علوم غیرزیستی دریا
Ramin Alaie Ruozbahani; Hamid reza Jafari; Gholamreza Nabi Bidhendi; Hassan Hoveidi
Abstract
Over the past few decades, the structure and cover pattern of land in the Khuzestan coastal zone has also not been immune to such changes. Therefore, monitoring the spatial combination and arrangement of Land cover/Land use and its changes in order to identify the current threats and opportunities is ...
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Over the past few decades, the structure and cover pattern of land in the Khuzestan coastal zone has also not been immune to such changes. Therefore, monitoring the spatial combination and arrangement of Land cover/Land use and its changes in order to identify the current threats and opportunities is considered essential. The aim of this research is to monitor or the trend of changes in landscape mosaic in the coastal regions of Khuzestan Plain in response to the growth and development of human activities. In this research, Landsat images were used across two time 2000 (Landsat 7 ETM+) and 2015 (Landsat OLI) for extracting Land cover/Land use map. In order to monitor the trend of changes, four metrics of land appearance MNND, MPS, PLAND, and NP have been used. The obtained results indicated that the spatial combination and arrangement of the landscape in the studied region have found greater heterogeneity and complexity with fragmentation, attrition, and patches. Further, with the addition of two types of use including sugar cane cultivation and industry as well as fish farming basins along with the development of industrial construction units have caused dramatic transformations in the land structure in terms of combination, continuity, and extension of valuable ecological patterns. Riparian forests with increase in the number of patches and 50% reduction in the PLAND metrics and wetlands with increase of 58% of NP metrics and 71% in MMND metrics in these regions with the maximum level of fragmentation are the most vulnerable environmental patterns.
ziba batvandi; Ramin Alaie Ruozbahani
Abstract
The main purpose of satellite image processing is preparing thematic and efficient maps, so choosing appropriate classification algorithm has important role in this case. In parametric methods such as maximum likelihood main problem is their dependence on the statistical distribution of input data. Artificial ...
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The main purpose of satellite image processing is preparing thematic and efficient maps, so choosing appropriate classification algorithm has important role in this case. In parametric methods such as maximum likelihood main problem is their dependence on the statistical distribution of input data. Artificial neural network is nonparametric classification method which is not dependent on any particular distribution and extract desired functions from within data. This study aimed to compare the efficiency of neural network and maximum likelihood to classify land cover Using Landsat Satellite Images. Determine classes and samples to classify land cover Using field operations, topographic maps, aerial photographs and maps were made and using the above information four classes vegetation cover, building, water and outdoor were selected. After applying two algorithms, the neural network and maximum likelihood on the Landsat 8 satellite image with OLI sensors, land cover map of the arvand coastal area was prepared. Multi-layer perceptron network neural network structure consists of three input neurons, seven intermediate neurons, and four output neurons. For network training, a back propagation algorithm has been used. with Kappa coefficient, the accuracy of the classification methods was evaluated. Based on the results, Artificial neural network method with kappa coefficient of 0.92 in comparison to maximum probability algorithm with kappa coefficient of 0.79 has a better performance in providing land cover map of the arvand coastal area which is due to Neural network is nonparametric and nonlinear.