---

Prelude: Due to the special characteristics and conditions of Security of the border, deployment of security in such areas is important.any form of insecurity in a border zone can seriously threaten different aspects of security in the country. Deployment of security along the border and in the border zone requires in the first place, investigation, and identification of natural and human-made features within the border zone. identification of natural and geographic features of a border zone is of paramount importance owing to the crucial role played by such bio-foundations in the social life and development of the border zone. They further contribute largely to borderline monitoring, management, and control systems.
Goal: The present research seeks to investigate the states of natural features, including the border stream, topography, and vegetation, in the Iran-Afghanistan border zone in Khorasan Razavi and further evaluates their impact on borderline and border zone security. materials and ways: the required data was extracted from the OLI sensor on the Landsat Satellite(2018), with the data then analyzed using GIS and remote sensing techniques in the ENVI )5.3(.
Conclusion: The findings showed that water scarcity and insufficient depth of Harirood River in most parts of the year set the scene for illegal trafficking of goods and drugs across the border, negatively impacting the border zone security. This further holds true for the seasonal lake of Namakar in the border zone between the two countries. Considering the topography, existing maps indicate that the presence of highlands in the vicinity of the Iran-Afghanistan borderline and extension of particular highlands into the mainland of Afghanistan have negatively influenced the border security.

---

The purpose of this study is to investigate the changes in the land use of Amol at the time of 1986-2020. The effect of stimulant factors in the growth of the city determines that population and immigration from villages are considered as the most important factors in the growth of Amol city. Also, in the process of formation of the tissue of Amol, the primary core of the city after Islam has had a major role. The study of its growth process after the first Pahlavi shows that development of roads and commercial land uses distribution, especially around the main ways are another stimulant agent in the urban growth of Amol which also forms the pattern and form of growth in Amol city. The data provided from satellite imagery clearly identifies the growth of Amol from 1986 to 2020. The built-in area shows an annual increase of 15% (135 hectares per year). The expansion of the city has occurred in all respects, but it is more obvious along the main road to the nearby villages. Business/ service areas have been established along the roads which show the rapid decline in agricultural lands and vegetable areas. Also, changes in agriculture in Amol and in rural settlements merged in the city and the central part of Amol city is seen under the influence of urban expansion and development.

---

Aims: Recent advances in landscape ecology and satellite data have provided an opportunity to change approaches in sustainable urban planning and have created a high potential for enhancing resilience in the interactions of social-ecological systems. In this research, by using the concept of spatial resilience and measuring the critical components and relationships of the socio-ecological landscape over time, the thresholds related to identity were quantitatively extracted to provide a solution for linking concrete management objectives and the theory of resilience.
Methods: By evaluating the ecological landscape of Qom city using PLAND, CA, NP, AREA-MN metrics and satellite data, the changes in landscape resilience of this city during thirty years based on the theory of spatial resilience were analyzed.
Findings: By defining and extracting identity thresholds, identity changes in the city landscape were predicted concerning resilience in the coming years. Then, by identifying the spatial-identity patterns of the city in different periods and measuring them based on resilience dimensions, measurable suggestions were presented to policymakers and planners to place the urban landscape in a new, resilient, and sustainable balance.
Conclusion: The landscape of the city of Qom in 2009 and 2019 has crossed the first and second thresholds of identity, and with the continuation of the current trend, in the next 20 years, traveling the third threshold (complete transformation of landscape identity) will happen, and if the process of reducing the green area structures continues, the landscape will reach an irreparable stage in terms of resilience.
 

---

---

Data and information play a special role in the transparency of water governance. On the other hand, witnessing contradictions in water resources data and information, inconsistent readings and narratives about water assets, outdated hardware equipment, and to some extent software enhancement in the preparation and presentation of water resources information compared to global advances, necessitates a serious review of water resources data collection and processing systems. In this regard, artificial intelligence methods, sensors, and remote sensing technologies are considered in accurate water resources accounting. This article is a systematic review of about 100 international articles that present the latest findings related to software and hardware equipment for monitoring hydrological cycle meta-indicators. These meta-indicators include precipitation, water depth/water level/flow velocity and discharge of rivers, and groundwater level. In each case, while providing a list of the most important technologies, the application level of these technologies in monitoring surface and groundwater resources in Iran was evaluated. The conducted studies prove the unfavorable application technologies in monitoring hydrological cycle in Iran. For example, out of a total of twenty-six known technologies related to surface flow measurements, only two technologies have been widely used Iran; four technologies have reached the knowledge frontier and widespread production by domestic knowledge-based companies, and eleven technologies have not yet reached the knowledge frontier Iran. In this paper, suggestions were presented to outline the path for developing new technologies for water cycle data collection and transformation in the modernization of Iran's water resources data collection and data processing infrastructure.

---

An ever increasing population needs more energy and food to be provided from limited resources. Many different problems such as floods and droughts frequently occur and even happen at extreme values throughout the world, since the inherent capability of the resources is not evaluated properly. Soil erosion, as one of the major types of land degra-dation, is supposed to cause serious problems for future and even present generations. However, evaluation of the magnitude and spatial distribution of fundamental types of soil erosion i.e. sheet, rill and gully erosion is an important task to be conducted in devel-oping countries where the necessary bases for development are required. An attempt has been made in the present paper to introduce a technique through which the condition of soil erosion is defined and mapped in the study area. All available and acceptably accu-rate information, such as the geologic sensitivity of the area to erosion, the land type and slope maps, are required for determination of homogeneous work units using overlying. The present character of soil erosion in the field is then evaluated by filling out the revised questionnaire forms adjusted on the basis of criteria mentioned by United States Bureau of Land Management (USBLM) and the final classification of each type of erosion is made according to the sum of scores obtained by each work unit. Finally, the overall situation is generalized in the fractional form. The presented technique has been implemented for more than 6 million hectares of the area of Iran and was able to reflect the governing conditions well and the results should be applied in the management of natural resources to achieve sustainable utilization. For the present study and for demonstrating the details of methodology, a small watershed located in the Markazi large watershed of Iran, known as Barahmoom, and comprising 4236.2 ha was selected as a case study. The presented soil erosion mapping technique can be applied for areas where only basic or very little data and information is available.

---

---

---

Detection of land use/cover changes in many different studies is one of the basic needs for environmental monitoring and management. Conversion of agricultural lands is one of the main issues related to urban planning. In this study an attempt has been made to study land use/cover changes through image processing techniques. Two landsat TM images of Isfahan area provided in 1990 and 1998 were atmospherically rectified and registered on each other. Images were then classified to ten different land use/cover classes using Bayesian classification algorithm. Training sites were generated using fuzzy logic approach. A post classification comparison approach was then used to create a change map. The results show a dramatic change on agricultural lands in this area during this period.

---

Landform maps show the earth’s surface phenomena and nature of the processes that have been working to produce them. These maps are valuable in spatial planning, agricultural purposes, environmental conservations and forecasts and natural hazards prevention. The study area is a vast province of semnan/Iran (area:96816 km2) with different physiographical conditions from mountainous areas to playas and desert landscapes. Because of difficulties for deep field surveys and time limitation, investigation of the region was done by RS data and some geomorphologic sampling. So, RS data with high spectral characteristics such as LANDSAT were used and combined with those of high spatial resolution such as IRS-PAN in ER-MAPPER 6.3 for better analysis and enhanced classification of geomorphologic features of the study area. Therefore, the region was classified in to 27 classes in ARC/VIEW 3.2a.Then, the results were loaded in Arc Info7.2.1 in order to generate a GIS-ready system. Also, DEM of the region was produced using topographic maps for further studies. The produced landform maps can be used for many environmental plans and reasonable adaptation by real world.

---

Aims: This study aims to evaluate the Soil Hydrological Response (SHR) under LU/LC using a field-oriented and remote sensing database in the Jiroft watershed, Iran.
Materials & Methods: Land use maps were extracted from Landsat images using the supervised classification method for 1987-2017. The results were validated against field data from 100 points, where we found the Kapp index to be greater than 80%, indicating an acceptable land-use classification. The LU/LC map was then projected for 2047 using the CA-Markov model. The Curve Number (CN) for each land use was determined from superimposing LU/LC and the soil hydrological group map. The Soil Conservation Services-Curve Number method (SCS-CN) was employed to estimate runoff.  
Findings: Good (densely vegetated) and moderate rangelands had a decreasing trend (i.e., -2.94% and -3.64% in 1987- 2017), while croplands, orchards, residential, and saline areas expanded (by 1.46%, 0.88%, 0.33%, and 7.21%). We found that agricultural lands, saline lands, and residential areas would increase by 0.75, 5.5, and 0.13%, by 2047, respectively.
Conclusion: We found a considerable increase (up to 6 mm increase) in runoff depth in some land-uses and more than 3.4% increase in the area of the high runoff producing class (IV).  We believe that higher runoff production potential and more intense and short rain showers should be considered seriously in terms of possible flash floods in the future.

---

A knowledge of soil surface conditions, especially desert crust, salt crust and desert varnish is useful for improving classification of remotely sensed data. Desert crust can generate high levels of reflectance, similar to those areas with high salt concentration and non-saline soil. Therefore, soil surface crusts might bias thematic remote sensing of soils. In this study, we evaluated the efficiency of the Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) reflective and thermal bands in detecting crusted surfaces and soil salinity conditions. The study areas were Ardakan, Damghan, Lut Desert, Qom, and Abarkooh which are located in arid regions of Iran. To assess the Landsat TM ther-mal data for detecting land cover types, the following steps were taken: 1) determination of correlation coefficients between TM wavebands, 2) assessment of the relationship be-tween TM thermal and TM reflective bands on land cover types, 3) assessment of the rela-tionship between soil salinity and TM Digital Numbers (DN), 4) two dimensional Feature Space (FS) analysis of the training samples, 5) field sampling, 6) image classification and accuracy assessment, and 7) comparison of surface reflectance of different soil surface types. The results show that the trend of correlation coefficients of TM6 with reflective bands is completely different from the correlation between reflective bands. The behav-iour of the thermal band on gypsiferous soils is completely different from that on saline soils. Moreover, with an increasing correlation between soil salinity and reflective bands, the correlation between soil salinity and the thermal band decreases. In image classifica-tion, the thermal band improved the separability of the crusted and gravely classes. Therefore the TM/ETM+ regions of the electro-magnetic spectrum have complementary capabilities for spectral separability of gravely and crusted surfaces. In general, selection of the TM/ETM+ thermal band combination is an important step for classifying the re-mote sensing data and for securing class separability of gravely and crusted surfaces in arid regions. We also concluded that TM/ETM+ thermal bands may contain information complementary to the TM/ETM+ reflective bands and therefore this combination of the TM/ETM+ thermal and reflective bands provide a viable method for soil salinity studies in arid regions.

---

Assessment of Evapo-Transpiration (ET) in the cases such as Irrigation programming, water basin evaporation determination, water balance calculation, water runoff estimation and climatological studies are important. It is possible to determine ET by field measurements. However these methods only can determine ET for the regions with the limited areas. This limitation has made the use of remote sensing techniques reliable for assessment of ET in a vast area. In this work, the amount of ET has been evaluated in an army wheat field located in the Golestan Province (North of the Iran) for May 5th and June 7th, 2003 using MODIS images. Surface albedo affects in the outcome of SEBAL that we estimate it using two methods, one using 1 and 2 bands of MODIS image (old method) and the other using 1 to 5 and 7 bands of MODIS image (new method). The comparison of the results of SEBAL to the results of other works showed the accuracy of the estimation of surface albedo using the new method is better than the old method. Also, the accuracy of SEBAL outcomes are relatively satisfactory and can be improved by further detailed studies.

---

Aims: This study aimed to propose an effective model for estimating soil moisture by integrating the optical trapezoid method with a deep learning Long Short-Term Memory (LSTM) model. The performance of the proposed model was compared with two other methods, i.e., Partial Least Squares (PLS) regression and Group Method of Data Handling (GMDH) multivariate neural network.
Materials & Methods: This study combined the optical trapezoid method with deep learning models to propose an effective model for soil moisture estimation in the Maragheh watershed. A total of 499 in-situ soil moisture data were collected. Relative moisture content was calculated using the optical trapezoid method and imported into the LSTM model, along with other inputs such as spectral indices and DEM-based derived variables. The performance of the mentioned models was assessed both with and without the optical trapezoid method to evaluate its efficacy on the performance of AI models.
Findings: The results demonstrate that the combined model of deep learning LSTM and the optical trapezoid method achieves satisfactory performance, with an R² of 0.95 and a RMSE of 1.7%. The PLS and GMDH methods performed moderately, both without the involvement of the optical trapezoid method and in the combined mode.
Conclusion: This study shows that the optical trapezoid method can improve the performance of deep-learning models in estimating soil moisture. However, considering the significant difference in computational costs among these models, choosing the appropriate model depends on the user's objectives and desired level of accuracy and precision.
 

---

Sensors onboard meteorological satellites such as MODIS and AVHRR are able to collect information adequate in frequency but with low spatial resolution. The problem can be overcome, if one finds a way to increase the quality of the vegetation indices through searching in each individual pixel of the images, employing concurrent higher spatial resolution images. The objective of this study was to investigate the enhancement of MODIS NDVI products by using NDVI from the ASTER sensor onboard the same platform, as MODIS. The ASTER averaged NDVI values computed using only vegetated pixels were compared to unadjusted MODIS NDVI. Two approaches for the comparison are introduced in this work. In the first one, vegetated ASTER NDVI compared with MODIS NDVI (AMII Model), and in the second one the difference between vegetated ASTER NDVI and MODIS NDVI was modeled against a code representing percentage of vegetation cover (AMDI Model). It is found that the MODIS NDVI index always reads lower as compared to the vegetated ASTER NDVI. It was also found that the difference between vegetated ASTER NDVI and MODIS NDVI for vegetation covers of less than 20% was greater than 0.1 and for vegetation covers of more than 80% as low as 0.01. This could produce erroneous results when introducing uncorrected NDVI values into the climatological models especially in the arid and semi-arid climates where the vegetation covers are sparse. Both AMII and AMDI models produce NDVI values higher than those calculated from MODIS. These models were tested using 10 samples where a RMSE of about 0.028 for AMII and 0.018 for AMDI was found out. It is revealed that AMII model increases the NDVI values up to 87% for pixels containing less than 10% vegetation while 5% for pixels with more than 80% vegetation covers. These increases for AMDI model were 84% and 6%, respectively.

---

Ecological studies based on field data have shown that vegetation phenology follows a relatively well-defined temporal pattern. This pattern, that is reflecting the cumulative temperature from the date of the beginning of the growth, can be represented by the use of a suitable model. Due to the spatial, temporal, and ecological complexity of these processes a simple method to monitor phenological behavior of the vegetation canopies through remote sensing has proven elusive. Employing ASTER images from different seasons, might make it possible to produce an algorithm for sugarcane phenological date estimation and as well to monitor different stages of the plant growth from cultivation to harvest. For this, a parameter, namely Physiological Date is employed. Based on the field collected data and selected ASTER Images, 133 Regions Of Interest (ROI) having different Phenological Dates (PD) in units of Degree-Days (DDs) were supplied. One hundred of these samples were taken for modeling and another 33 for testing the models. Such indices as NDVI and SAVI along with PDs for the ROIs were calculated. The correlation between these indices and PDs was investigated. This ended up with the introduction of two models of PANDVI and PASAVI respectively based on the use of NDVI and SAVI indices for PD assessment. PANDVI model showed a better correlation with the field recorded data although either of the models can be well enough predictive.

---

The geometric mean particle diameter (dg) and lime are two of the most important properties from the viewpoint of soil management. Nowadays remote sensing technology which has emerged walking with science development throughout the world, has made soil study faster, more facile and more cost-efficient. An investigation of soil dg and lime was performed in Pol-e-Dokhtar area by use of four sets of spectral data of IRS P6, LISS III obtained from the Organizations of Geography of Armed Forces and Aerospace of Iran, in September 7th 2007. Subsequently, Principle Component Analysis, Normalized Difference Vegetation Index, Soil Line Euclidean Distance and Unsupervised Classification was carried out for satellite data sets following image preprocessing operations. Through stratified randomized sampling method and according to the false color composite and photomorphic units of the main image, 95 samples were selected and eventually collected from 0-5cm depth of soil surface, likewise 43 samples from 5-20cm. Afterwards, dg and lime contents were determined for each sampled point in soil laboratory. By means of multivariate regression operations there were eventually shown pronounced relationships (P< 0.01) between soil dg and lime with green (R2adj= 0.78) and NIR (R2adj= 0.77) bands in the first sampling depth. In addition, this was true for the second sampling depth with green (R2adj= 0.57), NIR (R2adj= 0.55) and red (R2adj= 0.59) bands with lower coefficients of determination. Consequently it has been substantiated with evidence that dg and lime contents are able to impress soil spectral reflectance. So it is possible to find out about these parameters using satellite and ancillary data.

---

Quality of coastal water is of great importance in environment and other applications. Therefore, studying the quality of this water is quite vital. Traditional methods of water quality studies are time consuming processes that lead to pixel-based information and also impose a great deal of costs. Using stelllite images and remote sensing can play an important role in enhancing the outcome of these studies. In this research, by field sampling of secchi depth, simultaneous to the satellite pass; an experimental statistic-mathematical model was fitted for the acquired data in the field and the processed images of the IRS-LISS-III sensor. The results indicated good relations between the Secchi depth and the radiance received by the sensor. The fitted model can be used to map Secchi depth in coastal areas.

---

Due to some practical limitations, it is not possible to adopt images which have both high spectral and spatial resolutions. Image fusion is one of the methods that utilizes image supplementary information, to the effect that it combines the spatial information of images with high spatial resolution, By the use of these spectral information of the images with high spectral resolution, one can creates an image that has high spatial and spectral resolutions simultaneously. Current methods and algorithms of image fusion are not efficient enough to combine new satellite images due to some changes in these satellites; therefore, providing new methods of image fusion is of paramount significance for the above-mentioned satellites. Image fusion methods should desirably maintain the spectral and spatial information of the original images. In the present study, a new algorithm was introduced to combine the spatial information of the IKONOS images with the spatial resolution of 1m with the spectral information of the SPOT images with the spatial resolution of 20m. This algorithm is in the feature-level and is based on the Retinal Model. Other existent fusion models such as IHS, HPF, PCA, Wavelet and the hybrid Wavelet-PC were also applied to these images. The results of the spatial and spectral assessments of the combined images indicated that the spectral and spatial information of the proposed method are better. One of the major advantages of this method is that there is no need for resampling, which is a must in other methods.

---

The ability in assessment of Total Precipitable Water (TPW) is useful in the prediction of the amount of raining, dam over-flooding and the flood. To extract TPW, the algorithm of infra- red bands near the MODIS sensor images were used. The satellite TPW, was validated using radiosonde data. Due to the limitation of the algorithm implementation to the cloud free sky and stable atmosphere, the general atmospheric conditions in the satellite passing date were investigated using auxiliary curves produced by synoptic and higher level meteorological data. In this way, the calm and eddy free atmosphere were selected. Then MODIS images were supplied from Iran Space Agency for this satellite passage. Then the TPW data were estimated using radiosonde and thermodynamics equation. Then regarding the stability and lack of new air masses in the region for the selected days (using analysis of the ground data and atmospheric profiles), the TPW for the time of satellite passage was interpolated.For determination of and in the aforementioned algorithm, EVI and ENDVI indices were deployed. At the end, a regression between the TPW produced by satellite and the one calculated from the radiosonde. showed that for the Mehrabad weather conditions, the MODIS channels 18 and 19 are suitable. Using the ratio of the apparent reflectance in the water vapor absorption bands to the one in the non-absorbing band, the atmospheric water vapor transparency for each one of the water vapor bands was calculated. The TPW in the earth-sensor path was calculated by implementing MODIS infrared bands under different atmospheric conditions, taking into account sensor and zenith angles, and the water vapor transparency using band ratio technique.