JWSS - Journal of Water and Soil Science

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Of the applications of remote sensing and satellite images in natural resources is distinguishing and detection of changes in land surface. The image classification using Maximum Likelihood (MLC) is one the prevalent method which is used in a study of the application of TM and ETM+ satellite images to detect sediment deposition on an implemented floodwater spreading scheme. In order to implement the research, field sampling and checking were done using transect networking method by selection of 30 sample points in floodwater spreading area as well as another 30 control points in the study area. The results of the study are shown that detection of sediment deposition using MLC method by application of LANDSAT TM and ETM+ can lead to increase the precision of change detection up to 82 percent. Furthermore, the results also show that the trend and changes due to sediment deposition on water spreading area can be precisely detected. Considering the present and potential applicability of the applied method in distinguishing changes due to sediment deposition on land surface which is absorbed on 450 hectares of water spreading area in this research study, it can be pointed out that the use of this method in larger area could be tend to increase the precision of change detection and to decrease the required time.

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The aim of this study was to detect change of the forest area in the north of Iran between 1994 and 2001. The study area was covered by a 1:25000 topographic map (about 15000 ha) in Babol forests. The forest map of 1994 was extracted from 1:25000 topographic digital map. Landsat 7 ETM+ image dated July 30, 2001 was analyzed to produce the forest map for the end of the period. Since the evaluation of the image quality illustrated it less than ±1DN in the ETM 2, 4, 5, the rectification of the stripping distortion was ignored. There were also duplicate scan lines and sweep distortions in all the spectral bands. Orthorectification was implemented using ephemeris data and digital elevation model. Several spectral transformations such as rationing, PCA, Tasseled cap and image fusion (using Color space transformation and Spectral response method) were performed on the ETM+ data. The sample ground-truth map was prepared using GPS in 3% of the study area. In order to classify the image, hybrid classification method (digital and visual), using original and synthetic bands, was employed. At first the image was classified using maximum likelihood classifier. The most accurate map (overall accuracy and kappa coefficient equal to 94.56% and 0.89, respectively) was converted to the vector format and then it was edited on the basis of various color composites, fused images and other ancillary data. The obtained map showed overall accuracy and kappa coefficient equal to 96.39% and 0.927, respectively. The comparison of the classified map with the forest map of 1994, illustrated that 751 ha of forest area (equal to 8.2% of the previous forest area), were decreased. This includes a 417 ha increase (mostly reforested areas) and a 1168 ha decrease over the study period. The findings indicate the high potential of ETM+ data in forest mapping and change detection over the whole extent of the northern forest of Iran.

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To detect the rice paddis areas in Isfahan region, the IRS-1D data from PAN, LISS III and WiFS time series were used. Geometric, atmospheric, radiometric and topographic corrections were applied to various images from 2003 to 2004. Necessary preprocessing and various analyses as well as time series composite image analyses were applied and field sampling was done for appropriate times in 2003 and 2004. Image classification was applied using suitable training sites in various images. The SWIR band capabilities were useful for NDWI (Normalized Difference Water Index) to detect the rice paddies. On PAN and LISS III images, urban areas, roads, agricultural lands, non cultivated farms, rocks and brackish soils are detectable. The error matrix was calculated to assess the produced map accuracy using the ground truth data. The total classification accuracy was %91 and the Kappa index value was %89. The rice paddy areas was about 19500 ha in 2003, detected through LISS III data, and 20450 ha through WiFS data. The paddies were 21670 in 2004 through WiFS data. The results of this study confirmed that one can use the LISS III data to detect and determine the rice paddys areas with high accuracy, and WiFS data to estimate the paddies areas with acceptable accuracy.

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The natural forest and range stands of Zanjan province are located in mountainous areas. Inventorying and mapping of natural forest and range stands in mountainous areas are difficult and costly. Satellite data are suitable for this purpose. The Landsat ETM+ image data of 2002 are used for classification and mapping of natural forest stands in Zanjan province. For the purpose of data reduction and principal components extraction, the principal components analysis (PCA) was used. Just the scores of the first three PCs (PCA1، PCA2 and PCA3 (that accounted for 76.67 percent of the total variance were considered as new images for future analysis. A raster geographic information system (RGIS) database file was prepared and involved 7 ETM+ bands, 3 principle component analysis, 9 factor analysis and 8 vegetation indexes of image data. The correlation coefficients of 27 image layers and optimum index factors (OIF) of selected images were computed and 12 groups were found suitable for natural forest and range stands. Maximum liklelihood classification (MLC) method was used in this study. In order to test the accuracy of map, kappa index of agreement was calculated. The highest KIP belonged to three λ₃, λ₄, λ₅ Landsat image bands with KIP = 0.86. The highest OIF belonged to three PCA3, FA2 and MIR with value of 233.44 and lower OIF belonged to three λ₄, λ₅, λ₇ with value of 83.63. The overall, user’s and producer’s accuracy rates were 88.45, 73.69 and 70.23 percent respectively. The results of the study show that the Landsat ETM+ image data were appropriate for classification and mapping of natural forest and range stands in Zanjan province.

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The present study discusses a method used to produce updated information about vegetation cover in arid and semi-arid zones, using RS data and GIS technique. In this method, Landsat ETM⁺ data in 2002 was collected in an area of about 60000 ha in Nodoushan basin, Yazd, Iran. To collect the necessary ground data, 50 sites of different vegetation types were selected and the percentage of vegetation cover in each one was determined. Also, different vegetation and soil indices were derived and crossed with located sampling points using ILWIS software capabilities. To get the best fitted curve, the relationship between vegetation cover, as a dependent variable, and satellite data bands, vegetation indices and environmental factors, as independent variables were assessed. Therefore, a multiple linear regression model was established for the prediction of vegetation cover percentage in the studied area. Finally, a vegetation cover map with high a precision was produced. As a conclusion, it can be said that mapping of vegetation cover via remote sensing is possible even if its vegetation cover is sparse.

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‏Snow is a huge water resource in most parts of the world. Snow water equivalent supplies 1/3 of the water requirement for farming and irrigation throughout the world. Water content estimation of a snow-cover or estimation of snowmelt runoff is necessary for Hydrologists. Several snowmelt-forecasting models have been suggested, most of which require continuous monitoring of snow-cover. Today monitoring snow-cover patches is done through satellites imagery and remote sensing methods. MODIS have smaller Spatial Resolution and more bands in comparison with Meteorology Satellite like NOAA. Therefore, in this research we used MODIS data for creating snow cover imagery. Existence of cloud in the study area is a major problem for snow cover monitoring. Therefore, in this research snow cover area changes were estimated without MODIS data period, but with DEM imagery and regressions between temperature, height and aspect. For this purpose, on 10 Esfand when the image was suitable we estimated the snow cover area. In comparison with real image, precision of the method was confirmed.

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Remote sensing science and satellite data are widely used by researchers for agricultural studies. Vegetation spectral reflections recorded by satellite sensors have been used extensively for identifying plant types, plant cover, health community of plants and predicting yield. The TERRA satellite, with 5 sensors, provides an opportunity to observe land, atmosphere and ocean characteristics. The Moderate Resolution Imaging Spectroradiometer (MODIS) is on–board TERRA satellite. This sensor with 36 bands by 250m, 500m, and 1000m spatial resolution help us to study our environment. The MODIS vegetation indices are used to monitor photosynthetic activity radiation, change detection in plant communities, planted area estimation and plant health. A statistical analysis was done to analyze Near Infra Red (NIR) (841-876 nm) and Red (R) (620-670 nm) bands of MODIS images for a 16 day period. The images have been used for winter wheat in Mashhad (North East of IRAN) during agricultural season of 2004-05.Some image processing techniques were used to extract the related digital numbers (DN), showing the electromagnetic spectrum reflection for all of the pixels. The analysis shows a positive correlation between R and NIR spectrum (0.70 and 0.69) and decrease in NDVI (0.18 and 0.24) in the first and late wheat growth season. However, there is not such a good correlation in the middle of the season and NDVI increased very much. In spite of having wheat cover in the field, NIR reflection decreased very much in the late wheat growth season (0.5). Therefore the correlation relation between R and NIR band along with NDVI could be used effectively in precision agriculture management such as predicting of phonological stage, wheat yield estimation and wheat health condition.

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Remote sensing is the main technology for assessing expansion and rate of land cover changes. Knowing the different kinds of land cover changes and human activities in different parts of lands, as the base information for different planning is especially important. In this study, the land cover changes of Isfahan city that is consist of Isfahan and its` surrounded area was studied for the past 4 decades. For researching the study objectives, the aerial photos with scale of 1:50000 taken in 1955, MSS, TM and ETM+ images from Landsat satellite taken respectively in 1972, 1990 and 2001 and the topography maps of Isfahan city and its` surrounding were used. All of the aerial photos and satellite images with the nearest neighbor sampling were georegistered with the RMSe less than one pixel. For image processing, the best false colored composite image was first produced according to OIF index. Then land cover maps of the studied area were produced in 5 classes by using the combination of supervised and unsupervised classification and NDVI index. At the end, the produced maps compared with post-classification method. The results showed that the most urban area sprawl was occurred between 1972-1990 with the mean of 571 ha in a year and the least growth was come about between 1955-1972 with approximately 324 ha in a year. However, by declining the annual mean of green cover 1263 ha during 1955-1972, the most green cover demolition occurred in study area.

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Monitoring Land Use and Land Cover Changes have a significant role in environmental programming and management. Satellite data is an essential tool for detecting and analyzing environmental changes. Many change detection techniques have been developed which have advantages or disadvantages. Change Vector Analysis (CVA) technique is one such a method. This method is based on radiometric changes between two dates of satellite imagery. Main advantage of this method is that it provides direction and magnitude image of change. The aim of this study was to describe change vector analysis technique and it applies to detect land cover change in Isfahan area during an 11-year period. The data used for this study were two images Landsat: TM 05 June 1987 and 03 June 1998. Correction radiometric was not carried out because of the similar sensor and acquisition time of the remote sensing data. After geometric correction, the study area was selected from Landsat images. Change vector technique was applied to analyze magnitude and direction of change. The change map showed Kappa and overall accuracy coefficient of 63.19% and 74.4%, respectively. The results showed that the changed land cover was 3340 ha during this period. Overall, the results show that 1325 hectares (especially agricultural lands) have been converted into urban areas, agricultural areas were increased up to1385 hectares, and 435 hectares of agricultural areas were converted to other land use over the period of study. This study showed that CVA is a robust approach for detecting and characterizing radiometric change in multi-spectral remote sensing data sets.

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Land cover maps derived from satellite images play a key role in regional and national land cover assessments. In order to compare maximum likelihood and minimum distance to mean classifiers, LISS-III images from IRS-P6 satellite were acquired in August 2008 from the western part of Isfahan. First, the LISS-III image was georeferenced. The Root Mean Square error of less than one pixel was the result of registration. After creating false color composite and calculating transformed divergence index, the images were classified using maximum likelihood and minimum distance to mean classifiers into six categories including river, bare land, agricultural land, urban area, highway and rocky outcrops. The results of classification showed that the dominant land cover type is urban area, occupying about 6821.1 ha representing 38.86% of total area. The accuracy of maximum likelihood and minimum distance to mean classifiers was obtained using error matrix and Kappa analysis. According to the results, the maximum likelihood algorithm had an overall accuracy of 94.93% and the minimum distance to mean method was 85.25% accurate. The results illustrate that the maximum likelihood method is superior to minimum distance to mean classifier.

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Precision farming aims to optimize field-level management by providing information on production rate, crop needs, nutrients, pest/disease control, environmental contamination, timing of field practices, soil organic matter and irrigation. Remote sensing and GIS have made huge impacts on agricultural industry by monitoring and managing agricultural lands. Using vegetation indices have been widely used for quantifying net annual production on different scales. The aim of this study was to find a rapid method with acceptable precision for the identification and classification of agricultural lands under cultivation (wheat and barley, alfalfa and potatoes). We used multi-temporal AWiFS data and applied Boolean logic and unsupervised classification. Results indicated that Boolean logic approach had a higher accuracy and precision in comparison to unsupervised classification, although it is more complicated and time consuming.

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Study of land use effects on soil quality indicators leads to sustainable management and preventing progressive land degradation. The TM (1987) and ETM+ (2000 and 2005) data were used to study land use change effects in Mahan-Joopar area on soil quality indicators. Fifty random soil samples from 0-30 cm depth of each land use were taken using provided maps. Organic matter, microbial respiration potential, bulk density, pH, EC, and soil texture were investigated as soil quality indicators. Eight land uses including fruit orchards, woodlands, pistachio orchards, cultivated, barren, bare land, fallowed, and haloxylone land were detected. Results showed overall accuracies of 89.4, 95.2, and 91.7 % with kappa coefficients of 85, 92, and 88% for maps provided in 1987, 2000, and 2005, respectively. Generally, the investigated quality indicators showed that woodlands, fruit orchards, cultivated land, and pistachio orchards enhanced soil quality better than other land uses.

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This study aimed to evaluate the performance of TM satellite data acquired in June 2009 to map soil salinity in southeast of Isfahan province. Ground salinity data (EC) was collected within 9 pixels, covering an area of approximately 8100 m2 using stratified random sampling technique at 53 sample sites. Spectral indices including TM bands, BI, SI1, SI2 and SI3, PC1, PC2, PC3 and also multiple linear regression modeling and maximum likelihood classification techniques were applied to the geometrically corrected image. Results of regression analysis showed that the TM band 4 had the strongest relationship with EC data (R2=0.48) and also the relationship of the modeling image using TM 3, TM 4, TM5 and PC3 was significant at the 99% confidence level. The accuracy assessment of the stratified TM4 and modeling image into five classes including 0-4, 4-20, 20-60, 60-100 and EC>100 ds/m indicated that there was more than 86% agreement with the field measurements of EC data. Therefore, it can be concluded that the discretely classified salinity maps have higher accuracy than regression methods for identifying broad areas of saline soils, and can be used as appropriate tools to manage and combat soil salinization.

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Land use classification and mapping mostly use remotely sensed data. During the past decades, several advanced classification methods such as neural network and support vector machine (SVM) have been developed. In the present study, Landsat TM images with 30m spatial resolution were used to classify land uses through two classification methods including support vector machine and neural network. The results showed that SVM and neural network with the total accuracy of 90.67 % and 91.67% are superior. SVM had a better performance in separating classes with similar spectral profiles. In addition, SVM showed a better performance in delineating class borders in comparison with neural network method. In summary, both SVM and neural network showed satisfactory results but the method of support vector machine proved better with a difference of 1% and 2% in overall accuracy and kappa coefficient, respectively. This was an expected outcome because SVMs are designed to locate an optimal separating hyperplane, while ANNs may not be able to locate this separating hyperplane.

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Spatial estimation of evapotranspiration (ET) rates is essential for agriculture and water resources management. This study aimed to estimate ET v an ET estimation algorithm called Surface Energy Balance Algorithms for Land (SEBAL) and also by using TM June 2009 satellite data in Damaneh region of Isfahan province. To calculate the ET, all the energy balance components and related parameters including net radiation, surface albedo, incoming and emitting shortwave and longwave radiation, surface emissivity, soil heat flux, sensible heat flux, NDVI vegetation index, Leaf Area Index(LAI),  and surface temperature were extracted from the geometrically and radiometrically corrected TM images. Results showed that ET rate was about 7.2 mm day^-1 in agricultural areas, which was almost equal to 6.99 mm day^-1 extracted from the FAO Penman-Monteith method in the synoptic weather station of Daran. Results here indicate that the extraction of ET rate which is almost equal to plant water requirements from remote sensing data can be used in selecting appropriate plants for agriculture and rehabilitation purposes in extensive arid and semi-arid regions of Isfahan province where severe droughts and water shortage are major problems.

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Precipitation is an important element of the hydrologic cycle and lack of this data is one of the most serious problems facing research on hydrological and climatic analysis. On the other hand, using satellite images has been proposed by many researchers as one of practical strategies to estimate precipitation. The present paper aims to evaluate the accuracy of satellite precipitation data, provided by PERSIANN and TRMM-3B42 V7 in Gorganrood basin, Iran. To achieve this aim, two sets of daily precipitation ground-based data, 2003 to 2004 and 2006 to 2007, from six stations of Gorganrood basin, named; “Tamer”, “Ramian”, “Bahalkeh-ye Dashli”, “Gorgan Dam”, “Ghaffar Haji” and “Fazel Abad” have been used in this paper. The evaluation indices have been calculated and analyzed in different time scales, including daily, monthly and seasonal. The results indicated that the two above mentioned satellite models are not accurate in daily scale. However, they showed reasonable accuracy in monthly and seasonal scales. The highest correlations between satellites and recorded data in daily and monthly scales, for TRMM-3B42 V7 in “Gorgan Dam” and “Bahlke Dashlei” stations, are 0.397 and 0.404, respectively. The comparison of measured and satellite data of winter showed better agreement for PERSIANN model. However, TRMM-3B42 V7 shows better correlation in other seasons. The results also indicated that while TRMM-3B42 data displays higher correlation with measured data, PERSIANN provids better results in predicting the number of rainy days. 

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A limited number of agricultural weather stations measure moisture in the soil surface. Furthermore, soil moisture information may be required in areas where there is no weather station. The aim of the present study was to use Landsat 8 satellite images to estimate soil surface moisture in an area without agricultural meteorological stations. Gravimetric soil moisture for a total of 14 samples was calculated in the cold season in depths of 0-10 cm when Landsat 8 satellite was overpassing poor rangeland of North of Sabzevar. Furthermore, the first four principal components were extracted from seven Landsat-derived vegetation indices and bio-physical factors affecting soil moisture. Afterwards, the first four components were used to estimate soil surface moisture at the moment of the satellite passing the region using a multivariate linear regression and neural networks. The obtained results of instantaneous soil surface moisture showed that the neural networks had mean absolute percentage error of while classical regression analysis had mean absolute percentage error of 40%. The results also showed the benefits of using both in-situ soil moisture data and Landsat 8 satellite images to model instantaneous soil surface moisture content for areas lacking meteorological networks.
 

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