FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1967621805> ?p ?o ?g. }

• W1967621805 endingPage "270" @default.
• W1967621805 startingPage "258" @default.
• W1967621805 abstract "The identification of tree species is an important issue in forest management. In recent years, many studies have explored this topic using hyperspectral, multispectral, and LiDAR data. In this study we analyzed two multi-sensor set-ups: 1) airborne high spatial resolution hyperspectral images combined with LiDAR data; and 2) high spatial resolution satellite multispectral images combined with LiDAR data. Two LiDAR acquisitions were considered: low point density (approx. 0.48 points per m2) and high point density (approx. 8.6 points per m2). The aims of this work were: i) to understand what level of classification accuracy can be achieved using a high spectral and spatial resolution multi-sensor data set-up (very high spatial and spectral resolution airborne hyperspectral images integrated with high point density LiDAR data), over a mountain area characterized by many species, both broadleaf and coniferous; ii) to understand the implications of a downgrading of the data characteristics (in terms of spectral resolution of spectral data and point density of LiDAR data), on species separability, with respect to the previous set-up; and iii) to understand the differences between high- and low-point density LiDAR acquisitions on tree species classification. The study region was a mountain area in the Southern Alps characterized by many tree species (7 species and a “non-forest” class), either coniferous or broadleaf. For each set-up a specific processing chain was adopted, from the pre-processing of the raw data to the classification (two classifiers were used: support vector machine and random forest). Different class definitions were tested, including general macro-classes, forest types, and finally single tree species. Experimental results showed that the set-up based on hyperspectral data was effective with general macro-classes, forest types, and single species, reaching high kappa accuracies (93.2%, 82.1% and 76.5%, respectively). The use of multispectral data produced a reduction in the classification accuracy, which was sharp for single tree species, and still high for forest types. Considering general macro-classes, the multispectral set-up was still very accurate (85.8%). Regarding LiDAR data, the experimental analysis showed that high density LiDAR data provided more information for tree species classification with respect to low density data, when combined with either hyperspectral or multispectral data." @default.
• W1967621805 created "2016-06-24" @default.
• W1967621805 creator A5006095323 @default.
• W1967621805 creator A5055675691 @default.
• W1967621805 creator A5083031500 @default.
• W1967621805 date "2012-08-01" @default.
• W1967621805 modified "2023-10-17" @default.
• W1967621805 title "Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data" @default.
• W1967621805 cites W1984232434 @default.
• W1967621805 cites W1997120165 @default.
• W1967621805 cites W2000345291 @default.
• W1967621805 cites W2001094237 @default.
• W1967621805 cites W2005156666 @default.
• W1967621805 cites W2009269885 @default.
• W1967621805 cites W2014915963 @default.
• W1967621805 cites W2019965926 @default.
• W1967621805 cites W2021793377 @default.
• W1967621805 cites W2033510127 @default.
• W1967621805 cites W2042255844 @default.
• W1967621805 cites W2044283259 @default.
• W1967621805 cites W2045201279 @default.
• W1967621805 cites W2045716451 @default.
• W1967621805 cites W2051837164 @default.
• W1967621805 cites W2053154970 @default.
• W1967621805 cites W2054133252 @default.
• W1967621805 cites W2055314812 @default.
• W1967621805 cites W2065407071 @default.
• W1967621805 cites W2068624850 @default.
• W1967621805 cites W2069880880 @default.
• W1967621805 cites W2072262083 @default.
• W1967621805 cites W2073266896 @default.
• W1967621805 cites W2084291846 @default.
• W1967621805 cites W2097337758 @default.
• W1967621805 cites W2098057602 @default.
• W1967621805 cites W2101244453 @default.
• W1967621805 cites W2109191549 @default.
• W1967621805 cites W2118342735 @default.
• W1967621805 cites W2134312403 @default.
• W1967621805 cites W2136251662 @default.
• W1967621805 cites W2137608957 @default.
• W1967621805 cites W2143035263 @default.
• W1967621805 cites W2147153167 @default.
• W1967621805 cites W2150986124 @default.
• W1967621805 cites W2151426712 @default.
• W1967621805 cites W2161943337 @default.
• W1967621805 cites W2165796970 @default.
• W1967621805 cites W2166307050 @default.
• W1967621805 cites W2168365075 @default.
• W1967621805 cites W2315582853 @default.
• W1967621805 cites W2911964244 @default.
• W1967621805 cites W4240374930 @default.
• W1967621805 doi "https://doi.org/10.1016/j.rse.2012.03.013" @default.
• W1967621805 hasPublicationYear "2012" @default.
• W1967621805 type Work @default.
• W1967621805 sameAs 1967621805 @default.
• W1967621805 citedByCount "357" @default.
• W1967621805 countsByYear W19676218052012 @default.
• W1967621805 countsByYear W19676218052013 @default.
• W1967621805 countsByYear W19676218052014 @default.
• W1967621805 countsByYear W19676218052015 @default.
• W1967621805 countsByYear W19676218052016 @default.
• W1967621805 countsByYear W19676218052017 @default.
• W1967621805 countsByYear W19676218052018 @default.
• W1967621805 countsByYear W19676218052019 @default.
• W1967621805 countsByYear W19676218052020 @default.
• W1967621805 countsByYear W19676218052021 @default.
• W1967621805 countsByYear W19676218052022 @default.
• W1967621805 countsByYear W19676218052023 @default.
• W1967621805 crossrefType "journal-article" @default.
• W1967621805 hasAuthorship W1967621805A5006095323 @default.
• W1967621805 hasAuthorship W1967621805A5055675691 @default.
• W1967621805 hasAuthorship W1967621805A5083031500 @default.
• W1967621805 hasConcept C104541649 @default.
• W1967621805 hasConcept C113174947 @default.
• W1967621805 hasConcept C134306372 @default.
• W1967621805 hasConcept C154945302 @default.
• W1967621805 hasConcept C159078339 @default.
• W1967621805 hasConcept C173163844 @default.
• W1967621805 hasConcept C176641082 @default.
• W1967621805 hasConcept C205372480 @default.
• W1967621805 hasConcept C205649164 @default.
• W1967621805 hasConcept C33923547 @default.
• W1967621805 hasConcept C33954974 @default.
• W1967621805 hasConcept C41008148 @default.
• W1967621805 hasConcept C51399673 @default.
• W1967621805 hasConcept C58489278 @default.
• W1967621805 hasConcept C62649853 @default.
• W1967621805 hasConceptScore W1967621805C104541649 @default.
• W1967621805 hasConceptScore W1967621805C113174947 @default.
• W1967621805 hasConceptScore W1967621805C134306372 @default.
• W1967621805 hasConceptScore W1967621805C154945302 @default.
• W1967621805 hasConceptScore W1967621805C159078339 @default.
• W1967621805 hasConceptScore W1967621805C173163844 @default.
• W1967621805 hasConceptScore W1967621805C176641082 @default.
• W1967621805 hasConceptScore W1967621805C205372480 @default.
• W1967621805 hasConceptScore W1967621805C205649164 @default.
• W1967621805 hasConceptScore W1967621805C33923547 @default.
• W1967621805 hasConceptScore W1967621805C33954974 @default.

• next