DOI: 10.5061/dryad.5tb2rbpfg
title: Data from: Mangrove above-ground biomass and production are related to forest age at Low Isles, Great Barrier Reef

description: Above-ground biomass contributes a large proportion of mangrove carbon
 stock; however, spatio-temporal dynamics of biomass are poorly understood
 in carbonate settings of the Southern Hemisphere. This influences capacity
 to accurately project the effects of accelerating sea-level rise on this
 important carbon store. Here, above-ground biomass and productivity
 dynamics were quantified across mangrove age zones dominated by Rhizophora
 stylosa, spanning a tidal gradient atop a reef platform at Low Isles,
 Great Barrier Reef, Australia. Above-ground biomass was extrapolated
 across the forest using field plot data, allometry, a canopy height model
 derived from remotely piloted aircraft (RPA) LiDAR and regression
 analyses. Above-ground biomass production was calculated as mean annual
 biomass increments and canopy production was determined using RPA-derived
 multispectral imagery and a Normalised Difference Vegetation Index.
 Mangrove above-ground biomass was estimated at 519.7 ± 3.11 t
 ha-1 and increased with age up to the oldest forest (812.0 ± 12.9
 t ha-1), believed to be ~135 ± 40 years old. Above-ground biomass was
 explained by age and tidal position (r2 &gt;0.8), with a positive
 association between the two predictor variables. Above-ground biomass
 production peaked at lowest intertidal positions in the youngest forest
 aged &lt;11 years at 36.3 t ha-1 yr-1, steadying thereafter, with a
 mean of 12.5 ± 5.4 t h-1yr-1 across the island. Production in the canopy
 remained high until the oldest forest and was negatively associated with
 age and tidal position (r2 &gt;0.9). Declining production in the older
 zones corresponded to forest ageing, tidal positions becoming suboptimal
 for growth and increased exposure to prevailing winds and cyclones. By
 developing relationships between above-ground biomass accumulation and age
 and tidal position, this study informs parameterisation of models of the
 response of biomass to sea-level rise but requires additional information
 about relationships between substrate evolution and forest development and
 age.
description: Collection and analysis of field data A total
 of 18 field plots (10 × 10 m) were established in the *R.
 stylosa-*dominant forest to collect tree measurements. Plots were
 positioned to span mangrove age zones and varying forest structures,
 assessed using historic mapping and field observations, respectively.
 Plots 1A–D and 2A–D are positioned in the 1848–1928 forest and plot 3 are
 in the 1928–1945 forest but close to the margin of the 1945–1973 forest.
 Plots 4A–D are in the 1945–1973 forest but also overlap with the 1973–2012
 forest. Plots 5A and 5B are in the 1945–1973 forest and plots 6, 7 and 8
 are in the 1973–2012 forest. To calculate field plot
 AGB, tree trunk circumference at breast height (at 1.3 m) was measured and
 converted to DBH (cm). If trees were multi-stemmed, each stem was treated
 as a separate tree. For leaning trees, DBH was measured at 1.3 m along the
 natural height of the trunk, or above the highest prop root when this
 arose from the stem at a height above 1.3 m and this approach is
 consistent with the development of allometric equations from DBH.
 Individual tree heights were recorded in seven field plots but due to time
 constraints, one tree height was measured in plots 2A–D, a maximum tree
 height was measured in plots 1A–D and tree height data was not collected
 for the remaining five plots. Tree density was recorded as the number of
 individuals included in AGB calculations (i.e., above 1.3 m) and trees
 below this height were also tallied to determine shrub density. A total of
 825 trees were measured for DBH and a total of 641 trees were measured for
 tree height. AGB for each tree was determined using Equation 1, an
 allometric equation based on DBH: AGB (kg) = 10^A +
 B*log<sub>10</sub>DBH               (Equation 1)
 Where: 10 represents the antilog of a base 10 log from
 rearranging the equation from (log Biomass = A + B log DBH) (B. F. Clough
 & Scott, 1989). The equation coefficients were derived from the
 total AGB of both <em>R. stylosa</em> and <em>Rhizophora
 apiculata</em> in northeastern Queensland (B. F. Clough &
 Scott, 1989), where A is -0.9789 and B is 2.6848. This equation was
 developed from trees with a DBH range of 3–23 cm, and therefore there is
 some uncertainty in values outside this range.
 Collection and processing of remote sensing data
 RPA remote sensing was used in this study as high-density LiDAR
 and high-resolution optical imagery was required and there was no other
 airborne LiDAR data available. An RPA survey of the mangrove forest was
 conducted in July 2023 using a DJI Matrice 300 RTK, fitted with a Zenmuse
 L1 and optical sensor to collect LiDAR and imagery in the visible spectrum
 (red, green, blue), respectively. Multispectral imagery (five-band) was
 collected in June 2022 using a DJI Phantom 4 fitted with a MicaSense
 RedEdge-M™ sensor. RPA surveys were conducted at low tides where possible
 to optimise detection of the substrate surface using LiDAR and spectral
 bands, both of which are sensitive to water (Klemas, 2013). Given the
 density of mangrove at this site, RPA-based LiDAR sensing was identified
 as the optimal approach for developing digital elevation models (DEMs),
 digital surface models (DSMs) and canopy height models (CHMs) due its high
 spatial resolution and precision. Prior to the RPA
 survey, seven ground control points (GCPs) were placed across a range of
 elevations on the reef platform and within the mangrove forest. Trimble
 Real Time Extended Global Positioning System (RTX-GPS) points were also
 collected for easily identifiable features in the imagery such as
 microatolls and small mangroves on the reef platform. A total of 12 GCPs
 were surveyed using RTX-GPS (vertical error of 0.197 cm) to correct the
 vertical and horizontal offset of rasters. Initial
 processing of the LiDAR data and the 2023 RPA imagery was conducted in DJI
 Terra (v.3.9.4), generating a las file and orthomosaic (2.78 cm/pixel),
 respectively. A multispectral orthomosaic (8 cm/pixel) was created by
 stitching images for each of the five spectral bands (blue, green, red,
 red edge, near-infrared) in Agisoft Metashape Professional** (Metashape
 v.1.6.3). Further image analysis was performed using ArcGIS Pro (v.3.3.1;
 ESRI) and R Studio (v.4.3.3; R Core Team 2024). LiDAR
 ground points were classified to generate a DEM and DSM at 0.25 m
 resolution. The visible spectrum orthomosaic, DEM and DSM were corrected
 for horizontal offset by georeferencing using the GCPs and a
 1<sup>st</sup> order polynomial correction (RMS errors
 &lt;3 cm). A reliable RTX-GPS point was not available for the DJI
 Matrice 300 RTK base station at the time of the survey, which created a
 systematic vertical offset of the digital models. To rectify this, the
 average vertical offset between the DEM and GCPs was subtracted from both
 the DEM and DSM. The GCPs used for vertical correction were filtered to
 &gt;0.28 m AHD to ensure submerged features did not influence the
 correction. This threshold was selected upon inspecting the orthomosaic
 for submerged features, as LiDAR returns do not effectively penetrate
 water. To assess mangrove canopy greenness, the red and
 near-infrared multispectral orthomosaics were used to calculate
 NDVI. Mangrove structure was quantified using a CHM
 (pixel size: 0.25 m), by subtracting the DEM from the DSM. The CHM was
 clipped to the 2023 mangrove extent and was resampled to a pixel size of
 10 m (100 m<sup>2</sup>) to align with the field plot
 resolution for AGB. Field notes and GPS points guided the location and
 construction of square polygons representing the biomass plots (10 × 10
 m). Calibration of the CHM was performed by linear regression analysis
 between field plot mean tree height where all tree heights were measured
 (n = 7) and the corresponding CHM pixel value to improve accuracy of the
 models. The calibrated CHM is hereafter referred to as Cal-CHM.
 AGB was extrapolated across the mangrove forest using the Cal-CHM
 and a logistic regression relating field plot mean tree height and total
 plot AGB (<em>n</em> = 7) (Equation 2). As mangrove tree
 height asymptotes at a maximum height (Suwa et al. 2008), a logistic
 regression was most appropriate to explain the data. In some cases, linear
 regressions exhibit greater capacity to describe relationships between
 mangrove AGB and height (Suwa et al. 2008). However, they cannot
 effectively describe the AGB addition of very large trees that exhibit an
 asymptote in growth with age. Y = c / 1 +
 e<sup>-a * (<em>x</em> + b)</sup>             
 (Equation 2) Where: <em>a</em> is the
 growth rate estimated at 0.323, <em>b</em> is the inflection
 point estimated at 11.78 m, and <em>c</em> is the asymptote
 estimated at 31.77 t per plot. The field plot mean tree height was
 calculated for the seven plots with tree height data for all trees and is
 represented by <em>x</em>. The Cal-CHM,
 AGB, DEM and NDVI rasters were resampled to 10 m resolution, clipped to
 the 2023 mangrove extent and interior ponds were removed to reduce
 outliers resulting from poor accuracy of LiDAR and multispectral imagery
 in submerged features. Table. RPA survey
 details 
 
 RPA-multispectral RPA-LiDAR and optical 
 Date June 2022 July 2023 
 Drone, sensor Phantom 4, MicaSense RedEdge-M™
 sensor DJI Matrice 300 RTK,
 Zenmuse L1, optical sensor Flight speed (m s^-1^)   5 Altitude (m) 120 60 Conditions Fine-cloudy, winds &lt;15 km hr^-1^, low
 tide Fine-cloudy, winds
 &lt;15 km hr^-1^, low tide Point cloud details   566.9 pts m^-2^ (spacing 0.042), 611,195,800
 points, (16.4% ground, 83.6% undefined) 
 Optical image details Red, green, blue, near infrared, red edge, 505
 images per band (8 cm/pixel) Red, green, blue, 970 images, (2.78
 cm/pixel) 
 Table. DEM and DSM processing parameters. 
  
 
 
 DEM processing parameters
 DSM processing parameters
 LAS
 filter Ground
 All points 
 Interpolation type
 Binning Binning Cell Assignment Average Maximum Void Fill Method Natural Neighbour Linear Cell size (m) 0.25 0.25
description: # Data from: Mangrove above-ground biomass and production are related to
 forest age at Low Isles, Great Barrier Reef Dataset DOI:
 [10.5061/dryad.5tb2rbpfg](10.5061/dryad.5tb2rbpfg) ## Description of the
 data and file structure ### Files and variables #### File:
 AGB_89_calib_logistic_t.tif **Description:** AGB model of the mangrove
 forest at 10 m resolution, developed using field plot data, CHM from LiDAR
 (Matrice 300 RTK Zenmuse L1, 2023) and logistic regression (detailed in
 manuscript). #### File: LI_CHM_filled_10m_pos_calib.tif **Description:** 
 Canopy height model (CHM) at 10 m resolution, clipped to the mangrove
 extent. Calibration of the CHM was performed by linear regression analysis
 between field plot mean tree height where all tree heights were measured
 (n = 7) and the corresponding CHM pixel value to improve accuracy of the
 models.  #### File: LowIsles_DEM_10m_clip.tif **Description:**  Digital
 elevation model (DEM) LiDAR (Matrice 300 RTK Zenmuse L1, 2023) at 10 m
 resolution, clipped to the mangrove extent. #### File:
 NDVI_18102023_ref1_10m_v2.tif **Description:** NDVI at 10 m resolution
 clipped to the mangrove extent. From RPA-multispectral survey in 2022. 
 #### File: 2023Ortho_v2_copy_resample_5cm_refd.tif **Description:** RGB
 orthomosaic at 5 cm resolution from Matrice 300 RTK survey in 2023.
 Georeferenced using RTX-GPS ground control points.  #### File:
 LowIsles_Fieldplotdata2023.csv **Description:** Low Isles field plot data
 2023. Note that n/a refers to data that is not available because it was
 not collected in the field. Not all tree heights were measured due to time
 constraints. ##### Variables * Plot ID: Plot name (10 x 10 m plots) * ID:
 ID of each tree within the plot * Height (m): individual tree height
 (metres)  * DBH (cm): individual tree diameter at 1.3 metres (cm)  *
 Biomass (kg): Total tree AGB (kg), calculated using Clough and Scott
 (1989) Equation * Age_class: estimated age class (years common error) of
 the plot  ArcGIS Pro (v.3.3.1; ESRI) was used to create the digital
 models.