ResearchSpace

What lies beneath: detecting sub-canopy changes in savanna woodlands using a three-dimensional classification method

Show simple item record

dc.contributor.author Fisher, JT
dc.contributor.author Witkowski, TF
dc.contributor.author Erasmus, BFN
dc.contributor.author Mograbi, PJ
dc.contributor.author Asner, GP
dc.contributor.author Van Aardt, JAN
dc.contributor.author Wessels, Konrad J
dc.contributor.author Mathieu, Renaud SA
dc.date.accessioned 2016-01-20T09:32:27Z
dc.date.available 2016-01-20T09:32:27Z
dc.date.issued 2015-07
dc.identifier.citation Fisher, JT, Witkowski, TF, Erasmus, BFN, Mograbi, PJ, Asner, GP, Van Aardt, JAN, Wessels, KJ and Mathieu, R. 2015. What lies beneath: detecting sub-canopy changes in savanna woodlands using a three-dimensional classification method. Applied Vegetation Science, Vol. 18(3), pp. 528-540 en_US
dc.identifier.issn 1402-2001
dc.identifier.uri http://onlinelibrary.wiley.com/doi/10.1111/avsc.12160/abstract
dc.identifier.uri http://hdl.handle.net/10204/8340
dc.description Copyright: 2015 Wiley. Due to copyright restrictions, the attached PDF file only contains an abstract of the full text item. For access to the full text item, please consult the publisher's website. The definitive version of the work is published in Applied Vegetation Science, Vol. 18(3), pp. 528-540 en_US
dc.description.abstract Question: Increasing population pressure, socio-economic development and associated natural resource use in savannas are resulting in large-scale land cover changes, which can be mapped using remote sensing. Is a three-dimensional (3D) woody vegetation structural classification applied to LiDAR (Light Detection and Ranging) data better than a 2D analysis to investigate change in fine-scale woody vegetation structure over 2 yrs in a protected area (PA) and a communal rangeland (CR)? Location: BushbuckridgeMunicipality and Sabi Sand Wildtuin, NE South Africa. Methods: Airborne LiDAR data were collected over 3 300 ha in April 2008 and 2010. Individual tree canopies were identified using object-based image analysis and classified into four height classes: 1–3, 3–6, 6–10 and >10 m. Four structural metrics were calculated for 0.25-ha grid cells: canopy cover, number of canopy layers present, cohesion and number of height classes present. The relationship between top-of-canopy cover and sub-canopy cover was investigated using regression. Gains, losses and persistence (GLP) of cover at each height class and the four structural metrics were calculated. GLP of clusters of each structural metric (calculated using LISA – Local Indicators of Spatial Association – statistics) were used to assess the changes in clusters of eachmetric over time. Results: Top-of-canopy cover was not a good predictor of sub-canopy cover. The number of canopy layers present and cohesion showed gains and losseswith persistence in canopy cover over time, necessitating the use of a 3D classification to detect fine-scale changes, especially in structurally heterogeneous savannas. Trees >3 min height showed recruitment and gains up to 2.2 times higher in the CR where they are likely to be protected for cultural reasons, but losses of up to 3.2-foldmore in the PA, possibly due to treefall caused by elephant and/or fire. Conclusion: Land use has affected sub-canopy structure in the adjacent sites, with the low intensity use CR showing higher structural diversity. A 3D classification approach was successful in detecting fine-scale, short-term changes between land uses, and can thus be used as amonitoring tool for savannawoody vegetation structure. en_US
dc.language.iso en en_US
dc.publisher Wiley en_US
dc.relation.ispartofseries Workflow;15446
dc.relation.ispartofseries Workflow;15451
dc.subject Change detection en_US
dc.subject Ecosystemservices en_US
dc.subject Fire en_US
dc.subject Geology en_US
dc.subject Land use en_US
dc.subject Local Indicators of Spatial Association en_US
dc.subject Monitoring en_US
dc.subject Savanna en_US
dc.title What lies beneath: detecting sub-canopy changes in savanna woodlands using a three-dimensional classification method en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record