1. (Presented at SCGIS Conference, Monterey, US, 2016)
Mapping Spatial Distribution of Mangrove Species Using High Resolution Multispectral Data
(Presented at SCGIS Conference, Monterey, US, 2016)
ANDINA ANASTASIA KREY
J. DOLAND NICHOLS
DEBRA STOKES
SUMITH PATHIRANA
GREG LUKER
PETER SAENGER
DOUGLAS SHEIL
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2. Questions to make us care about this presentation…
1. Why should we care about mangrove forests?
2. Why do we need to use remote sensing to monitor them?
3. Why do we need to try ADS40 for this?
4. How do we assess the ADS40?
5. What are the results?
6. So, the conclusion is… ???
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3. / 12
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4. 1. Why should we care about mangrove?
Mangrove provides ecosystem services towards land and sea, shelters for birds, as well as providing nutrition for fish stock and absorb excessive amount of carbon.
They live in extreme habitat.
Despite their vital contribution to ecosystem services across the globe, mangrove habitat has contracted.
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5. 2. Why I need to use remote sensing for monitoring the mangroves?
Mangrove forests are difficult and potentially dangerous environments to navigate on foot.
As such, remote sensing offers an efficient means to map and monitor vegetation cover.
So, when some problem happens, we can know the location in advanced, and conduct some actions to make sure that our forest is sustainable.
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6. SENSOR CHARACTERISTICS OF AIRBORNE DIGITAL SENSOR (ADS) 40
3. Why do we need to try ADS40 for this?
SENSOR CHARACTERISTICS OF
AIRBORNE DIGITAL SENSOR (ADS) 40
Spectral Resolution
Red, Green, Blue, Near Infra Red
Spatial Resolution
50 cm
Type of Platform
Airborne
As remote sensing techniques advance, and data resolution improves, the potential to accurately map mangrove species distribution increases.
Medium spectrally, High spatially
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7. 4. How do we assess the ADS40? Ground data Collection
Remotely sensed Data Analysis
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8. COMPARING CLASSIFIED MAP TO GROUND DATA
Methods
PRE-PROCESSING
Clip into study site
Water mask out
Pre-survey
GROUND DATA COLECTION
Random sampling
comparing classified map with ground data
DATA PROCESSING
Visual interpretation
Maximum likelihood supervised classification
COMPARING CLASSIFIED MAP TO GROUND DATA
Conclusion on how reliable the method of using ADS40 for monitoring mangrove
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9. Real color of ADS40 and location of sampling plots on the ground
Supervised classification based on visual interpretation
Classified map
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10. Aegiceras corniculatum (%)
5. What are the results?
Forest types
Species distribution
User’s accuracy
Overall accuracy (%)
Overall Accuracy
Mangrove (%)
Non-mangrove (%)
Avicennia marina (%)
Aegiceras corniculatum (%)
Casuarina glauca (%)
Accuracy 80 65 75 77 35 63 64
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11. Aegiceras vs Avicennia Need higher res GPS
Well matched, but:
Aegiceras vs Avicennia
Need higher res GPS
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12. 6. So, the conclusion is… ???
Our method shows mediu accuracy, 64% overall accuracy in differentiating dominant species and land-covers within the mangrove (Avicennia marina, Aegiceras corniculatum, Casuarina glauca and bare land).
Ways to improve the method:
Using object based classification method, using a higher resolution GPS, combine with radar data if provided.
3. This finding supports another paper which says that spectral resolution might play more important role than spatial resolution, when conducting remote sensing for mangrove forests.
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13. TERIMA KASIH / THANK YOU…
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