Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. (December 2018)

• Record Type: Journal Article
• Authors: Ma, Qin; Su, Yanjun; Luo, Laiping; Li, Le; Kelly, Maggi; Guo, Qinghua
• Is Part Of: Ecological indicators. Volume 95(2018)Part 1
• Page Start: 298
• Page End: 310
• Publication Date: 2018-12
• Subjects: Forest fuel treatment -- Vegetation index -- Aboveground biomass -- LiDAR; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2018.07.050
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: Landsat-derived vegetation indices can map forest fuel treatment extents. LiDAR can quantify forest biomass changes induced by fuel treatments. Vegetation index changes weakly correlate to biomass change in sparse/dense forests. Vegetation index changes can poorly indicate understory treatments. Abstract: Forest ecosystems in the American west have long been influenced by timber harvests and fire suppression, and recently through treatments that reduce fuel for fire management. Precisely quantifying the structural changes to forests caused by fuel treatments is an essential step to evaluate their impacts. Satellite imagery-derived vegetation indices, such as the normalized difference vegetation index (NDVI), have been widely used to map forest dynamics. However, uncertainties in using these vegetation indices to quantify forest structural changes have not been thoroughly studied, mainly due to the lack of wall-to-wall validation data. In this study we generated forest structural changes in aboveground biomass (AGB) and canopy cover as a result of fuel treatments using bi-temporal airborne light detection and ranging (LiDAR) data and field measurements in a mixed coniferous forest of northern Sierra Nevada, California, USA. These LiDAR-derived forest structural measures were used to evaluate the uncertainties of using Landsat-derived vegetation indices to quantify treatments. Our results confirmed that vegetation indices can accurately map the extents of forest disturbance and canopy cover changes caused by fuel treatments, but the accuracy in quantifying AGB changes varied by the pre-treatment forest densities and treatment intensity. Changes in vegetation indices had relatively weaker correlations (coefficient of determination < 0.45) to biomass changes in forests with sparse (AGB < 100 Mg/ha) or dense biomass (AGB > 700 Mg/ha), than in forests with moderate-density (AGB between 100 Mg/ha and 700 Mg/ha) before the disturbances. Moreover, understory treatments (canopy height < 10 m) were poorly indicated by changes in satellite-derived vegetation indices. Our results suggest that when relating vegetation indices to AGB changes, researchers and managers should be cautious about their uncertainties in extremely dense or sparse forests, particularly when treatments mainly removed small trees or understory fuels.