Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 102; pp. 222 - 231
• Main Authors: Zhu, Xiaolin, Liu, Desheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2015
• Subjects: Aboveground biomass; Accuracy; autumn; Biomass; carbon footprint; Empirical analysis; Empirical model; Estimates; Forest inventory and analysis; Forests; Landsat; NDVI; normalized difference vegetation index; Ohio; remote sensing; Saturation; Seasonal time-series; Seasons; time series analysis; Ohio; Forest inventory and analysis; Seasonal time-series; NDVI; Empirical model; Landsat; Aboveground biomass
• DOI: 10.1016/j.isprsjprs.2014.08.014
• ISSN: 0924-2716

Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting. Landsat data have been widely used to provide efficient and timely estimates of forest AGB because of their long archive and relatively high spatial resolution. Previous studies have explored different empirical modeling approaches to estimate AGB, but most of them only used a single Landsat image in the peak season, which may cause a saturation problem and low accuracy. To improve the accuracy of AGB estimation using Landsat images, this study explored the use of NDVI seasonal time-series derived from Landsat images across different seasons to estimate AGB in southeast Ohio by six empirical modeling approaches. Results clearly show that NDVI in the fall season has a stronger correlation to AGB than in the peak season, and using seasonal NDVI time-series can result in a more accurate AGB estimation and less saturation than using a single NDVI. In comparing these different empirical approaches, it is difficult to decide which one is superior to the other because they have different strengths and their accuracy is generally similar, indicating that modeling methods may not be the key issue for improving the accuracy of AGB estimation from Landsat data. This study suggests that future research should pay more attention to seasonal time-series data, and especially the data from the fall season.