FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2021302788> ?p ?o ?g. }

• W2021302788 endingPage "294" @default.
• W2021302788 startingPage "283" @default.
• W2021302788 abstract "Erosion, in particular gully erosion, is a widespread problem. Its mapping is crucial for erosion monitoring and remediation of degraded areas. In addition, mapping of areas with high potential for future gully erosion can be used to assist prevention strategies. Good relations with topographic variables collected from the field are appropriate for determining areas susceptible to gullying. Image analysis of high resolution remotely sensed imagery (HRI) in combination with field verification has proven to be a good approach, although dependent on expensive imagery. Automatic and semi-automatic methods, such as object-oriented analysis (OOA), are rapid and reproducible. However, HRI data are not always available. We therefore attempted to identify gully systems using statistical modeling of image features from medium resolution imagery, here ASTER. These data were used for determining areas within gully system boundaries (GSB) using a semi-automatic method based on OOA. We assess if the selection of useful object features can be done in an objective and transferable way, using Random Forests (RF) for prediction of gully systems at regional scale, here in the Sehoul region, near Rabat, Morocco. Moderate success was achieved using a semi-automatic object-based RF model (out-of-bag error of 18.8%). Besides compensating for the imbalance between gully and non-gully classes, the procedure followed in this study enabled us to balance the classification error rates. The user's and producer's accuracy of the data with a balanced set of class showed an improved accuracy of the spatial estimates of gully systems, when compared to the data with imbalanced class. The model over-predicted the area within the GSB (13–27%), but its overall performance demonstrated that medium resolution satellite images contain sufficient information to identify gully systems, so that large areas can be mapped with relatively little effort and acceptable accuracy." @default.
• W2021302788 created "2016-06-24" @default.
• W2021302788 creator A5014462963 @default.
• W2021302788 creator A5024927915 @default.
• W2021302788 creator A5051300914 @default.
• W2021302788 creator A5082935691 @default.
• W2021302788 date "2014-07-01" @default.
• W2021302788 modified "2023-09-28" @default.
• W2021302788 title "Object-based gully system prediction from medium resolution imagery using Random Forests" @default.
• W2021302788 cites W1520812622 @default.
• W2021302788 cites W1565726714 @default.
• W2021302788 cites W1964262728 @default.
• W2021302788 cites W1971042695 @default.
• W2021302788 cites W1978110877 @default.
• W2021302788 cites W1983513512 @default.
• W2021302788 cites W1987185252 @default.
• W2021302788 cites W2004669825 @default.
• W2021302788 cites W2012913568 @default.
• W2021302788 cites W2021474047 @default.
• W2021302788 cites W2021929253 @default.
• W2021302788 cites W2032075694 @default.
• W2021302788 cites W2037308434 @default.
• W2021302788 cites W2044465660 @default.
• W2021302788 cites W2047145503 @default.
• W2021302788 cites W2048549837 @default.
• W2021302788 cites W2058172830 @default.
• W2021302788 cites W2061240006 @default.
• W2021302788 cites W2067594023 @default.
• W2021302788 cites W2081620141 @default.
• W2021302788 cites W2092685533 @default.
• W2021302788 cites W2109826612 @default.
• W2021302788 cites W2110985329 @default.
• W2021302788 cites W2119507795 @default.
• W2021302788 cites W2119534769 @default.
• W2021302788 cites W2122203277 @default.
• W2021302788 cites W2126652700 @default.
• W2021302788 cites W2143296882 @default.
• W2021302788 cites W2149064628 @default.
• W2021302788 cites W2155632266 @default.
• W2021302788 cites W2161548576 @default.
• W2021302788 cites W2169230132 @default.
• W2021302788 cites W2911964244 @default.
• W2021302788 doi "https://doi.org/10.1016/j.geomorph.2014.04.006" @default.
• W2021302788 hasPublicationYear "2014" @default.
• W2021302788 type Work @default.
• W2021302788 sameAs 2021302788 @default.
• W2021302788 citedByCount "45" @default.
• W2021302788 countsByYear W20213027882015 @default.
• W2021302788 countsByYear W20213027882016 @default.
• W2021302788 countsByYear W20213027882017 @default.
• W2021302788 countsByYear W20213027882018 @default.
• W2021302788 countsByYear W20213027882019 @default.
• W2021302788 countsByYear W20213027882020 @default.
• W2021302788 countsByYear W20213027882021 @default.
• W2021302788 countsByYear W20213027882022 @default.
• W2021302788 countsByYear W20213027882023 @default.
• W2021302788 crossrefType "journal-article" @default.
• W2021302788 hasAuthorship W2021302788A5014462963 @default.
• W2021302788 hasAuthorship W2021302788A5024927915 @default.
• W2021302788 hasAuthorship W2021302788A5051300914 @default.
• W2021302788 hasAuthorship W2021302788A5082935691 @default.
• W2021302788 hasConcept C114793014 @default.
• W2021302788 hasConcept C123157820 @default.
• W2021302788 hasConcept C127313418 @default.
• W2021302788 hasConcept C13772937 @default.
• W2021302788 hasConcept C154945302 @default.
• W2021302788 hasConcept C169258074 @default.
• W2021302788 hasConcept C181843262 @default.
• W2021302788 hasConcept C202444582 @default.
• W2021302788 hasConcept C205649164 @default.
• W2021302788 hasConcept C2778755073 @default.
• W2021302788 hasConcept C2992428861 @default.
• W2021302788 hasConcept C33923547 @default.
• W2021302788 hasConcept C41008148 @default.
• W2021302788 hasConcept C58640448 @default.
• W2021302788 hasConcept C62649853 @default.
• W2021302788 hasConcept C9652623 @default.
• W2021302788 hasConceptScore W2021302788C114793014 @default.
• W2021302788 hasConceptScore W2021302788C123157820 @default.
• W2021302788 hasConceptScore W2021302788C127313418 @default.
• W2021302788 hasConceptScore W2021302788C13772937 @default.
• W2021302788 hasConceptScore W2021302788C154945302 @default.
• W2021302788 hasConceptScore W2021302788C169258074 @default.
• W2021302788 hasConceptScore W2021302788C181843262 @default.
• W2021302788 hasConceptScore W2021302788C202444582 @default.
• W2021302788 hasConceptScore W2021302788C205649164 @default.
• W2021302788 hasConceptScore W2021302788C2778755073 @default.
• W2021302788 hasConceptScore W2021302788C2992428861 @default.
• W2021302788 hasConceptScore W2021302788C33923547 @default.
• W2021302788 hasConceptScore W2021302788C41008148 @default.
• W2021302788 hasConceptScore W2021302788C58640448 @default.
• W2021302788 hasConceptScore W2021302788C62649853 @default.
• W2021302788 hasConceptScore W2021302788C9652623 @default.
• W2021302788 hasLocation W20213027881 @default.
• W2021302788 hasOpenAccess W2021302788 @default.
• W2021302788 hasPrimaryLocation W20213027881 @default.
• W2021302788 hasRelatedWork W1524303412 @default.
• W2021302788 hasRelatedWork W1805865405 @default.

• next