فصلنامه سنجش از دور و GIS ایران
سال یازدهم شماره 2 (پیاپی 42، تابستان 1398)

AbstractMetropolises are horizontally drawn to the periphery due to the extreme concentration of power,capital,population,this physical development often occurs unevenly,which causes manyproblems including land use change,natural,socioeconomic transformation,and consequentlyenvironmental problems. Factors such as urban growth,land value,development density areamong the most important factors in the formation of urban sprawl,which transforms the innerstructure of cities,causes the heterogeneous development of urban indicators in periphery,and ittransforms the areas into unplanned spaces. In this research,urban sprawl patterns,analysis ofchanges were carried out during three periods,using GIS,remote sensing,as well as Landsat TMsatellite data,images of 1986,ETM+ 2000,ETM+ 2014. The findings of this researchshow that in the past three years,residential construction has increased dramatically in the region. Thearea of such construction rose from 4.89% in 1986 to 10.3% in 2014. In contrast,the area ofagricultural land decreased from 61.33% in 1986 to 44.7% in 2014. Moreover,scattered structuresand urban sprawl in the studied area were according to discrete sprawl pattern,which has led toinconsistencies in the agricultural sector,rural economy of the area. Furthermore,the expansion ofthe network of roads,highways,electricity networks,non-residential construction haveexacerbated the discontinuous urban sprawl in the study area. On this basis,attention to the integratedland use planning alongside urban,rural integrated planning might be a remedy to reduceagricultural instability,to change the use of valuable agricultural lands,rangeland.

Determination of precipitation in energy balance calculations are vital for hydrological studies and meteorology. Due to the importance of precipitation data in different sciences and the absence of a wide and appropriate rainwater meter, precipitation data needs to be estimated in some way. One of the ways to estimate precipitation is the use of satellite data In this study, GLDAS, CRU, GPCP, TRMM, CMAP and NCEP-NCAR models are evaluated with station data in Alborz, Qazvin, Zanjan, Kurdistan and Hamedan provinces. The results showed that GPCP, TRMM, CMAP and NCEPNCAR had good outcomes in these regions, among which GPCP and TRMM provided better results.In the evaluation of GPCP with mean stations in the study area in pixel 3 in 2003, the explanatory factor (R2), EF coefficients, average error margin (MBE), absolute error of error (MAE) and root mean square error (RMSE) were 0.96, 0.94, 3.13, 5.30, and 6.58, respectively. In addition, precipitation data model of GLDAS is evaluated with station data. Results show that GLDAS model is not very accurate in areas with high precipitation such as Rasht and Noshahr stations.

Drought is an important phenomenon which can be monitored based on weather data obtained from weather stations and remote sensing data. Remote sensing methods have offered significant relative advantages compared to the other methods for monitoring drought . Also , several drought indicators have provided in remote sensing for monitoring drought , but none of the common indicators in remote sensing did not have generalizability of time , climate and altitude and it is necessary the performance quality of these indexes 1) in climates, 2) in altitudinal zoning examined .This study also proved this hypothesis , to identify appropriate indicators in every altitudinal zone , and in every region the index considered the appropriate season to evaluate indexes . In this study , drought indices ,VCI ,VDI ,TCI and TVDI by LST parameter , NDVI and EVI have been evaluated. To evaluate climate and altitudinal indicators , first in the whole country and then in Hamadan province , climate and altitudinal zoning done and drought indexes for different climates and altitude was determined in two forms pixel-based and object-based (polygons) and compared to precipitation data TRMM sensors . The operation of drought indexes were analyzed to drought evaluation by taking account climate type , data acquisition season , altitude and area . The results of this research shows lack of generalizability of all indictors in terms of climate , altitude and time indicators and for example , in pixel evaluating of hot and dry climate , the highest correlation between VCI index and precipitation data was in June and the lowest correlation is in December.

The specific capabilities of satellite data in providing information from the Earth surface materials provide a possibility for producing the geological maps, and in this regard, the spatial and spectral resolutions of the utilized data are two fundamental characteristics in determining the precision and accuracy of the maps. In this research, the data sets of ASTER and Sentinel 2, due to their high spatial and spectral resolutions, were used to enhance the lithological units of the Sureyan complex, northeastern Fars. The metamorphosed sedimentary-volcanic complex of Sureyan is part of the Southern Sanandaj- Sirjan Belt, in Bavanat, Fars province. Investigating the spectral features of field samples, measured at the Shahid Chamran University of Ahvaz, and the spectra extracted from the imageries indicated that the main functional groups responsible for spectral features were Fe2+, Fe3+, OH, CO3, Al-OH, Mg-OH, and Fe-OH. Based on the mineralogical studies, these groups could be attributed to the occurrences of chlorite, muscovite, epidote, amphibole, calcite, and hematite, which were approved by studies of microscopic thin sections. The band ratios (6+8)/7, (7+5)/6, and (6+9)/(7+8) were conducted on 9 reflection bands of ASTER, and the principal components analysis, on 9 reflection bands of ASTER and Sentinel-2. These processing methods were successful in discriminating the chlorite-epidote schist, calk-schist, mica-schist, and the basalt and quartzite dykes as well. Comparing the results of this study to the field observations and the results obtained by laboratory investigations revealed that simultaneous use of ASTER and Sentinel-2 data and the applied processing methods could be successful in discriminating the lithological units of a metamorphic-sedimentary-volcanic complex.

Takab watershed basin is one of the most important basins of Lake Urmia. The basin is quite hilly and mountainous, and the runoff from its snow melting is of substantial significance. Snow accumulation in winter is considered to be crucial in the spring of the following year, and the water from snow melting is especially important for water facilities in a way that it results in serious floods when the snow melts with warm spring rain. Therefore, the prediction of snow melting seems necessary. Furthermore, managing water resource and reservoirs as well as planning of rivers hydrology would not be possible without considering this factor. The SRM snow melt runoff model was used to simulate the flow considering the 83-84 water years. Furthermore, to test the validity of the model, the 84-85 water years was used. Due to the fact that the MODIS images have the appropriate time resolution, such images have been used to estimate the underlying snow area. Results of the study showed that the use of snow cover maps, derived from MODIS images, is useful in predicting the runoff of the basin. The findings also show that the model has the ability to simulate the snowmelt runoff. To evaluate the model, two indexes, namely, the coefficient of determination and volume difference were used which were obtained as 0.75 and 27.84%, respectively. The obtained values indicate that the model has high accuracy in estimating the runoff from snow melting in this basin and represents the applicability of the model to other basins in the region.

Natural and human activities in coastal areas cause dynamic changes in land use and land cover. Rapid population growth in these areas accelerates the process of land use and natural land cover changes, and the transition to residential use and infrastructure development. This research was conducted to investigate and modeling land use changes in Anzali wetland basin between 1975 and 2015 using satellite imagery and predicting possible land use change in 2045 using the LCM model. In order to achieve quantitative and qualitative changes in the study area, the land use maps of the Anzali wetland basin have been produced based on Landsat satellite images for years 1975, 1989, 2000, and 2015. For this purpose, six land use classes including agriculture, rangeland, forest, wetland areas, urban lands, and wetland surface were considered. The accuracy of the land use maps was verified by overall accuracy and kappa coefficients using 323 points based on stratified random sampling and these two parameters were 87% and 0.71, respectively. The LCM model was used to detect and map the changes of different land use categories in the Anzali wetland basin during the periods 1975-1989, 1989-2000, 2000-2015, and predict land use changes in 2045. Analysis of the change detection matrix shows that during the period 1975 to 2015, the total change and transfer of different land uses to each other is 76648.14 hectares. The most changes among different land use during this time are related to the transfer of different land uses to agriculture for 49827.69 hectares, which is equivalent to 65% of the total changes of different land uses. Changing of different land uses to agricultural use is the main change in the uses of this period. forests (64%), rangelands (16%), wetland areas (10%), wetland surface (8%) and residential areas (2 %) have the largest share, respectively. Throughout the study, the expansion of urban land use has always been a positive trend in line with population growth. Based on these changes and by taking 7 independent variable and 8 sub-models, transition potential modeling was done using Artificial Neural Network. The results of modeling in most scenarios showed high accuracy (60.14 to 88.73 percent). To verify modeling accuracy, the standard Kappa coefficient (0.8948) and Null Successes error (77.9%), Hits (3.1%), Misses (15.9%), False Alarms (3.1%) were calculated and accuracy of the position and number of pixels in each class was determined. The ratio of Hits to the total pixels has changed (14.2) indicates that model results are acceptable in predicting land-use changes. Comparison of the results of the changes and conversion of land use classes in the period 2015 to 2045 (predicted) in the region shows that if the land utilization trend continues with current management mode, 10036.26 hectares of forest lands would change to agricultural lands (67.69%), rangeland (32.04 %), urban areas (0.16 %) and wetland surface, and considering the transfer of other uses to forestry, eventually the 9963.36 hectares of forest will be reduced during this period. In general, agriculture, rangeland, and urban areas will increase during this period.

One of the most important problems of researchers in atmospheric and hydrological studies is sometimes the lack of accurate and up-to-date data. In many mountainous and desert areas, synoptic stations and rain gauges are not distributed at the right distance. Therefore, the use of satellite data recorded by rainfall can be a good complement to the lack of this type of data. The purpose of this study is to evaluate the accuracy of satellite data versus observational data. In this study, the daily rainfall data recorded by the GPM sensor with a resolution of 0.1 0.1 in the Midwest of Iran for 691 days in the period from 12/3/2014 to 31/1/2016 has been investigated. For this purpose, the daily precipitation data of 31 synoptic stations were used as observational data, and the statistical indices R, EF, IA, slope, bias, Rmse were used to evaluate the data. The results showed that 74% of the stations had a strong correlation of 0.8-0.5. But based on over-estimated and underestimated indicators, it was found that the GPM sensor has a low precipitation rate of 58% of the stations, Also, for stations in the province of Kermanshah (with the exception of the Kengawer, which has a bias of zero), there is an over estimated rainfall.