Estimating forest litter fuel load by integrating remotely sensed foliage phenology and modeled litter decomposition,Remote Sensing of Environment

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Estimating forest litter fuel load by integrating remotely sensed foliage phenology and modeled litter decomposition
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2024-11-22 , DOI: 10.1016/j.rse.2024.114526
Yanxi Li, Yiru Zhang, Xingwen Quan, Binbin He, Sander Veraverbeke, Zhanmang Liao, Thomas A.J. Janssen

Litter on the forest floor, or from a fire perspective the litter fuel load (LFL), is a key driver of the occurrence and spread of surface fires and an important regulator of forest fire behavior. High-quality spatiotemporal LFL data are essential for modeling fire behavior and assessing fire risk in forest ecosystems. Traditionally, LFL is estimated from ground-based measurements, but they are difficult to implement on large spatial scales. While remote sensing techniques have the advantage of large-scale observation, they encounter challenges in retrieving LFL because forest canopies generally block signals from the forest floor. Here we present a new method based on modeled litter accumulation to estimate LFL dynamics, integrating litterfall influx from the forest canopy and decomposition outflux through a mass balance approach. Annual litterfall was estimated based on seasonal changes in foliage fuel load which are retrieved from Landsat imagery and a radiative transfer model, while the decomposition rate was derived from meteorological data. Litterfall and decomposition were quantified over the past 20 years with the difference between the two being LFL accumulating over time. We validated the estimated LFL using 105 ground-based measurements in Liangshan Yi Autonomous Prefecture, China, and this validation demonstrated a reasonably strong performance for estimating LFL (R2 = 0.67, root mean squared error (RMSE) = 2.56 Mg ha−1, relative RMSE = 31.61 %). Our method integrates remote sensing-based foliage phenology with the ecological process of LFL accumulation, enabling large-scale LFL monitoring for forest fire risk assessments.

更新日期：2024-11-22

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