Soghra Andaryani; Mohammad Hosein Rezaei Moghadam; khalil Valizadeh Kamran; Farhad Almaspour
Abstract
Forecasting models of Land Use/ Cover changes are the main resources for managers and policy-makers in order to develop a sustainable land management plan. Changes of Orchard-lands have an effect on water resources as well as soil permeability. Thus simulation of this land use changes, in areas where ...
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Forecasting models of Land Use/ Cover changes are the main resources for managers and policy-makers in order to develop a sustainable land management plan. Changes of Orchard-lands have an effect on water resources as well as soil permeability. Thus simulation of this land use changes, in areas where there is a shortage of water resources, can provide more information about the occurred changes during a specified time scales along current management. The present study was carried out to simulate and predict the spatial-temporal changes of the orchard by 2026. For this purpose, Geomod method was used to simulate spatial changes of the orchard. Due to the lack of ability of this model in temporal simulation, Markov chain analysis method was used to solve the mentioned problem with the error proportional of 0.012. Orchard was extracted using Landsat 5, 7, and 8 satellite data after necessary corrections as well as SVM in 1987, 2000, and 2013. Then, to understand the impact of each of the criteria used to change this type of land use, instead of Delphi methods, logistic regression, Fuzzy standardization and, after all, WLC were used. The ROC index was used to validate the model. The results showed, this model has a good performance to simulate spatial changes because of area under Curve 0.91 for both of the 2000 and 2013. In the 26-years period, there are 294 hectares of orchard development, and the hybrid model showed that this land use will increase to 304 hectares till 2026
Ali Akbar Nazari Samani; alireza oliaye; sadat feiznia
Abstract
Assessment frequency of springs has become an important issue for land use planning, especially water resource identification and environmental protection.The purpose of this study is to produce a spring occurrence potential map in Bojnourd Basin, based on a logistic regression method using Geographic ...
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Assessment frequency of springs has become an important issue for land use planning, especially water resource identification and environmental protection.The purpose of this study is to produce a spring occurrence potential map in Bojnourd Basin, based on a logistic regression method using Geographic Information System (GIS) and remote sensing (RS). The locations of the springs (359 springs) were determined in the study area. In this study, 14 effective factors including spring were used in the analysis: lineament density, distance to lineament, distance to drainage, drainage density, normalized difference vegetation index (NDVI), profile curvature, tangential curvature, surface rate, vector dispersion, precipitation, elevation, geology, aspect and slope. Binary logistic regression coefficients of the variables by selecting 300 spring randomly. 59 another spring were used for validation that 80.6% of the springs were correctly predicted. The accuracy of the model was measured using ROC curves which showed that accuracy is 86.6 percent which indicates that the model shows high accuracy in the analysis of spring occurrence potential in the study area. The results showed that the distance of lineaments, distance of drainage, drainage density, vegetation index, profile curvature, tangential curvature, vector dispersion, precipitation and slope have the greatest impact on the occurrence of springs. Finally, spring occurrence potential map was divided into four probably classes of very low, low, medium and high. According to the survey results, this method can be used to identify sources of groundwater in karstic zones and has important role in better management of the karstic Basins.
khabat Khosravi; Edris Marufinia; Ebrahim Nohani; Kamran Chapy
Abstract
In order to prevent any damages which can be caused by flood at Haraz watershed in the Mazandaran province, it is essential to prepare a flood susceptibility map using logistic regression. About 211 flood locations and 211 non-flood locations were first recognized. Ten flood conditioning factors such ...
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In order to prevent any damages which can be caused by flood at Haraz watershed in the Mazandaran province, it is essential to prepare a flood susceptibility map using logistic regression. About 211 flood locations and 211 non-flood locations were first recognized. Ten flood conditioning factors such as Slope, plan curvature, altitude, distance from river, topographic wetness index (TWI), stream power index (SPI), rainfall, landuse and normalized differences vegetative index (NDVI) were then identified. The maps of all affecting factors were prepared using ArcGIS10.1, ENVI 5.1 and SAGA GIS2 software and they were exported to raster formats. Flood locations were randomly divided into two groups: 70% (151 flood locations) and 30% (60 flood locations) for modeling and validation, respectively. Enter method was selected for weighing the 10 factors in SPSS.18. The factors with their corresponding weights were used in the ArcGIS software for generation of flood susceptibility map. The map was divided into 5 classes. ROC curve and area under curve (AUC) are used for the validation of derived map. The results indicated that for prediction rate, the AUC is 78.3%; thus, the logistic regression has a reasonable accuracy for flood susceptibility mapping. The findings of this research are useful and necessary for scholars, the Mazandaran Regional Water Authority (MRWA), Ministry of Energy, and other agriculture and natural resources-related organizations in order for mitigating losses and damages during flooding events.
Hamid Reza Moradi
Abstract
ABSTRACT Aim Of this research is landslide hazard zoning in Syahdare watershed using logistic regression. Therefore, outset landslide points recognized using air photography and extensive field studies. Then distribution of landslide map was makes. Then each effective element on landslide occurred for ...
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ABSTRACT Aim Of this research is landslide hazard zoning in Syahdare watershed using logistic regression. Therefore, outset landslide points recognized using air photography and extensive field studies. Then distribution of landslide map was makes. Then each effective element on landslide occurred for example slope, aspect, elevation, litho logy, land use, distance of road, distance of drainage, distance of fault and precipitation map makes in GIS environment. These data were saved in raster and vector format in GIS soft ware and they used for analysis with logistic regression. Logistic analysis obtained by Arc GIS 9.2 soft ware and SPSS. Results showed the most important elements in Land slide occurred in this area are slope, elevation, precipitation, distance of drainage and distance of fault respectively. Most of the land slides have occurred in the classes of 10 to 15 degree slope, elevation of 2350-2500 meters, precipitation (473-523 mm) are located. 50% Landslide is located at a distance of 30 meters of the stream. In this region the most landslides are occurrence in the 300 meter to fault distance. While the from 500 meter distance to the fault reduced number and susceptibility to landslides. The evaluation of accuracy model and the results obtained with three methods for the presence of all variables, 98.2 percent, 0.692 and 0.519 respectively. So showed that logistic regression had high accuracy in making landslide susceptibility map in study area.
Mohammad Reza Sarvati; Kazem Nosrati; Shima Hassanvandi; Babak Mirbagheri
Abstract
Landslides and slope instabilities are major hazards for human activities often causing economiclosses and property damages. Sikan River Basin (Ilam province) due to the topography, tectonic,lithology, and climate has enough potential for occurrence of this phenomenon. The objectives of thisstudy were ...
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Landslides and slope instabilities are major hazards for human activities often causing economiclosses and property damages. Sikan River Basin (Ilam province) due to the topography, tectonic,lithology, and climate has enough potential for occurrence of this phenomenon. The objectives of thisstudy were to determine effective parameters controlling the landslide occurrence and to preparezonation map of landslide risk in Sykan River Basin. In view of this, 11 geophysical characteristicsincluding (height, slop, slop direction), geomorphologic (the slop of land surface), geology (lithology,the distance from the fault), hydrography (the distance from the river), coverage, land use (land useand the distance from road, the distance from village), pedology (soil texture), and dependent variable(landslide distribution) were selected an independent variable and were analyzed using logisticregression model. The results showed that the influential factors on landslides occurrence in the basinare the distance from river, land use, the distance from village, the materials (lithology), slope, and theshape of land surface. Finally, the study area was classified into five major area based on landslideoccurrence risk which 19.1 km2 of total area had very low risk, 15.9 km2 had low risk, 14.9 km2 hadaverage risk and 14.6 km2 had high risk and 9.1 km2 had also very high risk. The model evaluationshowed a high accuracy 74.2% in the study area. The results of this study can be useful for landsliderisk management and for controlling the accelerated parameters.
Mohammad Ali Zare Chahouki; Lyla Khalsi Ahvazi; Hossein Azarnivand
Abstract
The aim of this study was providing plant species predictive habitat models by using logisticregression method. For this purpose, study area conducted in north east rangelands of Semnanmodeling vegetation data in addition to site condition in formation including topography, and soil wasprepared. sampling ...
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The aim of this study was providing plant species predictive habitat models by using logisticregression method. For this purpose, study area conducted in north east rangelands of Semnanmodeling vegetation data in addition to site condition in formation including topography, and soil wasprepared. sampling was done within each unit of sampling parallel transects and 1 vertical transectwith 750m length, each containing 15 quadrates (according to vegetation variations) were established.Quadrate size was determined for each vegetation type using the minimal area method. Soil sampleswere taken from 0-20 cm and 20-80 cm in starting and ending points of each transect. Logesticregression (LR) techniques were implemented for plant species predictive modeling. To plantpredictive mapping, it is necessary to prepare the maps of all affective factors of models. To mappingsoil characteristics, geostatistical method was used based on obtained predictive models for eachspecies (through LR method). The accuracy of the predicted maps was tested with actual vegetationmaps. In this study, the adequacy of vegetation type mapping was evaluated using kappa statistics.Predictive maps of Astragalus spp. ( κ =0.86), Halocnemum strobilaceum ( κ =0.51), Zygophylumeurypterum ( κ =0.58) and Seidlitzia rosmarrinus ( κ =0.6) with narrow amplitude is as the same ofactual vegetation map prepared for the study area. Predictive model of Artemisia sieberi ( κ =0.33),due to its ability to grow in most parts of north east rangeland of Semnan with relatively differenthabitat condition, is not possible.
Shahrebanoo Rahmani; Ataollah Ebrahimi; Alireza Davoudian
Abstract
The analysis of the relationship between spatial distribution of environmental factors andvegetation types is crucial for understanding mountainous ecosystems. In this research aGIS based approach was used to produce a vegetation map for Sabzkouh protected area inthe Chaharmahal-Va-Bakhtiari province. ...
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The analysis of the relationship between spatial distribution of environmental factors andvegetation types is crucial for understanding mountainous ecosystems. In this research aGIS based approach was used to produce a vegetation map for Sabzkouh protected area inthe Chaharmahal-Va-Bakhtiari province. To identify environmental parameters affecting thevegetation cover, 6 primary and secondary environmental parameters including hypsometric,slope steepness, slope direction, annual precipitation, temperature and sun radiation maps werederived from the study area DEM. To investigate the relationship between these factors andthe spatial distribution of vegetation cover, quantitative analyses using statistical techniqueslike Principal Components Analysis(PCA) were undertaken. Then, the spatial distributionof vegetation types was predicted using a multi-logistic regression. Results showed thattopographic variables derived from the DEM were very useful for indicating habitats ofrange and forest types. Although lack of information on the anthropogenic effects led to someuncertainties in the interpretation of spatial pattern of vegetation types, the topographic andclimatic variables, derived from the DEM, were considerably effective in modelling the spatialdistribution of vegetation types.
