Classifying and mapping general coral-reef structure using Ikonos data

Jill Maeder, Sunil Narumalani, Donald C. Rundquist, Richard L. Perk, John Schalles, Kevin Hutchins, Jennifer Keck

Research output: Contribution to journalArticle

70 Citations (Scopus)

Abstract

Monitoring coral reef, seagrass, and sand features using contemporary remotely sensed data may prove to be a cost-effective and time-efficient tool for reef surveys, change detection, and management. Previous attempts at subsurface feature discrimination with satellite remote sensing have been limited in accuracy due to the effects of pixel mixing associated with poor spatial resolutions. While aerial reconnaissance may offer higher spatial resolutions than satellite sensors, it is often limited by the high costs of planning and implementing the missions, image rectification, area that can be covered, and repeat coverage. In this study, the Ikonos satellite with a 4- by 4-m spatial resolution in the multispectral bands was used as a tool for subsurface feature identification. The Single-Image Normalization Using Histogram Adjustment was used for atmospheric corrections on the imagery. Classification was performed using bands 1, 2, and 3 (blue, green, and red) to maximize the water-penetration capabilities of the sensor. An accuracy assessment of the classification results was performed using in situ data collected at 62 points one day prior to the image being acquired. It was concluded that the Ikonos data were useful for discriminating sand, coral reef (at two depth intervals), and seagrass features (providing overall accuracies of 89 percent each for the two study areas). However, error still remained in discriminating small, diverse patch-reef features. This error (producers accuracy 67 percent) was found in the "Reef ≤ 5 m" class and was primarily attributed to the diversity of this spectral class, which may lead to a spectral signature based on the dominant cover type in a given pixel.

Original languageEnglish
Pages (from-to)1297-1305
Number of pages9
JournalPhotogrammetric Engineering and Remote Sensing
Volume68
Issue number12
StatePublished - Dec 1 2002

Fingerprint

Reefs
coral reef
spatial resolution
reef
seagrass
pixel
Satellites
sand
accuracy assessment
atmospheric correction
satellite sensor
histogram
cost
Sand
Pixels
imagery
penetration
sensor
remote sensing
Sensors

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences (miscellaneous)
  • Computers in Earth Sciences

Cite this

Maeder, J., Narumalani, S., Rundquist, D. C., Perk, R. L., Schalles, J., Hutchins, K., & Keck, J. (2002). Classifying and mapping general coral-reef structure using Ikonos data. Photogrammetric Engineering and Remote Sensing, 68(12), 1297-1305.

Classifying and mapping general coral-reef structure using Ikonos data. / Maeder, Jill; Narumalani, Sunil; Rundquist, Donald C.; Perk, Richard L.; Schalles, John; Hutchins, Kevin; Keck, Jennifer.

In: Photogrammetric Engineering and Remote Sensing, Vol. 68, No. 12, 01.12.2002, p. 1297-1305.

Research output: Contribution to journalArticle

Maeder, J, Narumalani, S, Rundquist, DC, Perk, RL, Schalles, J, Hutchins, K & Keck, J 2002, 'Classifying and mapping general coral-reef structure using Ikonos data', Photogrammetric Engineering and Remote Sensing, vol. 68, no. 12, pp. 1297-1305.
Maeder J, Narumalani S, Rundquist DC, Perk RL, Schalles J, Hutchins K et al. Classifying and mapping general coral-reef structure using Ikonos data. Photogrammetric Engineering and Remote Sensing. 2002 Dec 1;68(12):1297-1305.
Maeder, Jill ; Narumalani, Sunil ; Rundquist, Donald C. ; Perk, Richard L. ; Schalles, John ; Hutchins, Kevin ; Keck, Jennifer. / Classifying and mapping general coral-reef structure using Ikonos data. In: Photogrammetric Engineering and Remote Sensing. 2002 ; Vol. 68, No. 12. pp. 1297-1305.
@article{f92b3ce8d50d454382bd66a77be1ed4c,
title = "Classifying and mapping general coral-reef structure using Ikonos data",
abstract = "Monitoring coral reef, seagrass, and sand features using contemporary remotely sensed data may prove to be a cost-effective and time-efficient tool for reef surveys, change detection, and management. Previous attempts at subsurface feature discrimination with satellite remote sensing have been limited in accuracy due to the effects of pixel mixing associated with poor spatial resolutions. While aerial reconnaissance may offer higher spatial resolutions than satellite sensors, it is often limited by the high costs of planning and implementing the missions, image rectification, area that can be covered, and repeat coverage. In this study, the Ikonos satellite with a 4- by 4-m spatial resolution in the multispectral bands was used as a tool for subsurface feature identification. The Single-Image Normalization Using Histogram Adjustment was used for atmospheric corrections on the imagery. Classification was performed using bands 1, 2, and 3 (blue, green, and red) to maximize the water-penetration capabilities of the sensor. An accuracy assessment of the classification results was performed using in situ data collected at 62 points one day prior to the image being acquired. It was concluded that the Ikonos data were useful for discriminating sand, coral reef (at two depth intervals), and seagrass features (providing overall accuracies of 89 percent each for the two study areas). However, error still remained in discriminating small, diverse patch-reef features. This error (producers accuracy 67 percent) was found in the {"}Reef ≤ 5 m{"} class and was primarily attributed to the diversity of this spectral class, which may lead to a spectral signature based on the dominant cover type in a given pixel.",
author = "Jill Maeder and Sunil Narumalani and Rundquist, {Donald C.} and Perk, {Richard L.} and John Schalles and Kevin Hutchins and Jennifer Keck",
year = "2002",
month = "12",
day = "1",
language = "English",
volume = "68",
pages = "1297--1305",
journal = "Photogrammetric Engineering and Remote Sensing",
issn = "0099-1112",
publisher = "American Society for Photogrammetry and Remote Sensing",
number = "12",

}

TY - JOUR

T1 - Classifying and mapping general coral-reef structure using Ikonos data

AU - Maeder, Jill

AU - Narumalani, Sunil

AU - Rundquist, Donald C.

AU - Perk, Richard L.

AU - Schalles, John

AU - Hutchins, Kevin

AU - Keck, Jennifer

PY - 2002/12/1

Y1 - 2002/12/1

N2 - Monitoring coral reef, seagrass, and sand features using contemporary remotely sensed data may prove to be a cost-effective and time-efficient tool for reef surveys, change detection, and management. Previous attempts at subsurface feature discrimination with satellite remote sensing have been limited in accuracy due to the effects of pixel mixing associated with poor spatial resolutions. While aerial reconnaissance may offer higher spatial resolutions than satellite sensors, it is often limited by the high costs of planning and implementing the missions, image rectification, area that can be covered, and repeat coverage. In this study, the Ikonos satellite with a 4- by 4-m spatial resolution in the multispectral bands was used as a tool for subsurface feature identification. The Single-Image Normalization Using Histogram Adjustment was used for atmospheric corrections on the imagery. Classification was performed using bands 1, 2, and 3 (blue, green, and red) to maximize the water-penetration capabilities of the sensor. An accuracy assessment of the classification results was performed using in situ data collected at 62 points one day prior to the image being acquired. It was concluded that the Ikonos data were useful for discriminating sand, coral reef (at two depth intervals), and seagrass features (providing overall accuracies of 89 percent each for the two study areas). However, error still remained in discriminating small, diverse patch-reef features. This error (producers accuracy 67 percent) was found in the "Reef ≤ 5 m" class and was primarily attributed to the diversity of this spectral class, which may lead to a spectral signature based on the dominant cover type in a given pixel.

AB - Monitoring coral reef, seagrass, and sand features using contemporary remotely sensed data may prove to be a cost-effective and time-efficient tool for reef surveys, change detection, and management. Previous attempts at subsurface feature discrimination with satellite remote sensing have been limited in accuracy due to the effects of pixel mixing associated with poor spatial resolutions. While aerial reconnaissance may offer higher spatial resolutions than satellite sensors, it is often limited by the high costs of planning and implementing the missions, image rectification, area that can be covered, and repeat coverage. In this study, the Ikonos satellite with a 4- by 4-m spatial resolution in the multispectral bands was used as a tool for subsurface feature identification. The Single-Image Normalization Using Histogram Adjustment was used for atmospheric corrections on the imagery. Classification was performed using bands 1, 2, and 3 (blue, green, and red) to maximize the water-penetration capabilities of the sensor. An accuracy assessment of the classification results was performed using in situ data collected at 62 points one day prior to the image being acquired. It was concluded that the Ikonos data were useful for discriminating sand, coral reef (at two depth intervals), and seagrass features (providing overall accuracies of 89 percent each for the two study areas). However, error still remained in discriminating small, diverse patch-reef features. This error (producers accuracy 67 percent) was found in the "Reef ≤ 5 m" class and was primarily attributed to the diversity of this spectral class, which may lead to a spectral signature based on the dominant cover type in a given pixel.

UR - http://www.scopus.com/inward/record.url?scp=0036900422&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036900422&partnerID=8YFLogxK

M3 - Article

VL - 68

SP - 1297

EP - 1305

JO - Photogrammetric Engineering and Remote Sensing

JF - Photogrammetric Engineering and Remote Sensing

SN - 0099-1112

IS - 12

ER -