Journal of RS and GIS for Natural Resources
Year:2016 | Volume:7 | Issue:1|Papers Count:8

Rangeland is one of valuable renewable resources that has a special place in national development programing in many countries. This study aims to survey vegetation of saline lands around Urmia lake using satellite images in 2014. This study was conducted in the area about 353150 hectares in saline lands around Urmia lake. In this study, various pre-processings including geometric correction using topographic maps and atmospheric correction using dark object subtraction method were utilized. Field data were collected in 2014. Sampling was done in plant units with stratified random sampling method. In this study, multiple regression was used to examine the relationship between variables. In this model, the presence or absence of collinearity between the independent variables (vegetation indices and bonds) was first studied by calculating the correlation matrix. In this study, the best model was chosen due to higher value of R2.Considering the selected model, several variables have been chosen from all the independent variables and have been introduced as the most important factors in determining the vegetation map of the study area. Finally, the produced maps and sampling points were controlled in order to validate the accuracy of results. The results showed that the indices of MIRVI, PD312, PD322, TVI, VNIRI, INFRARED, MID-IRINDEX and bounds of B2, B3, B5, B6 have significant correlations at the 5% level with the field data and using backward regression models, the vegetation map was estimated.According to the results, much of saline land around Urmia lake has 0-20% vegetation; therefore, we concluded that the rangelands in the region are not in good conditions.

Since the foundation of landscape ecology, the correlation between spatial patterns and ecological processes has always been regarded as one of key topics in this discipline. In this context, landscape metrics provide valuable information for the interpretation of landscape patterns. It is clear that the scale of input data and the scale of analysis must be coherent in order to calculate and interpret landscape metrics correctly. One main method that is often used to assess the scaling effects on landscape pattern is to manipulate the grain size or pixel size in satellite images. In this study, The SPOT and LandSat satellite images of 1986 and 2010 and simulations and maps of Markov-cellular automata models of 2020 were used. The effects of spatial resolution on 8 metrics were evaluated using the software FRAGSTATS in class and landscape levels. The results showed that the changes in grain size have significant effects on landscape metrics and their changes in the future so that the increased grain size will lead to the deacreased number of patches (NP), patch density (PD), LSI and CONAG. In general, metrics showed two types of irregular and increase behaviors according to the reduced grain size; in this study, the changes in grain size are more sensitive than the other metrics. So, the application of these metrics in landscape studies shoulde be considerably paid attention.

Land Surface Temperature (LST) is one of important parameters that is measured using Remote-sensing tools and thermal bands of satellites. The importance of this issue is revealed when direct effects of temperature are shown on the increase and decrease of evaporation, evapotranspiration and as a result, the moisture content changes in the plant. In this study, the temperature of sugarcane canopy cover was measured by LandSat 8 satellite data in 8 sugarcane fields out of Salman Farsi Sugacane Industry involving 5 points from each field (totally 40 points); these points were irrigated in different days and measured by the infrared thermometer. The points were selected at the edges of fields with the intervals of 30 m in order to avoid the combination of them with the pixels with no vegetation. To calibrate the Split Window (SW) algorithm, the input data of water evaporation, emissivity and transmittance as well as LandSat 8 satellite images were applied. Results have shown that the estimation of vegetation temperature of sugarcane fields in different days of irrigation was of an acceptable accuracy. Also, in the points with the same vegetation, irrigation is the main factor for the changes of temperature. In this research, Residual Mean Error Square (RMSe), and Mean Average Error for the measured field temperature and extracted one by the satellite images were given as 0.925 and 0.766oC, respectively.

The aim of this study was to determine the appropriate location for the construction of solar power plants according to the criteria and factors of climate (temperature, radiation, precipitation, sundial, evaporated), topography (elevation, slope, aspect, distance to fault), environment (user land, rivers) and human environment (residential areas, roads) in Geographical information system (GIS) and hierarchical model in Ilam province. According to the importance and role of these factors, the statistics of parameters were analyzed in the software Excel and map of each criterion was prepared in GIS and the weight of each criterion was determined by Analytical hierarchical process (AHP). ArcGIS®9.3 software was utilized for the modeling and integration of data to produce the map of solar plant construction in four different classes (poor, moderate, good and very good). The results showed that the zones in very good class covered an area of 1510812500 m2; thus, the southern and western regions of Ilam province are the best places for the construction of solar power plants. Results also showed that GIS as a decision support system and AHP as a flexible model are appropriate for modeling spatial data and positioning the right place of solar power plants.

Land use changes act as a significant factor in the environmental changes and have become a global threat. Monitoring and prediction these changes by satellite images and models can help the planners and managers to make more conscious planning decisions.In this regard, the current research aimed to monitor, model and predict land use changes using CA-Markov model in Kohmare Sorkhi region, Fars province in 2024 for a period of 25 years (1987-2012). To implement the mentioned model, the land use map was first prepared by ETM+and TM sensors during three years (1987, 2000, 2012). Then, validation of maps and change detection process were performed. The results of change detection for the first period (1987-2000) and second period (2000-2012) with an accuracy of 83% and Kappa index of 88% have shown the greatest increase in the rangeland area (4224.24 ha) and the greatest decrease in the forest area (3953.75 ha). In the next stage, in order to calibrate the CA-Markov model, land use map for 2012 was predicted; on the other hand, regarding Error Matrix between the modeling land use map and the reference land use map, the Kappa index wad given as 75%. Finally, considering the previous stage, the land use map for the outlook of 2024 was predicted. The final results for 2024 indicated that the forest area would endure the great amount of changes in comparison with 2012. The forests would change into the irrigated agricultural area and rangelands which can be considered in sustainable development planning by decision makers.

Using satellite images is a simple and inexpensive way to identify the habitats and monitor the migratory pests such as locusts. Using remote sensing technology for locust control policies has shifted from treatment methods to preventive ones. Considering the effective management of insect pest infestations based on thorough knowledge of biology and ecology, this study aimed to evaluate the use of biophysical indices derived from satellite images in order to identify and monitor the locust habitats. For this purpose, we used biophysical indicators (vegetation indices, vegetation, water content indices, drought index and land surface temperature) derived from Landsat 8 (OLI/TIRS) images coinciding with in-situ data monitoring. Then, the information of indices was summarized in one image using principal component analysis. Finally, the primary locust habitat zoning map with high risk, medium risk and low risk was developed using in-situ data obtained from the monitoring and thresholding methods.The spatial accuracy of results was evaluated by locust observed data as reference data; on the other hand, the overall accuracy and Kappa coefficient for high-risk habitat were given as 62% and 74%, respectively. For moderate-risk habitat, they were also obtained as 87% and 71%, respectively. For all of three habitats, they were estimated as 94% and 88%.

The aim of this stydy is to proced the trend process of land use changes into dry land using satellite images, remote sensing and GIS softwares. To find out the magnitude of changes by the image sensor LandSat ETM (2000) and sensor of LandSat OLI (2014) using ENVI®5 software and unsupervised classification methods in Dehsard and kohsefeid, Kerman province. Land use classification map including rangelands, agricultural lands and gardens was specified. In order to produce the training points, field visits, GPS and Google Earth software were utilized. Supervised classification was used to assess the accuracy of classification images. Afterwards, KAPA coefficie was applied to calculate the precision of produced maps. Precision matrix was created for each map. For the detection and assessment of land use changes as compared to the others, Crosstab was used. The results of land use changes in two regions showed that in this regard, the rangelands had the most changes estimated as 77% and 73% for Dehsard and Kohsefid watersheds, respectively. The precision of classification maps was given as 98% for both watersheds. The results of this study showed that the expansion of agricultural activities concerning the rangeland ecosystems could change the rangelands into the lands with low efficiency and in two mentioned regions, 9% and 20% of changes occurred in the low efficiency lands in Dehsard nd Kohsefid watersheds, respectively.