FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384282009> ?p ?o ?g. }

• W4384282009 endingPage "121280" @default.
• W4384282009 startingPage "121280" @default.
• W4384282009 abstract "Compared to undisturbed areas, timber harvest activities, including road construction, skid trails, and stream crossings, exhibit the potential for causing erosion and sedimentation if forestry best management practices (BMPs) are not implemented. Forestry BMP monitoring serves as a tool to evaluate BMP implementation status. Conventional on-the-ground BMP monitoring has contributed substantially to assessing BMP implementation. However, conventional on-the-ground surveys can be time consuming. Unmanned aerial vehicles (UAVs) have been rapidly emerging as a new tool for local-scale monitoring. In this study, we evaluated the feasibility and potential of UAVs for monitoring forestry BMPs in the coastal plain of southeastern United States. We compared the performance of UAV-live feed surveys and UAV-created map surveys with conventional on-the-ground surveys across major BMP categories on 30 study sites using BMP guidelines and implementation survey questions from Alabama, Georgia, and Florida for the sites specific to that state (10 sites in each state). The UAV-live feed surveys involved real-time assessments conducted using the live feed function, enabling on-the-spot evaluations. Whereas the UAV-created map surveys entailed autonomous UAV flight to collect imagery specifically for generating maps and facilitating subsequent assessments. We found that using a UAV for monitoring BMPs efficiently provided an overview of a timber harvest area from above. UAV-live feed surveys were as effective as conventional on-the-ground surveys across all major BMP categories. Specifically, the Cramer's V correlation between the responses from UAV-live feed and conventional on-the-ground surveys for all BMP categories, was 0.95 (p < 0.0001) for detecting implemented BMPs and 0.86 (p < 0.0001) for detecting when BMPs were not implemented but needed to be. UAV-created map survey results vs. conventional on-the-ground surveys were not as effective (V of 0.79 for detecting implemented BMPs and 0.46 for detecting BMPs not implemented but needed). Waste and stream crossing BMP implementation were the most difficult to verify using the map surveys, which was not an issue with the live feed surveys due to the capability to evaluate those BMPs at different camera angles. While UAV-created map surveys may be less effective, they provide an observation record that includes a map of the site. This project also developed a standardized framework for using UAVs to monitor forestry BMPs, including instructions and procedures for conducting UAV-live feed and map surveys. In conclusion, a UAV using the live feed function in the field is a feasible option to monitor forestry BMPs." @default.
• W4384282009 created "2023-07-15" @default.
• W4384282009 creator A5000534663 @default.
• W4384282009 creator A5011010505 @default.
• W4384282009 creator A5053013035 @default.
• W4384282009 creator A5076819963 @default.
• W4384282009 creator A5085367509 @default.
• W4384282009 date "2023-10-01" @default.
• W4384282009 modified "2023-10-16" @default.
• W4384282009 title "Evaluating the feasibility and potential of unmanned aerial vehicles to monitor implementation of forestry best management practices in the coastal plain of the southeastern United States" @default.
• W4384282009 cites W1824707177 @default.
• W4384282009 cites W1999462172 @default.
• W4384282009 cites W2009095018 @default.
• W4384282009 cites W2023703534 @default.
• W4384282009 cites W2026869267 @default.
• W4384282009 cites W2027858785 @default.
• W4384282009 cites W2033027494 @default.
• W4384282009 cites W2060983504 @default.
• W4384282009 cites W2115069360 @default.
• W4384282009 cites W2151951367 @default.
• W4384282009 cites W2177134325 @default.
• W4384282009 cites W2184923025 @default.
• W4384282009 cites W2189965043 @default.
• W4384282009 cites W2473712856 @default.
• W4384282009 cites W2556502614 @default.
• W4384282009 cites W258107459 @default.
• W4384282009 cites W2734702010 @default.
• W4384282009 cites W2736508163 @default.
• W4384282009 cites W2752278344 @default.
• W4384282009 cites W2752740954 @default.
• W4384282009 cites W2774842738 @default.
• W4384282009 cites W2774869152 @default.
• W4384282009 cites W2792846923 @default.
• W4384282009 cites W2804467493 @default.
• W4384282009 cites W2859720860 @default.
• W4384282009 cites W2889749348 @default.
• W4384282009 cites W2891576094 @default.
• W4384282009 cites W2898121939 @default.
• W4384282009 cites W2911245182 @default.
• W4384282009 cites W2936137334 @default.
• W4384282009 cites W2946490275 @default.
• W4384282009 cites W2947446681 @default.
• W4384282009 cites W2969277557 @default.
• W4384282009 cites W2986323898 @default.
• W4384282009 cites W3015075142 @default.
• W4384282009 cites W3034594441 @default.
• W4384282009 cites W3045994033 @default.
• W4384282009 cites W3088311973 @default.
• W4384282009 cites W3095887927 @default.
• W4384282009 cites W3109287508 @default.
• W4384282009 cites W3166214904 @default.
• W4384282009 cites W3171042454 @default.
• W4384282009 cites W3184401950 @default.
• W4384282009 cites W3199677396 @default.
• W4384282009 cites W3206795908 @default.
• W4384282009 cites W4205618938 @default.
• W4384282009 cites W4206676069 @default.
• W4384282009 cites W4210973264 @default.
• W4384282009 cites W4213189272 @default.
• W4384282009 cites W4313120906 @default.
• W4384282009 cites W4313141308 @default.
• W4384282009 cites W2939755237 @default.
• W4384282009 cites W3040286127 @default.
• W4384282009 doi "https://doi.org/10.1016/j.foreco.2023.121280" @default.
• W4384282009 hasPublicationYear "2023" @default.
• W4384282009 type Work @default.
• W4384282009 citedByCount "0" @default.
• W4384282009 crossrefType "journal-article" @default.
• W4384282009 hasAuthorship W4384282009A5000534663 @default.
• W4384282009 hasAuthorship W4384282009A5011010505 @default.
• W4384282009 hasAuthorship W4384282009A5053013035 @default.
• W4384282009 hasAuthorship W4384282009A5076819963 @default.
• W4384282009 hasAuthorship W4384282009A5085367509 @default.
• W4384282009 hasConcept C100834320 @default.
• W4384282009 hasConcept C100970517 @default.
• W4384282009 hasConcept C107826830 @default.
• W4384282009 hasConcept C125620115 @default.
• W4384282009 hasConcept C133091792 @default.
• W4384282009 hasConcept C176262533 @default.
• W4384282009 hasConcept C205649164 @default.
• W4384282009 hasConcept C39432304 @default.
• W4384282009 hasConcept C62649853 @default.
• W4384282009 hasConcept C97137747 @default.
• W4384282009 hasConceptScore W4384282009C100834320 @default.
• W4384282009 hasConceptScore W4384282009C100970517 @default.
• W4384282009 hasConceptScore W4384282009C107826830 @default.
• W4384282009 hasConceptScore W4384282009C125620115 @default.
• W4384282009 hasConceptScore W4384282009C133091792 @default.
• W4384282009 hasConceptScore W4384282009C176262533 @default.
• W4384282009 hasConceptScore W4384282009C205649164 @default.
• W4384282009 hasConceptScore W4384282009C39432304 @default.
• W4384282009 hasConceptScore W4384282009C62649853 @default.
• W4384282009 hasConceptScore W4384282009C97137747 @default.
• W4384282009 hasFunder F4320309904 @default.
• W4384282009 hasLocation W43842820091 @default.
• W4384282009 hasOpenAccess W4384282009 @default.
• W4384282009 hasPrimaryLocation W43842820091 @default.
• W4384282009 hasRelatedWork W1973833646 @default.

• next