فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال دهم شماره 4 (زمستان 1398)

Land cover and land use are an important variable in natural land processes. Land use change in environmental protection programs and natural resource management plays an important role in the intensification of natural crises such as floods. The Gorganrood River basin in the Golestan province has historically experienced land use conversion. In this research was selected for land use classification using Landsat 8 OLI satellite images of the 25 June 2017. The goal of this study is to assess the accuracy of two approaches, pixel-based supervised classification and the object-oriented one base on quantity and allocation disagreement indexes. The accuracy assessment results indicated verified that for land use mapping the SVM algorithm using a 50 pixel segmentation in the object-based classification having a quantity disagreement of 2.03, an allocation disagreement of 4.58, and an overall accuracy of 92.65% and a kappa coefficient of 0.91 was more accurate than other algorithms in the object-based classification and other algorithms in the pixel-based classification. Based on this algorithm, the lowest of omission and commission error showed in forest lands and residential and industrial areas of 0.58% and 1.59% respectively. The highest of producer and user accuracy showed in forest lands and the water body of 99.44% and 99.41% respectively. The largest area of land use in the Gorganrood River basin is related to the Barren/Rangeland/Cropland class of 314110 ha. Finally, the SVM-SL50 algorithm in the object-based classification is suggested as an optimal classifier with a high accuracy for classification of land use classification maps in order to manage natural resources in Golestan province.

Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 satellite images were used to estimate the concentration of acidity, bicarbonate and sulfate parameters. Initially, Sentinel-2 satellite images were pre-processing and then bands and spectral indexes were determined to identify the significant relationship between the parameter values of water quality and images using the multivariate regression method. In the next stage, using Artificial neural network (ANN) and Adaptive Neuro fuzzy inference system (ANFIS) models, the relationship between Sentinel-2 satellite images and water quality parameters were modeled and then their accuracy was calculated for real values. The results showed that in the modeling of sulfate parameter using Sentinel-2 satellite, ANFIS model with relative error equal to 0.0773 and RMSe equal to 0.8014 has a higher accuracy compared to ANN models with relative error equal to 0.1581 and RMSe equal to 1.2477. While, the relative error of the results of the ANN model are obtained 0.0064 and 0.0556 for acidity and bicarbonate parameter, respectively, and RMSe is equal to 0.0702 and 0.2691, respectively.  The ANFIS model has a relative error of 0.0165 and 0.0722, and RMSe is 0.1975 and 0.3037 for acidity and bicarbonate parameter, respectively. Finally, using satellite images, the mentioned models were applied to prepare a qualitative map of each parameter along the part of the Karun river.

The purpose of this study was to assess the impact of the land use changes in the Minab city on the variations in the urban heat islands (UHI), using the random forest classification method and spatial statistics on TM and OLI Landsat images in 1988, 1998, 2008 and 2018. First, land surface temperature (LST) was calculated using the split-window and the single-channel algorithms. Land use map was generated using supervised classification random forest method and its performance was assessed by two criteria of overall accuracy and kappa coefficient. In order to survey spatial autocorrelation and clustering, pattern of hot spots, the two statistics of Anselin Local Moran's I and Getis-Ord GI were applied. In 1988, land uses of vegetated, barren, and urban built-up lands were occupied 30.1, 32.53 and 37.37 percent of the city area, respectively; in 2018, the areas of these land uses were 16.36, 9.56 and 74.08 percents, respectively. A threefold and twice-fold decrease in the area was observed for barren and vegetated lands, respectively; while the area of urban built-up lands had more than doubled. The calculated values for urban-heat-island ratio index (URI) in 1988, 1998, 2008, and 2018 were 0.45, 0.34, 0.11, and 0.22, respectively. The outcomes of two considered spatial statistics indicated the clustering, pattern for UHI of the Minab city. In addition, there was a good agreement between the results of Getis-Ord GI statistic (hotspots spatial analysis) and the Local Moran's I statistic (spatial autocorrelation) on the spatial pattern of heat and cool clusters.

The object of this research is evaluated of flood productivity on vegetation cover changes. For this purpose, 5 linear transects with a length of 50 m were installed before flooding in the year 2006. In each of these transects, two plots 3×3 were identified at a height of 50 m at the beginning and 1% in length, and within each of them a plot of 1×1 was laid out for uniform harvesting at equal distances, their coordinates using specific GPS and feature the percentages of canopy cover, forage production, bare soil percentage, density and litter before starting flood spreading in 2003 were estimated. In addition, the vegetation characteristic changes were measured in the same place after the flood propagation in the year 2019. Landsat satellite images for a period of 16 years (2003-2019) was also used to study vegetation changes before and after flood propagation. For this purpose, after applying pre-processing on the images, the vegetation index was adjusted according to the soil adjusted vegetation index (SAVI) and soil water index (SWI) over five different time periods. Field results showed that with the application of flood propagation application, the amount of forage production increased from 68 to kg/ha, the percentage of canopy cover increased from 5.9 to 31.4%, as well as the amount of litter from 2 to 16.8% and bare soil decreased from 92.1 to 51.8%. Statistical analysis of vegetation characteristics before and after flood propagation revealed a significant difference between these parameters (p<0.01). In addition, the results showed significant changes in the SAVI and SWI indices over the period. The SAVI and SWI indices increased from 0.027 and 0.5 in the year 2003 to 0.49 and 1 in the year 2019, respectively, and the trend of changes in these indices showed high correlation (R² = 0.65) in flood propagation area. The results of this study showed that flooding productivity by providing soil moisture in the study area improved germination and plant growth conditions, which resulted in the establishment and improvement of vegetation cover in the floodplain.

In this research, occurred changes in the forests around Khorramabad between 1986 and 2018 using TM and OLI Landsat images were investigated. For this purpose, after making the necessary atmospheric and geometric corrections, the images were classified by the maximum likelihood algorithm in five classes with a total accuracy of 95% and a kappa coefficient of 0.94. By overlaying the images, the amount of lost forest (34 km2) was determined and as a dependent variable was imported into the multilayer perceptron (MLP) model. In the GIS environment, were prepared the effective factors in the process of deforestation (independent variables); then by using MLP, the deforestation process in the years under review was determined. It was also land use changes was extracted that the results show the highest changes belonged to the forest to barren land changes and finally the deforestation forecast for 10, 20 and 30 years displays a decrease of 4.6% for the year 1407, 7.5% for the year 1417 and 9.3 for 1427. The results of the network training involving all variables with mean squared error (RMS) of 0.13 indicate that the MLP-based modeling is accurate and also, using Receiver Operating Characteristic (ROC) index, the real amount of deforestation was compared to the result of the MLP model; which showed the high accuracy of the MLP model with 0.88 of the ROC.

The areas of natural resources and vegetation in the Taft and Mehriz townships in recent decades have undergone changes due to its close proximity to the capital of Yazd province. The purpose of this study is to assess the extent and direction and prediction of land and vegetation changes in these two cities. In this study, Landsat 5 (1998, 2004 and 2008) and Landsat 8 (2017) satellite images in the period from May to June was used. Modeling land use/land cover changes were carried out based on supervised classification. The process of changes was analyzed using land change modeling and perceptron neural network method. The results showed that 3% (558.8 ha) of land and vegetation cover of Taft to Bayer and 1.3% (209.9 ha) were added to the urban lands and reduced from the lands of gardens by 4.3% (559.2 ha), this is the highest rating. The amount of 2.8% (678.8 ha) of land and vegetation in Mehriz Bayer and 1.7% (184 ha) has been changed to urban residential land. In terms of urban expansion, Mehriz has had the highest amount of reducing vegetation. The results show that land use and land cover changes in Taft city where more than Mehriz city so that most of these changes were related to gardens, but in terms of area of land use and land use conversion in Mehriz city has the highest value More intense.