Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach
Large-scale debris cover glacier mapping can be efficiently conducted from high spatial resolution (HSR) remote sensing imagery using object-based image analysis (OBIA), which works on a group of pixels. This paper presents the spectral and spatial capabilities of OBIA to classify multiple glacier c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/13/3202 |
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| author | Kavita V. Mitkari Manoj K. Arora Reet Kamal Tiwari Sanjeev Sofat Hemendra S. Gusain Surya Prakash Tiwari |
| author_facet | Kavita V. Mitkari Manoj K. Arora Reet Kamal Tiwari Sanjeev Sofat Hemendra S. Gusain Surya Prakash Tiwari |
| author_sort | Kavita V. Mitkari |
| collection | DOAJ |
| description | Large-scale debris cover glacier mapping can be efficiently conducted from high spatial resolution (HSR) remote sensing imagery using object-based image analysis (OBIA), which works on a group of pixels. This paper presents the spectral and spatial capabilities of OBIA to classify multiple glacier cover classes using a multisource approach by integrating multispectral, thermal, and slope information into one workflow. The novel contributions of this study are effective mapping of small yet important geomorphological features, classification of shadow regions without manual corrections, discrimination of snow/ice, ice-mixed debris, and supraglacial debris without using shortwave infrared bands, and an adaptation of an area-weighted error matrix specifically built for assessing OBIA’s accuracy. The large-scale glacier cover map is produced with a high overall accuracy of ≈94% (area-weighted error matrix). The proposed OBIA approach also proved to be effective in mapping minor geomorphological features such as small glacial lakes, exposed ice faces, debris cones, rills, and crevasses with individual class accuracies in the range of 96.9–100%. We confirm the portability of our proposed approach by comparing the results with reference glacier inventories and applying it to different sensor data and study areas. |
| first_indexed | 2024-03-09T10:25:01Z |
| format | Article |
| id | doaj.art-295b6a99557140fcb98fa3ce547fcebe |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T10:25:01Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-295b6a99557140fcb98fa3ce547fcebe2023-12-01T21:41:05ZengMDPI AGRemote Sensing2072-42922022-07-011413320210.3390/rs14133202Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis ApproachKavita V. Mitkari0Manoj K. Arora1Reet Kamal Tiwari2Sanjeev Sofat3Hemendra S. Gusain4Surya Prakash Tiwari5Department of Computer Science and Engineering, Punjab Engineering College (Deemed to Be University), Chandigarh 160012, Punjab, IndiaBML Munjal University, Gurugram 122413, Haryana, IndiaDepartment of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar 140001, Punjab, IndiaDepartment of Computer Science and Engineering, Punjab Engineering College (Deemed to Be University), Chandigarh 160012, Punjab, IndiaInstitute of Technology Management (DRDO), Mussoorie 248179, Uttarakhand, IndiaApplied Research Center for Environment & Marine Studies, The Research Institute, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaLarge-scale debris cover glacier mapping can be efficiently conducted from high spatial resolution (HSR) remote sensing imagery using object-based image analysis (OBIA), which works on a group of pixels. This paper presents the spectral and spatial capabilities of OBIA to classify multiple glacier cover classes using a multisource approach by integrating multispectral, thermal, and slope information into one workflow. The novel contributions of this study are effective mapping of small yet important geomorphological features, classification of shadow regions without manual corrections, discrimination of snow/ice, ice-mixed debris, and supraglacial debris without using shortwave infrared bands, and an adaptation of an area-weighted error matrix specifically built for assessing OBIA’s accuracy. The large-scale glacier cover map is produced with a high overall accuracy of ≈94% (area-weighted error matrix). The proposed OBIA approach also proved to be effective in mapping minor geomorphological features such as small glacial lakes, exposed ice faces, debris cones, rills, and crevasses with individual class accuracies in the range of 96.9–100%. We confirm the portability of our proposed approach by comparing the results with reference glacier inventories and applying it to different sensor data and study areas.https://www.mdpi.com/2072-4292/14/13/3202large-scaledebris covermultisourceOBIAglacier cover classes |
| spellingShingle | Kavita V. Mitkari Manoj K. Arora Reet Kamal Tiwari Sanjeev Sofat Hemendra S. Gusain Surya Prakash Tiwari Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach Remote Sensing large-scale debris cover multisource OBIA glacier cover classes |
| title | Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach |
| title_full | Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach |
| title_fullStr | Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach |
| title_full_unstemmed | Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach |
| title_short | Large-Scale Debris Cover Glacier Mapping Using Multisource Object-Based Image Analysis Approach |
| title_sort | large scale debris cover glacier mapping using multisource object based image analysis approach |
| topic | large-scale debris cover multisource OBIA glacier cover classes |
| url | https://www.mdpi.com/2072-4292/14/13/3202 |
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