Estimation of Forest Crown Density Using Pleiades Satellite Data and Nonparametric Classification Method

2018 ◽  
Vol 46 (7) ◽  
pp. 1151-1158 ◽  
Author(s):  
Siavash Kalbi ◽  
Mohammad Nabi Hassanvand ◽  
Javad Soosani ◽  
Kambiz Abrary ◽  
Hamed Naghavi
2011 ◽  
Vol 181 (24) ◽  
pp. 5435-5456 ◽  
Author(s):  
João Roberto Bertini ◽  
Liang Zhao ◽  
Robson Motta ◽  
Alneu de Andrade Lopes

2019 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Yogo Aryo Jatmiko ◽  
Septiadi Padmadisastra ◽  
Anna Chadidjah

The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in Bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.


2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Chaitanya B. Pande ◽  
Kanak N. Moharir ◽  
S. F. R. Khadri

AbstractIn this paper, we focus on the assessment of land-use and land-cover change detection mapping to the effective planning and management policies of environment, land-use policy and hydrological system in the study area. In this study the soil and water conservation project has been applied during the five years and after five years what changes have been found in the land-use and land-cover classes and vegetation. In this view, this land-use and land-cover mapping is a more important role to decide the policy for watershed planning and management project in the semiarid region. In an emerging countries, fast industrialization and urbanization impose a significant threat to the natural atmosphere. The remote sensing and GIS techniques are crucial roles in the study of land-use and land-cover mapping during the years of 2007, 2014, and 2017. The main objective of this is to prepare the land-use and NDVI maps in the years of 2008, 2014 and 2017; these maps have prepared from satellite data using the supervised classification method. A normalized difference vegetation index map (NDVI) was done by using Landsat 8 and LISS-III satellite data. NDVI values play a major role in monitoring the vegetation and variation in land-use and land-cover classes. In these maps, four types of land are divided into four classes as agriculture, built-up, wasteland, and water body. The results of study show that agriculture land of 18.71% (158.24 Ha), built-up land of 0.62% (5.31 Ha), wasteland of 40.33% (341.02 Ha), and water body land of 17.39% (147 Ha) are increased. Land-use and land-cover maps and NDVI values show that agriculture land of 22.97% (194.29 Ha), 5.46% (14.59 Ha), and 0.08% (0.22 Ha) decreases during the years of 2008, 2014, and 2017. The results directly indicate that the supervised classification method has been the accurate identified feature in the land-use map classes. This classification method has been given the better accuracy (95%) from spatiotemporal satellite data. The accuracy was also tally with ground-truth and Google earth information. These results can be a very useful for the land-use policy, watershed planning, and management with natural resources, animals, and ecological systems.


2007 ◽  
Vol 03 (03) ◽  
pp. 419-426
Author(s):  
ANTON BOUGAEV ◽  
ALEKSEY URMANOV ◽  
LEFTERI TSOUKALAS ◽  
KENNY GROSS

A novel method for reducing a training data set in the context of nonparametric classification is proposed. The new method is based on the method of R-clouds. The advantages of the R-cloud classification method introduced recently are being investigated. The separating boundary of the R-cloud classifier is represented using Rvachev functions. The method of key vectors extraction uses the value of the R-cloud function to quantify the disturbance of the separating boundary, which is caused by removal of one data vector from the design dataset. The R-cloud method was found instructive and practical in a number of engineering problems related to pattern classification.


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