Journal of RS and GIS for Natural Resources: 2 (2)

Open Access Article

1 - Evaluation of four algorithms for estimation of canopy cover of mangrove forests by using aerial imagery
Akbar Ghasemi Asghar Fallah Shaban Shataee Joibari

Today, it is important to use the ecological indicators, such as canopy cover for recognizing the special status of ecosystems, such as mangrove forests and also monitoring and evaluating changes through a specific period. This study aimed to investigate the sufficiency More

Today, it is important to use the ecological indicators, such as canopy cover for recognizing the special status of ecosystems, such as mangrove forests and also monitoring and evaluating changes through a specific period. This study aimed to investigate the sufficiency of parametric and nonparametric algorithms using the spectral data with high spatial resolution in the evaluation of canopy cover in the mangrove forest in the Bushehr province. The vegetative characteristics were studied at 20×20 square meter sample plots. 50 Sample plots were studied for the proposed vegetative characteristic such as diameter, Height and percentage of canopy cover of mangrove forest. The camera UltraCamX digital images which used in this study were harvested to the shooting operation on 2012.01.10. After conducting some proper Preprocessing and processing, the digital values corresponding to the ground samples were extracted from spectral bands and were considered as the independent variables while and the crown canopy percent per plot were considered as the dependent variable. Modeling was carried out based on 75 percent of sample plots using K-Nearest Neighbor methods, support vector machine, random forest and General linear model methods and the results were cross-validated using the remaining 25 percent. The results showed that the best estimates were obtained from the crown canopy percent with method Random Forest, k-NN, SVM and General linear model methods with a root mean square error of 13.57, 13.95, 14.88 and 17.73 percent and relative bias of -3.88, -4.62, -5.05 and -2.88 percent that Random Forest method had the best performance. The results of this study showed UltraCam X Arial spectral data had the high ability for estimating of canopy cover percent. Manuscript profile

Open Access Article

2 - Studying the effect of dust on vegetation changes (Case study: Shadegan wetland, Khuzestan)
Reza Bayat Somayeh Jafari Bagher Ghermezcheshmeh Amir Hossain Charkhabi

Wetlands and water ecosystems are important, especially in terms of environmental values. Mapping vegetation changes can provide valuable information and removing vegetation can cause environmental disasters such as dust. This study aimed to investigate and determine th More

Wetlands and water ecosystems are important, especially in terms of environmental values. Mapping vegetation changes can provide valuable information and removing vegetation can cause environmental disasters such as dust. This study aimed to investigate and determine the spatial and temporal variations in Khuzestan, Shadegan wetland coverage and these changes are analyzed with a dust storm data. Temporal and spatial variation of vegetation measured using Normalized difference vegetation index (NDVI) of MODIS images from 2000 to 2011, and vegetation cover changes were determined and different variables of dust (total annual density, maximum annual concentrations and annual average concentrations of dust)  from Ahwaz station were analyzed for detecting changes of vegetation cover. The results indicated that the total area of vegetation NDVI from 2000 to 2011 declined 7.36%. Also, the results showed the highest and lowest water area 25.67% and 19.72% belong to 2007 and 2000 respectively, and the highest and lowest vegetation area were 31.21% and 17.27 % in 2000 and 2004 respectively. According to statistics from 2002 onwards we have been faced with increasing dust storm which indicated a correlation between dust and wetland vegetation. Also worth noting is that the total annual concentration of dust and vegetation indices showed a higher determination coefficient (0.8516). Meanwhile, 2 month delay effect of dust has 0.8214 determination coefficient with NDVI. Manuscript profile

Open Access Article

3 - Monitoring and forecasting of land use change by applying Markov chain model and land change modeler (Case study: Dehloran Bartash plains, Ilam)
Seyed Reza Mir Alizadehfard Seyedeh Maryam Alibakhshi

Nowadays modeling and forecasting of land use changes by application of satellite images can be a very useful tool for describing relations between natural environment and human activities to help planners to make decisions in complicated conditions. There are various m More

Nowadays modeling and forecasting of land use changes by application of satellite images can be a very useful tool for describing relations between natural environment and human activities to help planners to make decisions in complicated conditions. There are various methods for forecasting of land uses and coverage, in which the Markov chain model is one of them. In this research, land use changes in Bartash plain in Dehloran which is located in Ilam province in the area of 135244 hectares in 3 time periods (1988, 2001 and 2013) of landSat satellite images, providing land use map in 6 classes (low density forest, medium-dense grassland, poor grassland, agricultural, alluvium sediments and non-vegetated lands) by application of  Kohonens neural network and also Markov anticipation model and Land change modeler (LCM) approach was predicted for the year 2030. The classification results showed the rate of demolition and a reduction of the area of low density forests and medium grassland land uses and increase in area of other land uses. Reduction of low density forest and the medium grassland area and increasing growth of other land uses demonstrated the overall destruction in the region and replaced with poorer land uses. At the end, by application of the Markov chain model and LCM modeling approach, land use changes were a forecasted for the year 2030. The results of changes anticipation matrix based on maps of years 2001 and 2013 showed that it is likely that in the period of 2013-2030, 45% of low density forest, 71% of medium grassland, 96% of poor grassland, 81% of agricultural lands, 93% alluvialvium sediments and 100% of non-vegetated lands remain changeless; non-vegetated lands have the most stability and low density forest have the least stability. Manuscript profile

Open Access Article

4 - Potential maps of prone defense centers in western forest of Ilam-Iran by using an analytical hierarchy process (AHP)
Mohammad Fallah Zazuli Reza Aghataher Mehrdad Zarafshar Mohsen Jafari

Oak forest in west of Iran has been always considered by terrorists. So, site selection with emphasis to passive defense principal is really necessary in this area. This research aimed to site selection of defense installations and determine of suitable areas of its gen More

Oak forest in west of Iran has been always considered by terrorists. So, site selection with emphasis to passive defense principal is really necessary in this area. This research aimed to site selection of defense installations and determine of suitable areas of its generation in the part of thin forests at Ilam province using analytical hierarchy process (AHP) and Geographic information system (GIS).  By using defense expert opinions, and a literature review eight effective intelligence layer in determining the talented defensive centers (lithology, distance from urban, distance from rural, slope, aspect, elevation, distance from drainage and distance from road) were selected and their maps were digitized in ArcGIS®9.3 environment. Prioritizing factors were done using expert opinions in the Expert Choice (EC2000). The results by priority criteria by pairwise comparison method showed that distance from residential areas (urban and rural area), distance from roads and lithology 0.351, 0.222, 0.160, and 0.109 had the highest effects on defense site selection, respectively. In contrast, elevation (0.021) and distance from the river (0.030) had the lowest effects. Finally, the results showed that Cenozoic geology units, distance from city 10000-15000 m, distance from roads >6000 m, slope percentage (10-20%), eastern aspect, elevation (1000-1500 m), distance from river >3000 and distance from roads 3000-5000 m were the most important factors for presentation of potential maps for building of military centers in the western forest area of Ilam. Manuscript profile

Open Access Article

5 - Calculating the physical properties of snow, using differential radar interferometry and TerraSAR-X and MODIS images
Seyed Ali Alhossaini Almodaresi Javad Hatami Ali Sarkargar

The process of saving snow in mountainous areas of water resources is important. According to studies conducted by about 60 percent surface water and 57% groundwater flow in snowy areas. In recent years, the importance and applications of synthetic aperture radar data ( More

The process of saving snow in mountainous areas of water resources is important. According to studies conducted by about 60 percent surface water and 57% groundwater flow in snowy areas. In recent years, the importance and applications of synthetic aperture radar data (SAR), according to a major advantage compared to other remote sensing systems are growing. In this study, using manufacturing satellites and MODIS algorithm Snow map snow cover and then with twelve radar image sensor TerraSAR-X and DInSAR in such a way that initially an image as the base image the rest of the images of the first image interferometry was performed between areas where snow cover the amount of displacement rather than results indicative of changes in depth of snow and then map snow depth maps of snow between October 2012 to May 2013. Mining was the next step, using Linear regression between the snow depth map of the DInSAR technique produced snow water equivalent depth data from ground stations were harvested SWE depth map of the results suggest overall accuracy of 91.3% and kappa coefficient consuming 84.45 Snow level map and map the depth of the snow by a factor of extension of 85% and RMSe of 2.78 to calculate the depth of snow water equivalent using the correlation between the data of snow depth derived from DInSAR and the ground water depth of snow a linear correlation coefficient of generalization 0.77 and RMSe of  2.97 was the result that was statistically at 99%. Manuscript profile

Open Access Article

6 - Assessment of clouds seeding project in increasing of water harvesting in the Fars province using remote sensing and geographic information system techniques
Mahboubeh Olumi Majumerd Mohammad Zare Samaneh Pourmohammadi

Drought and climate change phenomena have severe negative impacts on natural vegetation and agricultural section in Central Iran during the last decades. Cloud seeding is one of the efficient methods to reduce the effects of climate change. The purpose of this study was More

Drought and climate change phenomena have severe negative impacts on natural vegetation and agricultural section in Central Iran during the last decades. Cloud seeding is one of the efficient methods to reduce the effects of climate change. The purpose of this study was to investigate the effect of clouds seeding on rainfall in Fars province. November, December, February and April months in the water year of 2010-2009, selected as the prone month of precipitation in Iran, were evaluated cloud seeding projects in Fars province. Historical regression was used to evaluate the cloud seeding project. In the first step, raster monthly precipitation maps for each month of year in the period was 34 years (1977-2010) were plotted using the Kriging method to measure rainfall amounts of each year. Then, the volume of precipitation in April, February, January, and November in the target area stations were calculated and used as a dependent variable. Next, precipitation time series for each month of the period of 1977-2010 in the control area stations were calculated and entered into the regression as independent variables. Correlation between the volume of rainfall in both regions (target and control) were analyzed. Then, expected rainfall in the region was estimated and its confidence was determined using statistical methods. Comparing expected and actual rainfall, percentage of changes in precipitation due to clouds seeding in Fars province were determined. Results showed an increase of about 15% in precipitation in Fars province during the four months of the water year of 2009-2010. Manuscript profile

Open Access Article

7 - Soil salinity map preparation using spectral analysis of OLI sensor and field data (Case study: Southern parts of Malayer plain)
Davoud Akhzari Ahmad Asadi Meyabadi

Soil salinity in arid and semi-arid lands is one of the most important limiting factors which changes vegetation types and biomass due to natural resources production reduction. The Landsat 8 satellite images (2014) were used in this research to select the best satellit More

Soil salinity in arid and semi-arid lands is one of the most important limiting factors which changes vegetation types and biomass due to natural resources production reduction. The Landsat 8 satellite images (2014) were used in this research to select the best satellite indices for soil salinity evaluation. All soil samples were conducted in September 2014. Based on 77 points of measurement the distribution maps of sodium, magnesium, potassium, calcium, electrical conductivity and soil acidity were prepared by Kriging interpolation method which was developed in ArcGIS®9.3 software. After that, the correlations between the produced maps and ten remote sensing indices have been investigated by use of spatial regression. Maps of the distribution of sodium, potassium, magnesium, calcium, soil conductivity, acidity, salinity and alkalinity also prepared and proper regression models were presented. The results show that for the detection of distribution of electrical conductivity and sodium, according to correlation coefficient, the Salinity Index and Malayer Salinity Index were suitable indices. In order to detect the distribution of magnesium, calcium and potassium in the study area due to the high correlation coefficient (0.88), the normalized difference salinity index can be used. Due to the not significant difference of spatial regression of soil alkality, it could not be used. The results showed that the normalized difference salinity index can be used  for general measurement of all soil elements. According to the regression equation derived between indices and prepared maps of field studies, the optimal models for soil salinity mapping of the study area were determined and calibrated. Based on satellite data, obtained models of this study have suitable estimates of study elements because their coefficient of correlation is acceptable. With the completion, expansion and development of findings of this study, zonation lands without the need for sampling could be done. This method, while providing greater precision can also minimize the sample costs. Manuscript profile

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