Island carbon stocks and distributions
Total forest cover and aboveground carbon stock for seven main Hawaiian Islands was
estimated at 550,065 ha and 36.0 Tg (million metric tons), respectively (Fig. 2; Table 1). A map of estimated uncertainty indicated greatest absolute uncertainties of 20–40 %
in very high-biomass forests, with much lower uncertainties in low-to-moderate biomass
conditions (Additional file 1: Figure S6). Forest ACD varied widely by island (Fig. 3). Hawaii Island contained 57 % of the total forest cover of the State, and almost
20 Tg of the State’s forest carbon. Kauai, Maui and Oahu islands collectively accounted
for 36Â % of the total forest cover and 14.7 Tg of aboveground C. Molokai, Lanai, and
Kahoolawe together accounted for only 7 % of the State’s forest cover and less than
1.4 Tg C. The small northwest-most island of Niihau was not considered in this study.
Fig. 2. Spatial distribution of forest aboveground carbon density (ACD; Mg C ha
?1
) for the State of Hawaii at 30-m mapping resolution. A map of estimated uncertainty
is provided in Additional file 1: Figure S6. The islands are displayed so that their relative sizes are preserved
Table 1. Forest cover and aboveground carbon stock and density for each island and the State’s
Districts
Fig. 3. Distribution of forest area and total aboveground carbon stock (Tg = million metric
tons) for the main Hawaiian Islands. Percentages are given in terms of the entire
State of Hawaii
The highest forest ACDs were found on Hawaii Island, reaching 537Â Mg C ha
?1
. Maui supported the next highest ACDs, reaching 294Â Mg C ha
?1
. We also found extremely variable C stocks on each island (Additional file 1: Figures S7-S10). Aboveground forest C density varied up to three fold among State
Districts, which are the minimum State-level political units of civil governance (Table 1). On Hawaii Island, for example, forest ACD values varied from means of 30–93 Mg C ha
?1
across Districts, yet within Districts, spatial variation in forest ACD ranged from
50 to 111Â % of their District means. Moreover, three of nine Districts on Hawaii Island
contained two-thirds of the entire island’s forest C stock. The island with the most
variable inter-District forest C stocks was Maui.
Model comparison to FIA plots
Comparison of modeled ACD to values estimated from FIA plot inventory indicated good
precision (R
2
 = 0.67) and accuracy (average root mean squared error or RMSE = 30.4 Mg C ha
?1
) (Fig. 4). Bias was just 11.2 Mg C ha
?1
, and heteroscedasticity was similar to that derived in plot-inventory comparison
studies 27]. These map performances were particularly strong relative to the accuracy of the
equation used for estimating ACD from canopy height (Additional file 1: Figure S5).
Fig. 4. Comparison of Hawaii statewide map of forest aboveground carbon density (ACD) against
plot inventory-based estimates of ACD from the US Forest Service FIA plot-inventory
data
Here we note the challenges involved in comparing the FIA plot data to mapped C densities
based on remote sensing. First, there was an offset of about 6Â years between the time
the LiDAR flights were completed and the time the FIA measurements were taken in the
field. Second, the FIA data in Hawaii were geo-located using non-differentially corrected
global positioning system (GPS) instruments. This leads to plot location uncertainties
of up to 30Â m. The combination of relatively small size (18Â m radius), circular shape,
and non-contiguity of the FIA plots (see “Methodsâ€), explains higher uncertainty when
comparing to ACD estimates in 30 m × 30 m mapping cells. Asner et al. 28] found that mismatches in location and plot shape alone account for up to 15 % uncertainty
in field validation studies. Additionally, the allometric scaling applied to the FIA
field measurements can result in additional uncertainties of up to 50Â % of the plot
mean value 29], 30].
Given these, and other sources of uncertainty, we contend that the verification step
undertaken here was successful in validating the map results. Nonetheless, validation
with FIA or other plots could be significantly improved by more accurate GPS measurements
of plot locations, and by employing plot and sampling design that is better suited
to validating remotely-sensed estimates of ACD. Specifically, plots should be similar
in area to the final grid size and all trees 5Â cm dbh should have height and diameter
measured in each plot. Better allometry would also decrease uncertainty. Currently,
we employ species-specific allometric equations only for the two most dominant native
woody tree species (Metrosideros polymorpha and Acacia koa) and for four non-native tree species. Aboveground biomass for the remaining 114
tree species encountered in FIA plots was estimated using a general model for tropical
trees that incorporates diameter, height and wood density 31]. Species-specific allometry for large, widespread non-native tree species, such as
Falcataria moluccana, would almost certainly reduce uncertainty in estimates of their aboveground biomass.
Factors affecting carbon stocks
The geospatial analysis indicated that fractional canopy cover (FC) was the principal
driver of spatial variation in forest carbon stocks throughout the Hawaiian archipelago,
accounting for 27 % of the total variance in ACD (Fig. 5). Forest cover was closely followed by forest type, as defined using the vegetation-cover
classification, which accounted for an additional 24Â % of variation in ACD. Other
important factors included mean annual precipitation, vegetation structure, and cloudiness,
which individually explained 6–8 % of the ACD variation throughout the islands. Finally,
fire return factors, elevation and additional climate variables individually explained
1–4 % of the variability in carbon density.
Fig. 5. Contribution of each potential explanatory factor determining aboveground carbon density
(ACD) in the Hawaiian Islands. Fractional canopy cover (FC), non-photosynthetic vegetation
(NPV) cover, and bare surface cover (soils, rock, infrastructure) were derived from
sub-30Â m resolution Landsat-based satellite mapping of the islands (see 25]). Vegetation type was provided by the Hawaii State GAP vegetation map 34]. MFRI mean fire return interval; RFS replacement fire severity; LFS low fire severity; MFS mixed fire severity
Note that while the results presented in Fig. 5 account for co-variation in explanatory factors, many of them are ecologically and/or
geospatially convolved with one another. For example, forest FC is broadly related
to elevation and topographic aspect, with less forest cover often observed at high
elevations and on leeward aspects, although low forest FC was also observed in deforested
zones at lower elevations on windward aspects. Thus the factor rankings presented
here indicate an additional effect of elevation and aspect not already explained by
FC alone. Similar inter-factor co-variances occur among the model rankings in Fig. 5. Nonetheless, it is clear that FC and vegetation type explain much of the geographic
variation in forest carbon stocks.
Effects of biological invasion on forest carbon
Although this study is limited to a single time step, the current Hawaii vegetation
map allowed us to conduct the first statewide assessment of the large-scale effects
of alien plant species on forest C stocks. Numerous plot- to landscape-scale studies
have reported on this issue, with highly variable outcomes ranging from no effect
of invasion on carbon densities, to increases and decreases in ACD following invasion
17], 23], 28], 32], 33]. Such wide-ranging results stem from underlying variability in the mediating factors,
such as time-since-introduction, rates of invasion, relative changes in plant functional
and structure types, and environmental filters such as soils and climate. There is
thus a general need for large-scale, high-resolution assessments that go beyond local
contextual results.
The Hawaii State vegetation map was generated using manual and automated classification
of Landsat imagery against aerial photography 34]. Experience with this map in field studies indicates that the “alien-dominated†classes
are comprised of mature stands of non-native species, while “native-dominated†classes
are comprised of mature stands of native species, particularly dominated by the keystone
canopy species Metrosideros polymorpha and Acacia koa. We focused our analysis on these two groups because the Hawaii State vegetation
map alone does not provide sufficient detail to partition the mapped C results into
finer levels of invasion, particularly since the invasion process is ongoing and highly
dynamic (in favor of alien invasive species dominance). We further partitioned the
native- and alien-dominated groups by three major environmental filters known to mediate
C stocks: annual precipitation, elevation and substrate age (from volcanic activity
dating back to the early Pliocene) (Additional file 1: Table S1).
Our results show that, on medium-to-older substrates in both drier and wetter conditions,
the total area of alien-dominated forest exceeds that of native-dominated forest in
lower-elevation zones (Fig. 6a). In contrast, the majority of wetter, higher-elevation and/or older-substrate conditions
remain dominated by native forest cover. Critically, however, we found that ACD is
greater in native-dominated forests in low-to-medium elevation, dry-to-mesic regions
of the islands, whereas alien-dominated forests tend to have slightly higher ACD levels
in wetter environments across the board (Fig. 6b). At these broad multi-island scales, substrate age played only a small role in
determining the relative difference in alien- and native-dominated forest ACD. This suggests strong limiting
effects of nutrient-poor soils on growth and biomass accumulation for all species,
independent of origin 35]. In contrast, higher biomass of native forest canopies in drier zones on older substrates
may reflect evolutionary adaptation to these environments, as well as a lack of analog
tree taxa in the current alien species pool on the islands.
Fig. 6. Distribution of a forest area and b forest aboveground carbon density (ACD) for native-dominated and alien-dominated
forests throughout the State of Hawaii. The forests are reported here using the Hawaii
State GAP Vegetation map 34] partitioned by lava substrate age, elevation and mean annual precipitation
Our results are also suggestive of how native biological diversity intersects with
C storage, and how alien invasive species alter those relationships. For example,
higher-elevation, drier forests on older substrates may be dominated by alien forest
cover (smallest solid green dot; Fig. 6a), but native-dominated forests in similar environments support twice the stored
C on a per-area basis (Fig. 6b). Thus actions to conserve and restore high-elevation native ecosystems yield a
co-benefit of increased C storage. On the other hand, higher-elevation, drier conditions
on younger substrates are areas currently dominated by native forest cover (open small
red dot; Fig. 6a), but alien species can double the ACD levels in these environments (Fig. 6b). Forest managers and conservationists can use these landscape-scale relationships
as trade-offs in planning efforts to increase C storage while managing for biological
diversity 36], 37].
Forest carbon protections and opportunities
High-resolution C mapping also affords a way to assess current protections, threats
and opportunities for sequestered carbon and generating healthy forests via land-use
allocation and management 21]. Using land tenure data provided by the State of Hawaii, we quantified C stocks and
densities on State, federal and private reserves. Of the total aboveground forest
C stock found on the islands (36 Tg C), about 18.5 Tg C or 51Â % is officially protected
on State (e.g., Natural Area Reserves; Forest Reserves), federal (National Parks;
Wildlife Refuges) and private (The Nature Conservancy; Kamehameha Schools lands) lands
covering 257,691 ha (Fig. 7a, Additional file 1: Table S2). This is almost equally matched by forests outside of protected reserves,
which in total cover more land area at 292,374Â ha, but which contain 17.5 Tg of aboveground
C. This finding indicates that a large amount of forest C could be incorporated into
more formal reserve protections. Moreover, we found that reserve ACD averages 61.8 ± 22.3 Mg C ha
?1
, whereas non-reserve forests have carbon densities of 59.6 ± 34.2 3 Mg C ha
?1
(Fig. 7b). Combined, these results underscore the C-storage benefit of adding long-term protection
status to remaining island forests; Total forest aboveground C stock increases linearly
with increasing reserve area (Additional file 1: Figure S11).
Fig. 7. Distribution of forest a aboveground carbon stock and b aboveground carbon density on protective reserves managed by State, federal and private
organizations, as well as unprotected forested lands
On all islands, 189 State-managed reserves hold the vast majority of protected carbon
stocks—14.8 Tg C, while 25 federal and 14 private reserves contain just 2.8 and 0.9
Tg C, respectively (Additional file 1: Table S2). Carbon densities are highest in State reserves (66.3 ± 23.2 Mg C ha
?1
), followed by private (56.1 ± 19.3 Mg C ha
?1
) and federal reserves (41.4 ± 17.6 Mg C ha
?1
). Differences in forest carbon densities are reflective of the location of the reserves
(lowland vs. montane, wet windward vs. dry leeward) as well as species composition
and management. A desired outcome of this work is to provide forest managers and the
public with information to compare, for example, carbon stocks on a reserve-by-reserve
basis against environmental maps, to identify opportunities for increasing C densities
through conservation and management actions.
Replication on oceanic island chains
The approach we have developed and tested here for high-resolution mapping of aboveground
forest carbon density is intended for replication on oceanic islands worldwide, but
also any set of highly heterogeneous landscapes. The methodology is based on a previously
established strategy that relies on airborne LiDAR sampling of forests found across
a range of ecological conditions, but limited to one island 23]. Here we greatly advanced the approach by extending the initial LiDAR sampling of
a single island, via a machine-learning algorithm 38], 39], to the multi-island or archipelago scale using a suite of environmental maps and
satellite data that is, in combination, sufficiently sensitive to variation in the
LiDAR-based estimates of canopy height. Shared environmental characteristics among
neighboring islands usually include geology, climatic zones, and dominant vegetation
types. Satellite-based metrics of forest structure, derived from Landsat-based spectral
mixture analysis, are time-variant and key to the linkage with the LiDAR data. Strategically,
these Landsat-based metrics can be updated through time using the fully automated
CLASlite software 25].
The conversion of either LiDAR-scale or modeled canopy height to estimates of ACD
requires plot-aggregate allometric equations 40]. This worked well in Hawaii, relative to plot-estimated ACD from U.S. Forest Service
inventory data. The universal or regional plot-aggregate allometric equations proposed
by Asner et al. 40] have also worked reasonably well in other regions 17], 41], 42], and they tend to result in mismatches between LiDAR-based and field-based estimates
of ACD of 10–15 % when applied at 1-ha spatial resolution 26]. Nonetheless, application of these conversion equations to oceanic islands requires
further validation, particularly for isolated islands in which vegetation types (and
thus allometrics) may diverge from general databases.
There is an initial cost for installing a forest C monitoring program on any given
island chain or archipelago. It includes an initial airborne LiDAR survey of one island
or part of the archipelago, which varies widely in cost depending upon whether the
data are sourced from non-profit, government, or commercial organizations. Our LiDAR
data collection and processing cost was approximately $150,000 for the Island of Hawaii,
but costs have greatly declined since the data acquisition was made for this study
43]. The LiDAR component was followed by personnel and computing costs required to link
the LiDAR data to the satellite imagery and for validation work. However, the satellite
imagery was free of charge, and CLASlite is also currently available at no charge
44], thereby providing us with a low-cost way to complete the initial carbon map. Moreover,
the free imagery and software makes updates to the map extremely cost-efficient, likely
requiring the effort of a single geospatial technician for the State of Hawaii. Even
if field inventories could be done at large geographic scales on a spatially contiguous
basis, which is not possible, the recurring costs would be extremely high for each
monitoring step through time.