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Showing 18 results for Classification

N. Zahedifard, S. J. Khajeddin, A. Jalalian,
Volume 8, Issue 2 (7-2004)
Abstract

Satellite data use is finding global applications because they provide repeated cover, broad information, high electromagnetic spectral resolution, and software-hardware compatibilities. This study aims to evaluate of the Landsat TM data capabilities in land-use mapping of Bazoft River basin (Chahar Mahale Bakhtiary Province). Six spectral bands of the Landsate TM were employed to produce land-use map of the Region. The date of image acquisition was May 5th, 1998. Performance of the geometric correction completed with RMSE= 1.008 pixels. Various image enhacement methods (e.g. FCC, filtering and Vegetation Indices) were used to study the different land-covers. Field investigations were carried out using a GPS, 1:50000 scale topographic map and false color composites images. Heterogeneous land-use units were studied in 62 sample sites estimating percentage of vegetation cover. A regression analysis was performed between percentage vegetation covers and vegetation indices values of NDVI, RVI, SAVI, DVI, TSAVI1, NRVI and MSAVI2. Results show that NDVI, SAVI, TSAVI1, NRVI and MSAVI2 have high correlation coefficients. But RVI, DVI and PVI have low correlation coefficients. The resulting values of vegetation cover were density sliced to produce the land-cover map. After supervised classifications and density slicing of Vegetation Indices, classifacation accuracy was assessed and, finally, land-use map of the study area was produced with Hybrid classification method. Supervised classification with maximum likelihood method was the best technique for land-use mapping in the study area the total Kappa index was %87. In general, detection of some land-use classes through single date TM data is not feasible, these include: scattered forest trees with cultivated understory, annual grasses, and fallow lands. Also TM digital data are incapable of distinguishing small and separated rural constructions or soil-covered routes.
A. Sarreshtehdari,
Volume 9, Issue 4 (1-2006)
Abstract

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.
P. Shekari, M. Baghernejad,
Volume 9, Issue 4 (1-2006)
Abstract

Chenges in the soil characteristics is rather continuously. A method that takes this continuity into account would present a realistic pattern of soil distribution either in taxonomic or geographical space. The fuzzy set theory provides such an approach. In this study, the robustness of fuzzy clustering in soil pattern recognition was evaluated in a subcatchment of western Iran. The clustering carried out on the basis of minimization of an objective function in assigning membership values to each pedon in each fuzzy class. Fuzziness exponent values from 1.15 to 1.5 were used. The following validation of the resulted clusters (classes), optimal number of classes in whole, morphological and particle-size subsets were determined 8, 4, and 5 respectively. Plots of membership values across the landscape indicated class overlap and considerable contiguity. Considering low differentiation of these young soils and the high similarity among their properties, the method indicated a high capacity in recognizing different soil types over the study area. Furthermore, there was relationships between the soil fuzzy classes and landform. Thus, the method is capable in continuous classification, which could be so important in construction of continuous soil maps at low aggregation levels, e. g., pedon.
O. Rafieyan, A. A. Darvishsefat , M. Namiranian,
Volume 10, Issue 3 (10-2006)
Abstract

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.
S. J. Khajeddin, S. Pourmanafi,
Volume 11, Issue 1 (4-2007)
Abstract

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.
A.e. Bonyad, T. Hajyghaderi,
Volume 11, Issue 42 (1-2008)
Abstract

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.
S Falhakar, A Saffianian, S.j Khajeddin, H Ziaei,
Volume 13, Issue 47 (4-2009)
Abstract

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.
S. Soltani , L. Yaghmaei , M. Khodagholi , R. Saboohi ,
Volume 14, Issue 54 (1-2011)
Abstract

The temporal and spatial vegetation dynamics is highly dependent on many different environmental and biophysical factors. Among these, climate is one of the most important factors that influence the growth and condition of vegetation. Of the abiotic factors affecting the geographic distribution of vegetation type, climate is probably the most important. Ecological research has traditionally aimed to generalize vegetation types that are assumed to be homogenous. Most of climatic classifications related to bioclimate are focused on limited climatic factors such as temperatue, precipitation and combination of them. As climate is a compound phenomena using limited factors cannot show the climate of a region, and as a result most climatic factors must be considered in bioclimatic classification. Therefore, a climatic study using various climatic factors could reveal the effective factors in distribution of vegetation. In order to determine bioclimatic zones in Chahar-Mahal & Bakhtiari province using multivariate statistical method, 71 climatic variables, which were more important in plant ecological conditions, were selected and evaluated by the factor analysis. The factor analysis revealed that the first three factors which explain %91.8 of total variance among the selected variables were temperature, precipitation, and radiation. According to results and using hierarchical cluster analysis in Ward’s method, bioclimatic classification in Chahar-Mahal province was carried out and 5 bioclimatic zones were found. In addition, Chahar-Mahal province was classified by 4 traditional climatic classification methods (Koppen, Gaussen, Emberger and De Martonne) and those classes were compared to climatic classes obtained by multivariate statistical method. The latter comparison was suggestive of the fact that multivariate statistical method provides a more appropriate classification in comparison to the traditional methods, specially because more dominant vegetation species could be defined for each of the newly described climatic classes. Furthermore, dominant species were determined for each climatic region.
L. Khodakarami , A. Soffianian,
Volume 16, Issue 59 (4-2012)
Abstract

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.
M. Arabi, A. Soffianian , M. Tarkesh Esfahani,
Volume 17, Issue 63 (6-2013)
Abstract

Physicochemical characteristics of soil, land cover/use and human activities have effects on heavy metals distribution. In this study, we applied Classification and Regression Tree model (CART) to predict the spatial distribution of zinc in surface soil of Hamadan province under Geographic Information System environment. Two approaches were used to build the model. In the first approach, 10% of total data were randomly selected as test data and residual data were used for building model. In the second approach, all data were used to build and evaluate the CART model. Determination coefficient (R2) and Mean Square Error (MSE) were applied to estimate the accuracy of model. Final model included 51 nodes and 26 terminal nodes (leaf). Calcium carbonate, slope, sand, silt and land use/cover were determined by the CART model to predict spatial distribution of Zn as the most important independent variables. The regions of western Hamadan province had the highest concentration of Zn whereas the lowest concentration of Zn occurred in the regions of northern Hamadan province. The results indicate good accuracy of CART model using R2 and MSE indices.
M. Mokhtari, A. Najafi,
Volume 19, Issue 72 (8-2015)
Abstract

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.
S. Youneszadeh Jalili, M. Kamali, P. Daneshkar Arasteh,
Volume 20, Issue 78 (1-2017)
Abstract

Integrated management of watershed basins depends on deep knowledge of basic concepts such as the arrangement of lands and their uses. Location and distribution of agricultural land use help to balance water resources in the watershed basins. In this research with the help of satellite images of Landsat 5 and 8, and the method of maximum likelihood classification algorithm, land use types of water, barren areas and salt lands, and irrigated agriculture were studied in the Urmia watershed in the years 2010 and 2013.Then applications of modis images and product Urmia watershed land cover for years 2010 and 2012 were compared and finally modis and Landsat land covers in 2010 were compared. Results showed that the area of irrigated farmlands of Urmia basin has increased in the years between 2010 and 2013; while, the water zone has declined. Comparison between modis and landsat in 2010 showed that modis can estimate irrigated lands and water zone better than barren areas. The kappa coefficient for years 2010 and 2013 in Landsat images are 0/77 and 0/87, respectively.


V. Rahdari, A. R. Soffianian, S. Pourmanafi, H. Ghaiumi Mohammadi,
Volume 22, Issue 3 (11-2018)
Abstract

Determining the cultivation crops area is important for properly supplying crops. The aim of this study was mapping the cultivation area crops in Chadian city for spring and summer during 2015 by using the time series data of the Landsat 8 satellite of OLI imagery. At first, the under cultivation area was determined by setting a low threshold in the marginal pixels of the agricultural rain fed in the spring image NDVI index. The area cultivated with wheat and alfalfa was prepared by subtracting spring and summer NDVI values. Cultivation maps, which were cultivated with potatoes, corn and orchards, were prepared using the supervised classification with the FISHER method in a step by step manner. Spring and summer cultivation maps were combined; finally, the major cultivation crops maps were produced by the hybrid classification method. Map accuracy assessment was done by producing error matrix and calculating kappa coefficient, total accuracy, commission and omission error, producer, and use accuracy; in all indices, they had an acceptable value, showing the capability of OLI and the used methods in separating each cultivation.

P. Khosravani, M. Baghernejad, A. Abtahi, R. Ghasemi,
Volume 25, Issue 3 (12-2021)
Abstract

Soil classification in a standard system is usually defined based on information obtained from properties and their variations in different map units. The aim of this study was to compare soil genesis and morphological characteristics in different landforms with WRB and Soil Taxonomy (ST) Systems. From nine studied profiles, six profiles were selected as representative profiles and dug in Colluvial fans, Piedmont plain, and Alluvial plain physiographic units, respectively. Then, the soils were classified according to the pattern of the two systems. Also, variation analysis of variance (ANOVA) and comparing means were used to quantify interested soil properties. The results of soil physio-chemical properties at different landform positions were significant based on analysis of variance of the effect of physiographic units and soil depth at the level of 1 %. Soil classification results based on WRB indicated that WRB were recognized four reference soil groups (RSG) included Regosols, Cambisols, Calcisols, and Gleysols at the first level of WRB classification in comparison of ST with recognizing two order Entisols and Inceptisols could separate more soils. The soils were located on the alluvial plain with a high groundwater level in the WRB due to the creation of restrictive conditions for root development in contrast to the ST called “Aquepts” in the suborder level but in a WRB is classified as the “Gleysols” RSG. On the other hand, ST, unlike WRB, used the Shallow criteria at the family level to describe the shallowness of soils and the limitations of root development. Generally, the efficiency of each system varies despite the differences in their structure and depending on the purpose of using them.

M. Pajouhesh, H. Shekohideh, Z. Heydari,
Volume 25, Issue 3 (12-2021)
Abstract

Land use changes identifying to assess and monitor sensitive areas for sustainable planning and land management is essential. Remote sensing and the use of GIS technology as some of the most common methods in the world in monitoring land changes, especially, in the study of large areas. In this study, the trend of spatial land use changes in the area of Karun 3 dam was investigated. in the before and after the construction periods and dam intake using remote sensing and GIS over 27 years. In this study, the satellite imagery of Landsat 5 TM sensors from 1991 and 2008 and Landsat 8 OLI sensors in 2018 were analyzed and processed. Using object-oriented classification with land use maps for the three periods 1991, 2008, and 2018 with the overall accuracy of the Kappa index of 0.93 and 0.89 percent for 1991, 0.94, and 0.88 percent in 2008 and 0.93, respectively, and 0.86% in 2018 was prepared. The results showed that the water use of the region with an area of 37.68 square kilometers is increasing and agricultural lands and residential areas with an area of 1349.04 and 226.56, respectively, forest lands with an area of 1041.49 remained as the dominant cover of the region and rangelands by going through a decreasing trend of increase in both periods after forest use, with an area of 878.87, they had the largest area. According to the obtained results, it can be said that the construction of the Karun 3 dam has caused the flooding of agricultural lands and their conversion to another use, as a result of which the villagers were forced to migrate due to losing their jobs and abandoned residential areas become other uses.

S. Salehi, A.r Esmaili, K. Esmaili,
Volume 25, Issue 4 (3-2022)
Abstract

The objective of this study was to investigate how the earth dam is destroyed due to the effect of upstream and downstream slope of the body in overflow conditions. Therefore, eight models were provided that each model is constructed from the embankment dam with different upstream and downstream slopes (1:1, 2:1) and the soil properties (Sc) on breach formation. The time and method of dam break for flood discharges were investigated. The results showed that the upstream side slope of the embankment dam has less effect than the downstream side slope on the scour process resulting from the phenomenon and by increasing the downstream side slope of the embankment dam, the amount of erosion in the scour hole increases 28 %. Then, using nonlinear regression, relationships were presented to estimate the output flow rate and the location of the waterfall. A to the erosion and formation of the waterfall inside the body of sticky earth dams, two main outlines of the great waterfall and a series of waterfalls were presented. Finally, the formation of these waterfalls due to the effect of shear stress created during sediment erosion relative to the critical shear stress of the dam constituents was investigated and evaluated. Considering the limitations based on shear stress, the formation status of the type and the leaching pattern of the body of the cohesive earth dams during the overpass were estimated. Then, a general plan was presented to predict the behavior of the overflow stream in homogeneous and sticky soil.

M. Kyanpoor Kal Khajeh, Me. Pajouhesh, S. Emamgolizadeh,
Volume 26, Issue 3 (12-2022)
Abstract

Humans are always trying to change land to use natural resources to meet their needs. One of the land use changes that take place in order to benefit from sustainable water resources is dam construction. Dam construction has many positive and negative consequences for the environment from the beginning to use. The objective of this study was to investigate the effect of Gotvand Dam on the problem of collision of water flow path with salt domes and large volume accumulation of salt behind the dam lake. Images of the Landsat 5 satellite TM sensor for 1991, Landsat 7 satellite ETM+ sensor for 2008, and Landsat 8 satellite OLI sensor for 2020 were used to classify images, and prepare land use maps of the studied basin. Reviewing and evaluating the land use maps of the study area showed that agricultural lands are being developed after the operation of the dam. Also, barren lands were decreasing as well as the area's water content was increasing during the study period. In the second period of study (2008-2020), the population of the regions with an increasing area has been increasing. Also, the rangeland and meadows had a decreasing trend during the first and second periods. The results of classification accuracy using the object-oriented method for three periods of 1991, 2008, and 2020 were obtained as 0.92, 0.97, and 0.93, respectively. In general, it can be stated that the construction of the dam has increased the area under cultivation of land and by increasing population and urbanization in the construction area of the dam, destruction and reduction of rangelands occurred.
A. Salar, M. Shahriari, V. Rahdari, S. Maleki,
Volume 28, Issue 2 (8-2024)
Abstract

Unbalanced development of different land use/cover in basins without considering the contribution of all components, can cause serious damage to the stability of the entire basin. The development of agricultural areas by increasing the amount of water use and creating dams upstream of rivers are the most important threats to wetlands in many places. Jazmorian wetland is one of the seasonal wetlands in the south-east of Iran. The most important source of water supply for this wetland is the Halil-Rood River. To investigate the land use/cover changes of Jazmurian wetland and
 Halil-Rood River, the time series of Landsat satellite data for the years 1354, 1374, 1387, and 1401 were used in the present research. The Landsat satellite images were classified using a hybrid classification method and the land use/cover of the study area maps were prepared. The accuracy of the prepared maps for the latest image was calculated by preparing the error matrix, calculating the kappa index, and the overall accuracy of more than 0.8 and 9%, respectively. The investigation of the prepared maps showed that the area of land under water increased from 1354 to 1374 and then decreased from 119,552 hectares in 1374 to 723 hectares in 1401. The area of agricultural land increased from 2131 hectares in 1354 to 133913 hectares in 1387 and declined to 105795 hectares in 1401. The results of this study show that in this period, with the construction of a dam upstream of the Halil-Rood River, and the development of agricultural lands, the water volume level of the wetland decreased, and the wetland completely dried up in 1401. The present study indicates the necessity of considering different components of a watershed in development planning to achieve sustainable development.


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