جستجوی مقالات مرتبط با کلیدواژه « شاخص پوشش گیاهی » در نشریات گروه « کشاورزی »

Determining the organic carbon content of rangeland vegetation is essential for monitoring rangeland condition and facilitating reclamation efforts. Satellite data provides a valuable tool for conducting extensive vegetation studies. This research aimed to estimate the organic carbon content of vegetation using field assessments and remote sensing indices in the rangelands of Lashgardar protected area, Malayer. Leveraging Landsat time series images, this study utilized Landsat 8 data from the Operational Land Imager (OLI) sensor.

Field sampling was conducted in the rangelands of Lashgardar protected area on May 28, 2016. The dominant growth form in these rangelands is herbaceous-shrub, with Asteraceae family species being the most abundant. Forty points were randomly selected as the centers of sampling plots for plant biomass. To account for GPS accuracy error, a factor of twice the pixel size was applied, resulting in the selection of 40 plots measuring 30×30 m2 for field sampling. Subplots measuring 1×1 m2 were utilized to collect composite aboveground biomass samples from the central point and the four corners of each main plot. Samples were processed in the Rangeland Science Laboratory at Malayer University following coding for laboratory procedures. The organic carbon content of vegetation was determined using the loss on ignition (LOI) method after air-drying. Vegetation indices were extracted from Landsat 8 satellite images captured by OLI sensors, including digital bands 1 to 7 with a spatial resolution of 30 meters. Various vegetation indices such as Greenness, RVI, NDVI, IPVI, DVI, WDVI, ARVI, SAVI, TSAVI, BI, OSAVI, GEMI, EVI, LAI, and GARI were derived from Landsat images.

Comparative analysis of estimated organic carbon data with measured organic carbon content revealed that only the Green Atmospherically Resistant Vegetation Index (GARI) could effectively estimate the organic carbon content of vegetation. The best model was achieved using the GARI index for organic carbon estimation, represented as OC = 5.4 + 1.38 GARI, with an explanatory coefficient (R2) of 0.13 and Root Mean Square Error (RMSE) of 0.7. These findings suggest that remote sensing indices can serve as complementary methods in vegetation studies.

The GARI index demonstrated promising results for estimating organic carbon content in vegetation within the study area and is recommended as a suitable indicator for similar areas. However, the efficacy of each index may vary depending on specific area characteristics and vegetation types. It is advisable to conduct time-series studies with larger sample sizes tailored to the unique conditions of the study area to identify the most appropriate indices.

Given the significance of the Hyrcanian Forests, inscribed by the UNESCO as a world heritage site, it is essential to monitor the changes in and the devastation of the forest cover in this ecoregion for the planning and management of national lands. It is the objective of the present study to monitor changes in both land use and forest cover in Astara region using Landsat TM, OLI 1 & 2 sensors for the years 1995 and 2022. For this purpose, images captured on days with cloud covers of less than 10% were selected over three time intervals and the relevant enhanced Vegetation Index (EVI) values were determined based on Landsat inter-band relations. In the next stage, the index values were combined to derive the land use map using the support vector machine (SVM) algorithm. The results of accuracy evaluation showed that the overall accuracy and Kappa coefficients of the land use map for the year 1995 were equal to 89 and 92% and those for 2022 were 0.86 and 0.75%, respectively, indicating acceptable results. The results of land use changes in Astara city during the period from 1995 to 2022 showed that residential land use had increased by 7% equal to 2954 ha while rangeland and agricultural uses had decreased by 1 to 2% equal to 258 and 997 ha, respectively. However, an important land use along the Caspian coast line – that is, forest cover - stretched over an area of 34283 ha equal to 80% of the study area, which declined in 2022 to 32522 ha equal to 76%, showing a decrease of 4% equal to 1761 ha. It is clear that, owing to its rich archive of satellite images, the GEE system can be used as a strong and useful tool for monitoring and managing forest lands.

Awareness of the types of vegetation changes and human activities in different parts has particular importance as basic information for different planning. It is very difficult and expensive to collect information about the continuous changes in vegetation cover by conventional methods. Therefore, the use of new technologies such as remote sensing is very beneficial. The objective of the present research was to introduce the appropriate vegetation index and determine the vegetation cover of the Abshar network. NDVI, EVI, SAVI, and MSAVI vegetation indices were calculated from 2000 to 2021 every year and monthly in the Google Earth Engine system using Landsat 7 satellite images of the ETM+ sensor. Also, the SPI drought index was calculated using the precipitation statistics of Kohrang station in Excel software. The results of the comparison of four indices showed the superiority and higher performance of NDVI compared to the other three indices for detecting vegetation changes. Then, vegetation changes were calculated. The results showed that the trend of agricultural development in the Abshar network is downward and has a direct relationship with precipitation and the SPI drought index. Also, the results indicated that the SPI drought index was equal to -1.73in 2008, which showed a severe drought in the region. Comparing these results with the vegetation area showed that the vegetation area was 35721 hectares in this year and the year after the drought (2009), the vegetation area was 22950 hectares. Therefore, there was a decrease in precipitation and a sharp decrease in the SPI index in 2008, which led to a sharp decrease of 35% in the vegetation area in 2009.

Soil salinity is one of the most important environmental problems, especially in arid and semi-arid regions. Soil salinity is caused naturally and/or by humans. High soil salinity negatively affects crop growth and productivity and ultimately leads to land degradation. Monitoring and mapping of soil salinity Due to the serious problems of spreading this issue to regional ecology, food security and agricultural development in the early stages, it is necessary to implement an effective soil rehabilitation program to prevent and reduce soil salinity. Remote sensing science performs better than traditional methods for assessing soil salinity and offers fast and cost-effective techniques for monitoring and mapping soil salinity. Soil salinity can be identified using direct indices that are related to the properties of surface soil salts as well as indirect indices. The aim of this research is to review the challenges of selecting appropriate indices in soil salinity studies through the investigation of researches conducted in the field of soil salinity and spectral indices used in soil salinity cases, which are helpful in land management at the regional scale. It is worth noting that in this regard, the most common vegetation and salinity indices used to detect and mapping of soil salinity were discussed. Many researchers have used different remote sensing indicators to map soil salinity. Among these, BI Brightness index, SI salinity index, NDVI normalized differential vegetation index and NDSI normalized differential salinity index showed the highest correlation between data obtained from satellite images in salinity-affected soils. Choosing the most appropriate band or indices depends on the soil conditions, geographical area, climatic conditions, satellite data, physiography of the area and the type of land use.

A relatively accurate prediction of the concentration of pollutants and environmental variables, short-term and long-term, is an important step in reducing the damage caused by poor air quality. In this study, first by spatiotemporal analysis and then, using prediction techniques, to predict the concentration of fine atmospheric particles (PM2.5), temperature and vegetation cover index (NDVI) on the trend of PM2.5 in a period of 5 years (2017-2022) was discussed at the level of Iran. The data of PM2.5 concentration, temperature and vegetation index were extracted based on MERRA-2, FLDAS and MODIS satellite models. In the five-year study period, a somewhat downward trend was observed for the air concentration of PM2.5. The results showed the lowest annual average concentration of fine atmospheric particles in 2019 and 2020. Also, a strong correlation between PM2.5 concentration and temperature was obtained. The highest average concentration of PM2.5 occurred in the northwest, west, and southwest of Iran. In the next step, in order to predict the future concentration of PM2.5 air particles, temperature and vegetation index, the Exponential Smoothing approach was used in the Python statistical library (Statsmodels) to model monthly time series. Evaluation of the models with two criteria of root mean square error (RMSE) and coefficient of determination (R2) was done to minimize the estimation error and find the most suitable model among the eleven predicted models. The results show that double exponential smoothing models are more suitable for predicting PM2.5 concentration and triple exponential smoothing models with Holt-Winter trend are more suitable for predicting temperature and NDVI data. This study can help public and private institutions to better understand economic, health and environmental condition affected by air pollution effects by predicting the period when air pollution levels may be particularly high.

Hydraulic conductivity is one of the most important physical properties of soil, and knowing it plays a vital role in investigating the transport of solutes and pollutants in porous environments such as soil. This study aims to obtain saturated hydraulic conductivity pedotransfer functions (PTFs) using soil properties and satellite images. In this regard, the hydraulic conductivity of soil saturation was performed using the Inversed augerhole method in a part of the southwestern lands of Khuzestan province at 50 points. Then, at these points, surface samples of the soil were taken and soil properties such as soil texture, electrical conductivity, soil organic carbon, and saturated moisture were determined in the laboratory. In the next step, the indices of Sentinel-2 satellite images were calculated in three categories of soil, vegetation, and moisture indices and 11 PTFs, (PTF1-PTF11) for saturated hydraulic conductivity were obtained in four stages by combining soil properties and these indices. Finally, the spatial distribution of saturated soil hydraulic conductivity was obtained using the random forest model. The results of the modeling of PTFs of saturated hydraulic conductivity showed that among the 11 models with which PTFs of hydraulic conductivity were performed, the combination of three vegetation indices with soil-found early properties was the most effective for estimating the saturated hydraulic conductivity (PTF7). The values of R2, RMSE and MAE for this case were equal to 0.83, 0.40 and 0.166 respectively. Finally, the spatial distribution of saturated hydraulic conductivity using the Random Forest model showed that this model performs the spatial distribution of saturated soil hydraulic conductivity of soil well. Based on the obtained results, it can be found that the combination of soil properties with the indices obtained from the Sentinel-2 satellite images, creates PTFs of saturated hydraulic conductivity of the soil with very high accuracy.

In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R2, MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.

There are always challenges of spatial and temporal resolution in-situ measurement methods of factors affecting drought phenomena, and the presence of human operators is required. However, due to remote sensing's ability to measure data on the entire surface of the planet with an acceptable spatial and temporal resolution, its use in controlling and observing drought has grown more than ever, and it has become a powerful tool in the hands of experts. In this study, based on two components of surface soil moisture and modified vegetation index (EVI) by applying remote sensing data, a new agricultural drought index named (SMADIN) is proposed.

To achieve the goal of proposing a drought index based on soil moisture, surface soil moisture data from the SMAP satellite of 5 cm depth was used. These data were validated before use against daily field measurements provided by the Iranian Meteorological Organization over a 250-day period. Validation step error was evaluated using the root mean square error method between satellite data and field measurements. Furthermore, the EVI index was calculated using data from the TERRA satellite and the MODIS sensor. Eventually, an analytical method is used to propose a drought index based on soil moisture. In order to compare the performance of this index in different weather conditions, two regions were chosen, one representing a dry climate and the other a wet climate. Then, the correlation matrix was plotted by the Pearson method for SMADIN agricultural drought index versus vegetation health index (VHI) and the results were discussed.

Validation showed that field data measured in land use similar to remote sensing had an average root mean square error of 0.05 .The results indicate that the new agricultural drought index correlates up to 96% with VHI in the humid climate and 98% in arid regions. In addition, a 5-year comparison of the new SMADI and VHI time series in the study area demonstrates synchrony in peaks, minimums, increases, and decreases.

An agricultural drought index based on soil moisture is proposed in this study. We believe that, in recent years, when the lifetime of the SMAP satellite data exceeds 7 years, it is possible to use this index in future studies. Considering the possible error of SMAP and TERRA data in providing drought index, it is suggested to use this index in future studies in dry regions such as the central and southern regions of Iran.

In general, comprehensive watershed management depends on achieving an eco-hydrological balance of watersheds and improving the livelihood of stakeholders. One of the main principles of watershed management measures is sediment management through soil protection operations using new techniques such as the Sediment connectivity index. Sediment connectivity is an emerging term in watersheds that are often used to describe the internal relationships between runoff and sediment sources in the upper parts to the outlet of the relevant watershed. The purpose of this study was to monitor the spatial changes of sediment accurately, using structural Sediment connectivity index and analysis of sediment flow from upstream to the outlet in the basins, which was carried out in AbolAbbas watershed of Khuzestan during the summer period of years 2020-2021. The present study is based on the use of Borsley's proposed approach and cover land weighted layer. The results showed that the dimensionless index of sediment connection was estimated with a spatial accuracy of 30 m, ranging from -7.9 to 3.4 and obtained with an average of -5.50. The accuracy of the sediment connection index with determination coefficient of 0.56 has a good accuracy in monitoring the sediment transport potential from the upstream to the basin outlet. However, the results showed that the new method of sediment connectivity index in modeling sediment flow could be a pixel (cellular) tool to identify homogeneous areas in terms of distribution of sediment connection and make management decisions and programs.

Vegetation is one of the most important key components in arid regions for reducing of the effects of erosion and determining the severity of desertification. Decrease in vegetation leads to increase in surface albedo. Accessing and preparing desertification intensity map at the fastest possible time and at the lowest cost is one of the concerns of governments. In the present study, in order to identify the best vegetation index for preparing the desertification intensity map, MSIL-1C data of Sentinel 2 satellite in the arid region of Sistan has been used. For this purpose, the relationship between surface albedo and each of the different vegetation indices of the NDVI, RVI, DVI, PVI, SAVI and TSAVI were conducted. After determining the linear regression equation between the albedo and each of the mentioned indices, the relevant desertification intensity equation was calculated and the desertification intensity map of the studied area at 5 classes was prepared based on albedo and each of the mentioned indices. The results showed the strongest relationship in the study area was between albedo and NDVI, with a correlation coefficient of 0.63, and the lowest correlation of 0.37 was between the albedo and PVI indices. Based on the present study among the indices studied, the NDVI is the best for the preparation of maps of desertification intensity in the arid region of Sistan. Based on this index, 20.3% of the region was classified as severe and 32.9% of the region grouped into the moderate desertification class.

Desertification refers to the decreased biological potentials in the ecosystem of hyper-arid, arid, semi-arid, and humid semi-arid regions because of climate change and human activities. The phenomenon occurs due to a combination of direct and indirect factors whose intensity varies according to time and place, making the scientific, replicable, and systematic evaluation of desertification an essential task. Remote sensing technology which is based on spatial information collected at regular intervals by aircraft and satellites plays a prominent role in assessing and monitoring land degradation and desertification on a local, regional and global scale. On the other hand, Change Detection is a process that evaluates spatial changes in various phenomena caused by natural and human factors, using multi-time satellite images. As an effective method for detecting and describing land cover changes, the change vector analysis method provides information on spectral changes in terms of magnitude and direction. Therefore, considering the significance of determining the intensity of desertification in different parts of Iran and evaluating methods for investigating the changes in desertification intensity, the present study sought to evaluate desertification using the change vector analysis for different land-uses of the Gavkhoni basin.

This study used the change vector analysis (CVA) method to determine desertification changes in the Gavkhooni basin based on algorithm-driven classification, producing two components of magnitude and the direction of change. Moreover, to evaluate the intensity of desertification via the change vector analysis method, EVI and BSI were used for examining the study area's vegetation and bare soil. Possessed with 13 layers to assess the land use, the MCD12Q1 product with annual temporal resolution and spatial resolution of 500 m was used as a Level-3 network product in the sine image system to evaluate the land use. In addition, the IGBP standard was also used to assess land cover and land use.

The results of analyzing the changes made in the BSI during 2001-2005 and 2000-2016 indicated that throughout the latter period, BSI values decreased in central, western, northwestern, eastern, and southeastern regions of the study area. On the other hand, the results of analyzing the changes in the EVI revealed that during the same period, the index values increased in the west and northwest of the region, while the index value decreased in the eastern, southern, and southeastern parts of the region. Moreover, the results of analyzing the changes in desertification showed that the number of changes in some areas of the center, west, southwest, and southeast of the region was greater than other areas, which could be attributed to rehabilitation or destruction in the study area. The results of analyzing desertification-related changes in terms of direction suggested that the intensity of destruction in the center, south, east, southeast, and northeast of the region was higher than that of other regions. The rehabilitation has occurred in the northern, northwestern, and southwestern regions. Among the areas that were under rehabilitation process, 14.15%, 12.04%, and 10.31% of the basin area were found to be in the low, medium, and high rehabilitation classes, respectively. On the other hand, in terms of the extent of destruction in the region, 12.02% of the study fell under the medium destruction class, while 8.24% and 8.91% of the study area were placed under the low and high degradation classes, respectively. However, 34.33% had remained unchanged in terms of desertification status. According to the results of analyzing the intensity and direction of changes in each land use, 0.42% of agricultural lands were found to be in the high destruction class. Furthermore, the greatest percentage of high rehabilitation class belonged to grasslands, which covered 5.40% of the study area.  However, 28.5% of the area which comprised of barren lands was divided under the trend-free class. It was also found that 1.88% of non-dense shrubs and 0.36% of residential lands were under the high destruction class.

As desertification is among the serious ecological crises in today's world, it is necessary to well identify and recognize the causes and processes involved in desertification on a regional and global scale. Therefore, this study used the vector analysis method to evaluate the desertification status in different land-uses of the Gavkhoni basin. The multivariate CVA technique was used in the pixel-by-pixel analysis of bands or spectral indices. The changes that occurred throughout two different periods (as mentioned earlier) were identified by placing the quantitative value of the pixels on the two axes of the Cartesian plane, out of which two componential elements, i.e., magnitude and direction were obtained.In general, the results of the present study indicated that while the east and center of the Gavkhoni basin were in a state of destruction and desertification, the bare soil in the western and northwestern regions of the Gavkhoni basin had been replaced by vegetation due to agricultural activities and cultivation and that these regions were in a state of rehabilitation. Therefore, the vector analysis model is recommended to be used for analyzing changes in other basins. In fact, unless a more accurate and better evaluation model is introduced, this model could be used confidently to assess the severity of future desertification.

Regarding the heavy damage of droughts to plant communities and soil moisture, the present study aimed to derive the SPEI drought index using the climatic data, temperature and rainfall, and to calculate the EVI vegetation cover and CWSI soil moisture indices through satellite data processing. Then variations of the indices and their correlation with drought was addressed. Looking at the trend of drought variations in Isfahan province shows a frequent drought over a 19-year period. The most severe droughts happened in 2001, 2008, and 2010 at which the minimum indices vegetation cover and soil moisture were observed. The trend of the variations of SPEI, EVI, and CWSI revealed their ascending trend by 88.54%, 13.14%, and 90.72% and their descending trend by 2.93%, 78.33%, and 9.28%, respectively. Out of the increases of the indices, 6.93%, 6.32%, and 63.56% were significant at the P < 0.05 level, respectively. Out of all decreases, 0.33%, 7.45%, and 8.75% were significant at the same level, respectively. The test of the correlation of SPEI index with soil moisture and vegetation cover indices indicated that the effect of SPEI on these two indices was positive across 87.23% of the province and negative across 56.56%. As a result quantity and quality of the vegetation cover and soil moisture have declined. The information produced by these climatic data and satellite imagery can considerably help policymakers and planners to cope with the drought and its impacts on vegetation cover and soil moisture.

Land degradation is a multifaceted phenomenon, which is caused by various variables, including climate, land use changes and socio-human activities. In order to investigate the effects of climatic parameters on land degradation in five second degree watersheds (South Baluchistan, Bandar Abbas - Sedij, Kal - Mehran, Hillah and Mond) located in the entire Persian Gulf and Oman Sea Watershed, observational data E32 of synoptic stations were used in the mentioned catchment areas for of 31- year period (1988-2019). The IDW algorithm was used to map the climatic parameters. The results of the change detection showed that the trend of temperature class changes of 25– 27.5 follows an increasing rate of 19.03%, and the precipitation class is less than 150 mm in the region. The region is also facing an increasing trend of 17.3%. The trend of the evaporation parameter is such that the 2500-2750 and 300-3250 mm classes with the changes of -5.4, 8.3 percent, respectively, have the most decreasing and increasing effects. Moreover, the wind speed classes of less than 2 and 3-4 meters per second with changes of 5.7 and -7.5 percent show the highest increase and decrease respectively, based on the findings of the regression model, there is a significant relationship at the 0.05% level between the climatic variables (precipitation, temperature, evaporation and wind speed) on one hands and the vegetation index and salinity and the precipitation parameter on the others show the greatest effect. Considering that the four mentioned climatic variables explain 47.6% and 40.5% of the changes in the dependent variable of vegetation index and salinity, respectively, it can be concluded that part of the changes in vegetation and salinity are due to the conditions. As the climate prevails in the region, the poor vegetation and salinity were constantly fluctuating during the study period; consequently, the process of degradation followed an increasing and decreasing rate. Therefore, being aware of the effects of climatic parameters on the fluctuation of vegetation and salinity indices in a long period of 31 years, it is possible to make the necessary predictions for the optimal management of natural resources, especially during droughts. This enables the concerned authorities to control the development stages of land degradation in the coastal catchment areas of ​​the Persian Gulf and the Sea of ​​Oman.

The weather fluctuations are important influential factors on vegetation cover. Therefore, this study was conducted to identify elements of climate changes including temperature and precipitation, and its effect on vegetation changes in Sefidkuh protected area located in Lorestan province using Landsat satellite images. So, at first the climate was determined and zoned based on de Martonne climatic index using temperature and rainfall data of 9 synoptic stations in the Lorestan province for a term of 17 years from 2000 to 2017. Then, vegetation index was computed and zoned using ETM+ images of Landsat 7 satellite in 2004 and OLI images of Landsat 8 in 2017 and finally, the relationship between climate change and vegetation was investigated. According to the results of this study and based on the de Martonne climatic index, the highest level of the area (7737.8 ha) was related to semiarid class with good cover in 2004. On the other hand, the lowest level was related to very humid class with weak cover (0.7 ha) and very humid class with excellent cover (4.5 hectare). But, these classifications have been changed, and the lowest level is related to the excellent cover (4,4169 hectares). In fact, the area of vegetation with good and excellent cover has decreased from 2017 to 2004 (from approximately 37000 to 14000 ha), and the climate of study area has changed to semi-dry.

For estimating evapotranspiration of landscape, in addition to vegetation coefficient, coefficients related to microclimate, plant density adjustment and specific plant species adjustment must also be calculated. In this study, using Landsat 7 satellite images and Mashhad Synoptic Station data, the actual evapotranspiration of landscape was calculated then, using climatic and environmental parameters obtained from satellite images and Seabl model, a relationship between microclimate coefficient and air temperature as well as relationship between density coefficient and normalized difference index of vegetation was established. Finally, the obtained relationships were evaluated by field monitoring of soil moisture in Ghadir Park of Mashhad. By determining the locations of field locations on the satellite image the relationship between NDVI index and the landscape density coefficient was investigated. The results showed that the best model presented using linear regression as Kd = 0.9941 * NDVI + 0.5058 with a correlation coefficient of 0.98 that the maximum percentage of error between calculated evapotranspiration for landscape using extracted relationships and measured values from moisture weight monitoring is less than 24%. due to severe water deficit in the forest landscape and insufficient water availability of the plant, evapotranspiration calculation using the Sabal model is inappropriate and has many errors. Therefore, instead of using the Sebal model, the extracted relationships can be used to determine the vegetation coefficients and the water requirement of urban landscape.

Soil moisture is of particular importance in the study of water resources and agriculture. The microwave electromagnetic spectrum does not have the physical limitations of other radiometric spectral in measuring soil moisture, but they often have very large pixel dimensions (more than 10 km). In this study, in order to apply soil moisture remote sensing data at the farm scale, using field soil moisture measurement data in the Nishabour plain during 2017-2019, calibration of AMSR2 retrieval data was performed. It turned out that altitude and vegetation changes are among the key factors affecting calibration accuracy. Based on the theory of thermal inertia and with the help of MODIS sensor images, the interactions of the average daily soil moisture and the daily surface temperature difference between the two MODIS and AMSR2 sensors were investigated. Using it, downscaling linear relationships of soil moisture images were obtained to convert the image dimensions from 25 km to 1000 m. Validation of R2 statistical indices with a minimum of 0.73 and a maximum of 0.84, MAE and RMSE with a range of 1.6 to 4, showed that the algorithm used in the downscaling is well able to reflect the interactions between rainfall, soil moisture, vegetation and changes in canopy temperature profile and this feature reinforces its application in agrometeorological studies.

Drought is one of the environmental events and an integral part of climate change in any region and has significant effects. Due to the benefits of monitoring the combined climatic, hydrological, and agricultural conditions, vegetation indices (NDVI), land surface temperature index (LST), and standardized precipitation index (SPI) were used for analyzing drought intensity in Sistan and Balochestan province. To obtain NDVI and LST indices, MODIS satellite images were used in the months of growth seasons (September to October) from 2000 to 2019. Three stations of Iranshahr, Zahedan, and Zabol were selected to calculate the SPI index due to their long-term statistics (2000-2019), low statistical deficiencies, and appropriate distribution. A geographic Information System (GIS) was used to zoning the drought intensity. In this regard, after examining the types of interpolation models, the IDW method was used to prepare raster maps. Then, the obtained maps were classified from high to low SPI using the opinion of geography experts, and the drought zoning map was prepared. This study showed that the NDVI and LST indices experienced the highest intensity from 2008 to 2011 and the lowest intensity from 2003 to 2008, respectively. According to the SPI index, the trend of drought changes in the whole region in 2000-2006 and 2009-2014 is in the class close to moderate and high drought, respectively. The results indicate that the study area is sensitive to medium and short-term droughts. Most of the droughts occurring in this region are mild and moderate droughts, and drought is more likely to happen in the central and northern areas of the province.

Landslides are one of the major types of slope instability which cause enormous financial losses and casualties; therefore, it is imperative to consider them in the study of geomorphology, erosion and sedimentation in important watershed. The aim of the present study was to investigate and rank the landslide susceptibility using a random forest method in the Sadat Mahalleh Watershed, Sari. Elevation from the sea level, geology, land use, slope (degree, direction, shape and length), distance from linear elements such as faults, streams and roads, normalized difference vegetation index and slope form were considered as effective parameter in the landslide occurrence. Zoning landslide sensitivity was achieved by coding in the R and GIS10.3 soft wares. In order to determine the weight of each of the factors and classes effective in zoning the landslide sensitivity and validation of landslide potential prediction maps, the abundance ratio method and the receiver operating characteristics (ROC) were used. The results indicated that among the effective factors, vegetative cover and land use, had the most paramount impact on the landslide occurrence in the study area. A landslide sensitivity map was prepared using the random forest method by dividing into five class, namely: very low (3.85%), low (5.38%), moderate (23.08%), high (50%) and very high (7.69%) sensitivities. Validation of the results of the landslide sensitivity zoning maps, using the relative factor characteristics index diagram, indicated that the area under the curve (AUC) for random forest method was 0.709 and its standard error was 0.10. It is reasonable to claim that the forest method provided an acceptable accuracy for mapping the landslide sensitivity. A landslide sensitivity map provides prone complete and accurate information for natural resource managers to detect the landslide areas.

Surface soil moisture is one of the important variables in hydrological processes that affects the exchange of water and energy flow in relationship between land surface and atmosphere. Precise assessment of spatial and temporal variations in soil moisture is crucial for numerous environmental studies. Recent technological advances in satellite remote sensing indicates that the soil moisture can be measured using remote sensing methods. The purpose of this study is to estimate biophysical indices and evapotranspiration using SEBAL algorithm and to present soil moisture index using principal component regression method in the east of Bakhtegan Lake, Fars province. For this purpose, five Landsat 8 satellite images for March, April, May and June 2017 were selected and initially corrected. Meteorological data of Marvdasht synoptic station was used to execute SEBAL algorithm. Soil moisture index was modeled using biophysical indices such as albedo, net radiation flux, soil heat flux, evapotranspiration, and etc. by using principal component regression. TVDI index was used to validate the model. The coefficient of determination (R2) and F index are equal to 0.966 and 1651581.9, respectively, which indicates the high efficiency of the model to obtain soil moisture index for each pixel in different areas with different conditions and diverse vegetation. In addition to temperature and vegetation, other biophysical indicates of the region that affect the soil moisture should be taken into account.

Drought is a natural phenomenon that occurs in almost all parts of the world. The effects of this crawling phenomenon are more pronounced in arid and semi-arid areas due to their annual rainfall. In contrast to traditional methods based on meteorological stations observations that focus more on weather drought, the use of remote sensing and satellite imagery as a useful tool for monitoring agricultural drought has been considered. In the present study, the aim of comparing and assessing agricultural drought monitoring in Urmia Lake basin using VCI, VHI, TCI vegetation cover indices during the years 2000 to 2011 is using Madison. For this purpose, the NDVI index was first calculated from the images of Madis during June, July, August and September. Then, by comparing the mean of this index during these months, Shahrivar was selected with the maximum value as the month of the indicator. With regard to the minimum and maximum NDVI index in the months of September 2003 and 2008, VCI, VHI, TCI dash mapping maps were prepared. In order to evaluate the performance of agricultural drought indices, correlation coefficients were calculated for VCI, VHI and TCI profiles with SPI Meteorological Index. The results showed that the remote sensing index had a good accuracy in estimating the spatial and temporal dispersion of agricultural drought, so that the correlation coefficient between the VHI and SPI index was 0.86, which indicates that the index is consistent with the SPI meteorological index.