@article{fdi:010042711,
  title = {{R}egional-scale seagrass habitat mapping in the {W}ider {C}aribbean region using {L}andsat sensors : applications to conservation and ecology},
  author = {{W}abnitz, {C}. {C}. and {A}ndr{\'e}fou{\¨e}t, {S}erge and {T}orres-{P}ulliza, {D}. and {M}uller-{K}arger, {F}. {E}. and {K}ramer, {P}. {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}eagrass meadows occupy a large proportion of the world's coastal oceans and are some of the most productive systems on {E}arth. {D}irect and indirect human-derived impacts have led to significant seagrass declines worldwide and the alteration of services linked to their biodiversity. {E}ffective conservation and the provision of sustainable recovery goals for ecologically significant species are limited by the absence of reliable information on seagrass extent. {T}his is especially true for the {W}ider {C}aribbean region ({WCR}) where many conservation initiatives are under way, but are impaired by the lack {O}f accurate baseline habitat maps. {T}o assist with such a fundamental conservation need using high-resolution remote sensing data, both environmental and methodological challenges need to be tackled. {F}irst, the diversity of environments, the heterogeneity of habitats, and the vast extent of the targeted region mean that local expertise and field data of adequate quality and resolution are seldom available. {S}econd, large-scale high-resolution mapping requires several hundred {L}andsat 5 and 7 images, which poses substantial processing problems. {T}he main goal of this study was to test the feasibility of achieving {L}andsat-based large-scale seagrass mapping with limited ground-truth data and acceptable accuracies. {W}e used the following combination of methods to map seagrass throughout the {WCR}: geomorphological segmentation, contextual editing, and supervised classifications. {A} total of 40 {L}andsat scenes (path-row) were processed. {T}hree major classes were derived ('dense seagrass', 'medium-sparse seagrass', and a generic 'other' class). {P}roducts' accuracies were assessed against (i) selected in situ data; (ii) patterns detectable with very high-resolution {IKONOS} images; and (iii) published habitat maps with documented accuracies. {D}espite variable overall classification accuracies (46-88%), following their critical evaluation, the resulting thematic maps were deemed acceptable to (i) regionally {P}rovide an adequate baseline for further large-scale conservation programs and research actions; and (ii) regionally re-assess carrying capacity estimates for green turtles. {T}hey certainly represent a drastic improvement relative to current regional databases.},
  keywords = {{S}eagrass ; {C}oral reef ; {T}halassia testudinum ; {E}tm ; {I}konos ; {M}illennium coral reef mapping project ; {C}onservation management ; {H}abitat database ; {B}iodiveisity ; {S}ea turtle ; {C}helonia mydas},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {112},
  numero = {8},
  pages = {3455--3467},
  ISSN = {0034-4257},
  year = {2008},
  DOI = {10.1016/j.rse.2008.01.020},
  URL = {https://www.documentation.ird.fr/hor/fdi:010042711},
}