TY - JOUR
T1 - A Protocol for Improving Mapping and Assessing of Seagrass Abundance Along the West Central Coast of Florida Using Landsat TM and EO-1ALI/Hyperion Images
AU - Pu, Ruiliang
AU - Bell, Susan
PY - 2013/9/1
Y1 - 2013/9/1
N2 - 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 ( R 2 = 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.
AB - 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 ( R 2 = 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.
KW - image optimization
KW - submerged aquatic vegetation (SAV)
KW - fuzzy synthetic evaluation
KW - leaf area index
KW - biomass
KW - remote sensing
UR - https://digitalcommons.usf.edu/geo_facpub/347
UR - https://doi.org/10.1016/j.isprsjprs.2013.06.008
U2 - 10.1016/j.isprsjprs.2013.06.008
DO - 10.1016/j.isprsjprs.2013.06.008
M3 - Article
VL - 83
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
ER -