Next Article in Journal
Vertically Averaged and Moment Equations for Dam-Break Wave Modeling: Shallow Water Hypotheses
Previous Article in Journal
Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin
Open AccessArticle

Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir

School of Spatial Informatics and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3231; https://doi.org/10.3390/w12113231
Received: 28 September 2020 / Revised: 11 November 2020 / Accepted: 16 November 2020 / Published: 18 November 2020
(This article belongs to the Section Hydrology and Hydrogeology)
Debris-covered glaciers are common features on the eastern Pamir and serve as important indicators of climate change promptly. However, mapping of debris-covered glaciers in alpine regions is still challenging due to many factors including the spectral similarity between debris and the adjacent bedrock, shadows cast from mountains and clouds, and seasonal snow cover. Considering that few studies have added movement velocity features when extracting glacier boundaries, we innovatively developed an automatic algorithm consisting of rule-based image segmentation and Random Forest to extract information about debris-covered glaciers with Landsat-8 OLI/TIRS data for spectral, texture and temperature features, multi-digital elevation models (DEMs) for elevation and topographic features, and the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) for movement velocity features, and accuracy evaluation was performed to determine the optimal feature combination extraction of debris-covered glaciers. The study found that the overall accuracy of extracting debris-covered glaciers using combined movement velocity features is 97.60%, and the Kappa coefficient is 0.9624, which is better than the extraction results using other schemes. The high classification accuracy obtained using our method overcomes most of the above-mentioned challenges and can detect debris-covered glaciers, illustrating that this method can be executed efficiently, which will further help water resources management. View Full-Text
Keywords: Random Forest; Landsat; ITS_LIVE; movement velocity; the eastern Pamir; glacier mapping Random Forest; Landsat; ITS_LIVE; movement velocity; the eastern Pamir; glacier mapping
Show Figures

Figure 1

MDPI and ACS Style

Lu, Y.; Zhang, Z.; Huang, D. Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir. Water 2020, 12, 3231. https://doi.org/10.3390/w12113231

AMA Style

Lu Y, Zhang Z, Huang D. Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir. Water. 2020; 12(11):3231. https://doi.org/10.3390/w12113231

Chicago/Turabian Style

Lu, Yijie; Zhang, Zhen; Huang, Danni. 2020. "Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir" Water 12, no. 11: 3231. https://doi.org/10.3390/w12113231

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop