Abstrak/Abstract |
-Spatially explicit information on aboveground seagrass carbon
stock (AGCseagrass) is required to understand the role of seagrass
as a nature-based solution to mitigating and adapting to climate
change. Remote sensing provides the most effective and efficient
means to map AGCseagrass. This research aimed to assess the accuracy of multispectral images to map AGC in seagrass meadows with
different characteristics. Kemujan Island in Indonesia was selected
to represent patchy beds with relatively low seagrass percent cover
and low water clarity, whereas Labuan Bajo of Indonesia served an
example of continuous beds with a relatively high percent cover
and water clarity. WorldView-2 is a high-spatial-resolution multispectral image with six visible bands, by which water can be penetrated. Several image processing techniques, including sunglint and
water column correction, principal component analysis, and cooccurrence texture analysis, were applied to the atmospherically
corrected WorldView-2 images. These datasets were used as inputs
in the AGC empirical model using seven regression algorithms,
namely, single-band linear regression, band-ratio linear regression,
stepwise regression, random forest regression, support vector
regression, extreme gradient boosting, and multivariate adaptive
regression spline. Seagrass field data collected using photo quadrat
technique were used to train the regression model and assess the
accuracy of the resulting AGCseagrass maps. The results indicated
that WorldView-2 can be used to map AGC in different seagrass
meadows with a consistent accuracy. The most accurate AGCseagrass
map for Kemujan Island had a root mean square error (RMSE) of
4.11 g C m−2 for the aboveground stock in the range of 0–28.70 g
C m−2, and for Labuan Bajo, the RMSE of the map was 9.73 g C m−2,
with aboveground stock range of 0.31–64.38 g C m−2. Model crossvalidation revealed that the mapping model can be site-specific or
robust depending on the characteristics of the field-derived
AGC
seagrass data used to train the regression algorithm. For example, the model developed for Labuan Bajo seagrasses, which had
a higher AGC variance, can be successfully applied to Kemujan Island with its lower AGC variance, but not vice versa. This finding is
a key factor in the future development of a robust AGCseagrass
mapping model that is applicable to various seagrass meadows as
a stepping stone to accelerating the filling of gaps in the global
seagrass dataset |