Improving mangrove above-ground biomass estimates using LiDAR. (5th May 2020)
- Record Type:
- Journal Article
- Title:
- Improving mangrove above-ground biomass estimates using LiDAR. (5th May 2020)
- Main Title:
- Improving mangrove above-ground biomass estimates using LiDAR
- Authors:
- Salum, Rafaela B.
Souza-Filho, Pedro Walfir M.
Simard, Marc
Silva, Carlos Alberto
Fernandes, Marcus E.B.
Cougo, Michele F.
do Nascimento, Wilson
Rogers, Kerrylee - Abstract:
- Abstract: Tree height is a key parameter to accurately quantify above ground biomass (AGB) of trees. Approaches that integrate airborne light detection and ranging (LiDAR) with mapped extents of forests may improve estimation of mangrove heights by providing considerably more measurements of mangrove tree heights than can be achieved using field-based measurements alone. In this study, we present a validated method for quantifying mangrove AGB that was demonstrated for a mangrove forest at Guarás Island, Brazil. The application of LiDAR to estimate mangrove height was confirmed by correlating 89 tree heights measured in the field with LiDAR-derived mangrove heights, resulting in highly robust relationships for Avicennia germinans, Laguncularia racemosa and Rhizophora mangle (R 2 = 0.90–0.97, RMSE of 1.24–0.67 m and RMSE% of 11.26%–25.97%). These relationships were used to calibrate a LiDAR-derived canopy height model (CHM) and develop robust relationships between the calibrated-CHM and field-based estimates of AGB (R 2 = 0.85–0.92, RMSE of 3.1 kg–42.53 kg, RMSE% of 20.66%–43.81%). This relationship was then applied to the CHM whilst accounting for tree density to estimate mangrove AGB. Total mangrove AGB per hectare was estimated to be 246.90 t ha −1, corresponding closely with previous mangrove AGB measurements within the region. This study found that mangrove height and AGB are statistically related and these relationships can be applied to allometric equations forAbstract: Tree height is a key parameter to accurately quantify above ground biomass (AGB) of trees. Approaches that integrate airborne light detection and ranging (LiDAR) with mapped extents of forests may improve estimation of mangrove heights by providing considerably more measurements of mangrove tree heights than can be achieved using field-based measurements alone. In this study, we present a validated method for quantifying mangrove AGB that was demonstrated for a mangrove forest at Guarás Island, Brazil. The application of LiDAR to estimate mangrove height was confirmed by correlating 89 tree heights measured in the field with LiDAR-derived mangrove heights, resulting in highly robust relationships for Avicennia germinans, Laguncularia racemosa and Rhizophora mangle (R 2 = 0.90–0.97, RMSE of 1.24–0.67 m and RMSE% of 11.26%–25.97%). These relationships were used to calibrate a LiDAR-derived canopy height model (CHM) and develop robust relationships between the calibrated-CHM and field-based estimates of AGB (R 2 = 0.85–0.92, RMSE of 3.1 kg–42.53 kg, RMSE% of 20.66%–43.81%). This relationship was then applied to the CHM whilst accounting for tree density to estimate mangrove AGB. Total mangrove AGB per hectare was estimated to be 246.90 t ha −1, corresponding closely with previous mangrove AGB measurements within the region. This study found that mangrove height and AGB are statistically related and these relationships can be applied to allometric equations for specific species to improve mangrove AGB estimates. This study demonstrates the capacity for LiDAR-derived tree heights to replace traditional approaches to estimating AGB and improving estimates of mangrove blue carbon storage. Application of LiDAR to determine tree heights will be particularly useful where mangrove is extensive and/or remote. Graphical abstract: Image 1 Highlights: Mangrove height and AGB are statistically related. Lidar derived mangrove heights can improve biomass and carbon storage estimates. Amazonian mangrove biomass was estimated by correlating tree biomass with Lidar. Individual tree biomass was corrected for density using mangrove plot density data. This approach improves biomass estimates where fieldwork can be difficult. … (more)
- Is Part Of:
- Estuarine, coastal and shelf science. Volume 236(2020)
- Journal:
- Estuarine, coastal and shelf science
- Issue:
- Volume 236(2020)
- Issue Display:
- Volume 236, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 236
- Issue:
- 2020
- Issue Sort Value:
- 2020-0236-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-05-05
- Subjects:
- Mangrove -- LiDAR -- Above-ground biomass -- Allometry -- Blue carbon
Estuarine oceanography -- Periodicals
Coasts -- Periodicals
Estuarine biology -- Periodicals
Seashore biology -- Periodicals
Coasts
Estuarine biology
Estuarine oceanography
Seashore biology
Periodicals
551.461805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/02727714 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ecss.2020.106585 ↗
- Languages:
- English
- ISSNs:
- 0272-7714
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3812.599200
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 13580.xml