Detection and Measure Carstensz Glacier Area Changes using Machine Learning Technique
Rizaldi Suwandi1, Sani Muhamad Isa2

1Rizaldi Suwandi*, Student, Computer Science Department, Bina Nusantara University, Jakarta, Indonesia.
2Sani Muhamad Isa, Lecturer and Researcher, Computer Science Department, Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1397-1404 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8531019320/2020©BEIESP | DOI: 10.35940/ijitee.C8531.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Using satellite data for acquiring glacier outlines has become more popular in the last decade. Glacier change assessment is the main goal for deriving glacier outlines. It’s important to make the best method to generate the glacier outline as there most of the glacier outline is made with manual delineation and spectral thresholding. This research used a machine learning model to deriving the glacier pixels from satellite data. The model trained using more than 80 thousand of a glacier and non-glacier pixels. The model that trained has been proved to able classified a glacier pixel with more than 99% accuracy in one of the best experiments. The NDSI (Normalized Difference Snow Index) proved to be the key feature to classifying glaciers and shown to be one the best combination with NDSI + GLCM + TIFF (Band 4). This model hopefully can be further expanded and installed directly in satellite so we can instantly make a glacier outline without any manual delineation or spectral thresholding needed. 
Keywords: Glacier Area, Image Classification, Machine learning, Remote Sensing.
Scope of the Article: Classification