Journal of RS and GIS for Natural Resources
Year:2017 | Volume:8 | Issue:3 (28) |Papers Count:10

Predicting the potential distribution of plants in response to climate change is essential for their conservation and management.Amygdalus scoparia is a wild almond species native to Iran Therefore, this study aimed at predicting the effect of climate change on the geographical distribution of A. scoparia in Chaharmahal and Bakhtiari province in the central Zagros region. In this regard, we used 5 modeling approaches, Generalized Linear Model (GLM), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Generalized Boosting Method (GBM) and Random Forest (RF) to determine relationships between the occurrence of species and environmental factors under the ensemble framework by using Biomod and R software. The results showed that AUC values greater than 0.9 and functioning of all models been excellent. The mean temperature of the driest quarter and Annual precipitation had the most important role for habitat suitability of this species and (85%) changes inA. scoparia distribution was justified. The results of the model showed that 9%, (148680 ha) of in Chaharmahal and Bakhtiari province for the A. scoparia have had high habitat suitability. Area of suitable habitat was calculated by ArcGIS software on current and future climate conditions.Under RCP4.5 and RCP8.5 climate scenario A. scoparia might lose (Respectively 43% and 59%) of its climatically suitable habitats due to climate change factors, by 2050, while in a number of areas (135% and 140%), the current unsuitable habitats may be converted to suitable. The results of this study can be used in planning, conservation and rehabilitation ofA. scoparia.

Since the most cultivated areas allocated to dry land wheat in Kermanshah province, therefore the climatic zoning wheat cultivation seemed necessary for this province. To reach the research objectives was used the climatic data 6 synoptic stations (1989-2012) and climate data 8 climatological stations (2008-2013). Initially for analyzing and performance steps, used software SMADA for different distribution possibilities, selected planting date and the best statistical distribution Then extracted map precipitation, temperature and thermal stresses and applied contribution of each layer in the layer zoning, So that the highest percentage of participation is related to precipitation The germination period With 31.5% and the lowest percentage of participation is related to heat stress (30) degrees Step reaching With 8.1%. The results showed that Between Elements of climate, precipitation and temperature There are important factors of wheat cultivation in the end By combining layers To the Method Weighted overlap in ArcGIS environment, were extracted wheat climatic zoning map.The results showed that the very appropriate areas with an area of 16.7% in parts of the northwest, west and southwest, appropriate areas with an area of 14.36% in the northwest, west and southwest, the average areas with 15.27% in the southwest, west, central, north, northeast and southeast and poor areas with 53.64%, is located more in central and northeast. This research can be used in order to the more prominently capability of geographical information systems in the composition and production of spatial and attribute data, and help managers and decision makers to access information and provide appropriate model according to the type of crop planning.

Land surface temperature (LST) is an essential parameter in the energy exchange between the earth surface and atmosphere. It is widely used in various scientific fields, such as climatology, hydrology, agriculture, ecology, public health and environmental science where the time series analysis of LST is vital. One of the methods to estimate LST is to use thermal remote sensing technique and infra-red satellite imageries. But, the time series satellite data are commonly prone to miss data, outliers (spatially and temporally) due to clouds, aerosols, cloud masking algorithm malfunctioning and sensor errors. In this study, to solve the problem of missing data (gaps) and outliers Harmonic ANalysis of Time Series algorithm (HANTS) was used. The day and night MODIS LST products (MOD11A1) were used in 2015, with 1 kilometers and daily spatial and temporal resolution, respectively. The study area covers most part of Iran, Turkmenistan and the Caspian Sea, which belongs to an image frame that in the sinusoidal MODIS frame system has the horizontal and vertical number of 22 and 5 (h22v05), respectively.The quality evaluation of original data showed that on average 36.8 and 35.6 percentage of data was covered by a cloud by day and night time. The results of the HANTS algorithm illustrated that the Root Mean Square Error (RMSE) between the original and reconstructed data were 3.87 and 2.68 Kelvin during the day and night time. The results of this study indicate that HANTS algorithm can effectively solve the problem of gaps and outliers and improve the quality of data used in time series LST of MODIS.

Object based image analysis (OBIA) techniques are known as new methods in the domain of remote sensing image processing methods which are having significant potentialities for a variety of applications in pedology sciences. In this regard OBIA known as approach which integrate spectral and spatial pattern of satellite images for obtaining more accurate results. This approach has developed against of pixel based methods which are facing serious challenges due to the similarity index in spectral properties. The main objective of this study is to analyze soil salinity and apply an integrated approach of Fuzzy-object based for monitoring changes in soil characterize in the eastern area of Uremia Lake which has been under an environmental impact of lake drought. For this goal, Landsat 7 satellite images (ETM+) of 2000 and Landsat 8 (OLI) satellite image, for 2015 was prepared. In doing so, first pre-processing steps on satellite images were established. Accordingly, soil salinity trends of agricultural croplands in eastern area were evaluated using fuzzy object based image analysis approach. For this goal, we employed the object based features including: NDVI, spectral indices, brightness and NDSI. The results of this research indicated that, significant progress in increasing salinity areas while the soil salinity rate measured up about 21.54 % (833.18 Km2). According to the results, the salty barren lands with positive slope 19.7 % represent a positive growth rate while it has increased from 15.3 % to 35.05 %. Results indicated the critical environmental situation for the agriculture croplands located in the Eastern area of Uremia Lake which requires the attention of decision makers and authorizations in the East Azerbaijan province.

In recent decades, with the expansion of human activities on the natural environment, a variety of hazards, including landslides, have had serious human and financial damage.As a result, landslide susceptibility assessment and identifying the critical areas for watershed protection seems to be necessary. In this study, landslide susceptibility using combined fuzzy and Analytic Network Process (FANP) methods has been modelled on the Farub Roman basin. To achieve this goal, four clusters; topography, biological, hydro-climate and geological and criteria such as elevation, slope, aspect, curvature, distance from roads, land use, normalized difference vegetation index (NDVI), distance from rivers, drainage density, rainfall, soil moisture index, distance from faults and lithology have been considered. The results showed that the Fuzzy-analytic network process model is appropriate for landslide susceptibility modelling for as much as in model validation through the Relative Operating Characteristic (ROC) curves, the AUC, 0.83 has achieved with the standard error; 0.07, of the P-value equal to zero. For assessing the landslide susceptibility in the Farub Roman basin based on the results of the ROC curve, fuzzy ANP model evaluated very well. In addition, the results showed that, 66% of the known landslides have been found in areas with high and very high sensitivity. Due to the estimation of the high and very high sensitivity of landslides; 51% of the total area, the implementation of studying watershed protection seems to be necessary.

Natural hazards cause every year around the world, including Iran harmful damages. A Vark basin in the Lorestan province due to a variety of geological properties such as lithology, tectonic and climatic conditions, is taken as areas with potential environmental risks into account. To reduce the damage caused by these risks, earliest and foremost task is to determine areas with high potential risk. In order to evaluate environmental hazards in this basin, each informational layer has been identified and registered using the satellite images ETM+ (2016 year), geological maps, topographic maps and field visits (2017 year). Then, using the informational layers of the slope, aspect, elevation, land use, geology, the epicenter of the earthquake, fault, drainage and rainfall in ArcMap software and based on an AHP-Fuzzy method of the map, the risk of landslide, floods, erosion and earthquake have been prepared. In this study, in order to map the environmental risks using expert judgment, the Quartet Hazards of the region were weighted and then overlapped. Based on the results achieved, 9.03, 20.84, 27.68, 27.41 and 15.03 percent of the area ranked at-risk classes very low, low, medium, high and very high respectively. The results of the environmental risk map of the region show that landslides and flooding are a larger role in the creation of high-risk zones.

The earth’s crust has not been fixed during the geological history and reshaped continually under the influence of internal and external factors. Uplifting or subsidence in some areas of the earth’s crust, especially in thin ones has been led to some changes in its surface which cause to destroy natural phenomena and human-made structures. In the present research amount of the earth’ vertical displacement in the Tehran lands using a time series analysis based on short location baseline (SBAS) and the differential radar interferometry with synthetic aperture technique (DINSAR) has been assessed.Accordingly, 19 images of the ENVISAT ASAR satellite (C band) and 11 images of the TERRA SAR satellite (X band) have been used in which the time span was 1680 and 187 days, respectively. After the image processing, maps of the earth’s surface displacement for all dates were calculated than the primary image and the map of the earth’s surface vertical displacement per day was provided for each sensor. Assessing results of two sensors indicated that, subsidence was moderately 0.761 mm and 0.777 mm per day for ASAR and TERRA SAR sensors, respectively. Results also represented that, some areas showed uplifting, in which the amount of uplifting for ASAR and TERRA SAR sensors were 0.529 and 0.476 per day, respectively. Generally, considering that the date and wavelength were different, obtained results for uplifting and subsidence areas were closed for both sensors.

Land use/cover mapping is one of the most common applications of remote sensing data.Remote sensing data by providing updated digital information, repetitive coverage, reduce costs and the possibility of processing and high potential for the preparation of land use/cover maps in natural resources, is of paramount importance. In this study, the land use and cover map prepared using Google Earth and the Operational Land Imager image sensor (OLI) of Landsat 8 satellite and methods of visual interpretation (GE images), supervised classification, neural networks and object-based classification methods (Landsat 8 images), and compared with each other. In order to evaluate the accuracy of the classification, the overall accuracy, Kappa coefficient, producer’s accuracy and user’s accuracy were used. The results showed that the visual interpretation method with overall accuracy and Kappa coefficient of 99.4 and 0.99, in comparison to the object-based, supervised and artificial neural networks (with an overall accuracy of 94, 82 and 60.8, and a Kappa coefficient of 0.92, 0.77 and 0.50) are more reliable.According to the map of visual interpretation, the rangelands with an area of 946687 ha and water bodies in the area of 217.42 ha were the largest and smallest land use/covers, respectively. In terms of accuracy, the visual interpretation method using Google Earth images had the highest accuracy, but it is time-consuming and cost-effective. In contrast, object-based method with acceptable accuracy and with low cost and time is the best method for land use/cover mapping.