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A protocol for improving mapping and assessing of seagrass abundance along the West Central Coast of Florida using Landsat TM and EO-1 ALI/Hyperion images

Authors
Journal
ISPRS Journal of Photogrammetry and Remote Sensing
0924-2716
Publisher
Elsevier
Volume
83
Identifiers
DOI: 10.1016/j.isprsjprs.2013.06.008
Keywords
  • Image Optimization
  • Submerged Aquatic Vegetation (Sav)
  • Fuzzy Synthetic Evaluation
  • Leaf Area Index
  • Biomass
  • Remote Sensing
Disciplines
  • Biology
  • Computer Science
  • Ecology
  • Geography
  • Mathematics

Abstract

Abstract Seagrass habitats are characteristic features of shallow waters worldwide and provide a variety of ecosystem functions. Remote sensing techniques can help collect spatial and temporal information about seagrass resources. In this study, we evaluate a protocol that utilizes image optimization algorithms followed by atmospheric and sunglint corrections to the three satellite sensors [Landsat 5 Thematic Mapper (TM), Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion (HYP)] and a fuzzy synthetic evaluation technique to map and assess seagrass abundance in Pinellas County, FL, USA. After image preprocessed with image optimization algorithms and atmospheric and sunglint correction approaches, the three sensors’ data were used to classify the submerged aquatic vegetation cover (%SAV cover) into 5 classes with a maximum likelihood classifier. Based on three biological metrics [%SAV, leaf area index (LAI), and Biomass] measured from the field, nine multiple regression models were developed for estimating the three biometrics with spectral variables derived from the three sensors’ data. Then, five membership maps were created with the three biometrics along with two environmental factors (water depth and distance-to-shoreline). Finally, seagrass abundance maps were produced by using a fuzzy synthetic evaluation technique and five membership maps. The experimental results indicate that the HYP sensor produced the best results of the 5-class classification of %SAV cover (overall accuracy=87% and Kappa=0.83 vs. 82% and 0.77 by ALI and 79% and 0.73 by TM) and better multiple regression models for estimating the three biometrics (R2=0.66, 0.62 and 0.61 for %SAV, LAI and Biomass vs. 0.62, 0.61 and 0.55 by ALI and 0.58, 0.56 and 0.52 by TM) for creating seagrass abundance maps along with two environmental factors. Combined our results demonstrate that the image optimization algorithms and the fuzzy synthetic evaluation technique were effective in mapping detailed seagrass habitats and assessing seagrass abundance with the 30-m resolution data collected by the three sensors.

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