Abstract
The reflectance of the Earth’s surface is significantly influenced by atmospheric conditions such as water vapor content and aerosols. Particularly, the absorption and scattering effects become stronger when the target features are non-bright objects, such as in aqueous or vegetated areas. For any remote-sensing approach, atmospheric correction is thus required to minimize those effects and to convert digital number (DN) values to surface reflectance. The main aim of this study was to test the three most popular atmospheric correction models, namely (1) Dark Object Subtraction (DOS); (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and (3) the Second Simulation of Satellite Signal in the Solar Spectrum (6S) and compare them with Top of Atmospheric (TOA) reflectance. By using the k-Nearest Neighbor (kNN) algorithm, a series of experiments were conducted for above-ground forest biomass (AGB) estimations of the Gongju and Sejong region of South Korea, in order to check the effectiveness of atmospheric correction methods for Landsat ETM+. Overall, in the forest biomass estimation, the 6S model showed the bestRMSE’s, followed by FLAASH, DOS and TOA. In addition, a significant improvement of RMSE by 6S was found with images when the study site had higher total water vapor and temperature levels. Moreover, we also tested the sensitivity of the atmospheric correction methods to each of the Landsat ETM+ bands. The results confirmed that 6S dominates the other methods, especially in the infrared wavelengths covering the pivotal bands for forest applications. Finally, we suggest that the 6S model, integrating water vapor and aerosol optical depth derived from MODIS products, is better suited for AGB estimation based on optical remote-sensing data, especially when using satellite images acquired in the summer during full canopy development.
Highlights
A forest ecosystem is an important and manageable carbon sink that plays a critical role in reducing carbon concentrations in the atmosphere [1,2,3]
We applied the three atmospheric correction methods (DOS, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), 6S) to the suitable ETM+ images selected from the first test—8th August 2010 and 20th May 2011 and applied the k-Nearest Neighbor (kNN) algorithm and accuracy assessment to determine the best method for above-ground forest biomass (AGB) from the perspective of estimation accuracy
Landsat ETM+ imagery and National Forest Inventory (NFI) field survey data were used to evaluate the accuracy of above-ground biomass estimation using the kNN algorithm with the three atmospheric correction methods Dark Object Subtraction (DOS), FLAASH, and 6S
Summary
A forest ecosystem is an important and manageable carbon sink that plays a critical role in reducing carbon concentrations in the atmosphere [1,2,3]. The spatial distribution of above-ground forest biomass (AGB) is necessary for calculating the net flux of terrestrial carbon and supporting climate change modeling studies [4,5,6]. The traditional methods of AGB estimation are based on field sample plots [7,8]. AGB is modeled by using diameter-at-breast-height measurements that are easy to obtain in field samples. The quality of satellite imagery relies on environmental elements including topographic and atmospheric conditions [11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
