جستجوی مقالات مرتبط با کلیدواژه « شاخص پوشش گیاهی » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

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.

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.

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.

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.

Rangeland vegetation is one of the most important components of arid ecosystems and it is necessary to determine changes in rangeland vegetation under drought and wet years. The present study aimed to investigate the relationship between satellite indices and SPI index in Qom rangelands. For this purpose, the SPI index was calculated in moving averages of 1, 3, 5 and 7 years. In the next step, using Landsat images and after making the necessary adjustments to the images, the vegetation map was prepared using NDVI, MSAVI and EVI indices. Finally, correlation coefficients were used to investigate the relationship between satellite image indices and SPI index. The results showed a moderate and good correlation between MSAVI satellite indices and SPI index at peak vegetation growth months with a one month moving average of SPI index. The results of this study show that to estimate agricultural drought through remote sensing, the MSAVI index is a very suitable method and can be used for estimating drought in areas where meteorological stations are scattered (or nonexistent). Because the number of sampling points in satellite images is far greater than the number of meteorological stations.

Drought assessment and monitoring using traditional methods rely on rainfall data, which are limited in arid lands and often is very difficult to obtain near real time and costly. In contrast, remote sensing technology is a method for monitoring of large-scale drought.In this research, drought condition was analyzed using drought indices such as TVDI and NDVI from MODIS sensor data for the Yazd-Ardakan plain, Iran. First, relationship between the drought indices with climatic elements were detected. Coefficient of correlation between TVDI and SPI_6 and SPI_12 were 0.68 and 0.71, respectively. Correlation between NDVI and SPI_6 and SPI_12 were 0.49 and 0.51, respectively. Point correlation between TVDI and SPI_6 in 2004 (as a normal year), 2007 (dry) and 2012 (wet year), were 0.64, 0.78 and 0.67 and for the SPI_12 in the above-mentioned years were 0.65, 0.79 and 0.69, respectively. In other word, efficiency of the TVDI in 2007 is better than the other two years. Correlation of NDVI and SPI_6 in 2004, 2007 and 2012, were 0.41, 0.50 and 0.56, respectively. The correlation between NDVI and SPI_12 in 2004, 2007 and 2012, were 0.52, 0.57 and 0.59, respectively. TVDI which takes into account thermal and reflective bands, and soil moisture, is more accurate than the NDVI, which considers only amount of vegetation of the study area. Results showed that the relationship between vegetation and temperature is negative, while, the relationship between vegetation and precipitation is positive. Using of TDVI can compensate defects of the NDVI and used for identifying and monitoring drought.

The complexity of drought phenomenon hinders our full understanding of its impact. Field sampling, Geographic Information Systems, SPI and NDVI, EVI and SAVI indices derived from 16-day interval MODIS images during 2000-2015 were used to better understand the effects of drought on vegetation In recent study, ground true map was prepared by sampling and field surveys and vegetation cover data was obtained from 32 sampling units in 320 plots over the entire study area. Then, the correlation between field sampling data and vegetation indices was estimated and vegetation cover models were produced for different indices. In this study, precipitation data of 14 stations within and around the study area were used and SPI was calculated at the same time scales with the vegetation indices to study the effect of drought on vegetation. The results showed that NDVI has had the highest correlation coefficient (R2=0.56) amongst the indices so it was selected for vegetation cover percentage mapping. Investigating NDVI rates and drought index in different temporal periods, 9-month SPI was found to have the best correlation with NDVI. On the basis of SPI analysis, it was found that the study area had the most severe drought in 2012 and the best wet condition in 2004. The similar trend was observed in NDVI. The comparison of classified images between 2004 and 2012 (with 42 % changes in poor vegetation) indicates the effect of drought on vegetation in the study area.

Due to incorrect policies in the water resources sector and excessive withdrawal of groundwater resources, water quality in most plains of the country has decreased. Therefore, the quality of water for drinking and agriculture is important in qualitative terms.With this aim in this study, the desertification changes in Khash plain were studied using two criteria of groundwater and vegetation. Shooler and Wilcox diagrams were drawn up for AquaChem software for the years of 2001, 2008 and 2015. Also, spatial variations were made on Arctic plain surface using Arc GIS software and vegetation changes trend were also done using MOD13A2 sensor images and remote sensing techniques. The results showed that the number of wells that have an unpredictable TDS in the years 2008 and 2015 compared to 2001 have been increased by comparing the Schuler diagram. However, the level of lands with good drinking status decreased during the years 1387 and 1394 compared to 1380 and was increased by the level of acceptable drinking water. According to Wilcox's diagram, the result is that most wells are salty to very salty and their use is harmful to agriculture or requires measures. The results of the vegetation change trend also indicated that the trend is to reduce vegetation and the average level of the NDVI index decreases with the passage of time.

Study of effective factors on sediment load of river basins has attracted more attentionin watershed management. In spite of the short record length, sediment load measuredin stations can be used in such studies. In this study, 20 sub-basins with measuredsediment data was identified and some 48 physiographic, climatic, geologic, andvegetation index factors were extracted for the sub-basins using GIS. Surface curvatureand satellite image-based vegetation indices were considered for the first time. Based onfactor analysis, four factors namely total area, percent of convex area, percent area withnorthwest aspect and percent area with NDVI>0.4 were the main factors. Clusteranalysis was applied to delineate homogeneous regions, which led to two regions. Theresults indicated that the factors mentioned above are the most influential factors onsediment load.

In Iran, like many other developing countries, high population growth rate causes unfairly uses of natural resources and consequently land cover change. Therefore, detection of land cover (rangelands, irrigated and rainfed agricultural lands, urban areas…) changes can influence local planning and natural resource management. Present study efforts to find a rapid and exact method of recognition different land covers using Landsat satellite data. Methods used in this research were image enhancement, false color composite (FCC), principal components analysis (PCA) and Image classification, i.e. normalized different vegetation index (NDVI) and supervised classification. A GIS environment, ILWIS software, was used. Results showed that irrigated agriculture, rainfed agriculture, rock out crop, rangeland classes (fair, moderate, poor condition) could be separated with overall accuracy of 89%.