Vegetation type and land cover mapping in a semi-arid heterogeneous forested wetland of India: comparing image classification algorithms

2021 ◽  
Author(s):  
Kundan Deval ◽  
P. K. Joshi
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Vegetation Type ◽  
Forested Wetland ◽  
Land Cover Mapping ◽  
Classification Algorithms ◽  
Semi Arid

scholarly journals LAND USE AND LAND COVER MAPPING – A CASE STUDY OF AHMEDABAD DISTRICT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 189-193
Author(s):  
D. Rawal ◽  
A. Chhabra ◽  
M. Pandya ◽  
A. Vyas
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Spectral Reflectance ◽  
Land Cover Classification ◽  
Land Cover Mapping ◽  
Classification Algorithms ◽  
Classification Technique ◽  
Object Based

Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

LAND COVER MAPPING WITH ADVANCED CLASSIFICATION ALGORITHMS

2018 ◽  
Vol 13 (3) ◽  
pp. 41-50
Author(s):  
Çiğdem Şerifoğlu Yılmaz Çiğdem Şerifoğlu Yılmaz ◽  
Oğuz Güngör Oğuz Güngör ◽  
Hamdi Tolga Kahraman Hamdi Tolga Kahraman
Keyword(s):  
Land Cover ◽  
Land Cover Mapping ◽  
Classification Algorithms

scholarly journals A hierarchical approach of hybrid image classification for land use and land cover mapping

2018 ◽  
Vol 22 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Vahid Rahdari ◽  
Alireza Soffianian ◽  
Saeid Pourmanafi ◽  
Razieh Mosadeghi ◽  
Ghaiumi Mohammadi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Land Cover Mapping ◽  
Hierarchical Approach

scholarly journals SENTINEL-1 IMAGE CLASSIFICATION FOR CITY EXTRACTION BASED ON THE SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 297-302 ◽  
Author(s):  
A. Jamali ◽  
A. Abdul Rahman
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Land Cover ◽  
Image Classification ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
The Impact

Abstract. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.

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