Journal of RS and GIS for Natural Resources
Year:2019 | Volume:10 | Issue:1 (34) |Papers Count:7

Vegetation is one of the criteria indicating the production potential of the land. So that the quality and quantity of vegetation in each region is a criterion for determining its production potential. In this research, vegetation indices of Landsat 8 digital data were used to estimate vegetation cover and biomass in the protected area of the central Alborz in June 2016. To study the correlation between vegetation cover and biomass with satellite data, 27 samples were obtained randomly in the region. Linear regression was used to determine the relationship between cover percentage and biomass values with remotely sensed vegetation indices. The results showed that among selected vegetation indexes, Enhanced vegetation index (EVI) and Visible atmospherically resistant index (VAR) had the highest correlation coefficient with vegetation percentage 0. 53 and 0. 52, respectively. Therefore, these vegetation indices are appropriate for estimating vegetation cover at a 5% significance level. And in calibration, the correlation coefficient for the wet weight of vegetation and the indices of Global environment monitoring index (GEMI), Simple ratio (VR) and Enhanced vegetation index (EVI) were 0. 43, 0. 41 and 0. 41, respectively even though their estimations were unsuccessful in the validation stage. According to the results of this research, it is recommended that the indicators for the estimation of the quality and quantity of vegetation should be used in which the atmosphere and soil impacts are considered.

Land surface temperature (LST) is an important factor in global warming studies. Therefore, considering its negative effects on human life, it is very important to study this phenomenon. The aim of this study was to investigate the effects of vegetation indices and surface characteristics of Sanandaj city on land surface temperature changes. For this purpose, the land surface temperature was calculated in ArcGIS and vegetation indices and the surface Sanandaj city were derived from Landsat images in ENVI software. The results of regression analysis and correlation of LST with synoptic station data were 0. 45 and 0. 20 respectively, which shows a positive relationship. The results of the overall accuracy and the indices shows that the correlation analysis the precise indices (enhanced buildup and bareness index, index-based built up the index, normalized difference built up the index, urban index), positive relationship and low precision indices (normalized difference bareness index, new built up the index, normalized difference water index, modified normalized difference water index) have the opposite correlation with land surface temperature. The results of the temperature situation in the vegetation classes indicate that the high temperature was observed in the low vegetation classes in the early summer of 1988, 1999, 2008, 2012, 2014 and 2015, and the lowest temperature in dense vegetation classes at the end of spring, 1989, 1993, 2000, 2007, 2010 and 2016. Therefore, the reduction of vegetation and growth of builtup areas has an important role in increasing the land surface temperature, and spectral indices to extract buildup areas from satellite images can provide acceptable results.

The land surface temperature (LST) plays a vital role in a wide range of scientific researches including climatology, hydrology, natural resources and etc. There are some determining factors which affect the land surface temperature, such as the kind of surface elements, topography and environmental conditions and also the amount of incoming radiation to the surface. The objective of this study is to investigate the effect of topographic parameters, climatic conditions and downward radiation on land surface temperature using remote sensing data. For this purpose, the Landsat 8 satellite image, ASTER digital elevation model, MODIS water vapor product (MOD07) on 24 July 2018, topography and climate map of Kerman province were used. To calculate the LST and downward shortwave and longwave radiation to surface the single channel and SEBAL energy balance algorithms were used, respectively. Finally, using statistical analysis the relationship between LST and independent variables, including elevation, slope, aspect, vegetation cover index and downward radiation to the surface were studied. The results of the study shown that the correlation coefficient between the LST and each of the independent parameters is more than 0. 7. Also, the relationship between LST and topographic, normalized difference vegetation index (NDVI) and downward radiation parameters at the 95% level was significant. The results of the mean of LST values in climatic conditions, including extra-dry, dry, semi-dry, Mediterranean, semiwet and wet indicate that climates classes with higher LST relative to climates classes with lower LST have means of elevation, NDVI lower and mean longwave downward radiation to surface higher.

The aim of this study was to investigate the canopy cover and seed production of medicinal-economic Sumac (Rhus coriaria) seeds and locating susceptible areas of Sumac in rangelands of Khakriz village, Bilesavar County, Ardabil province. For this purpose, three sites (a total of 60 hectares) with Sumac cover and in each site three transects were parallel and perpendicular to the direction of the slope with intervals of 100 meters, were established. Along each transect used 10 plots of five square meters with 20-meter intervals using a random-systematic sampling (a total of 90 plots) and the position of each plot was recorded using GPS. Sumac seeds in each plot were harvested by clipping and weighted method. Maps of environmental factors (elevation, slope, aspect, annual precipitation, and temperature) created using digital elevation models (DEM) in ArcGIS and then values of each environmental factor extracted for each Sumac site and the questionnaire of ratings were completed in this regard. Then, to determine the areas susceptible to cultivating Sumac in the Khakriz rangelands, the analytic network process (ANP) method, was used. The results showed that the areas with Sumac cover had 657. 88 kg/ha seed production and 263. 12 kg/ha of powdered seed production. The results of ANP indicated that 157. 12 hectares of the area were very suitable, 138. 10 hectares were suitable, 110. 60 hectares were medium, 46. 01 hectares were poor and 46. 17 hectares were unsuitable for Sumac cultivating. The results can be used to exploit, cultivate and increase the cover of Sumac in specified areas.

The western forests and rangelands of Iran in Zagros habitats have mainly been destroyed by various reasons in recent years. The preparation of the land cover map in these sites is the first step to protect them and to prevent further destruction. The aim of this research was to select the best algorithm for land cover mapping in a part of Ilam site using the Sentinel-2 image. After providing Sentinel-2 the supervised classification of it was performed by seven different algorithms (maximum likelihood, minimum distance from the average, mahalanobis distance, spectral angle mapper, spectral correlation mapper, support vector machine, neural network). For accuracy assessment of the land cover maps, the stratified random points were created and found in the field. In the field visit, after determining the current land cover of each point in the plot area, the real land cover of each point was compared with the defined land cover of the same point in the pixel area based on classification results and the accuracy of the algorithms was evaluated. The results showed that the support vector machine algorithm had the highest accuracy in providing the land cover map with a general accuracy of 79% and a Kappa index of 0. 70. The analysis of the land cover map obtained from this algorithm showed that the dense forest area was 319. 64 ha, semi-dense forest area was 361. 44 ha and sparse forest area was 1832. 36 ha from the total area of the study area (16085. 31 ha). Also, the rangeland area was 7352. 78 ha, the garden area was 62. 32 ha, the agricultural area was 658. 42 ha and understorey agriculture was 4504. 64 ha. For optimal management of this sensitive ecosystem, land cover mapping using this algorithm in certain temporal intervals is essential to investigate the forests and rangelands change and to control the human-made land uses.

Soil salinity is considered as one of the potential environmental hazards. The purpose of this study was to find the best index and the most suitable relationship for estimating soil salinity and its mapping using remote sensing data. At the first step, random sampling was performed using fishnet method and surface soil electrical conductivity (EC) measurements. Then, the threshold levels (92%, 95%, and 98%) were applied to the output images of each indicator. The methodology included using the least squares fitting (LS-fit) technique and principal components analysis (PCA) for halite and gypsum minerals, determining the correlation between the output of indices and ground data, and performing clustering and factor analysis between EC and output images. In order to select the best model derived from Landsat-8 band combinations and the amount of salinity, collinearity test, Durbin-Watson test, and backward multivariate regression were employed. The Cohen‘ s kappa coefficient was also applied to evaluate the multivariate regression formed by Landsat-8 bands. The performance of the indicators was evaluated based on four criteria of root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE) and R-squared (R2). The results of the factor analysis showed the smallest distance between the EC, salinity index (SI) and brightness index (BI). The SI with an amount of 0. 89 had the highest Pearson correlation with EC. In the dendrogram diagram, SI index with EC was placed in a cluster, and the RMSE, MBE, MAE and R2 values of the SI index were estimated to be 0. 16, 0. 11, 0. 12, and 0. 76, respectively. Compared to the rest of the indicators and linear, multivariate regression (with Cohen‘ s kappa coefficient of 60%, ), the SI index has provided better outcomes.

Predicting land use changes using satellite imagery is now a useful tool for helping planners in complex situations. The purpose of this study was to detect and predict land use changes during the 28-year period (1986-2014) by CAMarkov model in the Safarood-Ramsar watershed of Mazandaran province. In this research, land use and NDVI maps were prepared using Landsat TM (1986), ETM+ (2000) and OLI (2014) satellite images. The accuracy of the CA-Markov model was estimated using the Kappa index of 87%. In order to calibrate the CA-Markov model, the land use map was prepared in 2014, and the Kappa coefficient of the mapping from modeling and user base map (2014) was 82%. The results showed that during the period between 1986 and 2014, the area of forest lands decreased by 10. 26% and the total area of residential areas increased by 3. 27%. The land use map for the years 2021 and 2028 was predicted by the CA-Markov model. The results showed that during the period 2014-2028, forested lands and rangelands decreased by 4. 92% and 1. 7%, respectively. Residential areas will increase by 8. 04% and the agricultural land will change slightly, indicating the changes in land use to residential land.