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This article addresses a new reserve estimation method which uses fuzzy modeling algorithms and estimates the reserve parameters
based on spatial variability. The proposed fuzzy modeling approach has three stages: (1) Structure identification and preliminary
clustering, (2) Variogram analysis, and (3) Clustering based rule system. A new clustering index approach and a new spatial
measure function (point semimadogram) are proposed in the paper. The developed methodology uses spatial variability in each
step and takes the fuzzy rules from input-output data. The model has been tested using both simulated and real data sets.
The performance evaluation indicates that the new methodology can be applied in reserve estimation and similar modeling problems 相似文献
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Bulent Tutmez Uzay Kaymak A. Erhan Tercan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(7):1013-1023
Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis. 相似文献
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Aydın Kahriman Alkan Günlü Uzay Karahalil 《Journal of the Indian Society of Remote Sensing》2014,42(3):559-567
The objective of this study was to investigate the relationship between crown closure and tree density in mixed forest stands using Landsat Thematic Mapper (TM) reflectance values (TM 1- TM 5 and TM 7) and six vegetation indices (SR, DVI, SAVI, NDVI, TVI and NLI). In this study, multiple regression analysis was used to estimate the relationships between the crown closure and tree density (number of tree stems per hectare) using reflectance values and vegetation indices (VIs). The results demonstrated that the model that used SR and DVI had the best performances in terms of crown closure (R2?=?0.674) and the model that used the DVI and SAVI had the best performances in terms of tree density (R2?=?0.702). The regression model that used TM 1, TM 3 together with TM 4 showed the performances of the crown closure (R2?=?0.610) and the regression model that used TM 1 showed the performances of the tree density (0.613). Results obtained from this research show that vegetation indices (VIs) were a better predictor of crown closure and tree density than other TM bands. 相似文献
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