研究目的
To quantify the abundance and distribution of permafrost region disturbances (PRDs) across the Arctic and Subarctic using remote sensing data.
研究成果
PRDs are widespread and significant, with lake area loss dominating, fires extensive in boreal regions, and RTS highly localized. Findings emphasize the need to incorporate PRDs into land surface models for accurate permafrost carbon feedback projections.
研究不足
The study is limited to 30-m resolution data, which may not detect smaller features; reliance on available datasets with potential quality issues; and the 16-year observation period may not capture long-term trends fully.
1：Experimental Design and Method Selection:
Used time-series analysis of 30-m resolution Landsat imagery from 1999 to 2014, combined with machine-learning classification (Random Forest method) to detect and delineate PRDs such as lake changes, wildfires, and retrogressive thaw slumps.
2：Sample Selection and Data Sources:
Analyzed four continental-scale transects in North America and Eurasia covering ~10% of the permafrost region, using Landsat surface reflectance data preprocessed by USGS ESPA.
3：List of Experimental Equipment and Materials:
Landsat satellites (TM, ETM+, OLI), auxiliary data including permafrost maps, DEMs, and climate reanalysis data.
4：Experimental Procedures and Operational Workflow:
Applied trend analysis using Theil-Sen algorithm on multispectral indices, classified land cover changes, extracted objects for lakes, fires, and RTS, and performed post-processing to remove noise and errors.
5：Data Analysis Methods:
Used statistical methods for trend analysis, machine learning for classification, and object-based image analysis for feature extraction.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
