فصلنامه سنجش از دور و GIS ایران
سال چهارم شماره 2 (پیاپی 14، تابستان 1391)

Land use pattern, due to increasing human activities and interference on land in order to meet different needs, is constantly changing. Most of land use optimization as a complex problem in the real world inherently involves multiple conflicting objectives. In this research, the capability of non dominated sorting genetic algorithm II (NSGA-II) with implying two type crossover operators, namely as two dimensional cross over (TDC) and uniform cross over (UC) in order to multi-objective optimization of land-use allocation has been evaluated in the Taleghan watershed. The multi-objective problem described in this paper consists of four objective functions: maximizing economic return, maximizing suitability, maximizing compactness-consistency, maximizing runoff and minimizing erosion. The model was implemented in two ways, with guidance and without guidance in generating the initial population. Results show that the crossover operator performance is completely under the influence of initial population. The comparison of the examined crossover operators shows that there is a significant advantage of using TDC operator in generating land use allocation patterns with respect to the given objective functions. The results showed that after the optimization in the study area, erosion rates declined while profits increased

Study of Landforms and their spatial distribution in Geomorphology and other environmental science is important. In this study presented a quantitative method using morphometric parameters that extracted from digital elevation model for the classification of arid region landforms. Geomorphometric parameters used include slope, profile curvature, longitudinal curvature, and minimum and maximum curvatures that are appropriate for Identification of landforms morphometric Properties and are largely associated with geomorphological processes. Map of geomorphometric parameters in the raster format extracted using SRTM digital elevation model in the region Maranjab. Classification was done by supervised classification Maximum likelihood classification (MLC) using landform training samples from area of study. The results of the classification showed in seven class include: alluvial fans, cross and mixed sandy Dune, barkhan, stone knives, stone hills, and playa so result show playa have the greatest expansion in the region. Results of statistical evaluation of landform classes with three parameters minimum, maximum and average are presented. The assessment accuracy of classification was performed using the base Geomorphology map of Maranjab region. The results show that the classification overall accuracy is 81% and the kappa index is 0.73.

Today, technological transition and industrialization of cities has led to an important and essential change in urban ecology which created undesirable impact on human societies. One of the most important and the simplest ways is development of parks and urban green spaces to reduce the different impacts of environmental vulnerabilities. The distribution of urban green space has a high importance in cities. Since there are not appropriate models for site selection of urban land uses to determine the regions, capacities from environmental view point; the present study attempts to investigate the distribution of urban green space in the city of Sari the political center of Mazandaran province. The city has an area of 28.52 km2 with a population of 261293. Also the city is covered by 374904 m2 green space and 113385 m2 public parks. Evidences show that the existed urban green space has not distributed in a hierarchical order; the urban park and green space per capita are 0.39m2 and 2m2 respectively which indicates obvious difference with the given standard (9m2) of the country. This research aimed to investigate quantitative and qualitative dispersion of urban parks to select the appropriate locations for the neighborhood parks using an integrated AHP fuzzy in GIS environment. So, first the effective parameters and criteria for urban park selection were identified and then the data base of these criteria was created in GIS. Second, a questionnaire was completed by the professional experts to rank the preferences of the criteria; the weight of each criterion was obtained using FAHP and the informational-descriptive layers of criteria were integrated in GIS with respect to the calculated weights and analytical functions. Finally, the desirability map was produced by overlaying the informational layers and accordance to the existed land uses for the different parts of the city which are categorized in three classes: highly appropriate, appropriate and inappropriate. The results revealed that 18.8 percent of the city area is highly appropriated, 33.7 percent is appropriate and 47.8 percent placed as inappropriate areas which have limitations for establishing neighborhood

Drought is a severe dilemma which has influence on economy, agronomy, environment and society. In the recent decades, many indices based on satellite images have been presented by researchers. The objective of this study was to improve Modified Perpendicular Drought Index (MPDI) by improving its main input parameter, which is the vegetation fraction. Calculation of vegetation fraction was considered by using an ALOS imagery.Sheitoor basin located in Yazd province (part of the central arid area of Iran) was selected as the study area. The canopy cover percentage of 52 sample plots (50m by 50m) was measured in the field in June 2008. Seventeen vegetation indices and different types of neural networks algorithms have been used. Regression models were used to assess the relationship between the field data and the calculated reflectance data. Results revealed that the vegetation indices which are based on soil line parameters can estimate the fraction of vegetation more accurately compared to other indices (R2>0.63, RMSE<4%). On the other hand, neural network results showed that these algorithms could estimate the vegetation fraction even more accurate than vegetation indices(R2> 0.74,RMSE < 2%), since it benefits from having more input parameters. Finally, Drought severity of the area was mapped using the estimated soil line parameters and the vegetation fraction which was estimated using the neural network algorithm.

Tree species identification is essential to forest type mapping. Remote sensing data, especially high spatial and radiometric resolution digital aerial images can be useful to this goal achievement. This research is aimed at the evaluation of digital aerial images in order to identify tree species in two study areas located in a natural mixed forest. Radiometric and geometric qualities of these images were evaluated and all images were corrected geometrically. Some appropriate transformations were performed and utilized. The hierarchical image object network was constructed and various alternatives of segmentation were tried and qualitatively evaluated. Nearest Neighbor classifier based on Fuzzy logic, leads to tree species map. Training samples and ground truth map were prepared through fieldwork. The result of accuracy assessment showed the overall accuracy more than 70% and Kappa Index of Agreement (KIA) equal to 0.46 and 0.5 in these areas. Main tree species presents different accuracies in two areas. Applying these aerial images and the object-based method in other forest areas together with the consideration of the influence of using proper elevation data on the accuracy of the produced map is suggested.

In order to prepare information about spatial distribution of rice fields, Multi-Spectral and Multi-Temporal data can be helpful because in addition to the rice, all fields could be covered by mixture of water and soil regarding the time of crop calendar. In this study, we developed a special mapping algorithm that uses NDVI and LSWI2105 time series data derived from MODIS 16-day 250 meter vegetation indices (MOD13Q1) product of MODIS imagery to identify paddy rice fields. This algorithm works based on the sensitivity of LSWI2105 to the surface moisture and NDVI to the vegetation chlorophyll content. As a result, during the period of Rice Transplanting stage because of the field flooding, LSWI2105 will be increased and after the rice growing it will be decreased, and at the same time NDVI has a reverse behavior. In this research two methods has been developed to define the relationship between NDVI and LSWI2105 to detect the location of paddy rice fields in North part of Iran in 2011. Our results were validated with ground field data at 183 well-distributed sample points. The overall Accuracy of two methods are 67.21% and 86.87% respectively. The results of this study indicated that the paddy rice mapping based on MOD13Q1 could potentially be applied at moderate spatial scales to monitor paddy rice agricultural fields on a timely basis.

. The growth and development of cities are depended to land information which is very important for planning, development and control of land resources. Therefore planning and development require an effective managerial system. The multipurpose digital cadastre system provides a comprehensive guideline to obtain geographical information in the framework of GIS. This research intended to investigate all of the geo-spatial tools relevant to multipurpose digital cadastre system based on the existing conditions and facilities in the country. So the paper aimed: 1. identifying the problems of land recording 2. Investigating the theoretical background of multipurpose cadastre based on the concept of sustainable development. 3. Proposing a conceptual structure for systematic development of multipurpose cadastre regarding to the guidelines of base cadastre model 4. Conducting the multipurpose digital cadastre in the zone 8 of Tabriz and finally evaluating the strength, weakness, opportunities and challenges of materializing this model in the city via strategic analysis. On the base of the findings, the research came to conclusion that multi-purpose Cadastre could provide a firm background for land recording.