فصلنامه سنجش از دور و GIS ایران
سال ششم شماره 3 (پیاپی 23، پاییز 1393)

Among the usual interpolation methods, kriging and co-kriging are frequently used in the interpolation of precipitation data as one the best linear unbiased estimators, Despite these advantages, there models show smoothness representation and because they are based on regional averages of the data, they predict maximum and minimum values lower and higher than real values respectively. Therefore, using these models alone is not sufficient in cases where the target is assessment of risk and study of variability. Variability of phenomenon could be measured by uncertainty index. In the study in order to calculation of local and spatial uncertainty of precipitation, geostatistical simulation algorithms CO-SGS and SGS were used. The main result of the study showed that, in simulation sample SGS and CO-SGS algorithms would be able generate the Max and Min probable value making variance as close as to the main data. The difference simulation variance is very low with main samples, in contrast, the difference of variance between main samples and interpolation method is very high. The result also showed that the mentioned algorithms could be able to compute the local and spatial uncertainty of the precipitation by different simulation.

Employing recent technological advances in surveying and mapping soil salinity is a step forward in controlling saline soils. The aim of this study was to map the topsoil salinity, the depth of 0-5 cm, using different methods within the environmental context of the area around Tashk & Bakhtegan Lake, with the area of 8062 ha, that in this region soil salinity appears to be a major threat to agriculture production. We used three different methods to produce soil salinity map and then compared the results with the soil salinity data that were measured on the ground. A set of 143 soil salinity sample, electrical conductivity of the water extracted from saturated past (ECe), was systematically sampled on a 750-m grid and was used to assess two mapping methods; regression models (RM) and ordinary kriging (OK). As a third method, supervised classification (Scl) of LISS-III sensor satellite images was employed. We used linear, power and exponential regression models for estimating of salinity values. In these regression models, digital numbers of the satellite images were set as independent variables and ECe values as dependent variable. In order to provide a prediction map of the soil, the salinity data were interpolated using the ordinary kriging method. In case of the satellite images, we classified the training pixels with maximum likelihood algorithm and then the land cover map was prepared. Our results revealed that regression models could not appropriately predict the salinity values and the vegetation indexes had poor correlation with the topsoil salinity values. The salinity percentages obtained from OK and Scl were nearly similar where the salinity was high (≥16dS/m), but differed in other salinity classes. Therefore, in the supervised classification of LISS-III sensor data the bare soil surfaces with high salinity (≥16dS/m) were successfully identified and separated from the rest of the soils. The regression model estimated 100 % of the study area as saline soil. The kriging method predicted 87.6 % of the area to be classified as saline soils (> 4dS/m), while supervised classification predicted that to be 62.5 %. Each of these methods has constrains. Therefore, we recommend the integration of these methods for estimating of soil salinity.

Geothermal energy serves as a renewable and clean energy. Thanks to its great advantages such as relatively harmless, low costs and environmental friendly, it may be a good substitute for fossil fuels. In the present study, a geothermal survey is conducted in an area prone geothermal Ferdows of South Khorasan province in eastern Iran using ETM data Landsat 7 geo-referenced to topography map in scale 1:50000 of Ferdows city. Pixel number of thermal bands was converted to spectral radiance and then radiance temperature was measured. NDVI index was calculated from the visible bands in and near infra-red bands and subsequently radiance potential layer obtained. Earth surface temperature was determined by integrating both reflective and radiative temperatures. The method of least squares fitting, was used to produce layered zones of iron oxides and clay minerals and regions faults was extracted from map in scale 1:100.000. Through integration of produced layers using weighted overlapping method, geothermal prone area in Ferdows city was recognized. The potential geothermal of Ferdows in east part of Iran were evaluated and identified with the key factors associated with the formation of geothermal resources. Synthesizing the information layers, prone areas in order to geothermal energy utilizing were recognized. Hence, two resources of geothermal energy within the area were identified, which is spatially correlated with geothermal evidences such as hot spring and two inactive volcanoes. Based on the outcomes of this research, the remote sensing approaches are cost effective for determining surface temperature anomalies and area geological features such as alterations and rock units’ identification. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

There exist a number of factors that affect the quality laser scanner. In other words, the accuracy of a terrestrial scanner is limited extensively by systematic errors and thus must be calibrated. Indeed, calibration is a prerequisite for obtaining 3D precise and reliable data from point clouds. Until now, several models have been proposed to improve the accuracy of laser scanner data, most of which include both physical empirical parameters which are produced by observing point residuals, As a result, these models are just usable solely for those observations. The authors of have previously developed a new general parametric model based on the internal structure of laser scanner which can be used for a variety of TLS instruments. Due to of the importance of stability of parameters in a model, stability of them and the correlation between them needs to be investigated precisely, a task which is addressed thoroughly in this paper through a number of practical experiments. The results show that this model with a relative stability can improve the accuracy of TLS data.

Remote sensing can be used as a powerful tool by using data from different sources and combine them for vegetation and land cover classification. Pasture type classification provides key information for analysis of agricultural productivity, carbon accounting and biodiversity.The firstdata set thatused in thisstudyLandsatTM (Thematic Mapper)optical image and the second ENVISAT ASAR radar image for the study area located within the North-West of Tehran (South Alborz). In this study after applying several methods which all of them are non-lambertian and regarding to evaluate them, topographic correction was performed for optical image. The usefulness and improvement of using texture features extracted from optical and radar images in integration with spectral bands of the optical image has been evaluated on the final classification results and genetic algorithm used to select features that are independent to derive the most accurate results. In another part of the study, the impact of elevation data and optical image vegetation indices evaluated on final classification result and optimal bands selected. The results indicate increase in the overall accuracy and maximum likelihood Kappa coefficientfrom 77.04 and 0.7317 for original optical image to 78.71 and 0.7495 incaseof usinggenetic algorithm and 83.37 and 0.8036 incaseof usingelevation data and vegetation indices.

In the last two decade the use of Aerial Laser Scanner (ALS) or LiDAR (Light Detection and Ranging) sensor in geomatics engineering and surveying application has augmented significantly. The main reason of the mentioned phenomenon is the reliability and accuracy of the data obtained by LiDAR sensors. The output of LiDAR is unclassified 3D point cloud. Classification of the LiDAR point clouds in different and distinguished classes is the first step in applying such data in different geomatics applications. The purpose of this article is to classify Full- Waveform LiDAR data with the compilation of geometric and physical parameters of each point in the point cloud. First of all the geometrical parameter is extracted from raw 3D coordinate of the points. This geometrical parameter is the calculation of the relational association of the point in the construction of a plane with the help of 3D Hough transform. The feature vector also includes physical features that exclusively belong to full-waveform LiDAR. These features are amplitude of the pulse, width of the pulse and the number of the returned pulse. After the construction of the feature vector for each point, the next step is to classify the point cloud into three classes; bare earth, building and vegetation with the utilization of Support Vector Machines classification method. The final step is accuracy assessment of the classification method. The results are promising; 81.04% Overall Accuracy, 0.69 Kappa Coefficient and 79.21% Average Accuracy.