 that had provided by image was used to evaluate the map from Markov model. Kappa for this map was 53 %. Post classification was used to investigate the area with high accurate prediction and error in prediction. Results have shown because of high decrease in vegetation cover in non-core zone and increase vegetation cover in core zone this model couldn’t predict by high accuracy. But this model is valuable in small scale, in order to general view of future.