Journal of RS and GIS for Natural Resources
Year:2021 | Volume:12 | Issue:2 (43)|Papers Count:6

Background and Objective: Biological soil crusts are a collection of lichens, mosses, fungi, cyanobacteria, etc. that are part of the soil ecosystem. Estimation of density and distribution of biological soil crusts in arid and semi-arid regions of Iran, which is the subject of soil erosion and wastage is very important. Methods based on remote sensing techniques are important in terms of cost and time less efficient methods to achieve this goal. Segzi plain is one of the critical points of wind erosion in Iran and identifying and determining the distribution of biological soil crusts as a soil modifier is an effective step in reducing wind erosion in the region. In this research, BSCI (Biological Soil Crust) index has been used to prepare the distribution map of lichen-dominated biological soil crusts. Materials and Methods: The study area is part of the Sajzi Desert (Central Deserts of Iran) which is located in Isfahan province of Iran. The study area with an area of 199. 5 hectares is spread between the eastern lengths of 51o52'32" to 52o27'41" and the northern widths of 32o33'31" to 32o55'01". The average slope of Segzi plain is 1. 08 percent and its average height is 1680 meters. According to the statistics of East Isfahan Meteorological Station (Shahid Beheshti Station), the average annual rainfall in the region is 106 mm. According to the Dumarten climatic classification, the climate of the region is dry and according to the Amberge classification it is cold. The BSCI index is a combination of the relationships used to estimate vegetation and bare soil surface, and its mathematical relationship is the slope of the soil line. To calculate the soil line in an area, one must first separate the pixels that have bare soil and no vegetation. In order to calculate the soil line equation, in four seasons of a year, images of Landsat OLI 8 satellite related to 2018 were downloaded from the site of the US Geological Survey and 20 to 30 pixels of pure bare soil were extracted by drawing the reflection values of these pixels in the red and infrared band. Red near soil line coefficients were calculated for each season in the Segzi Plain. Based on BSCI index, lichen-dominated biological soil crust are identified using at least VIS-NIR spectral reflection and the slope between the red and green bands compared to bare soil and dry vegetation. Using ENVI software, the distribution shells of biological shells with lichen dominance were prepared in four seasons since 2018 in Segzi plain. Then, the prepared maps were validated based on land points and the total accuracy and kappa index were calculated in all four seasons. The collected lichen samples were identified based on their morphological characteristics and using a stereomicroscope, conventional microscope and common color reagents such as potassium hydroxide (KOH). After applying the BSCI index on the Landsat OLI 8 satellite image, using ENVI software, spectral profiles related to 4 points of Segzi plain in four seasons of the year were prepared and the spectral reflection in four seasons of the year in different points were examined. Results and Discussion: The slope of the soil line is lower in the rainy season, which coincides with the growth of herbaceous and annual plants, compared to the summer season, which has the least amount of rainfall, and the annual plants have dried up and become extinct. In May, the slope of the soil line was minimal (0. 39) and in late summer it has its maximum value (0. 78). In fact, the slope of the soil line has decreased from mid-August to May, and then has increased with the loss of annual vegetation and the increase of bare soil surface. The distribution maps of bio-shells in all four seasons of the year were validated during field visits and the year it was found that the highest accuracy of the map related to the map produced from Landsat 8 image is related to summer with 94% total accuracy and Kappa index equal to 0. 7412. Interpretation of the spectral profiles of the BSCI index shows that the reflections of the spectra related to the zephyr and strain prepared on the lichen dispersion points are very close to each other and also the spectral profiles of the mid-autumn and early spring are quite consistent. Whereas in the faults, which did not cover the biological crust, the amount of reflection was higher and there was a slight difference between the reflection diagrams of autumn and spring. Although the reflectance values of a range of agricultural lands and the distribution points of biological crusts are very close to each other, the spectral diagrams of all four seasons are very different from each other. But in all seasons of the year and in all places, the least reflection has occurred in the beginning of winter and the most reflection has occurred in summer. The climate of Segzi plain is Mediterranean and precipitation occurs in the cold season of the year. Simultaneously with the increase of precipitation from the middle of autumn, annual plants and mosses at the base of shrubs begin to grow and reach their peak in early winter and again at the beginning of spring. Decreases in rainfall have reduced their density. If the winter spectrum has the least reflection in all places. While in late summer, when the annuals and mosses have dried up, it has had the greatest spectral reflection. In Fasaran, which is a barren area and a landfill, it has shown its maximum reflection. Therefore, the BSCI index relative to the percentage of organic matter has a significant error in the detection of biological soil crust and where the organic matter is high may not provide accurate diagnosis of soil bioshells. Of course, since the BSCI index is defined for the detection of throat compounds in lichen tissues. The error rate for organic matter is reduced to a minimum. As it has been observed in the final map, there is no cover of biological soil crusts in Fasaran and only soil biological crusts are observed in the areas around Fasaran in the agricultural areas. In agricultural areas, due to human intervention and cultivation, the amount of annual plants is different from the field of natural resources in different seasons of a year have become. Conclusion: Spectral similarity of the most important soil surface, including vegetation, the involvement of human factors in increasing or decreasing soil organic matter, bare soil, etc. limits the efficiency of the BSCI index and therefore in the time period of satellite images and regional conditions have a great impact on It has the accuracy of BSCI index.

Background and Objective: Soil organic carbon in rangeland ecosystems has a variety of functions such as increasing soil fertility, controlling erosion, increasing soil water permeability and, reducing the effects of greenhouse gases. Therefore, it is a key indicator in determining soil health that affects all physical, chemical, and biological properties of soil. The large area of the country's rangelands causes a serious challenge to the use of traditional methods in estimating soil organic carbon. In such situations, the use of remote sensing capabilities can be considered as a suitable option for monitoring the organic carbon of the country's rangeland soils. The aim of this study was to determine the most important spectral factors affecting topsoil organic carbon in two summer rangelands. Materials and Methods: This research was carried out in two summer rangelands of Lazour and Asaran. The first rangeland (Lazour) with an area of 8150 hectares and an average height of 2875 meters is located in the range of eastern longitudes 52. 514 to 52. 694 degrees and northern latitudes 35. 855 to 35. 934 degrees in Tehran province. The second Rangeland (Asaran) with an area of 5642 hectares and an average height of 2465 meters is located in the range of eastern longitudes 53. 265 to 53. 392 degrees and northern latitudes 35. 804 to 35. 882 degrees in Semnan province. In this research, the data of the OLI sensor of the Landsat 8 satellite were used. After preprocessing satellite imagery of the studied areas, Top of Atmosphere (TOA) reflectance layers of bands 2 to 7 along with the variables of surface albedo, Clay index, Carbonate index, Grain Size index, NDVI, brightness, greenness, and wetness index of Tasseled cap transformation were calculated. In each of the target areas, using Digital Elevation Model (DEM) maps, the slope, aspect, and hypsometric maps were prepared and by combining the last three layers with each other, a map of homogeneous sampling units was obtained. Soil sampling was performed using the stratifiedrandom sampling pattern. In this way, in each of the homogeneous units, according to its area, several soil samples were randomly taken from a depth of zero to 20 cm and the amount of organic carbon of the samples was measured using the Walkley-Black method. Results and Discussion: The results of this study showed that the spectral variables of Top of Atmosphere (TOA) reflectance layers of bands 2 to 7 along with the variables of surface albedo, Clay index, NDVI, brightness, greenness, and wetness index of Tasseled cap transformation have a significant correlation with topsoil organic carbon (p<0. 01). Also, the results of factor analysis by principal component analysis (PCA) with eigenvalues greater than one showed that the total cumulative variance explained by the 12 variables is 91. 74%, which was explained by two factors. The first factor (soil color) explained 76. 6% of the variance and the second factor (vegetation and soil texture) explained 15. 14% of the variance. Conclusion: The results of this study confirm the existence of a significant relationship between topsoil organic carbon and spectral factors extracted from Landsat 8 OLI sensor data in semi-steppe rangelands. Because of the large area of rangelands in Iran, the use of traditional methods in estimating soil organic carbon is not possible due to the need to spend a lot of time and money. And in such situations, the use of Remote sensing (RS) capabilities can be considered as a suitable option for monitoring the topsoil organic carbon in the rangelands.

Background and Objective: Today, land use change in many countries has become an important challenge that has many effects on the environment. Accordingly, the study of land use change at different scales is one of the important issues in the proper management of natural resources and environmental change at various levels. Therefore, being aware of land use changes and investigating their causes and factors in several time periods, and predicting land use changes in the future can be properly planned to reduce adverse effects, which has been considered by planners and city managers. They help in land use planning. Also, converting land uses to each other and changing the use of vegetation is known as an important issue. Therefore, the purpose of this study is to monitor and predict land use changes and land cover in Abbasabad urban area in the future; Using these changes, appropriate management measures can be taken to preserve and rehabilitate lands. Materials and Methods: A combination of an automated cell model and Markov chain in the Abbasabad urban area was used to predict land use change; The relevant images were taken from the TM and OLI sensors of the Landsat 8 and 5 satellites at the USGS site. Four user classes, including zone class built with code number 1, vegetation class with code number 2, water resources class with code number 3, and barren land class with code number 4, were separated for Abbasabad urban area. Obtained USGS. In order to extract land use classes, after checking several methods, object-oriented classification method and support vector machine (SVM) algorithm were used due to better efficiency. Evaluation of Babian satellite imagery classification The overall accuracy and kappa coefficient were performed for three periods of time. Each of these classified maps was evaluated by drawing an error matrix. 250 sample points were used to prepare this matrix. The type of sampling was stratified sampling. Also, to determine land use changes in 2030, classified maps were used and with the help of TerrSet software, changes made in classes and their percentages were obtained, and using the CA-MARKOV model, changes of different classes based on matrices. The possibility of transfer was predicted. Results and Discussion: The results during 1997, 2006, and 2017 show that the constructed area has an increasing trend and the uses of vegetation, barren lands, and water resources have a decreasing trend and 23279 hectares of lands in the region are built area dedicated. The kappa coefficient calculated for 1997, 2006, and 2017 is 0. 86, 0. 89, and 0. 89, respectively. Markov chain forecasting model with 85% accuracy stated that the trend of land use change for 2030 will be the same as in previous years, and this indicates that the conversion and change of land uses will proceed as before, and it is necessary to mention this point that the identical uses of vegetation to vegetation cover the largest area during the years 2006 to 2017, and this shows that in this area, vegetation is still stable and has undergone less changes. Conclusion: The output of the 13-year forecast map for 2030 in this study indicates the appropriate accuracy of the CA-MARKOV model. In addition, this output shows that this method can be trusted for short-term planning. These forecast maps can be a good guide for managers and urban planners. To achieve better results, it is recommended to use a combination of automated cell model and Markov chain to monitor and predict changes nationwide. The results of this study, in addition to helping to reduce the volume of input data, but also in the processing of classified images and in predicting them for the future.

Background and Objective: Problem The use of various transformations to improve the accuracy of data extraction from satellite images is increasing sharply. In the meantime, the choice of optimal conversion is very important and will affect the output results. Due to the correlated nature of remote sensing images, the use of various transformations to improve the accuracy of information extraction from these images is essential. According to the studies, the purpose of this study is to investigate different methods of image conversion in improving the process of classification of satellite images and increasing the accuracy of land use maps. Considering that the study area and in general the northern regions of Iran are facing special conditions of entanglement of land uses, so the use of various conversion methods as well as the combined method proposed in this study increases the accuracy and the accuracy of the output information and finally the possibility of more detailed separation and review of uses and identification of factors changing them for future planning. Materials and Methods: In this study, in order to evaluate the performance of principal component analysis methods, independent component analysis, and minimum noise fraction method, Sentinel-2 satellite images of Rezvanshahr city were used. Gram-Schmit algorithm was used to integrate this data with each other and achieve a resolution of 10 meters. After applying the necessary pre-processing and merging the images together, all three transformations were applied to the image, as well as a combination of the components of these three methods. Then, the results of the transformations were classified into 8 user classes using the maximum likelihood algorithm. Using Sheffield coefficient and statistical calculations between the obtained components, the combination of the first components of principal component analysis, the first component of minimum noise fraction, and the second component of independent component analysis were selected as the optimal combination. General knowledge of the area and accordingly the visual interpretation of the outputs, as well as the perception of 120 ground points by GPS, has been the basis for assessing the accuracy of the output maps. Results and Discussion: After applying the required preprocessors, each of these algorithms was applied to the image, and the output of each was classified into 8 user classes using the Maximum Likelihood algorithm. The results of output maps showed that the conversion of principal component analysis, considering that it considers Gaussian distribution for variables and tries to decompose the extracted components, is weak in samples with non-Gaussian distribution and shows low performance. The minimum noise fraction algorithm works similarly to the principal component analysis algorithm, except that it classifies the noise better. This algorithm has less error in separating classes and this factor has resulted in better performance and higher accuracy than the other two conversions. In the independent component analysis algorithm, the image correlated bands of the study area have been converted to independent components and new information has been extracted from the area. The visual interpretation shows the high accuracy of the classification result and an error matrix (confusion) is used to quantify the accuracy of the classified image. The results of the evaluation of overall accuracy and kappa coefficient showed that the classification of the original image without applying transformations and with the same training samples of output with an overall accuracy of 76% and kappa coefficient of 0. 78 had the highest error. Also, the results of other outputs for classification resulting from principal component analysis conversion are 80% overall accuracy and kappa coefficient of 0. 83, respectively, for classification resulting from minimum noise fraction conversion, total accuracy of 85% and kappa coefficient of 0. 88 and for the classification obtained from the analysis of independent component analysis, the overall accuracy was 77% and the kappa coefficient was 0. 80. After selecting the optimal combination of components of principal components analysis methods, independent component analysis and minimum noise fraction method and selecting the first components of principal component analysis algorithms and minimum noise fraction and the second component of total component analysis to 92% independent coefficient and Kappa increased 0. 94. Conclusion: In this study, after evaluating the conversion performance of principal component analysis, independent component analysis, and minimum noise fraction method, an optimal combination of components of these transformations was proposed. As the results of the research showed, the classification of the original image without conversions and with the same training samples had low overall accuracy and kappa coefficient. The results show the close performance of these transformations to each other, which indicates the existence of both Gaussian and non-Gaussian distributions of variables. MNF conversion has minimized the amount of data noise and results in better output than ICA and PCA conversion. Since these transformations alone are not able to extract all the components of the image, so a combination of the components of these transformations based on the Sheffield coefficient was chosen to assume the Gaussian and non-Gaussian distributions of the variables with the least possible noise.

Background and Objective: Over the past 100 years, the country has lost about 90 percent of its per capita renewable water. About 90% of the country's renewable water resources are allocated to the agricultural sector. With the increase in the area of pistachio orchards and the increase in demand for water on the one hand and the limited water resources in the region, on the other hand, the imbalance between supply and demand for water is sharply increasing. In this regard, the most important step to prevent water loss is the uniform distribution of water on the field, optimal at each stage of growth. About 99% of the water absorbed by the plant is used for evapotranspiration. Therefore, studying this phenomenon can play an important role in determining the water needs of plants. It is difficult to measure the actual evapotranspiration outside the laboratory. Many experimental methods have been developed to estimate actual and potential evapotranspiration using meteorological and climatic data. But most of these methods are only able to estimate potential evapotranspiration and do not estimate the actual amount of it. In contrast, remote sensing methods have been developed that are a good solution for estimating the actual evapotranspiration. Satellite imagery with global coverage and repetitive Acquisition has made it possible to monitor evapotranspiration at the field level and during plant growth. Various studies have been conducted to estimate the actual evapotranspiration of agricultural areas using satellite images, which indicate the acceptable accuracy of these methods. However, most of this research is related to agricultural fields and no significant research has been done to estimate evapotranspiration at the orchards. Vegetation at the farms is uniform and homogeneous compared to orchards, so the estimation of vegetation index, which is one of the inputs of the SEBAL model in orchards is more difficult than agricultural fields, which can affect the final accuracy. Therefore, the main purpose of this study is to estimate the amount of evapotranspiration in the pistachio orchard using the SEBAL algorithm and evaluate the accuracy of estimation. Also, this research has been Materials and Methods: The present research has been carried out in pistachio orchards in Zarandieh city of Markazi province. The gardens had three different irrigation systems including flood irrigation systems, surface, and subsurface drip irrigation systems. Actual evapotranspiration is estimated using water balance and SEBAL algorithm. Meteorological data from Imam Airport Synoptic Station and Landsat8 satellite imagery has been used to estimate evapotranspiration using the SEBAL algorithm. Actual evapotranspiration is estimated at satellite overpass times during the growing season. To select hot and cold pixels in the SEBAL algorithm, the semiautomatic method proposed by Oldmo is used, which minimizes user participation in the selection of hot and cold pixels. To evaluate the accuracy of evapotranspiration estimation, the information of soil moisture sensors in the orchard has been used. 28 sensors measure soil moisture in different parts of the orchard. Using the soil moisture values, the actual evapotranspiration was estimated using the water balance method and used as a reference value. Results and Discussion: A comparison of the results of the SEBAL algorithm and water balance method showed that the SEBAl algorithm was able to estimate the actual evapotranspiration in different parts of the orchard with an RMS error of 0. 57. In addition, the correlation between the values estimated by the two methods was equal to 0. 82, which indicates the appropriate capability of the SEBAL algorithm in estimating evapotranspiration values. The correlation between the actual evapotranspiration estimated from the SEBAL model and the reference evapotranspiration is 0. 76. In addition, in the research, changes in the evapotranspiration in different parts of the garden and also gardens with different irrigation systems including flood, surface, and subsurface drips have been investigated. The results show that the orchard with subsurface irrigation had the lowest average of evapotranspiration on different dates. Considering that evapotranspiration is equal to the sum of evaporation from the soil surface and transpiration from the plant, this decrease can be attributed to the decrease in evaporation from the soil surface. In addition, evapotranspiration heterogeneity can be observed in all parts of orchards with the same irrigation system on all dates. For example, in the orchard with a flood irrigation system, parts of the garden show low evapotranspiration, which can be due to the lack of smoothing of the surface and lack of proper moisture in these areas. Obviously, the same amount of moisture accumulates in other parts of the garden and is inaccessible through deep percolation. This uneven distribution is also observed in the garden with a surface drip irrigation system. For example, the middle part of the garden with surface drip irrigation always shows a higher amount of evapotranspiration, which can indicate the loss of water in this part, due to the miss-operation of the dripper. To evaluate the difference in evapotranspiration in different irrigation systems, the average, minimum, maximum, and standard deviation values of evapotranspiration in orchards related to three different irrigation systems have been calculated. The results showed that in all dates, the ranges and standard deviation of evapotranspiration in the flood irrigation system were higher than in other systems, which indicates the lack of uniform irrigation in the orchard. Also, on all dates, the average amount of evapotranspiration in the orchard with a surface drip irrigation system has been more than flood irrigation system. Vegetation in orchards with drip irrigation systems (surface and subsurface) was denser compared to the flood irrigation systems. Conclusion: In this study, the actual evapotranspiration of pistachio orchards has been estimated using satellite imagery and the SEBAL algorithm. The results of the study indicate the appropriate accuracy of the SEBAL algorithm in estimating the actual evapotranspiration of the orchards. Compared with the water balance method, the correlation coefficient was 0. 82 and the root means the square error was 0. 57. In addition, comparing the moisture situation in different parts of the orchard and in orchards with different irrigation systems has shown that by estimating the actual evapotranspiration using satellite imagery, appropriate information can be obtained on how to distribute moisture in the garden. This information provides valuable information on the optimal management of water resources and increases irrigation efficiency. Other results of this research include the significant difference between surface and subsurface drip irrigation methods. The results show that using subsurface irrigation methods can effectively reduce irrigation water loss due to evaporation from the soil surface. The results show that in areas where there is no access to information from soil moisture sensors or direct measurements of evapotranspiration, the use of the SEBAL algorithm and remote sensing methods can provide appropriate information for optimal water management.

Background and Objective: Sisangan forest park is one of the important habitats of Buxus Hyrcana in Iran. Unfortunately, the park has suffered from dieback in recent years, and many Box trees have been destroyed. Monitoring and management of this zone can be effective in controlling, protecting, and supporting it. However, due to the destruction of Box trees, on a large scale, it is not possible to accurately estimate the area using the available data. On the other hand, manual measurements are also very timeconsuming and tedious. Therefore, a way must be found to do this process accurately and automatically. Unmanned aerial vehicles (UAV) have made this possible by using highly accurate sensors (spatial resolution). Another solution that can be used to automatically separate dieback trees from green trees is to use different classification methods. The aim of this study is to prove the ability of low-cost UAV data with conventional sensors to detect and zoning areas that have suffered Dieback. Since the cost of UAVs with multispectral sensors (red edge band and near infrared) is very high, it should be possible to reduce this cost. Since the cost of UAV with multispectral sensors (red-edge and near-infrared band) is very high, it should be possible to reduce this cost. Materials and Methods: Sisangan Forest Park has located 30km to the east of Nowshahr County, Mazandaran province, at latitude 36º 33′ 30″ to 36º 35′ 30″ N, and longitude 51º 47′ to 51º 49′ 30″ E. This park is both a tourist destination and many important plant species of the country grow in it. One of the most important of these species is the Buxus Hyrcana. But unfortunately, in recent years they have become snag due to pests and insect infestations. Multirotor UAVs have been used in this research. The camera installed on this device is capable of capturing 20 megapixel images. Imaging operations were performed on December 28, 2017, at 10: 00 AM, which lasted 45 minutes. The study area was visited for field sampling and its different points were identified in terms of density of snags and preserved Buxus Hyrcana. Then, three circular pieces with a radius of 60 meters and an area of 1. 13 hectares were designed in the zone and the density of snag stands and preserved Buxus Hyrcana stands were determined in these three samples. In each plot, 50 training points were recorded in the places where the Buxus Hyrcana stands were located and also 50 points were recorded in the places where the preserved Buxus Hyrcana stands, floor grass cover, and blackberry was located. In this study, in order to evaluate the accuracy of UAV images in identifying and classifying zones covered with Dieback, the smallest Dieback stands with the smallest canopy width were also recorded. Because UAV images require geometric corrections, they were first corrected geometrically and geographically. They were classified with ENVI software. According to the above explanations, 100 points were recorded in each sample plot, 75 of which were monitored for the classification process and 25 of which were used to evaluate the classification accuracy. Three monitored artificial neural network classification algorithms, maximum likelihood and minimum distance were used to classify these images. Finally, after performing each of the classification steps, a low-pass filter with a size of 3 by 3 pixels was used for smoothing the images. Kappa coefficients and overall accuracy indices were also used to evaluate the results. Results and Discussion: In this number of sample plots, 579 stands were measured. Buxus Hyrcana was by far the most frequent in the zone. European hornbeam, Parrotia persica, and Oak were in the next ranks, respectively. The results showed that the artificial neural network algorithm had the best results compared to the other two algorithms. But the results of the artificial neural network also fluctuate according to the condition of the sample piece. This algorithm with an overall accuracy of 97. 47% and a kappa coefficient of 0. 94 had the best results in the separation and detection of the Buxus Hyrcana snags in the sample plot with the dominance of Buxus Hyrcana snags. After the artificial neural network algorithm, the maximum likelihood algorithm showed more favorable results in separating the Buxus Hyrcana snag stands. The minimum distance algorithm showed good results, but it was not as accurate as of the previous two algorithms. All three algorithms showed poorer results in separating the bases in the sample plot with the dominance of live bases in the sample than the other two sample plots. The sample piece with the predominance of live and green bases compared to the other two sample pieces has more phenomena and effects and in terms of image texture, there are many significant differences compared to the other two sample pieces. All three algorithms showed poorer results in separating the stands in the sample plot by dominance the preserved stands in the sample than the other two sample plots. The sample plot with the predominance of preserved stands compared to the other two sample plots has more phenomena and in terms of image texture compared to the other two sample plots has a lot of significant differences. In this sample plot, in addition to the presence of preserved and snag stands, grass cover and blackberry accessions can also be seen. In this study, the results of classification and detection of Buxus Hyrcana snags using an artificial neural network algorithm were much better than the maximum likelihood and minimum distance algorithms. One of the reasons for the better results of the artificial neural network algorithm is its nonlinearity and non-parametricity. But in classification by traditional algorithms such as statistical methods, they have lower accuracy because they have less flexibility. Parametric types of traditional methods, such as the maximum likelihood algorithm, due to depending on Gaussian statistics, if the data are not normal, cannot have the desired accuracy in classifying and separating classes from each other. In traditional algorithms such as maximum likelihood and minimum distance algorithms, training data play a vital role. In these methods, it is assumed that the distribution within the training samples should be normal so that if this condition cannot be met, the classification accuracy will be greatly reduced. While artificial neural network methods operate based on the characteristics and structure of the data itself. Conclusion: The results of this study showed that using the data and ordinary images of a low-cost UAV, it is possible to study the condition of Dieback after the outbreak of the disease and determine its area. Despite the high cost of purchasing expensive sensors to monitor vegetation status, these methods presented in this article can be done at a much lower cost. This method can be of great help to the relevant institutions in determining the area of snag coatings.