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High-resolution global maps of tidal flat ecosystems from 1984 to 2019.
Murray, Nicholas J; Phinn, Stuart P; Fuller, Richard A; DeWitt, Michael; Ferrari, Renata; Johnston, Renee; Clinton, Nicholas; Lyons, Mitchell B.
Afiliación
  • Murray NJ; College of Science and Engineering, James Cook University, Townsville, Queensland, Australia. nicholas.murray@jcu.edu.au.
  • Phinn SP; Remote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Queensland, 4072, Australia.
  • Fuller RA; School of Biological Sciences, The University of Queensland, St Lucia, QLD, Australia.
  • DeWitt M; Google Inc., 1600 Amphitheater Parkway, Mountain View, CA, 94043, USA.
  • Ferrari R; Australian Institute of Marine Science, Townsville, 4810, Australia.
  • Johnston R; Google Inc., 1600 Amphitheater Parkway, Mountain View, CA, 94043, USA.
  • Clinton N; Google Inc., 1600 Amphitheater Parkway, Mountain View, CA, 94043, USA.
  • Lyons MB; Centre for Ecosystem Science, School of Biological, Earth and Environmental Science, University of New South Wales, Sydney, Australia.
Sci Data ; 9(1): 542, 2022 09 06.
Article en En | MEDLINE | ID: mdl-36068234
ABSTRACT
Assessments of the status of tidal flats, one of the most extensive coastal ecosystems, have been hampered by a lack of data on their global distribution and change. Here we present globally consistent, spatially-explicit data of the occurrence of tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation. More than 1.3 million Landsat images were processed to 54 composite metrics for twelve 3-year periods, spanning four decades (1984-1986 to 2017-2019). The composite metrics were used as predictor variables in a machine-learning classification trained with more than 10,000 globally distributed training samples. We assessed accuracy of the classification with 1,348 stratified random samples across the mapped area, which indicated overall map accuracies of 82.2% (80.0-84.3%, 95% confidence interval) and 86.1% (84.2-86.8%, 95% CI) for version 1.1 and 1.2 of the data, respectively. We expect these maps will provide a means to measure and monitor a range of processes that are affecting coastal ecosystems, including the impacts of human population growth and sea level rise.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article País de afiliación: Australia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article País de afiliación: Australia