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Assessing the poten...
Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species
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- Karlson, Martin (författare)
- Linköpings universitet,Tema Miljöförändring,Filosofiska fakulteten
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- Ostwald, Madelene, 1966 (författare)
- Linköpings universitet,Gothenburg University,Göteborgs universitet,Centrum för globalisering och utveckling (GCGD),Göteborgs miljövetenskapliga centrum, GMV,Gothenburg Centre for Globalization and Development (GCGD),Centre for Environment and Sustainability,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology,Tema Miljöförändring,Filosofiska fakulteten,Centrum för klimatpolitisk forskning,University of Gothenburg, Sweden; Chalmers, Sweden
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- Reese, Heather, 1964 (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management,Swedish University of Agriculture Science, Sweden
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- Bazie, Hugues Roméo (författare)
- University of Ouagadougou, Burkina Faso
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- Tankoano, Baolidioa (författare)
- Polytech University of Bobo Dioulasso, Burkina Faso
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(creator_code:org_t)
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- Elsevier BV, 2016
- 2016
- Engelska.
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 0303-2434 .- 1872-826X. ; 50:August, s. 80-88
- Relaterad länk:
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http://dx.doi.org/10...
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://research.cha...
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https://res.slu.se/i...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- High resolution satellite systems enable efficient and detailed mapping of tree cover, with high potential to support both natural resource monitoring and ecological research. This study investigates the capability of multi-seasonal WorldView-2 imagery to map five dominant tree species at the individual tree crown level in a parkland landscape in central Burkina Faso. The Random Forest algorithm is used for object based tree species classification and for assessing the relative importance of WorldView-2 predictors. The classification accuracies from using wet season, dry season and multi-seasonal datasets are compared to gain insights about the optimal timing for image acquisition. The multi-seasonal dataset produced the most accurate classifications, with an overall accuracy (OA) of 83.4%. For classifications based on single date imagery, the dry season (OA = 78.4%) proved to be more suitable than the wet season (OA = 68.1%). The predictors that contributed most to the classification success were based on the red edge band and visible wavelengths, in particular green and yellow. It was therefore concluded that WorldView- 2, with its unique band configuration, represents a suitable data source for tree species mapping in West African parklands. These results are particularly promising when considering the recently launched WorldView-3, which provides data both at higher spatial and spectral resolution, including shortwave infrared bands.
Ämnesord
- LANTBRUKSVETENSKAPER -- Annan lantbruksvetenskap -- Miljö- och naturvårdsvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Other Agricultural Sciences -- Environmental Sciences related to Agriculture and Land-use (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Skogsvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Forest Science (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Remote Sensing (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
Nyckelord
- Tree species mapping
- WorldView-2
- Agroforestry
- Parkland
- Sudano-Sahel
- Tree species mapping
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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