Assessment of multi-resolution image data for mangrove leaf area index mapping

Kamal, Muhammad, Phinn, Stuart and Johansen, Kasper (2016) Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sensing of Environment, 176 242-254. doi:10.1016/j.rse.2016.02.013


Author Kamal, Muhammad
Phinn, Stuart
Johansen, Kasper
Title Assessment of multi-resolution image data for mangrove leaf area index mapping
Journal name Remote Sensing of Environment   Check publisher's open access policy
ISSN 0034-4257
1879-0704
Publication date 2016-04-01
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.rse.2016.02.013
Open Access Status Not Open Access
Volume 176
Start page 242
End page 254
Total pages 13
Place of publication New York, NY United States
Publisher Elsevier
Language eng
Subject 1111 Soil Science
1907 Geology
1903 Computers in Earth Sciences
Abstract The increasing development pressures from urbanization, aquaculture and tourism on worldwide coastal environments and the ecosystem services that mangroves provide make it essential to monitor and manage these environments more effectively. Measuring and monitoring mangrove structure through variables like Leaf Area Index (LAI) is an essential part of this action. This study investigated the effects of different mangrove environmental settings, satellite image spatial resolutions, spectral vegetation indices (SVIs) and mapping approaches for LAI estimation. We compared and contrasted the ability of WorldView-2 (WV-2), ALOS AVNIR-2 (AVNIR-2) and Landsat TM (TM) image data (2 m, 10 m and 30 m pixel sizes, respectively), to estimate LAI through regression analysis at sites in Moreton Bay (Australia) and Karimunjawa Island (Indonesia). We also investigated the effect of different pixel averaging windows (3 × 3, 5 × 5, and 7 × 7 pixels) and multi-resolution segmentation scale parameters (10, 20, 30, 40 and 50) applied to the WV-2 image for LAI estimation. The results showed that LAI estimation using remote sensing data varies across sites and sensors. Estimation of LAI in this study was influenced by the local spatial variation of mangrove phenological stages and canopy cover. The regression analyses showed significant coefficient of determination (R) values ranging from 0.50 to 0.83 across different sensors (TM, AVNIR-2, WV-2), segmentation scales (10, 20, 30, 40, 50) and SVIs (SR, NDVI, SAVI, EVI). The sensor and SVIs assessment identified the ALOS AVNIR-2 and NDVI as the optimal estimators of LAI, with R = 0.83, RMSE = 0.54 for Moreton Bay, and R = 0.82, RMSE = 1.31 for Karimunjawa Island. The optimum image pixel size for estimating LAI was related to the average canopy size (about 10 m in diameter) and the field sampling size (10 m). Image segmentation significantly increased the LAI estimation accuracy by approximately 14% for both sites. The findings of this study provide an understanding of the relationship between image spatial resolution, field sampling size and spatial variation of mangrove vegetation for estimating LAI. These findings can be potentially used as a guide for selecting the optimum imagery for LAI estimation.
Formatted abstract
The increasing development pressures from urbanization, aquaculture and tourism on worldwide coastal environments and the ecosystem services that mangroves provide make it essential to monitor and manage these environments more effectively. Measuring and monitoring mangrove structure through variables like Leaf Area Index (LAI) is an essential part of this action. This study investigated the effects of different mangrove environmental settings, satellite image spatial resolutions, spectral vegetation indices (SVIs) and mapping approaches for LAI estimation. We compared and contrasted the ability of WorldView-2 (WV-2), ALOS AVNIR-2 (AVNIR-2) and Landsat TM (TM) image data (2 m, 10 m and 30 m pixel sizes, respectively), to estimate LAI through regression analysis at sites in Moreton Bay (Australia) and Karimunjawa Island (Indonesia). We also investigated the effect of different pixel averaging windows (3 × 3, 5 × 5, and 7 × 7 pixels) and multi-resolution segmentation scale parameters (10, 20, 30, 40 and 50) applied to the WV-2 image for LAI estimation. The results showed that LAI estimation using remote sensing data varies across sites and sensors. Estimation of LAI in this study was influenced by the local spatial variation of mangrove phenological stages and canopy cover. The regression analyses showed significant coefficient of determination (R2) values ranging from 0.50 to 0.83 across different sensors (TM, AVNIR-2, WV-2), segmentation scales (10, 20, 30, 40, 50) and SVIs (SR, NDVI, SAVI, EVI). The sensor and SVIs assessment identified the ALOS AVNIR-2 and NDVI as the optimal estimators of LAI, with R2 = 0.83, RMSE = 0.54 for Moreton Bay, and R2 = 0.82, RMSE = 1.31 for Karimunjawa Island. The optimum image pixel size for estimating LAI was related to the average canopy size (about 10 m in diameter) and the field sampling size (10 m). Image segmentation significantly increased the LAI estimation accuracy by approximately 14% for both sites. The findings of this study provide an understanding of the relationship between image spatial resolution, field sampling size and spatial variation of mangrove vegetation for estimating LAI. These findings can be potentially used as a guide for selecting the optimum imagery for LAI estimation.
Keyword Leaf area index
Spatial resolution
Mangroves
Segmentation
Regression analysis
Spectral vegetation indices
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
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