Alaska Permanent Snow and Ice Classification for the National Resources Inventory Survey

2675 

Contributed Abstract 

Poster 

Yingchao Zhou (1), Zhengyuan Zhu (1)

(1) Iowa State University, N/A

Zhengyuan Zhu  
Iowa State University

Yingchao Zhou  
Iowa State University

Yingchao Zhou  
Iowa State University

Permanent snow and ice plays a crucial role in Earth's ecological system, affecting both climate and hydrology. However, their accurate classification remains challenging, especially in remote areas where field data collection is difficult. In this study, we leverage data from the 2007 Alaska National Resources Inventory (NRI) survey, publicly available remote sensing data, and global glacier inventory data to improve the classification of permanent snow and ice in Alaska. More specifically, our aims are: (i) develop machine learning methods to classify permanent snow and ice using extensive publicly available remote sensing data which is in line with the NRI definition; (ii) produce annual maps of permanent snow and ice in Alaska to assist in the sampling design and statistical inference of future surveys. To overcome issues of class imbalance and improve model training, we integrate multiple data sources, create relevant variables, and use Random Forests algorithm for classification, achieving 98.6% accuracy. Furthermore, we apply a cross-conformal prediction approach to quantify the uncertainty in the Random Forests prediction.

Permanent snow and ice|Random forests classification|Remote sensing data| | |

Section on Statistics and the Environment

Environmental and Ecological Monitoring