Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 8; no. 5; p. 369
• Main Authors: Lopez-Serrano, Pablito M, Corral-Rivas, Jose J, Diaz-Varela, Ramon A, alvarez-Gonzalez, Juan G, Lopez-Sanchez, Carlos A
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2016
• Subjects: Algorithms; Atmospheric correction; Biomass; Estimates; Forest biomass; Forests; Landsat; Mathematical models; multivariate adaptive regression splines; radiometric correction algorithms; Reflectance; Reflectivity; Remote sensing; Satellite imagery; Satellites; Solar radiation; Temperate forests; terrain features
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs8050369

Solar radiation is affected by absorption and emission phenomena during its downward trajectory from the Sun to the Earth's surface and during the upward trajectory detected by satellite sensors. This leads to distortion of the ground radiometric properties (reflectance) recorded by satellite images, used in this study to estimate aboveground forest biomass (AGB). Atmospherically-corrected remote sensing data can be used to estimate AGB on a global scale and with moderate effort. The objective of this study was to evaluate four atmospheric correction algorithms (for surface reflectance), ATCOR2 (Atmospheric Correction for Flat Terrain), COST (Cosine of the Sun Zenith Angle), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) and 6S (Second Simulation of Satellite Signal in the Solar), and one radiometric correction algorithm (for reflectance at the sensor) ToA (Apparent Reflectance at the Top of Atmosphere) to estimate AGB in temperate forest in the northeast of the state of Durango, Mexico. The AGB was estimated from Landsat 5 TM imagery and ancillary information from a digital elevation model (DEM) using the non-parametric multivariate adaptive regression splines (MARS) technique. Field reference data for the model training were collected by systematic sampling of 99 permanent forest growth and soil research sites (SPIFyS) established during the winter of 2011. The following predictor variables were identified in the MARS model: Band 7, Band 5, slope ( beta ), Wetness Index (WI), NDVI and MSAVI2. After cross-validation, 6S was found to be the optimal model for estimating AGB (R2 = 0.71 and RMSE = 33.5 Mg times ha-1; 37.61% of the average stand biomass). We conclude that atmospheric and radiometric correction of satellite images can be used along with non-parametric techniques to estimate AGB with acceptable accuracy.