Abstract
Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R2(calibration) = 0.89, R2(validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha−1, and RMSE(validation) = 14.80 t·ha−1). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.
Highlights
Forests play a crucial role in the global carbon cycle (C), capturing CO2 from the atmosphere and storing large quantities of organic matter
The main steps of the global workflow to produce the aboveground biomass (AGB) map were the following: (i) individual tree AGB was estimated from field measurements, and the values were added for each plot; (ii) lidar metrics were extracted from the cloud of height-normalized points; (iii) the predictive models of plot AGB were adjusted and compared for their performance; (iv) the biomass map was generated based on the best predictive model; and (v) uncertainty of pixel-level and landscape-level predictions was analyzed
The present study underscores the effectiveness of lidar as a means to estimate mangrove forest AGB in areas with different degrees of disturbance
Summary
Forests play a crucial role in the global carbon cycle (C), capturing CO2 from the atmosphere and storing large quantities of organic matter. Tropical forests’ C stocks were estimated to be ~471 ± 93 PgC—that is 55% of the total C of the world’s forests [6]. This estimation included C in live biomass (above and below ground), soil carbon, deadwood, and litter. Mangrove forests store more carbon than other ecosystems on an area-specific basis, with a mean carbon stock estimate of 956 t·C·.ha−1 compared with 241 t·C·ha−1 for rainforests [1,9]. Soil stocks are typically much larger than in most other forests and are estimated to account for 49–98% of the total carbon storage [1]. All human threats on mangroves can impact the mangrove carbon cycle
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
