The valuable Arasbaran forest is a complex and dynamic ecosystem that has always been subject to extensive fires. The purpose of the present research is to utilize the technology of remote sensing and geographic information system and the technical abilities of the Google Earth Engine system in order to prepare a fire occurrence map in the rangelands and forests of Arasbaran. In order to choose the appropriate method and type of satellite between Sentinel2 and Landsat8, the separability index was used. Accordingly, the RdNBR differential index was selected among the different indicators of fire detection to prepare the final map of the last 9 years and the cumulative fire map. Based on the accuracy assessment of the resulting map quantitatively, 84% of the actual fire points recorded by the General Directorate of Natural Resources and Watershed of East Azarbayjan province were placed at a distance of 200 meters from fire polygons extracted from satellite images, which showed the high accuracy of the fire map. The field visit also showed a good match among the fire areas resulting from the processing of satellite images with the existing situation of the region. The current research showed the high potential of these two satellites as well as the extraordinary ability and facilities of the Google Earth Engine system in providing a huge amount of remotely sensed data and advanced processing on them to prepare fire occurrence maps. The advantages of Landsat8 compared to Sentinel2 are having thermal bands and more time series. The spatial and radiometric resolution of both are almost similar, and the low-temporal resolution of Landsat8 will be compensated by combining it with Landsat 9 images. Therefore, in line with the results of similar studies, Landsat8 is generally preferable to Sentinel2. For the correct and scaled spatial data from Arasbaran region, it is suggested to create an integrated and large-scale Geo-database due to the lack of accuracy.

Introduction Nowadays, natural resources are exploited for the purpose of economical development in developing countries. Expansion of agricultural lands, supply of charcoal and fuel wood and wood production play an important role in forest degradation which affects biodiversity, soil conservation, the quantity and quality of water and the global climate conducted to the importance of forest conservation and reforestation. Therefore quantitative assessment of forests is required for conservation programs and forest monitoring is defined as a tool for sustainable forest management. Today, remote sensing techniques and satellite images can widely provide functional information in environmental studies. In this work, Sentinel-2 satellite images with high spatial, temporal and spectral resolution were applied to determine the area, distribution and density of the Arasbaran forestsas well as other land use classes in the area. Materials and Methods Arasbaran area is located in Qaradagh mountainous region inthe north of East Azerbaijan province between 38°, 25′,59 " N-39°, 20′,7. 7 " N latitude and 46°, 09′,18 " E-47°, 16′,5. 3 " E longitude which covers an area of 551211 hectares and the deciduous forests of this area are known as the 11th Biosphere Reserved in Iran. The altitude varies from ca. 256 m tomore than 2000 m. the importance of the area is in having a rich flora (about 1334plant species) and unique vegetation among the vegetation of the country. For the first time, the Sentinel-2 images with a combination of high spatial and temporal resolution were used to classify the land use of the area. The best band combination was found for bands 2, 3, 6, 12 and NDVI index. Land use classification included dense, semi-dense, sparse and very sparse forests as well as rangeland, agriculture, residential area-bare soil, garden and water was implemented using 9 different algorithms in a pilot area to find the best algorithm. 280 training sample points were collected from all different land use classes in the area. Consequently, supervised classification technique and Maximum Likelihood algorithm with the Kappa coefficient of 0. 886 and anoverall accuracy of 89. 6% was identified as the best classification method for the Arasbaran area. Accuracy assessment of the final map was done using ground control points and Google earth images with a total accuracy of 95%. Finally creating an error matrix with 880 ground reference test pixels revealed the accuracy indices. Results The final land use map of the Arasbaran area based onthe Supervisedclassification technique and Maximum Likelihood algorithm was created. Based on the results, the accuracy assessment of the final map showed that the Kappa coefficient and the overall accuracy of the classified map were 0. 88 and 89. 8% respectively. The forest distribution and canopy cover density map were extracted from the land use area map. The total area of forests with a canopy cover of more than 5%, obtained 131019 ha consisting of 39% dense forest, 36% semi-dense forest, 17% sparse forest and 8% very sparse forest. In addition, the largest type of land use accounted for rangeland with 270000, forest with 131019, agriculture with 101974, residential area-bare soil with 30028, garden with 15434 and water with 2756 hectares respectively. Based on the error matrix table and correct classified points as well as total ground control points, the highest user’, s and producer’, s accuracy belonged to the densed forest class as well as the lowest user’, s accuracy and lowest producer’, s accuracy belonged to garden and agriculture classes respectively. Conclusion The results conducted supervised pixel-based image classification based on the Maximum Likelihood algorithms an acceptable method. It can be because of well-distributed training sample points, the high spatial resolution of Sentinel-2 images or Environmental heterogeneity of the area. According to the results, dense forests declined(from 56910 to 50628 ha)however semi-dense and sparse forests have increased (from 35280 to 47930 ha)with respect tothe last forest survey project in the Arasbaran area in 2003. In addition, the results revealed an overlap between agriculture and garden as well as rangeland and residential area-bare soil classes because of multi culture of crops and fruit trees together as well as dried or low vegetation cover of rangelands in the area. These results can provide useful information for decision-making and sustainable forest management for reducing forest degradation and it seems to be an important next step to manage these forests based on conservation policies.

Water quality monitoring through remote sensing involves establishing a reliable relationship between light reflectance (at specific wavelengths of bands) and water parameters collected in situ (Barrett et al, 2016). Estuaries can play an important role in changing the water quality of lakes, and according to studies, satellite images with high spatial resolution (better than 30m) have been used to study water quality. So, in Chalous River estuary has been selected due to its environmental importance in this research, and its qualitative parameters of salinity, temperature and pH have been studied. Also, to achieve these goals Sentinel2 and Landsat8 satellite images are used and the efficiency of these two satellites to determine the relationship between different physical and chemical parameters of water quality evaluated by comparing with the field measurements. Since Sentinel2 multispectral images comprise more bands compared to older multispectral images such as Landsat8, it is worthwhile to evaluate the possibility of evaluating water quality parameters using Sentinel2 multispectral data. In addition, the main mission of Sentinel2 satellite is continuous environmental monitoring, which if the significant relationship between satellite data and seawater quality parameters is proven, many concerns about environmental monitoring will be resolved and the lack of updated imagery data for monitoring changes in seawater quality will be addressed.

Heavy metals pollution is one of the main drawbacks of using wastewater for irrigation. Exploring the pollution of heavy metals in a vast area needs frequent experimental measurements, which is mostly time and money consuming. In this study, the image of Sentinell2 satellite was used to evaluate the heavy metals pollution of wastewater irrigated area in south of Tehran, IRAN. For this aim, 30 soil-surface samples were collected from the study area and the concentrations of Pb, Cu and Ni were determined using atomic absorption spectroscopy. Then the relation between the heavy metals concentrations and reflectance in the bands or the ratio of the bands at the corresponded sampling points was determined by applying the stepwise regression method. The developed models were applied on the satellite image for zoning the heavy metals concentrations in the study area. Finally, the accuracy of the developed models was examined by Root-Mean-Square Error (RMSE) and Pearson correlation coefficients. The results showed that the amounts of RMSE for the equations of Pb, Cu and Ni were 1. 90, 2. 54 and 1. 59 ppm respectively while the amounts of R were 0. 81, 0. 75 and 0. 73 for these metals that showed a promising match between estimated and measured results.

Evapotranspiration is one of the most important factors in the hydrology cycle and is one of the determinants of energy equations at ground level and water balance. Most of the ground-based methods use point measurements to estimate evapotranspiration. Remote sensing has the ability to estimate the amount of evapotranspiration and examine its spatial distribution. In this study, Landsat8 and Sentinel2 satellite images combined to estimate the actual daily evapotranspiration of sugarcane in the Mirza Kouchak khan Agro industrial Company, Khuzestan province, using the SEBAL model at six dates. Validation of SEBAL model performance was performed in two modes: using integrated images and using Landsat 8 images with lysimeter data. The results indicated that the SEBAL model with Landsat 8 satellite images with (R2=0. 88), and the SEBAL model with Landsat 8 and Sentinel 2 satellite images with (R2=0. 90), Overall, it was well correlated with the lysimeter method and estimated similar and and appropriate results.

Background and Objective: Urban green space has a very important role in the sustainability of the city. Green space per capita is one of the main factors to planning and management of a city. In this study, green space per capita estimation of Khomein city was investigated. Material and Methodology: Satellite imagery was used to measure green space and parks per capita. For this purpose, using Sentinel-2 imagery, land use map was developed in four classes including: Bare lands, habitat, urban parks and farmlands. The urban park layer was extracted and combined to the four district of Khomein city layer. Then, the green space per capita of each area was calculated. Findings: Green space per capita survey using remote sensing approach shows a severe shortage of urban green space in this city, especially zone 3 in the southeast of the city with 1. 66 m2 per capita, meanwhile, this zone had the lowest value among all zones. In this city, green space has an inappropriate distribution and the highest green space per capita value was 3. 43 in zone 1. Zones 2 and 4 had an average of 3. 22 and 3. 27 m2, respectively. Discussion and Conclusion: the results show that the green space per capita of the city is very low compared to the standards and requires special attention of decision makers to increase and expand the green space in the city. So in the near future, a comprehensive analysis of the green space and the reasons for this shortage should be addressed.

IntroductionGroundwater is among the most precious natural resources for human health, economic development and environmental diversity. Since the measurement of groundwater parameters and water quality is difficult, costly and far from being available, interpolation techniques are an easy solution. At the same time, there is a strong correlation between groundwater quality and land use in areas with sensitive aquifers. Changes in land use caused by factors such as rapid growth and expansion of urban centers, rapid population growth, and the lack of land, the need for increased production and the evolution of technologies are important concerns. The literature review shows that the quantitative and qualitative decline in groundwater is a global crisis. As a result, the factors affecting the quantitative and qualitative decline in groundwater range from climate factors to socio-economic factors.In the current research, find an answer to the poor condition of the Isin Plain aquifer by looking at the relationship between some hydrological factors and changes in cultivation pattern of the region is the main goal. For this purpose, the water table and EC of groundwater were interpolated using geostatistical methods. Using satellite imagery, the trend of culture pattern changes over time was obtained. Finally, the relation between the factors on the Isin plain was established. Material and MethodsFor this purpose, the quantity and quality of groundwater in eastern and western Isin plains were interpolated using the Kriging and IDW methods, during the four statistical years of 2004, 2011, 2018, and 2021 and the time series of 2004-2021. The RMSE statistic was used to evaluate the performance of the methods.Then, satellite images and ground truth data was used for land use change classes to investigate the land use changes during the cropping season, along with the determination of changes in the quantity and quality of groundwater in the eastern and western Isin plains for the mentioned years. Satellite data including Landsat 5 multi-temporal satellite images in 2004, 2011, and 2018 and Landsat 8 and Sentinel 2 images for February 2021 were obtained from the USGS.Following preparation of the related images using the flash module, atmospheric and radiometric corrections were performed. Then, the corrections information was extracted into the text file appended to each image. With field survey, the coordinates of the representative pixels were determined and seven land use classes of gardens, vegetables, bare lands, residential and industrial areas, saline lands, and Prosopis Cineraria and Juliflora species were determined. The maximum likelihood classification method was used to separate seven main land use classes based on 127 training samples. For the purpose of assessing accuracy, an error matrix was created for the producer's accuracy, the user's accuracy, the overall accuracy, and the kappa coefficient calculation. Finally, to examine the relationship, the land use map and the groundwater and EC interpolation maps were overlapped into the Arc Map software environment. Results and DiscussionBy comparing the interpolation methods of IDW and Kriging with the RMSE validation technique, it was found that the best interpolation method for estimating water table and EC is Kriging, followed by the IDW method. A review of the land use maps of the Eastern and Western Plains of Isin showed the increase and decrease of different land use categories over the years under study. The overall accuracy and Kappa coefficient were over 82% and 0.79, indicating the acceptable accuracy of the classification and maps obtained. The results of overlapping land use maps and spatial changes in ground water indicate that the location of agricultural land, especially gardens in the eastern Isin plain and vegetables in the western Isin plain, is compatible with the areas of having low water table. The results of overlapping the land use map obtained from Landsat 8 data and EC spatial changes showed the highest amount of EC in can be observed in Prosopis Cineraria and Juliflora species and residential and industrial uses in eastern and western Isin plain. The results obtained from Sentinel2 indicate that the value of EC was significant in the bare lands of eastern Isin and in the saline lands of western Isin. However, the increase in agricultural use, especially for gardens and vegetables, and the pairing with areas with the lowest water table indicates an over-extraction of groundwater for agricultural purposes. On the other hand, the significant extent of bare lands and the upward trend of saline lands, residential and industrial areas, and matching with areas with high EC and the adaptation of maximum EC with Prosopis Cineraria and Juliflora species uses may be a warning for poor condition of the Isin plain aquifer.

Surface soil moisture is an important variable that plays a crucial role in the management of water and soil resources. Estimating this parameter is one of the important applications of remote sensing. One of the remote sensing techniques for precise estimation of this parameter is data-driven models. In this study, volumetric soil moisture content was estimated using data-driven models, support vector regression (SVR) and multi-layer perceptron artificial neural network (ANNMLP) method. The parameters of the two models are optimized by the Genetic optimization algorithm. Estimation of volumetric soil moisture content with the two top models was performed using two types of radar image (Sentinel 1) and optics image (Sentinel 2), in which optimized optics image bands were identified by the Genetic optimization algorithm. After estimating the volumetric soil moisture map, four outputs of the two methods are compared. The best estimate of the volumetric soil moisture content has been achieved by the support vector regression (SVR) method with the Sentinel 1 image. The worst estimate of the volumetric soil moisture content has been achieved by the multi-layer perceptron artificial neural network (ANN-MLP) method with the Sentinel 2 image. The accuracy of this study was calculated by the square of correlation coefficient of the measured volumetric soil moisture content and the estimated volumetric soil moisture content, which the best and worst correlation coefficients, respectively, 0. 659 for Sentinel1 image using support vector regression method and 0. 409 for Sentinel2 image using multilayer perceptron neural network method have been calculated. The root mean square error (RMSE) is also used to calculate the error of the methods. The lowest and highest errors were calculated by 0. 291( 𝑚 3 𝑚 3) for Sentinel1 image with support vector regression and 0. 4612( 𝑚 3 𝑚 3) for Sentinel2 image with Multilayer Perceptron Artificial Neural Network.

Background and Objectives: In Golestan province, the suitability of climatic condition to produce most of the agricultural products has led to high diversity in crop production, so this province has the first rank in terms of cultivating and producing oilseeds, especially soybean, in Iran. This research was carried out at four major watershed basins of Golestan province, Mohammad Abad, Qaresoo, Zaringol, and Gharnabad. This study was aimed to estimate the area under rice-and soybeans-cultivation in the aforementioned watershed basins. For this, Sentinel2 satellite images were used for the first time using different supervised classification methods (Maximum likelihood, the minimum distance of average and the Mahalanobis distance). Materials and Methods: In this study, two Sentinel-2 satellite images of August and September of 2016 were used to identify, detect and evaluate the cultivated area of rice and soybean as two summer crops. This research was carried out at four watershed basins of Golestan Province (Mohammad Abad, Qaresoo, Zaringol, and Gharnabad). Radiometric, atmospheric, and geometric corrections were made after downloading the images of the study area. Then, band compounds, pseudo-color combinations, image mosaics and rational band calculations were carried out, and the NDVI vegetation index was used to detect vegetation cover from other land uses, and finally, a land use map and crop layer was produced. Results: Results of this study showed that the soybean cultivation area which is an alternative plant for rice in summer cropping, has decreased compared to past years. In the present study, two Sentinel-2 satellite images of August and September of 2016 were used to identify, detect and evaluate the cultivated area of rice and soybean as two summer crops in four watershed basins of Golestan province. To compare the outputs of the three classification methods, training and test samples were used. In order to evaluate the accuracy of the classification results, the generated map was analyzed using the GPS-registered ground control point. The Maximum likelihood classification with kappa coefficient and overall accuracy of 92% and 95. 5% was selected as the superior method for rice. In this method, the rice cultivation area was estimated 32911 hectares with an 18% bias compared to the Agricultural Jihad statistics (27839 hectares). Whereas for soybean, the minimum distance method with kappa coefficient and the overall accuracy of 88% and 95. 2% was selected as a superior classification method. Based on the results, the soybean cultivation area was estimated as 28359 hectares, with a bias of 13%, compared to the Agricultural Jihad statistics (25083 hectares). Conclusion: Sentinel2 satellite images have a high potential for quick land detection and providing crops cultivation area maps in a regional scale. Also, the rice cultivation area has been increased compared to past years, while has been decreased for soybean.

The signs and symptoms of the water crisis in Iran appear to lead to the decline in groundwater levels, land subsidence, soil erosion, dust storms, and the drying of wetlands, rivers and lakes. In this situation, the accurate identification and conservation of water resources is essential to reduce the effects of water scarcity. This research was conducted to better identify the water areas and assess the accuracy of aqueous clearances from non-aquatic environments using spectral measurements of distance from the Lar dam in the southwest of Damavand Peak. Four NDVI, NDWI Gao, NDWI McFeeters and MNDWI spectral indices were used to identify the water mass and distinguish it from other natural and artificial effects on Landsat 8 and Sentinel2 images. The results show the highest overall accuracy is related to the MNDWI index and the lowest overall accuracy is related to the NDWI Gao index. The kappa coefficient also represents a better separation of water zones from land in the MNDWI index. It is suggested that similar investigations be carried out in different areas of the country simultaneously to identify the aquifers so that the spectral indexes can be studied and evaluated more appropriately due to changing environmental conditions.