Journal of RS and GIS for Natural Resources
Year:2014 | Volume:5 | Issue:3|Papers Count:10

The aims of the present research were comparison between land use maps produced through IRS-WiFS and MODIS satellite images taken from Semirom and Brojen regions. Various preprocessing, including image rectification was applied with geo-referencing of the image to a registered image with an RMSe 0.5 pixel for MODIS and 0.35 pixel for IRS-WiFS. The atmospheric and topographic corrections were applied using subtraction of dark objects method and the Lambert method accordingly. Image processing including FCC, PCA, vegetation indices and supervised classification were employed to produce the land use maps. Two data sets of both IRS-WiFS and MODIS were used for this study. Finally the produced maps were controlled for their accuracies. Land-use map of MODIS produced with9 category and accuracy of 79.61 and with Kappa of 72.6, and land use map of IRS-WiFS produced with 8 category and accuracy of 90.2 and Kappa of 77.9. Result showed that land use maps produced with IRS-WiFS data sets have very high accuracy. Results showed the same for land cover map produced with MODIS data sets.

Timely and accurate change detection of earth surface features is extremely important for understanding relationships and interaction between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. In this study, images of landsat (TM) 1988 and landsat (ETM+) 2001 were analyzed using five change detection techniques in 80470 hectares in the region of Daresher, Ilam province. Change detection techniques considered were image differencing, image rationing, NDVI differencing, change vector analysis (CVA) and post-classification comparison. In this study, from statistical method for determining the threshold level was used which from the change threshold was achieved. In this study, threshold level was set at ±1 standard deviation from the mean. After determine optimal threshold, areas having decreasing change, increasing change and no change was determined. Based on ground data and field visit and Google Earth, accuracy assessment of change detection techniques was carried out using overall accuracy and Kappa coefficient. According to the results, NDVI differencing with overall accuracy of 98.5 and Kappa coefficient of 97% showed the highest accuracy among applied change detection techniques and on the contrary, band rationing with overall accuracy 72.5 and Kappa coefficient of 50% had lowest accuracy in land use/land cover change in study area.

The objective of this research was presenting a case model for determining of range suitability of Taleghan region for sustainable bee keeping activities. After considering the factors affecting range suitability for beekeeping, final suitability map was created by using the suggested method of FAO and GIS software. Random sampling was done in vegetation types, using 30 (1´1m) quadrates along three 200m long transect. Investigation on quality and characteristics of rangelands showed that three sub models of vegetation cover (flowering period, the rate of attractiveness of plants and vegetation cover percent), environmental factors (roads and access roads to vegetation, elevation, temperature and soil characteristics) and hydrology (water resources) played the main role in the determination of range suitability for bee keeping. Decreasing of nectar or pollen in the vegetation cover, an existence of low quality plants (classes III and IV) along with the shortage of flowering period, poor soil conditions and lack of roads in some vegetation types were among the most limiting factors of range suitability for bee keeping in the study area. In contrast, suitable distribution of water resources, elevation and temperature increased the rangelands suitability for bee keeping. According to our results from 37977.2 hectares of studied rangelands, 235 hectares (0.61%) classified as S1 of suitability (without limitation for bee keeping), 7798 hectares (20.53%) classified as S2 class (with limitation), 9961 hectares (26.29%) classified as S3 class (with high limitation), and 8861 hectares (23.33%) classified as N class (non suitable). Generally, 21% of the area had an acceptable score as excellent suitability for bee keeping.

In this study, we investigated the effect of agricultural land concentrations of heavy metals including Copper, Arsenic and Zinc and mapping of soil contamination potential the elements in the areas studied using the collected data, GIS, Geo-statistics and remote sensing were conducted. First, using 135 surface soils (0-20cm) classified random systematic sampling in the region area 7262 square kilometers were collected and total element concentrations, soil characteristics, including the pH and organic matters were measured. Interpolation for heavy metals concentrations were done by geo-statistics methods, and assisting location correlation analysis, interpolation suitable method was chosen using MAE and MBE function. For heavy metals concentration maps, Copper and Zinc, we used Ordinary Kriging and exponential model and for Arsenic Ordinary Kriging and Spherical models. For analyzing the metal’s concentration distribution maps of pollution time series satellite images were used. For this purpose, five time series of satellite images of IRSP6 sensor Awifs (6 March, 3 April, 27 April, 9 June, 18 July and 16 August) were prepared. Using conventional classification methods and advanced satellite imagery maps of Land Use in 2009 was prepared. Finally fuzzy classification method map due to having the higher kappa coefficient as a final land use map was selected. Site analysis of studied heavy metal interpolation maps assisted by GIS and remote sensing assistive showed that Copper and Zinc have geological and agricultural origins. And Arsenic has originated from bedrock, but agricultural activities according to excessive consumption of chemical fertilizers can increase most of these elements in soil.

In order to achieve sustainable development, it is necessary to obtain and adopt planning procedures based on Multi Criteria Evaluation of natural environment. Since biophysical (natural) environment has limited ecological capabilities for human use, ecological capability assessment, as an essence for environmental studies and with the aim of preventing existing crises, yields proper grounds for environmental planning. The analysis of land capability and sufficiency for urban development is one of the main categories with which urban planners deal. In this paper, by Analytic Hierarchy Process (AHP) through the perspective of Multi Criteria Evaluation (MCE) Approach and within the Geographic Information Systems (GIS), ecological capability of the suburbs surrounding Tabriz city was assessed (natural and human in terms of 12 criteria) to examine the physical development of Tabriz city and final mapping of the region was provided. This mapping shows regions suitable for physical urban development of the city. The results and finding of this research were applied by urban planners.

Land use mapping is one of the key factors in studies of environment and natural resources management. Mapping land use is often one of the most expensive parts of natural resources and environmental projects. Satellite data is one of the fastest and most cost-effective methods for mapping land use that is available for researchers. In recent years, researchers from the different methods of land use maps have been produced using this data. There is the different method to classify the images. Each method has advantages and disadvantages. The aim of this research is to determine the best images nine supervised classification methods to extract land use map of the Noor city by ETM+ sensor. The results showed that the SVM classification by 0.9503 factor kappa coefficient and 90.94% overall accuracy is better than other methods. The accuracy of the order of priority 9 that is, SVM, Neural network, Mahalanobis distance, Maximum likelihood, Minimum distance from the mean, Spectral angle mapper, Spectral information divergence, parallel piped and binary code. All the research results of this study can be using the correct classification. Land use maps can be extracted with higher accuracy.

Usage of modern technologies such as GIS and RS in plant ecosystems studies and especially land cover mapping is needed to recognition of these instruments efficiency and also identification of the best methods for applying them. This study aimed to compare the efficiency of three common supervised classification methods of satellite data (Minimum to Distance, Parallelepiped and Maximum Likelihood) to identification of plant groups in Goloul-via-Sarani protected area, Northern Khorasan Province, Iran. In order to this, 143 training samples (>30m2) were collected from areas that shown a homogenous plant species composition. These data recorded by GPS device and so transported to a GIS database. Satellite data included Landsat ETM+, and IRS-P6 LISS III that were prepared and analyzed by ENVI 4.2 software. Amount of efficiency for each method was evaluated by measurement of overall accuracy (OA) and Kappa coefficient (KC) criteria. Results showed that ML method makes the highest accuracy for two data series (OA=90.35, 82.19 and KC=0.878, 0.772 for Landsat and IRS data respectively). In the face, PP method showed the worst results (OA=67.09, 58.76 and KC=0.593, 0.478). These results suggested that collection of sufficient training samples from natural classes and surveying probability of image pixel's dependency on these classes can be useful for classification of plant groups.