High-resolution mapping of seasonal snow cover extent in the Pamir Hindu Kush using machine learning-based integration of multi-sensor data

Mahmoodzada, Abdul Basir; Das, Pragyan; Varade, Divyesh; Akhtar, Mohd Arslaan; Shimada, Sawahiko

doi:10.1007/s11600-023-01281-4

Acta Geophys.

2024

DOI: 10.1007/s11600-023-01281-4

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High-resolution mapping of seasonal snow cover extent in the Pamir Hindu Kush using machine learning-based integration of multi-sensor data

Abdul Basir Mahmoodzada,

Pragyan Das,

Divyesh Varade

et al.

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Cited by 2 publications

References 69 publications

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Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape

Amin,

Imtiaz,

Haroon

et al. 2024

J geovis spat anal

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Mapping land cover (LC) in mountainous regions, such as the Gilgit-Baltistan (GB) area of Pakistan, presents significant challenges due to complex terrain, limited data availability, and accessibility constraints. This study addresses these challenges by developing a robust, data-driven approach to classify LC using high-resolution Sentinel-2 (S-2) satellite imagery from 2019 within Google Earth Engine (GEE). The research evaluated the performance of various machine learning (ML) algorithms, including classification and regression tree (CART), maximum entropy (gmoMaxEnt), minimum distance (minDistance), support vector machine (SVM), and random forest (RF), without extensive hyperparameter tuning. Additionally, ten different scenarios based on various band combinations of S-2 data were used as input for running the ML models. The LC classification was performed using 2759 sample points, with 70% for training and 30% for validation. The results indicate that the RF algorithm outperformed all other classifiers under scenario S1 (using 10 bands), achieving an overall accuracy (OA) of 0.79 and a kappa coefficient of 0.76. The final RF-based LC mapping shows the following percentage distribution: barren land (46.7%), snow cover (22.9%), glacier (7.9%), grasses (7.2%), water (4.7%), wetland (2.9%), built-up (2.7%), agriculture (1.9%), and forest (1.2%). It is suggested that the best identified RF classifier within the GEE environment should be used for advanced multi-source data image classification with hyperparameter tuning to increase OA. Additionally, it is suggested to build the capacity of various stakeholders in GB for better monitoring of LC changes and resource management using geospatial big data.

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Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape

Amin,

Imtiaz,

Haroon

et al. 2024

J geovis spat anal

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Sensitivity of Sentinel-1 C-band SAR backscatter, polarimetry and interferometry to snow accumulation in the Alps

Jans,

Beernaert,

De Breuck

et al. 2025

Remote Sensing of Environment

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High-resolution mapping of seasonal snow cover extent in the Pamir Hindu Kush using machine learning-based integration of multi-sensor data

Cited by 2 publications

References 69 publications

Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape

Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape

Sensitivity of Sentinel-1 C-band SAR backscatter, polarimetry and interferometry to snow accumulation in the Alps

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