Journal of RS and GIS for Natural Resources
Year:2011 | Volume:2 | Issue:1|Papers Count:11

Land cover mapping is important for much planning and management activities. Today, satellite images and remote sensing techniques are extensively used in all sectors, including agriculture and natural resources because they provide updated data and high analyzing abilities. In this study, in order to produce the land cover maps for part of Hamedan province, digital satellite data IRSP6 (Awifs time series data) was used. First, satellite images with geometric correction of a mean square error of less than 0.48 pixels were applied. For image classification, Perceptron’s classification method of artificial neural network with three layers was used, employing vegetation index maps (NDVI) as auxiliary data. Finally, the land cover map of the study region was classified into thirteen classes. To assess the classified land cover map precision, it was controlled for ground truths with a GPS. Kappa coefficient and overall classification accuracy of neural network methods were estimated 86 and 88 percent, respectively.  The results confirmed that the neural network classifier was capable of generated land cover maps and cultivation‎ pattern‎ with high accuracy.

Land use based on its capability and production potential, and preparation of economically affordable and applicable allocation plans is one of the proper management factors in maintaining of Irreversible natural resources such as water and soil. The purpose of this study is to obtain different allocation plans for Cheragh-Veis watershed and to select the most economical and suitable of them by using the economic factors in regard to economic-social conditions of watershed residents. In order to perform the land allocation process, quantitative -comparative and multi objective land allocation methods were used, which after the evaluation and determining of land abilities by merging the maps in the GIS environment and prioritizing the land uses, two allocation maps were obtained. For economical evaluating of these two logistics projects, economic indexes: establishment costs, gross margin and benefit-cost ratio were calculated. The results showed that the benefit-cost ratio index for obtained allocated plans from the quantitative -comparative and multi objective land allocation methods were 4.043 and 1.54, respectively. With regard to once a watershed plan will be economically applicable that its benefit-cost ratio index is greater than 1, therefore both logistics projects economically affordable and is applicable.

The objective of this research was presenting a case model for determining of range suitability of central 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 vulnerability of karst areas of Kermanshah Province to collapse has been investigated using Geographic Information System (GIS). Geological, hydrological, topographical, land use and vegetation parameters, have been used in order to investigate the vulnerability of karst areas to collapse. For this purpose, boundary maps for layers of fault, drainage systems, roads and urban residential areas were created and reclassified. In order to determine the degree of vulnerability of karstic areas to various parameters weighted and overlay techniques were used. The result of overlapping geological and hydrological parameters shows that 23.67 percent of the karstic areas have a  high and very high vulnerability, 59.32 percent of the karstic areas equivalent to 5,211 km2 in the slope of less than 8, are sensitive areas to collapse, 38.27 percent of karstic areas affected by land use and vegetation parameters with high and very high vulnerability, and finally, 45.41 percent of karstic areas affected by all parameters have  high and very high risk to collapse.

The aim of this study is to determine suitability classes for sheep grazing in terms of forage production in rangeland of Fereidoonshahr. Four information layers were crossed used of Geographical Information System (GIS). The ratio of available forage to the whole produced forage in each vegetation type was calculated in three different modes. In the first method, According to the coefficient of the palatability and proper use factors, the utilizable forage of species of class I, II and III were calculated, in second method for each vegetation type and in the third method only utilizable forage species class, I and II according to the coefficient of the palatability and allowable use factors was determined. The results of this study showed that in the first method, the amount of grazing capacities is 20709 animal units per grazing season. 15.98% of the rangelands belong to the S1 class, 37.50% to the medium class (S2), and is the rest to be low suitability class (S3). In the second method, the amount of grazing capacities is 15989 animal units per grazing period and 96% of rangelands are located in low suitability class (S3). In the third method, the amount of grazing capacities is 3190 per grazing period and 16.53% of the rangeland are in the middle suitability class (S2), 15.98% at the low class (S3) and 67.48% at unsuitable class (N). Based on this study, the most important factors for suitability decline is the low rate of available forage for the livestock, low forage production of classes, I, II, and low palatability percentage. Based on this study, the most important factor in reducing the low level of competence Meadow's area available to livestock fodder due to reasons such as production plants small classes, I and II, the low percentage of palatability and operational level allowed. High livestock number (22170 animal units) on one hand and high land deterioration rate on the other calls for lower stocking rate in hope of better future for these rangelands.

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.

The aim of this study was evaluated changes in land use and cover the period 1987 to 2005 in the Tange-Bostanak watershed in Fars province. The land use map, particularly time series maps, can give a precise evaluation of lands change and degradation in land use planning. In this paper for land use mapping of Tange-Bostanak watershed in Fars province images of Landsat 4 (TM) and IRSP6 were used. According to the sensor differences, initially geometric and radiometric corrections as a pre image processing were considered. Some more preprocessing such as image acquisition times, band combinations using Optimum Index Factor, Matrix Variance-Covariance Analyses among different bands were also considered. Land use maps using supervised classification (Maximum Likelihood) for 1988 (past land use) and 2005 (present land use) for change detection (between 18 yeas) were produced. The accuracy of the produced maps using overall accuracy and Kappa statistic were calculated. Overall accuracy was 0.88 and 0.86, and Kappa statistic was 0.92 and 0.91, respectively. Results show that from 1988 to 2005 barrier lands has increased from 0.05% to 0.21%, and a dry farming land has also increased from 0.51% to 1.65%. On the other hand, forest lands with moderate rangelands have decreased from 40.7% to 12.33% and rangelands have also reduced from 4.06 to 3.65%. Respectively Statistical analyses in the level of 1 and 5% showed that the irrigated farming lands on the 1988 land use map were 92.7 and 93.1%, and the forest with dry farming 55.88 and 55.47% as the highest and lowest significance. Results for the land use map of 2005 were 90.7 and 91.02% for the irrigated farming, and 56.38 and 58.06 for the forest with dry farming as the highest and lowest significance. The large amount of spatially detailed information derived from digital images, ground surveys, and digital terrain models and handled within a GIS, offers new opportunities for watershed parameterization.