Jalal Abdolahi; Mohammad hasan Rahimian; Mohammad hosein Savaqebi
Volume 14, Issue 3 , January 2007, , Pages 289-301
Abstract
Today, various indices have been developed for monitoring of vegetation cover in different climatic condition, which cause variation in aspect and spectral reflectance. Therefore, an index can give different values in different conditions. In addition, sparse vegetation and soil background are the other ...
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Today, various indices have been developed for monitoring of vegetation cover in different climatic condition, which cause variation in aspect and spectral reflectance. Therefore, an index can give different values in different conditions. In addition, sparse vegetation and soil background are the other limitations. Hence, combination of some indices can provide sufficient real information in such areas. Contribution of each parameter can be obtained from a statistical method. However, there is no guarantee that the high correlation coefficients would get a good vegetation cover map. It depends on the originality of each predictor variable. The main objective of this study was to identify some probable limitations of Landsat ETM+ images for mapping of vegetation cover in arid and semi-arid zones, especially in drought conditions. In addition, it suggests a method for mapping of sparse vegetation cover in such areas. For this purpose, vegetation cover percentages were measured in two dry and rainy years (2000 and 2002) in the Nodoushan basin, Yazd, Iran. Afterwards, Landsat ETM+ images of two mentioned dates were acquired and different indices were derived. In addition, some environmental factor maps were generated and aligned with other variables (e.g. DEM, Slope and Aspect maps). These data were analyzed using a multiple linear regression method and built regression equations of the form: vegetation cover (%) =1X1+2X2+…+ for each year. Xi’s are independent variables (Satellite data bands, different indices and environmental factors) and’s are regression coefficients and is a constant. According to the equations, vegetation cover maps were generated using ILWIS software capabilities. Then, their accuracies were determined. Results show that the 2002 map (rainy year) is more reliable than the 2000 map (dry year). It was also found that if a drought was occurred in the arid zones, soil background would be dominant and therefore, vegetation indices would not be able to estimate vegetation cover confidently
Morteza Akbari; Hamid reza Karim zadeh; Reza Modares; Bahareh Chakoshi
Volume 14, Issue 2 , January 2007, , Pages 124-142
Abstract
Desertification is the ecologic and biologic reduction which may occur naturally or non naturally. Desertification process usually effects arid and semi arid region and reduce the land efficiency very rapidly. This study has been conducted to evaluate and classify desertification with the aim of geographic ...
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Desertification is the ecologic and biologic reduction which may occur naturally or non naturally. Desertification process usually effects arid and semi arid region and reduce the land efficiency very rapidly. This study has been conducted to evaluate and classify desertification with the aim of geographic information system and remote sensing techniques. In this study, TM data of 25 shahriver 1369(16 October 1990) and ETM+ data of 16 shahrivar 1380 (7 October 2001) were used. Geometric, radiometric and sun height angle correction were carried out on the images. Supervised and unsupervised classification were used for both two images and 9 land uses were classified and compare quantitatively due to geomorphology and desertification attributes. Each main and partial desertification factor was input to geographic information database and scaled to make the map of desertification severity based on recommended method for Iran and FAO map of desertification vulnerability. The result of desertification severity which shows desertification condition, velocity and potentiality, showed that natural desertification occurs in 35 % of area with different medium, high and very high desertification severity. Anthropogenic desertification includes 65 % of study area with high and very high condition. But in desertification vulnerability map, very high desertification vulnerability was determined in 20 % of area with simultaneous human and animal effect and high desertification vulnerability was determined on 80 % area with only animal effect. According to the map, for arid region of north of isfahan, it was identified that range land use change to agriculture, improve agricultural patterns, over grazing, incident economic condition and ground water over exploitation are the most important anthropogenic factor, and drought and soil and water salinity are the most important natural factors. Desertification trend is enhancing to north west of the study area which will make trouble for rangelands, residential and industrial areas.
Abolghasem Dadrasi Sabzavari; Mojtaba Pakparvar
Volume 14, Issue 1 , January 2007, , Pages 33-52
Abstract
Desertification is one of the most difficult issues which has been taken into consideration in the world. The main objective of this study is determination of lands affected by desertification using remote sensing and geographic information systems, in Sabzevar. To meet the objective, Satellite ...
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Desertification is one of the most difficult issues which has been taken into consideration in the world. The main objective of this study is determination of lands affected by desertification using remote sensing and geographic information systems, in Sabzevar. To meet the objective, Satellite imagery of Landsat TM- 29 April 1987 and 13 May 2001 were selected and used, after haze and geometric correction. Principal Components (PC) and False Color Composit (FCC) were calculated, finally FCC521: RGB was selected for background of classification because of the best correlation. The Satellite images were classified by the maximum likelihood algorithm on the basis of field data. A study was conducted to determine the iso line of change in groundwater salinity and depth, in the study area, by using subsurface water data ( quality and quantity). The results show that on non saline parts and in the area of gypsum, the changes are negligible. The area of land uses are more or less the same except in agriculture which the land has increased by 3632 ha and in urban area which developed 672 ha. Merging and processing the whole data shows that the agriculture lands increased from 1987 to 2001. More salinity and less depth of groundwater are the results of groundwater data analysis, specially in south east part of the region
Jalal Abdollahi; Mohammad hasan Rahimian
Volume 14, Issue 2 , January 2007, , Pages 156-170
Abstract
Remotely sensed data are able to monitor some characteristics of the environment and also their spatial structure. The latter one is the first and main step in field data interpolations. Therefore, spatial analysis of some field data is possible by employing of related satellite data bands. In this study, ...
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Remotely sensed data are able to monitor some characteristics of the environment and also their spatial structure. The latter one is the first and main step in field data interpolations. Therefore, spatial analysis of some field data is possible by employing of related satellite data bands. In this study, as an example, Landsat ETM+ thermal band (band 6) was acquired to determine the spatial structure of surface temperature distribution. For the purpose of evaluation and selection of the best interpolation model, the band 6 data was crossed with a regular sampling grid and therefore, a dataset was constructed. Using geo-statistical analysis, empirical semi-variogram was calculated and various mathematical models were fitted to its points (e.g. gaussian, exponential, circular and spherical). Afterwards, the fitted models were applied to generate different temperature distribution maps using kriging interpolation approach. Finally, the optimum model which could better predict temperature changes and distribution was recognized. Result of the study shows that the exponential model would be better to predict and estimate surface temperature in un-sampled locations in the area of studied. So, the model can be used for interpolation of field temperature data with a high confidence level. The represented method can be developed for all the other environmental factors which could better characterized by remotely sensed data, like minimum and maximum temperature, evapotranspiration and so on.
Ebrahim khalifeh; Mohammad reza Kavianpoor; Mojtabah Pakparvar; Sajad Vafaei
Volume 14, Issue 2 , January 2007, , Pages 204-221
Abstract
Wind erosion is one of the major problems in arid and semiarid regions, where the erosion and sand transport caused by wind can lead to serious problems for civilians live in those areas. The regions under wind erosion, includes the sources, transition and deposition areas. Amongst them, identification ...
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Wind erosion is one of the major problems in arid and semiarid regions, where the erosion and sand transport caused by wind can lead to serious problems for civilians live in those areas. The regions under wind erosion, includes the sources, transition and deposition areas. Amongst them, identification of the sources areas is significantly important, which affects our investigations, evaluation and control over wind erosion. It has been found that the application of Remote Sensing (RS) techniques and Geographical Information Systems (GIS) is considerably useful to find the sources of sand in wind erosion. In this study, a combination method based on Image Processing and Wind Analysis (IPWA) has been introduced and used to identify the sources of deposited sand in areas under wind erosion. The method is based on utilizing and composing of image processing techniques such as image enhancement, threshold, false color composition, filtering, and Spectral Profile Testing and Analysis. The results are compared with other investigations to validate the method. It was observed that the results of the present method are well comparable with those of previous researches which are highly based on field investigations. Without extensive field measurements, the method reduces the time and the cost of investigation and provides a basis for repeating the operation at any time and everywhere, which is required.
Ebrahim khalifeh; Mojtabah Pakparvar; Mohammad reza Kaviyanpoor
Volume 14, Issue 3 , January 2007, , Pages 403-420
Abstract
Active sand dunes particularly barkhans are serious threats for adjacent civilians live, agricultural lands and economic activities. Deployment quality and motion paths of these dunes are main targets in wind erosion studies in deposit regions. These targets need the active and inactive dunes to be identified. ...
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Active sand dunes particularly barkhans are serious threats for adjacent civilians live, agricultural lands and economic activities. Deployment quality and motion paths of these dunes are main targets in wind erosion studies in deposit regions. These targets need the active and inactive dunes to be identified. Edges and borders are evident characteristics of wind deposits and sand dunes. Inactive (old) dunes are more voluminous and smooth. Furthermore active dunes have distinct feature. Also temperature, humidity and plant cover variation lead to change surface and feature of inactive dunes. These changes can’t be observed in one band images. Therefore making uses of filters particularly non directional edges enhancement and band ratio techniques are main ideas to process the images of these dunes especially about their activity and displacement. In present research filter and band ratio techniques have been used and consequently, third band in Profile Testing and Spectral Analysis method (in this case study b5 of Landsat ETM+) and ratio of first and second bands (in this case study b62/b4) have been introduced respectively as appropriate band and ratio for these techniques to identify and separate active (new) and inactive (old) sand dunes.
mahmood Goudarzi; Mahdi Farahpour
Volume 14, Issue 3 , January 2007, , Pages 432-446
Abstract
According to reports, the problem range managers face is impossibility of distinguish between dry land farming and the rangeland, as the reflectance between dry land farming and rangeland are the same. One solution is usage of temporal images, i.e. times that reflectance between these two features is ...
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According to reports, the problem range managers face is impossibility of distinguish between dry land farming and the rangeland, as the reflectance between dry land farming and rangeland are the same. One solution is usage of temporal images, i.e. times that reflectance between these two features is high. For the experiment rainfed cereal farms and rangelands in Taham region of Zanjan province, characterized as a semi arid area, was selected. Crop calendar (seeding, sowing, harvesting) of rainfed crop was drawn. Outstanding dates, over which differences between natural vegetation and crops was high, were distinguished. Corresponding Landsat ETM images, 8 August 2002, and IRS images (multi-spectral and panchromatic), 16 October 2002, were used. Images were georeferenced using available topographic map to a Universal Transverse Mercator projection using control points. For preparation of base map (ground true) IRS panchromatic images were interpreted and land use map was made through digit screen checking boundaries of the map lead to preparation of the final map. Via Image Classification Techniques, supervised (maximum likelihood and Box classification), unsupervised and principal components analysis were used to create a map. The maps were overlaid on base map (ground true) and accuracy of classified map was assessed. The result showed that it is not possible to distinguish between dry land farms and rangeland with the assistance of image classification technique, in panchromatic images however dry lands could be easily distinguished through the pattern, shape and texture found on images.
Jalal Abdollahi; Hosein Arzani; Naser Baghestani; Mohammad Hasan Rahimian
Volume 13, Issue 3 , February 2006, , Pages 162-171
Abstract
Remote sensing is a method to produce updated information in vast area. Describing the model for utilization and processing satellite data in regard to developing a method for mapping forage production of arid regions were the purpose for this study. For this purpose Landsat ETM+ data used at Nodushan ...
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Remote sensing is a method to produce updated information in vast area. Describing the model for utilization and processing satellite data in regard to developing a method for mapping forage production of arid regions were the purpose for this study. For this purpose Landsat ETM+ data used at Nodushan area in 2002 . The study area covers about 60000 hectares and the annual precipitation is about 140-300 mm. in order to correct the necessary data 50 sites with different vegetation types using 25 quadrant of 1*2 m size and then awareged were used. To study the dependent variable of vegetation relations with independent variables of satellite data, vegetation indices and environmental factors; multiple linear regression analysis were manipulated using SPSS software. Then a suitable model was selected which caried predict the vegetation properties of the study area. Finally, production map was produced using ILWIS software. According to the results, mapping of forage production via remote sensing is possible even when its vegetation cover is less than 25%.
Mahmood Goudarzi; Mahdi Farahpour; Alireza Mosav
Volume 13, Issue 3 , February 2006, , Pages 265-277
Abstract
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 ...
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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%.