scholarly journals THE BOTTOM SUBSTRATE SHALLOW WATER MAPPING USING THE QUICK BIRD SATELLITE IMAGERY

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Vincentius Siregar
Keyword(s):  
High Resolution ◽  
Shallow Water ◽  
Satellite Imagery ◽  
Transformation Method ◽  
Live Coral ◽  
Bottom Substrate ◽  
Accuracy Test ◽  
Coral Sand ◽  
Water Mapping

The objective of this study was to explore the capability of high resolution satellite data of QuicBird to map the characteristics of the bottom shallow water (habitat) using the transformation method of two bands (blue and green) by implementing "depth invariant index" algorithm i.e., Y = ln Band 1 - (ki/kj) ln Band 2. The result provide more detail information on the characteristic of the bottom shallow water comparing to the used of original band (RGB). The classification of the transformed image showed 6 classes of bottom substrats i.e., Live coral, Death, Coral, Sand mix coral, Sand mix algae, andMacro algae with Sand. The accuracy test of the map derived from the classification was about 79%.Keywords: bottom shallow water, Quick Bird image, depth invariant index, classification

scholarly journals THE BOTTOM SUBSTRATE SHALLOW WATER MAPPING USING THE QUICK BIRD SATELLITE IMAGERY

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Vincentius Siregar
Keyword(s):  
High Resolution ◽  
Shallow Water ◽  
Transformation Method ◽  
Live Coral ◽  
Bottom Substrate ◽  
Accuracy Test ◽  
Coral Sand ◽  
Water Mapping ◽  
Macro Algae

<p>The objective of this study was to explore the capability of high resolution satellite data of QuicBird to map the characteristics of the bottom shallow water (habitat) using the transformation method of two bands (blue and green) by implementing "depth invariant index" algorithm i.e., Y = ln Band 1 - (ki/kj) ln Band 2. The result provide more detail information on the characteristic of the bottom shallow water comparing to the used of original band (RGB). The classification of the transformed image showed 6 classes of bottom substrats i.e., Live coral, Death, Coral, Sand mix coral, Sand mix algae, and<br />Macro algae with Sand. The accuracy test of the map derived from the classification was about 79%.</p><p>Keywords: bottom shallow water, Quick Bird image, depth invariant index, classification</p>

scholarly journals Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Mary K. Bennett ◽  
Nicolas Younes ◽  
Karen Joyce
Keyword(s):  
Image Processing ◽  
Coral Reef ◽  
High Resolution ◽  
Satellite Imagery ◽  
Coral Cover ◽  
Google Earth ◽  
Live Coral ◽  
Coral Sand ◽  
Google Earth Engine

While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic and environmental disturbances have caused these ecosystems to decline globally. Current coral reef monitoring methods include in situ surveys and analyzing remotely sensed data from satellites. However, in situ methods are often expensive and inconsistent in terms of time and space. High-resolution satellite imagery can also be expensive to acquire and subject to environmental conditions that conceal target features. High-resolution imagery gathered from remotely piloted aircraft systems (RPAS or drones) is an inexpensive alternative; however, processing drone imagery for analysis is time-consuming and complex. This study presents the first semi-automatic workflow for drone image processing with Google Earth Engine (GEE) and free and open source software (FOSS). With this workflow, we processed 230 drone images of Heron Reef, Australia and classified coral, sand, and rock/dead coral substrates with the Random Forest classifier. Our classification achieved an overall accuracy of 86% and mapped live coral cover with 92% accuracy. The presented methods enable efficient processing of drone imagery of any environment and can be useful when processing drone imagery for calibrating and validating satellite imagery.

SHALLOW WATER HABITAT MAPPING AND REEF FISH STOCK ESTIMATION USING HIGH RESOLUTION SATELLITE DATA

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Vincentius P. Siregar ◽  
Sam Wouthuyzen ◽  
Andriani Sunuddin ◽  
Ari Anggoro ◽  
Ade Ayu Mustika
Keyword(s):  
High Resolution ◽  
Reef Fish ◽  
Satellite Imagery ◽  
Field Survey ◽  
Coral Cover ◽  
Fish Density ◽  
Fish Stock ◽  
Benthic Habitat ◽  
Live Coral ◽  
Stock Estimation

Shallow marine waters comprise diverse benthic types forming habitats for reef fish community, which important for the livelihood of coastal and small island inhabitants. Satellite imagery provide synoptic map of benthic habitat and further utilized to estimate reef fish stock. The objective of this research was to estimate reef fish stock in complex coral reef of Pulau Pari, by utilizing high resolution satellite imagery of the WorldView-2 in combination with field data such as visual census of reef fish. Field survey was conducted between May-August 2013 with 160 sampling points representing four sites (north, south, west, and east). The image was analy-zed and grouped into five classes of benthic habitats i.e., live coral (LC), dead coral (DC), sand (Sa), seagrass (Sg), and mix (Mx) (combination seagrass+coral and seagrass+sand). The overall accuracy of benthic habitat map was 78%. Field survey revealed that the highest live coral cover (58%) was found at the north site with fish density 3.69 and 1.50 ind/m2at 3 and 10 m depth, respectively. Meanwhile, the lowest live coral cover (18%) was found at the south site with fish density 2.79 and 2.18  ind/m2 at 3 and 10 m depth, respectively. Interpolation on fish density data in each habitat class resulted in standing stock reef fish estimation:  LC (5,340,698 ind), DC (56,254,356 ind), Sa (13,370,154 ind), Sg (1,776,195 ind) and Mx (14,557,680 ind). Keywords: mapping, satellite imagery, benthic habitat, reef fish, stock estimation

scholarly journals Classification of Soil Desalination Areas Using High Resolution Satellite Imagery in Saemangeum Reclaimed Land

2013 ◽  
Vol 46 (6) ◽  
pp. 426-433 ◽  
Author(s):  
Kyung-Do Lee ◽  
Shin-Chul Baek ◽  
Suk-Young Hong ◽  
Yi-Hyun Kim ◽  
Sang-Il Na ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Reclaimed Land ◽  
Soil Desalination ◽  
High Resolution Satellite Imagery
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