hide
Free keywords:
-
Abstract:
Accurate estimates of aboveground biomass (AGB) are crucial to assess terrestrial C-stocks
and C-emissions as well as to develop sustainable forest management strategies. In this study we used
Synthetic Aperture Radar (SAR) data acquired at L-band and the Landsat tree cover product together
with Moderate Resolution Image Spectroradiometer (MODIS) normalized difference vegetation index
(NDVI) time series data to improve AGB estimations over two study areas in southern Mexico.
We used Mexican National Forest Inventory (INFyS) data collected between 2005 and 2011 to calibrate
AGB models as well as to validate the derived AGB products. We applied MODIS NDVI time
series data analysis to exclude field plots in which abrupt changes were detected. For this, we used
Breaks For Additive Seasonal and Trend analysis (BFAST). We modelled AGB using an original
field dataset and BFAST-filtered data. The results show higher accuracies of AGB estimations
using BFAST-filtered data than using original field data in terms of R2 and root mean square error
(RMSE) for both dry and humid tropical forests of southern Mexico. The best results were found
in areas with high deforestation rates where the AGB models based on the BFAST-filtered data
substantially outperformed those based on original field data (R2
BFAST = 0.62 vs. R2
orig = 0.45;
RMSEBFAST = 28.4 t/ha vs. RMSEorig = 33.8 t/ha). We conclude that the presented method shows
great potential to improve AGB estimations and can be easily and automatically implemented over
large areas.
