Journal of RS and GIS for Natural Resources
Year:2016 | Volume:7 | Issue:3|Papers Count:10

the aim of this study is to assess the potential of beekeeping and determination of attractiveness range plants used bee by geographic information system (GIS) in 2015 in Char-Bagh summer rangeland, Golestan. Accordingly, the final model of beekeeping potential of combining the four main criteria of nectar and pollen composition of plants (20 points), distance from water sources (10 points), roads and access routes (10 points) and the average temperature during the course of beekeeping (10 points) was determined.After the vegetation type’s floristic-physiognomic method, sampling the vegetation types in the area delimitation random-systematic method to deploy 3 transects 300 m and 30 plots were made according to the type of vegetation. Water resources map as well as the road map was drawn using Global position system (GPS), field visit and geographic information system (GIS). Nectar and pollen 134 plant species from 80 genera and 31 plant families’ favorite bee detected. Family Asteraceae, Lamiaceae and Fabaceae, respectively, with 29 (21.8%), 23 (29.17%) and 19 (14.28%) species with the highest frequency nectar and pollen plants in the region. The results showed that plants attractive class II and III, the most appearances (60.9%) and class V least of (2.3%) in the region and with regard to regular visits on the field and during the flowering period May to August are the most plants in Char-Bagh summer rangelands. The results show that using the GIS module beekeeping potential of the pasture area 17.62% (1562.4 hectares) average potential in the floor (S2), 72.41% (6419.76 hectares) on the low potential (S3) and 9.96% (883.5 ha) in the class of potential (N) is located. According to the results area of about 90.03% (7982.17 hectares) has been potential the principles of apiculture, beekeeping can be attempted with the principles and also earn money by reducing grazing pasture helped to revive.

Land surface temperature (LST) is a key parameter in environmental studies particularly for drought monitoring. Due to the ground limitations to measure the LST on a large scale, thermal remote sensing is a unique method for estimating LST. The aim of this article is comparing between LST estimation in single and multi-channel method using Landsat 8 thermal and reflective bands. Necessary ground data from meteorological stations Farabi (Khuzestan) and Karaj (Alborz) were taken to coincide with the dates and times of Landsat 8 overpasses. In this article Land surface emissivity and atmospheric water vapor content are major inputs for single and multi-channel LST estimation. After correction, processing and calculation of interest, LST were estimated. For result evaluation, statistical indices such as Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R2) were used. Results show the high value of R2 in all LST estimation method in comparison with ground measurement. In single channel using band 10 highest accuracy with MAE about 1.04 and 0.98 degrees in Karaj and Farabi station was seen respectively. The lowest and highest value of RMSE is in the single channel method (band 10) and multi-channel method (band 10 and 11) respectively.Study area conditions in terms of temperature; land cover and water vapor content affect the results and appropriate thermal band selection. Take-in consideration, especially using multi-band LST estimation method is suggested.

Landscape ecology is mainly based on the patch-corridor-matrix model. Although this model is efficient and has been successfully used in landscape ecology studies, but it is believed that this model cannot consider the continuous heterogeneity. This fact has encouraged researchers in the field to find new indicators for landscape analysis in a continuous framework. The aim of this study is a comparison of continuous and discrete indices in measuring Gorgan forest landscape fragmentation based on the moving window technique. This technique was performed on a classified map derived from SPOT satellite image in year 2010 using a maximum likelihood algorithm and on NDVI vegetation index from a Landsat satellite image of the year 2010. Window sizes were considered in 60, 90, 150 and 300 meters and six landscape class-level metrics were selected for the comparison including LPI, LSI, SPLIT, MESH, AI and PLAND. To assess the correlation between the output images of each of these metrics, the Spearman rank correlation coefficient was used. The results of the statistical comparisons of different window sizes showed that the values of the correlation coefficient were increased with increasing window size, as the high correlation values were seen when the window size was 300m belonging to PLAND and SPLIT metrics. All of the metrics had minimum correlation values in the window size 60m and the LSI metric had the lowest correlation (0.33).

The aesthetic value of landscape is considered as one of the most important natural resources created by the interaction of different environmental and visual variables.These values are among the factors that have a large impact on land quality and habitat conditions.This study was implemented with the aim of combining visual and environmental elements of aesthetic quality assessment of Gharahsoo watershed in the southwestern Golestan Province of Iran based on Fuzzy and multi criteria decision making models. First, the evaluation criteria, including various aspects of physical, biological, socio - economic and visual features of the study area were selected based on a literature review and expert’s opinion. Fuzzy analytical hierarchy process (FAHP) method was used for weighting. In the next stage, data analysis was conducted based on a fuzzy technique for order preference by similar to ideal solution (FTOPSIS) method.The weighting results of evaluation criteria showed that the criteria vegetation type, waterfall viewshed, vegetation density, proximity to rivers and naturalness with values 0.206, 0.155, 0.114, 0.114 and 0.076 respectively, are priority to other criteria in the aesthetic quality assessment of the study area. Man-made elements, including urban and rural settlements, roads and power transmission lines have received the lowest priority.Also, the results proved that there are aesthetic quality classes in various types in the study area, including most of the forest landscape in the central and south of the study area, as well as the southeast and west parts.

Desertification is recognized as a main problem in the arid and semi-arid areas.Therefore, identification and prediction of the effective factors in development of desertification are very important for better management of these areas. The main purpose of this study was evaluating the accuracy of an artificial neural network model for predicting the desertification process and selects the most effective criteria on desertification in the Dehloran plain by using the Iranian model for desertification potential assessment (IMDPA). In IMDPA model, water and climatic were selected as effective factors in desertification. In this model, three indicators for climate criteria; annual precipitation, drought index (Standardized precipitation index; SPI and continued drought and for water criteria; ground water table depletion, sodium absorption ratio, Cl, electrical conductivity (EC) and total dissolved solids were evaluated. Each index was rated using of IMDPA model. Then desertification intensity and criteria maps were prepared using a geometric average for predicting period in ArcGIS®9.3. Final data were entered into neural network to predict. The results showed that the neural network model has a high efficiency for predicting the desertification process in the study area.The accuracy of the model was about 80% and mean square error (MSe) was less than one.In addition, the climate factor and the index of EC were found the most effective variables for predicting the desertification process. In 2015-2016 predicted the most important probable criteria affecting the intensity of desertification were climate and water with weighted average 2 (moderate in sub-class1, 2 and 3), 1.84 (moderate in subclass 1and 2), respectively.

In recent decades Caspian forest has been attacked by human intervention. Easy access, abundance and diversity of valuable forest products led to an increase in population density, development of new residential areas and activities of deforestation. Change detection is essential in the assessment and management of natural resources. The aim of this study, was to monitor changes in forests of Siyahmezgi watershed in two time periods (2000 and 2015), using LandSat ETM+ (2000) and OLI (2015) images. Images were geometric corrected using 20 ground control points that are randomly taken from all over the watershed area, and topographic maps. After selection of the best indicators of using Bhattacharyya distance, image classification using an artificial neural network algorithm was performed. The results of classification of neural network method of Siyahmezgi watershed in two time periods (2000 and 2015) showed that overall accuracy is equal to 95.75% and 95.96%, respectively. The area of forest lands during 2000 and 2015 has been reduced in size 213.55 ha. In addition, in this area dense rangelands have declined, but during this period the extent of dry farming and semidense rangelands have 169.95 and 9.6 hectares were added, respectively.

In order to protect the reasonable and sagely of wetlands and also recognize the changes in their characteristics which can be caused by natural factors or human activities used remote sensing techniques and satellite image analysis. This study aimed to evaluate land use changes of Hoor Al Azim Wetland using LandSat ETM+ (2003) and OLI (2014) satellite images. After geometric and atmospheric correction, maximum likelihood and post-classification techniques were used to detect land use/cover changes. The overall classification accuracy and the Kappa coefficient for the produced maps to 2003 and 2014 were 0.91 and 0.89, respectively. Map classification of 2014 showed that the area of Hoor Al Azim wetlands has been decreased from 84300 to 45500 hectares. The results of change detection showed during the study residential, and rangeland area had increased, but agricultural and wetland had reduced. The findings of this study denoted that remote sensing data can provide appropriate information for specifying land use changes due to their repeatability, and broad vision. This approach will support adaptive management of wetlands such as Hoor Al Azim wetland.

The aim of this study was assessed desertification potential of the Dashte Abbas in the semiarid region of Ilam provience18028.8 hectares by using the Iranian model of desertification potential assessment (IMDPA) on soil and vegetation cover indicators.The geometric average for each index including effective soil depth, soil texture, electrical conductivity of soil, percent of gravel, the operation of the plant, the revitalization of vegetation and vegetation condition was obtained using ArcGIS®9.3 and the status map of each criteria was prepared. Finally, by combining and determination of geometric mean desertification intensity map was obtained. The desertification intensity map based on soil criteria demonstrated that over 4843 hectares (28.86% of the total area) and 13185 hectares (73.13% of the total area) has been taken in low and moderate classes, respectively. Also the obtained results from geometric average of vegetation cover criteria indices showed that 7005.99 hectares (38.86% of the total area) in the lower classes and 407.45 hectares (2.26% of the total area) and 10615.35 hectares (58.88% of the total area) has been taken in moderation and Severe classes, respectively.The results of the assessment the considered Catteries indicating that vegetation cover criteria with values of 2.6 is the most influential criteria in the severity of desertification in the study area. Accordingly, it can be said that the quantitative value of the desertification intensity of the total area has been taken in moderate class.