Generation of High Temporal Resolution Fractional Forest Cover Data and Its Application in Accurate Time Detection of Forest Loss

Abstract

Fractional Forest cover holds significance in characterizing the ecological condition of forests and serves as a crucial input parameter for climate and hydrological models. This research introduces a novel approach for generating a 250 m fractional forest cover product with an 8-day temporal resolution based on the updated GLASS FVC product and the annualized MODIS VCF product, thereby facilitating the development of a high-quality, long-time-series forest cover product on a global scale. Validation of the proposed product, employing high spatial resolution GFCC data, demonstrates its high accuracy across various continents and forest cover scenarios globally. It yields an average fit coefficient of determination (R2) of 0.9085 and an average root-mean-square error of 7.22%. Furthermore, to assess the availability and credibility of forest cover data with high temporal resolution, this study integrates the CCDC algorithm to map forest disturbances and quantify the yearly and even monthly disturbed trace area within two sub-study areas of the Amazon region. The achieved sample validation accuracy is over 86%, which substantiates the reliability of the data. This investigation offers a fresh perspective on monitoring forest changes and observing forest disturbances by amalgamating data from diverse sources, enabling the mapping of dynamic forest cover over an extensive time series with high temporal resolution, thereby mitigating data gaps and enhancing the precision of existing products.