Long term Earth observation data stored in Google Earth Engine (GEE) can be ingested and derived to biologically relevant environmental variables that can used as the predictors of a species niche. The aim of this research was to create a script using GEE to generate biologically meaningful environmental variables from various Earth observation data and models in Indonesia. Elevation and bathymetry raster data from GEBCO were land masked and benthic terrain modelling were done in order to get the aspect, depth, curvature, and slope. HYCOM and MODIS AQUA dataset were filtered using spatial (Indonesia and surrounding region) and temporal filter (from 2002–2017), and reduced to biologically meaningful variables, the maximum, minimum, and mean. Water speed vector (northward and eastward) data were also converted in to scalar unit. In order to fill data gaps, kriging was done using Bayesian slope. Results shows the water depth in Indonesia ranges from 0 – 6827 m, with slope ranging from 0 – 34.33°, aspect from 0 – 359.99°, and curvature from 0 – 0.94. Variables representing water energy, mean sea surface elevation ranges from 0 – 0.85 m, and mean scalar water velocity 0 – 4 m/s. Mean surface salinity ranges from 20.09 – 35.32‰. Variables representing water quality includes mean of particulate organic carbon which ranges from 25.31 – 953.47‰ and mean of clorophyll-A concentration from 0.05 – 13.63‰. These data can be used as the input for species distribution models or spatially explicit decision support systems such as Marxan for spatial planning and zonation in Marine and Coastal Zone Management Plan.

1. GENERATING BIOLOGICALLY RELEVANT ENVIRONMENTAL
DATA FROM REMOTE SENSING IMAGERIES AND
OCEANOGRAPHIC MODELS TO SUPPORT SPATIAL
PRIORITIZATION OF MARINE BIODIVERSITY CONSERVATION
AND MANAGEMENT IN INDONESIA
Safran Yusri1, Vincentius P. Siregar2, Suharsono3
1Yayasan TERANGI
2Fakultas Perikanan dan Ilmu Kelautan, Institut Pertanian Bogor
3Pusat Penelitian Oseanografi, Lembaga Ilmu Pengetahuan Indonesia
safran.yusri@terangi.or.id

2. Background
• Both ecosystem managers and modelers
require environmental data that influenced
the distribution of a species.
• During the zonation process, spatial
prioritization requires input of environmental
variables (habitat)
• Data are abundant but downloading and
analysis difficult

3. Cloud computing
• The rise of cloud computing boosted the
availability of environmental data and provide the
computing power needed for large scale analysis
of such data.
• Google Earth Engine (GEE)
• Objective: to generate biologically relevant
environmental variables to support to support
spatial prioritization of marine biodiversity
conservation and management in Indonesia using
GEE

4. Methodology

5. Results

6. GEBCO -
Bathymet
ry

7. MODIS AQUA
Chlorophyll A
Concentration

8. Modis Aqua Normalized
Fluorescence Line
Height

9. MODIS AQUA Particulate
Organic Carbon

10. HYCOM Salinity

11. HYCOM Scalar Water Speed

12. MODIS AQUA Sea
Surface
Temperature

13. HYCOM Sea
Surface
Elevation

14. HYCOM Eastward
Water Velocity

15. Discussion
• One of the most used of environmental
variables is determining the probability of a
species or a group of organism presences in a
particular area.
• some areas in Indonesia with sea surface
temperature higher the limit and up to 38°C.
These area are more susceptible to coral
bleaching

16. • Results shows that offshore reefs in Seribu
Island tend to experience higher water speed
and sea surface elevation.
• These two variables are representing water
energy.
• Franklin et al. (2013) also found that location
high coral cover sites tend to have low water
energy characteristic.

17. Conclusion
• This script can ingest long term earth
observation and oceanographic model in
order to develop biologically meaningful
environmental variables on marine
environment.
• The data generated from this script can be
used directly in spatial prioritization or
inderctly from species distribution models and
other biodiversity research