FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2101478587> ?p ?o ?g. }

• W2101478587 endingPage "1241" @default.
• W2101478587 startingPage "1230" @default.
• W2101478587 abstract "article i nfo During the last three decades, the large spatial coverage of remote sensing data has been used in coral reef research to map dominant substrate types, geomorphologic zones, and bathymetry. During the same period, field studies have documented statistical relationships between variables quantifying aspects of the reef habitat and its fish community. Although the results of these studies are ambiguous, some habitat variables have frequently been found to correlate with one or more aspects of the fish community. Several of these habitat variables, including depth, the structural complexity of the substrate, and live coral cover, are possible to estimate with remote sensing data. In this study, we combine a set of statistical and machine- learning models with habitat variables derived from IKONOS data to produce spatially explicit predictions of the species richness, biomass, and diversity of the fish community around two reefs in Zanzibar. In the process, we assess the ability of IKONOS imagery to estimate live coral cover, structural complexity and habitat diversity, and we explore the importance of habitat variables, at a range of spatial scales, in the predictive models using a permutation-based technique. Our findings indicate that structural complexity at a fine spatial scale (∼5 to 10 m) is the most important habitat variable in predictive models of fish species richness and diversity, whereas other variables such as depth, habitat diversity, and structural complexity at coarser spatial scales contribute to predictions of biomass. In addition, our results demonstrate that complex model types such as tree-based ensemble techniques provide superior predictive performance compared to the more frequently used linear models, achieving a reduction of the cross-validated root-mean-squared prediction error of 3-11%. Although aerial photographs and airborne lidar instruments have recently been used to produce spatially explicit predictions of reef fish community variables, our study illustrates the possibility of doing so with satellite data. The ability to use satellite data may bring the cost of creating such maps within the reach of both spatial ecology researchers and the wide range of organizations involved in marine spatial planning." @default.
• W2101478587 created "2016-06-24" @default.
• W2101478587 creator A5004242756 @default.
• W2101478587 creator A5019118021 @default.
• W2101478587 creator A5034583583 @default.
• W2101478587 date "2010-06-01" @default.
• W2101478587 modified "2023-10-05" @default.
• W2101478587 title "Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques" @default.
• W2101478587 cites W1608489469 @default.
• W2101478587 cites W1875061881 @default.
• W2101478587 cites W1970227588 @default.
• W2101478587 cites W1971689326 @default.
• W2101478587 cites W1972155321 @default.
• W2101478587 cites W1981722323 @default.
• W2101478587 cites W1997647946 @default.
• W2101478587 cites W2001408032 @default.
• W2101478587 cites W2006391342 @default.
• W2101478587 cites W2007045546 @default.
• W2101478587 cites W2011435702 @default.
• W2101478587 cites W2015985888 @default.
• W2101478587 cites W2017947281 @default.
• W2101478587 cites W2024046085 @default.
• W2101478587 cites W2026257972 @default.
• W2101478587 cites W2027191688 @default.
• W2101478587 cites W2032637162 @default.
• W2101478587 cites W2039830881 @default.
• W2101478587 cites W2042578013 @default.
• W2101478587 cites W2044484650 @default.
• W2101478587 cites W2057455419 @default.
• W2101478587 cites W2065757246 @default.
• W2101478587 cites W2095803840 @default.
• W2101478587 cites W2097776394 @default.
• W2101478587 cites W2099107563 @default.
• W2101478587 cites W2099628503 @default.
• W2101478587 cites W2102986689 @default.
• W2101478587 cites W2105012295 @default.
• W2101478587 cites W2106091379 @default.
• W2101478587 cites W2116653338 @default.
• W2101478587 cites W2119475325 @default.
• W2101478587 cites W2120269059 @default.
• W2101478587 cites W2132675942 @default.
• W2101478587 cites W2135695572 @default.
• W2101478587 cites W2136224223 @default.
• W2101478587 cites W2143425926 @default.
• W2101478587 cites W2144749488 @default.
• W2101478587 cites W2147817115 @default.
• W2101478587 cites W2151295828 @default.
• W2101478587 cites W2152583725 @default.
• W2101478587 cites W2152686492 @default.
• W2101478587 cites W2160116837 @default.
• W2101478587 cites W2167087245 @default.
• W2101478587 cites W2168707907 @default.
• W2101478587 cites W2169439425 @default.
• W2101478587 cites W2180935476 @default.
• W2101478587 cites W2911964244 @default.
• W2101478587 cites W4212883601 @default.
• W2101478587 cites W4241607140 @default.
• W2101478587 cites W4244469798 @default.
• W2101478587 doi "https://doi.org/10.1016/j.rse.2010.01.007" @default.
• W2101478587 hasPublicationYear "2010" @default.
• W2101478587 type Work @default.
• W2101478587 sameAs 2101478587 @default.
• W2101478587 citedByCount "97" @default.
• W2101478587 countsByYear W21014785872012 @default.
• W2101478587 countsByYear W21014785872013 @default.
• W2101478587 countsByYear W21014785872014 @default.
• W2101478587 countsByYear W21014785872015 @default.
• W2101478587 countsByYear W21014785872016 @default.
• W2101478587 countsByYear W21014785872017 @default.
• W2101478587 countsByYear W21014785872018 @default.
• W2101478587 countsByYear W21014785872019 @default.
• W2101478587 countsByYear W21014785872020 @default.
• W2101478587 countsByYear W21014785872021 @default.
• W2101478587 countsByYear W21014785872022 @default.
• W2101478587 countsByYear W21014785872023 @default.
• W2101478587 crossrefType "journal-article" @default.
• W2101478587 hasAuthorship W2101478587A5004242756 @default.
• W2101478587 hasAuthorship W2101478587A5019118021 @default.
• W2101478587 hasAuthorship W2101478587A5034583583 @default.
• W2101478587 hasConcept C111368507 @default.
• W2101478587 hasConcept C115540264 @default.
• W2101478587 hasConcept C127313418 @default.
• W2101478587 hasConcept C144024400 @default.
• W2101478587 hasConcept C18903297 @default.
• W2101478587 hasConcept C19165224 @default.
• W2101478587 hasConcept C205649164 @default.
• W2101478587 hasConcept C2781316041 @default.
• W2101478587 hasConcept C2909208804 @default.
• W2101478587 hasConcept C39432304 @default.
• W2101478587 hasConcept C505870484 @default.
• W2101478587 hasConcept C53565203 @default.
• W2101478587 hasConcept C62649853 @default.
• W2101478587 hasConcept C77044568 @default.
• W2101478587 hasConcept C86803240 @default.
• W2101478587 hasConceptScore W2101478587C111368507 @default.
• W2101478587 hasConceptScore W2101478587C115540264 @default.
• W2101478587 hasConceptScore W2101478587C127313418 @default.
• W2101478587 hasConceptScore W2101478587C144024400 @default.

• next