جستجوی مقالات مرتبط با کلیدواژه « سنجش از دور » در نشریات گروه « اکولوژی »

Mangrove ecosystems have abundant ecosystem services and play a significant role in the coastal ecosystems sustainability. Thus, the aim of this research is to investigate the temporal and spatial trends of mangroves habitat changes in the Nayband marine national park and the Bahu-kalat protected area, as well as determining the intensity of fishing activities as human threats to these mangroves. To examine the temporal and spatial trends of these mangroves, four plant indices, namely NDVI, SAVI, LAI, and RVI, were estimated and to determine the intensity of fishing activities, a map of mangrove habitat extent was drawn, consisting of 189 and 391 grid cells with dimensions of 4x4 kilometers, covering the coastal waters of the Nayband and Govater mangroves. Additionally, data on the geographical location and the number of vessels in the fishing ports of Bushehr and Sistan and Baluchestan provinces were collected. The findings of this investigation demonstrate an expansion in the area of all the studied mangroves from 1990 to 2019. On average, an increase in the Bidekhoon habitat from 32.96 hectares to 123.93 hectares was observed in Nayband Gulf, while in Gwadar Gulf, the area expanded from 275.76 hectares to 396.72 hectares. The reduction of mangroves in this region between 2000 and 2010 can be attributed to various factors, including local community harvesting of mangrove shoots, pollution from oil and gas industries, tourism, and the influx of non-degradable waste due to the proximity of these habitats to residential areas. Similarly, in Gwadar Gulf, the primary factor contributing to changes in hydrology and the subsequent decline of these forests since 2015 has been aquaculture. The most significant factors influencing the growth and establishment of the studied mangrove habitats are increasing temperatures and rising sea levels. Furthermore, the results showed that based on the current status of the studied mangrove habitats, the geographical distribution of fishing ports and the number of fishing vessels present in them, the Govater mangroves are subjected to higher fishing activity intensity compared to the Nayband mangroves.

In order to make decisions for regional planning and achieve sustainable development, it is necessary to quantify land use and land cover changes. In this study, the land use and land cover maps of the Zayandehrood Dam watershed were prepared for the period of 1991 to 2021, using Landsat satellite images, and the changes that occurred in this period were revealed. Using the land change modeler (LCM), land use and land cover and their future changes for 2051 were modeled and predicted. The results showed that in the period between 1991 and  2021, the coverage of poor rangelands with 51,871 hectares of change had the largest decreasing trend, and the agricultural class had the largest increasing trend with 71,478 hectares of change. The largest decline in the period from 2021 to 2051 is related in the coverage of the fair rangelands class with 66192 hectares, and the agricultural class potentially has the largest increasing trend with 70328 hectares of change. The findings of this research will be useful for policymakers and planners. They can use the findings of this study for spatial planning in the region, managing the process of land use and land cover changes for sustainable development.

Spatiotemporal changes of net primary production (NPP) is one of the essential indicators in determining rangeland ecosystem condition. Therefore, the aim of current research was to map and monitor the actual and potential NPP of rangeland ecosystems in Kohgiluyeh and Boyer-Ahmad province, using CASA and Miami models, MODIS and field data from 2009 to 2018. The accuracy of the produced NPP maps was assessed in 253 sampling sites located in different vegetation types with good, fair and poor rangeland conditions, using linear regression. Results showed that the difference between actual and potential NPP was greater than 56 gr C/m2/month, which can be a sign of human impacts and interferences in the rangeland ecosystems of the region. The highest and lowest relationships between modeled and field productions were observed in the Astragalus spp. - Bromus tomentellus vegetation type with good rangeland condition (R2=0.84) and Astragalus sieberi- Stipa capensis vegetation type with poor rangeland condition (R2=28), respectively. The present research findings indicated the importance of drought conditions, vegetation type and rangeland condition in estimating the actual and potential NPP and the difference of these productions can be used as an index to determine and monitor the condition of rangeland ecosystems.

Land use/cover change is one of the main factors in environmental changes. The changes directly affect the value of ecosystem services. The purpose of this research is to evaluate the effect of land use/land cover change in the Kashkan area on its ecosystem services. This research was carried out in three stages, including the analysis of land cover changes, prediction of changes, and carbon storage and sequestration. Landsat satellite images of TM, ETM+, and OLI sensors were used to prepare land cover maps in three periods of 1370, 1385, and 1400. In the TerrSet software environment, LCM modeler was used to predict land cover change in 1430 and ESM modeler was used for carbon storage and sequestration. Carbon storage data were extracted from IPCC emissions. Validity was evaluated using an error matrix. The results show that forest ecosystems have had the most land cover changes and the most hot spots of carbon loss are in forests. In 1370, about 44.35% of the area, equivalent to 4113.8 square kilometers, was made up of forest ecosystems, while in 1400, it decreased to 26%, equivalent to 2411.7 square kilometers. According to the future study scenario in 1430, about 1205.8 square kilometers of the area will be forest ecosystems, and between 1400 and 1430, about 158,000 mg of carbon will be released. The results can be used by environmental planners and managers to improve and protect the natural covers, especially forests, to protect ecosystem services and their continuity.

In order to manage the forests and optimal and sustainable utilization of the forest, it seems necessary to know the information on the volume of the residual stand. In this study, a systematic randomized inventory was carried out in 186 circular 10-acre plots in the educational and research forest of Darabkola, Sari, Golestan, Iran and the volume of each plot was obtained. In the next step, the physiographic layers of the area were prepared using the topographic map and the vegetation characteristics were prepared using the LISS-III image of the IRS-P6 satellite with a resolution of 23.5. After preparing physiographic layers and vegetation characteristics, their value was calculated for all plots. Then, regarding these variables and using two methods of multilayer perceptron neural network and multiple regression model, modeling was done. The results showed that the multiple linear model could model the volume changes in the region with higher accuracy (R2=0.75 and RMSE=0.3). The results of this research can be used in management planning and as one of the effective factors in the design of logging routes and forest roads so that areas with larger volumes are covered more.

In this study, the efficiency of two fuzzy methods and the integrated desertification index in Torbat-e-Heydariyeh, Khorasan Razavi province, have been compared using enhanced vegetation indices, vegetation condition index, salinity index, synthetized drought index, and temperature index for 2000 and 2020. The indices were normalized using maximum-minimum and fuzzy methods and weighted by analytical hierarchical method (AHP). Next, by weighted overlay combination and IDI method, the intensity of desertification was determined. The results showed that in the fuzzy method, 45% (1676 Km2) of the region suffers from severe and very severe desertification, and a major section (ie 55%, 2048 sq. Km) has mild and moderate intensity. In comparison, in the IDI method, no area fell into the very severe class, but at the same time, 67% (2496 Km2) of the total area fell into the extreme class. Accordingly, although the two methods have classified the area at risk of desertification, but this classification in the fuzzy method has been much stricter than the IDI method. The IDI method tends to overestimate desertification conditions. The comparison between the measured field data and similar values ​​in the obtained maps showed that the IDI method (kappa index of 0.73) was more compatible with the ground truth than the fuzzy method (kappa index of 0.54). Therefore, it can be concluded that the IDI method, although more efficient, has also overestimated the desertification in the region. Finally, this method is proposed to evaluate desertification in the region against the fuzzy method.

In recent years, the dust events frequency and the drought severity have increased in many arid regions of Iran, so it is very important to discover the relationship between these two phenomena in these regions. In the present study, the drought situation of Kerman province using the standardized precipitation-evaporation index (SPEI) and the frequency of dust events using codes 07, 08, 09, 35-30 and 98 recorded in synoptic stations during 1990-2018 has been investigated. In addition, Palmer Drought Severity Index (PDSI) and Aerosol Optical Depth (AOD) were also used to confirm the results obtained from meteorological data. The trends of changes in drought indices, dust occurrences and AOD were analyzed using the Mann-Kendall test and their correlation with each other was analyzed using the Pearson correlation coefficient. The results showed that Kerman faced the most severe drought events in the period from 1998 to 2010, and the longest drought with a duration of 21 years and an intensity of 11.84 occurred in the northwest of the province. The temporal analysis of dust events showed that intensification of their activity in the spring and winter seasons, especially in the months of March, April and May. The results of the analysis of SPEI changes showed that the drought in Kerman had an increasing and significant trend, while the trend of changes in the frequency of dust occurrences had a decreasing and insignificant trend. Analysis of changes using satellite data also proved the significant increasing trend of PDSI and non-significant decreasing trend of AOD, especially in hot seasons. Correlation analysis between SPEI and dust events showed that the intensification of dry conditions in November had a significant effect on the intensification of dust events in Kerman province (r = -0.46; P-value<0.05), while in other months, it has no significant effect on the dust events frequency. The results of PDSI and AOD confirmed the weak correlation between these two phenomena in Kerman province. The results also showed that the effect of drought time delays on the dust phenomenon in this province was weak and insignificant.

Land degradation rapidly increased in developing countries. Changes in climate and land use in Fars province in the past few decades have intensified the process of destruction and desertification. In the present research, satellite data was used to investigate the temporal and spatial changes of vegetation and its relationship with climate changes in the climate samples of Fars province in the years 2000 to 2020. The trend of changes of these variables in time with the Mann-Kendall method and in determining the time of change and spatial correlation, Pettit's test and Pearson's correlation test were used, respectively. The trend of NDVI in arid and semi-arid climates is increasing and the point of change is from 2010 onwards. Based on this, it can be expected that in most regions of Fars, we see a decrease in the ratio of precipitation to potential evaporation (increase in evaporation) and the decadence trend is increasing. Precipitation has no trend and the surface temperature is decreasing. The spatial pattern of NDVI and precipitation trend is increasing in more than 70% of the region (south), and AI and LST are decreasing in more than 65% of the region (central). NDVI changes spatial correlation with LST, precipitation and AI variables showed that the type of relationship and the strength of correlation were different in climatic regions. The strongest correlations were seen in the cold ultra-arid climates in the northeast and the temperate Mediterranean located in the northwest of the province.

Evapotranspiration is a complex phenomenon that depends on many factors and data, so determining it is very difficult and costly. In most methods mainly aimed at estimating evapotranspiration, point measurements have been used to estimate this variable. Therefore, it is only suitable on a local scale and cannot be extended to large basins due to the dynamics and regional changes of evapotranspiration (ET). One of the remote sensing algorithms for estimating real evapotranspiration is the Surface Energy Balance Algorithms for Land (SEBAL). In this algorithm, by estimating all energy components on the land surface such as pure radiation flux, soil heat flux, and tangible heat flux, using the energy balance equation, evapotranspiration is used. The aim of this study is to compare the estimation of real evapotranspiration using SEBAL algorithm in wheat and rapeseed crops with the results of estimating evapotranspiration by Penman-Monteith-FAO method in the field of Esmaeil Abad Agricultural Research Station in 2019-2020. Single-sample T-test of ground surface temperature reflection index showed that the spectral reflection of wheat and rapeseed crops during phenological growth period had a significant difference. The results showed that the computational values of the two models are relatively good and the mean value of square root of error (RMSE) in estimating real evapotranspiration for wheat and rapeseed plants was 3.04 and 2.09 mm/day, respectively, and the coefficient of explanation (R2) was 0.78 and 0.81, respectively. The results showed that SEBAL algorithm model in comparison with Penman-Monteith-FAO model (based on up-to-date meteorological data) The amount of evapotranspiration for wheat and rapeseed plants is lower.

Drought is a continuous period of lack of rainfall that causes damage and reduced yields in crops and has a direct impact on the quality and quantity of water and agricultural resources in the region. Remote sensing technology is Geographic Information Systems (GIS) are useful tools for processing and interpretation of spatial data and can be used for drought monitoring. The purpose of this study is to determine the most appropriate remote sensing indicators and to present a composite drought index based on the intelligent artificial neural network method. Based on the results, the best remote sensing indicators to determine the risk of drought in the region are vegetation indices, rainfall, and land surface temperature. The results were evaluated based on standardized precipitation index (SPI) values ​​obtained from meteorological stations. Accordingly, the accuracy of the results of the multivariate regression method was R2 = 0.62 and the multilayer perceptron neural network was R2 = 0.91. Therefore, the multilayer perceptron neural network method is more robust than the multivariate regression method to create a more accurate hybrid drought index. According to the results, there is a potential for monthly drought in most areas of Kermanshah province. Moreover, the annual drought potential is observed in the eastern regions of the province. Islamabad, Songor, and Harsin are cities with a low risk of drought.

Most of the water needs in arid and semi-arid regions is supplied through groundwater, so it is important to study the quantitative and qualitative changes of these waters and the factors affecting them. Land use changes can cause changes in groundwater quality. Here, land use changes in the period 2003 to 2020 in Damghan watershed were studied using Landsat 8, 7 and 5 satellite images, eCognition software and GIS. 1760 wells data in Semnan province were used for intermediation and accurate preparation of groundwater quality maps and extraction of geostatistical maps. Also, the information of 58 deep wells inside the watershed was used to analyze and study the groundwater quality changes. The results showed that the area of rangelands, forests and agricultural lands decreased and the area of gardens, urban areas, barren lands and surface water resources (due to the construction of the dam) increased. During this period, groundwater quality changes included Cl increased by 1.01 mEq, SO₄-2, decreased by 0.34 mEq, HCO3- increased by 0.46 mEq, Mg increased by 0.32 mEq, Ca decreased by 2.25 mEq, Na increased 3.58 mEq, electrical conductivity increased  by 44.81 μmohs / cm, Total dissolved solids increased by103.88 mg / l and pH increased by 0.09. These changes were evaluated with R software and the results showed that changes in groundwater quality are related to land use changes.

High-precision precipitation data at the time and place scale, which is a main component of hydrological models, will greatly help to manage the water resources. Hence, in this research, evaluating precipitation data accuracy as well as their origin correction in Quantile method (RQUANT) was used to improve the performance of satellite data in the Yamchi Dam Basin in Ardabil province. The satellite data used included PERSIANN-CCS, PDIR-Now and GPM precipitation data on an hourly, daily and monthly time scale for the region's rainy months over 11 years that were selected by moderate precipitation, standard deviation and coefficient of variation. Comparison of the results obtained from GPM satellite with precipitation data showed that this product has better performance than PERSIANN-CCS and PDIR-Now models on a monthly scale. GPM data on a monthly scale had a MAE index and RMSE 4.66 and 9.70 mm per day, respectively and a correlation coefficient of 0.74; while these values for PERSIANN-CCS and PDIR-Now models were equal to 24.24, 62.03 and 0.36 and 07.09, 13.52, 0.27, respectively. Thus, the GPM satellite precipitation product was provided better performance on a daily scale. Since hourly data was required in flood analysis, then in this paper, precipitation data on an hourly scale was evaluated; the statistical values for GPM satellite were 0.91, 2.66 and 0.06, respectively. In general, among the study precipitation products, GPM precipitation gives better results, although on a daily and hourly scale, the desired results were not obtained compared to the measured data.

Due to the advances made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can cover all surface water areas. To monitor. In this study, the capability of Sentinel-2 and Landsat-8 satellite images to estimate the concentration of water quality parameters including acidity, electrical conductivity, total soluble solids, alkalinity and surface water temperature were investigated. First, the Sentinel-2 and Landsat-8 satellite images were pre-processed and then the appropriate bands were determined to identify a significant relationship between the values of each water quality parameter and the images were determined using linear regression and their accuracy was calculated for the actual values. The results show the superiority of Sentinel-2 images with Pearson coefficient, standard error and RMSe for PH parameter equal to 0.58, 0.28 and 0.29, EC equal to 0.847, 78.7 and 77.22, TDS equal to 0.895, 45.8 and 44.37 and for the alkalinity parameter only through Landsat-8 images with Pearson coefficient, standard error and RMSe equal to 0.473, 22 and 21.8, respectively. Both satellite images can be used for the water surface temperature parameter due to the high Pearson coefficient. These values are 0.871, 2.9 and 2.85 for Landsat-8 and 0.752 for Sentinel-2, respectively. 0, 14.4 and 4.06.

The study of statistics and information on annual damages caused by floods in Iran indicates the extent of flood damage to natural resources. The use of geospatial information and remote sensing technologies can be very helpful in reducing these damages and better planning. In the present study, these technologies have been used for flood zoning in the Karun River in Ahvaz city. The zoning of the study area was done by combining hydraulic model and ArcGIS software through HEC-Geo RAS extension along 33 km of this river. In order to extract the border of the river banks in the urban area of Ahvaz, the supervised classification and Landsat satellite images were used. After introducing the geometric information to the model, the value of the Manning roughness coefficient is determined for different periods and then the return periods of 2, 50, and 100 years are introduced. The maximum speeds in the return periods were 2.6, 6.15, and 7.2 m/s, respectively, which are within the allowable flood speeds. This means that if the river banks are organized and fenced, scouring, erosion, and sedimentation will not occur and water will flow in the main canal without destruction. Preparation of flood warning systems based on precipitation with a high return period that leads to extreme floods, walling, beach construction, and river management in unstable and erodible sections of the river and improving the slope of the bed and canal dredging in the required sections, including the proposed executive solutions for organizing Karun River.

Vegetation changes can change the rainfall and temperature cycle and also climate fluctuations, especially temperature and precipitation parameters, have significant effects on vegetation. Climate change causes restriction for plant activities which cause changes in vegetation indices including NPP. Considering that Qazvin plain has been affected by climate fluctuations and drought in recent years, in this study, the relationship between NPP and temperature, standard deviation of temperature, precipitation and standard deviation of precipitation was investigated in this plain. Annual NPP was gained using MODIS satellite imagery for 2001-2020 and using meteorological data, the average annual temperature, total annual rainfall and standard deviation of these two parameters were calculated for each year. Then using linear regression analysis in TerrSet, the relationship between NPP and meteorological parameters was determined. The results showed that the most important factor affecting NPP was related to the temperature and its standard deviation, when they are entered into the equation simultaneously which was effective in 48.8% of the area. The least effective variable was related to the entry of the precipitation and its standard deviation into the equation, which was effective in only 0.4% of the region. Due to the seasonal precipitation, it can be concluded that the standard deviation is not very effective on NPP. Among the land uses in the region, the sensitivity of NPP to temperature, precipitation and their standard deviation in agricultural lands was more than other land uses.

Anzali Wetland in Iran as one of the most valuable wetlands registered in the Ramsar Convention is being destroyed by environmental factors and human activities. In the last two decades, among various satellite images, radar images have played a special role in wetland monitoring. Radar is an all-weather sensor and it is sensitive to surface roughness and moisture, they serve as a valuable source for quick and accurate monitoring of wetlands. However, similarities in backscattering coefficients of different wetland classes and relatively difficult processing – in comparison to optical images- are the most important factors that limit their application. In this study, the capabilities of SAR images in the classification of Anzali wetland and the three main land use classes around the wetland (i.e. agricultural lands, reeds, and built-up areas) were evaluated. Two radar images; Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) and Sentinel 1 captured in 2018 were used. The texture parameters of the two images have been extracted. The images and their extracted texture layers have been fused by the feature-level method and further classified by the random forest method. The overall accuracy of feature-level fusion is equal to 75% and the kappa coefficient is equal to 0.62. The evaluation results related to producer and user accuracy are 100% and 83.33%, respectively, show the high capability of radar images in the classification and detection of wetlands. However, some errors have been observed in the separation of agricultural lands, reeds, and built-up areas.

In the present study, long-term (during 1999 to 2019) and seasonal changes (during 2018) of the area of Gorgan Bay and its relationship with the changes in precipitation and Caspian Sea water level have been studied. The long-term water body was calculated by using the time series of Landsat8, Landsat5 and Sentinel-2 satellites imagery, and employ the MNDWI2 index. Also, data from Topix and Json satellites were used to study the changes of Caspian Sea water level and TRMM satellite data was used for precipitation changes. The results showed that the water body of Gorgan Bay has decreased significantly during the study period and this trend continues. The long term changes in the water area of Gorgan Bay have a high correlation (0.92) with the amount of fluctuations in the water level of the Caspian Sea. During this 20-year period, the changes in the water level of the Caspian Sea have been about 120 cm. But the correlation between water area and precipitation in the long period is low (0.1). This trend is quite the opposite in a short period of one year and since the changes in the water level of the Caspian Sea in a period of one year is very small (5 cm), so the water area of Gorgan Bay has a very low correlation (0.09) with it. But in the same period, there is a relatively moderate correlation between rainfall changes and the area of Gorgan Bay water body with a delay of one month.

One of the main scientific topics on the effects of global climate change is to assess changes in the carbon cycle in rangelands. Net Primary Production (NPP) is an important component of this cycle, in terms of carbon storage, and a key indicator for assessing the ecosystem function. This research aimed to investigate the correlation between NPP and ocean-atmospheric oscillations, monthly and seasonally, from 2000 to 2016 in the north of Iran’s Alborz Mountains. Net Primary Production of terrestrial vegetation was extracted from MODIS data and used in a model along with ocean-atmospheric oscillations. Multivariate regression analysis was used to investigate the simultaneous and lagged status in different timescales. Mann-Kendal test was used for trend analysis in different seasons over the studied period. Results showed that the highest NPP values were 2.06 and 1.30 g C m-2 d-1 in spring and summer and the lowest were 0.68 and 0.55 g C m-2 d-1 in autumn and winter, respectively. The trend of NPP variations was significantly different in autumn and winter. Overall, it was showed that NPP was affected by climatic variables, especially precipitation, and variables  related to ENSO indicator are the main factors affecting precipitation, thereby affecting NPP in the north of Iran.

Flood monitoring and zoning play an important role in reducing the damage caused by this natural crisis. The purpose of this paper is to investigate the risk of flooding of April 2019 in Khuzestan using Landsat-8 data. First, image processing was performed in ENVI 5.3 software and then MNDWI and NDWI indices were used to monitor the floods. Then, the flood hazard map was prepared in ArcGIS10.4 software. The results show that the southern and southwestern parts of the province are in a very severe situation and the central and southeastern parts are in a very hazardous condition, which is one of the most prone flood areas in the province. Also, monitoring of flood maps in Khuzestan province shows that there is a complete similarity between the recent flood and flood zoning map. Examination of the maps showing that the recent floods occurred mostly in the western, southern and southwestern parts. Spatial survey of floodplain areas shows that the cities of Hoveyzeh, Azadegan Plain, Ahvaz, Khorramshahr, Bandar Mahshahr, Abadan and especially Shadegan have been flooded more than other cities. Meanwhile, Shadegan city has been affected by floods based on MNDWI and NDWI indices of 191349 and 174813 hectares, respectively, which shows the highest rate compared to other cities in the province. In general, according to the results, the use of remote sensing data and MNDWI and NDWI indices for flood monitoring, as well as the use of geographic information system for flood risk zoning in related studies are recommended.

The present research was carried out with the aim of identification and separation of zizyphus spina christi and Astragalus sp. species as well as providing a distribution map of the mentioned species using UltraCam-D digital images in a part of the mountainous Bushehr province. Different techniques of enhancement were applied including Texture Analysis, Principal Component Analysis (PCA) and ratio of the bands. Five band groups were selected including main bands, band set obtained from image texture analysis, main bands along with indices and the first obtained band from PCA and a collection of the best bands obtained from OIF. The training samples were produced through field method. Then, 70% of the samples were applied for various classifier pixel-based algorithms including, Mahalanobis Distance classification, Maximum Likelihood classification, Neural Net Classification, Support Vector Machine (SVM) and Random Forest classification. Verification of the results was carried out using 30% of actual ground samples. Results of assesment of images classified by various algorithm showed that the maximum overall accuracy (85.69%) and kappa coefficient (0.72) in separating the three classes of zizyphus spina christi, Astragalus sp. and soil from the other mixed vegetation cover are for classification by Mahalanobis Distance Classification algorithm applied on group of four main band, PC1, NDVI and SAVI. In general, the results of classification by pixel-based method represent proper efficiency of UltraCam-D digital data for identification and separation of desert regions species particularly zizyphus spina christi from Astragalus sp and shrub species.