Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system

Moses Azong Cho; Renaud Mathieu; Gregory P. Asner; Laven Naidoo; Jan van Aardt; Abel Ramoelo; Pravesh Debba; Konrad Wessels; Russell Main; Izak P.J. Smit; Barend Erasmus

doi:10.1016/j.rse.2012.07.010

Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system

Moses Azong Cho, Renaud Mathieu, Gregory P. Asner, Laven Naidoo, Jan van Aardt, Abel Ramoelo, Pravesh Debba, Konrad Wessels, Russell Main, Izak P.J. Smit, Barend Erasmus

Research output: Contribution to journal › Article › peer-review

183 Scopus citations

Abstract

Mapping savanna tree species is of broad interest for savanna ecology and rural resource inventory. We investigated the utility of (i) the Carnegie Airborne Observatory (CAO) hyperspectral data, and WorldView-2 and Quickbird multispectral spectral data and (ii) a combined spectral. +. tree height dataset (derived from the CAO LiDAR system) for mapping seven common savanna tree species or genera in the Sabi Sands Reserve and communal lands adjacent to Kruger National Park, South Africa. We convolved the 72 spectral bands of the CAO imagery to eight and four multispectral channels available in the WorldView-2 and Quickbird satellite sensors, respectively. A combination of the simulated WorldView-2 data and LiDAR tree height imagery was also assessed for species classification. First, the simulated WorldView-2 imagery provided a higher classification accuracy (77% ± 3.1 (mean ± standard deviation)) when compared to the simulated Quickbird (65% ± 1.9) and CAO (65% ± 1.2) data. Secondly, the combined spectral. +. height dataset provided a slightly higher overall classification accuracy (79% ± 1.8) when compared to the WorldView-2 spectral only dataset. The difference was however, statistically significant (p<0.001; one-way analysis of variance for 30 bootstrapped replicates (n=100) of the independent validation dataset). Higher classification accuracies were observed for trees with large crowns such as S. birrea, S. africana and A. nigrescens as compared to trees with small crowns. Species composition and diversity maps of trees with large crowns were consistent with established knowledge in the area. For example, the results showed higher tree diversity (number of different species per ha) in the Sabi Sands game reserve than in the communal areas. This study highlights the feasibility of remote sensing of tree species at the landscape scale in African savannas and the potential applicability of WorldView-2 sensor in mapping savanna tree species with a large crown.

Original language	English (US)
Pages (from-to)	214-226
Number of pages	13
Journal	Remote Sensing of Environment
Volume	125
DOIs	https://doi.org/10.1016/j.rse.2012.07.010
State	Published - Oct 2012
Externally published	Yes

Keywords

Carnegie Airborne Observatory
Hyperspectral remote sensing
Land use
LiDAR WorldView-2
Savanna tree species

ASJC Scopus subject areas

Soil Science
Geology
Computers in Earth Sciences

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10.1016/j.rse.2012.07.010

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@article{834b92e1508a410db74c0bbf9fa98fa2,

title = "Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system",

abstract = "Mapping savanna tree species is of broad interest for savanna ecology and rural resource inventory. We investigated the utility of (i) the Carnegie Airborne Observatory (CAO) hyperspectral data, and WorldView-2 and Quickbird multispectral spectral data and (ii) a combined spectral. +. tree height dataset (derived from the CAO LiDAR system) for mapping seven common savanna tree species or genera in the Sabi Sands Reserve and communal lands adjacent to Kruger National Park, South Africa. We convolved the 72 spectral bands of the CAO imagery to eight and four multispectral channels available in the WorldView-2 and Quickbird satellite sensors, respectively. A combination of the simulated WorldView-2 data and LiDAR tree height imagery was also assessed for species classification. First, the simulated WorldView-2 imagery provided a higher classification accuracy (77% ± 3.1 (mean ± standard deviation)) when compared to the simulated Quickbird (65% ± 1.9) and CAO (65% ± 1.2) data. Secondly, the combined spectral. +. height dataset provided a slightly higher overall classification accuracy (79% ± 1.8) when compared to the WorldView-2 spectral only dataset. The difference was however, statistically significant (p<0.001; one-way analysis of variance for 30 bootstrapped replicates (n=100) of the independent validation dataset). Higher classification accuracies were observed for trees with large crowns such as S. birrea, S. africana and A. nigrescens as compared to trees with small crowns. Species composition and diversity maps of trees with large crowns were consistent with established knowledge in the area. For example, the results showed higher tree diversity (number of different species per ha) in the Sabi Sands game reserve than in the communal areas. This study highlights the feasibility of remote sensing of tree species at the landscape scale in African savannas and the potential applicability of WorldView-2 sensor in mapping savanna tree species with a large crown.",

keywords = "Carnegie Airborne Observatory, Hyperspectral remote sensing, Land use, LiDAR WorldView-2, Savanna tree species",

author = "Cho, {Moses Azong} and Renaud Mathieu and Asner, {Gregory P.} and Laven Naidoo and {van Aardt}, Jan and Abel Ramoelo and Pravesh Debba and Konrad Wessels and Russell Main and Smit, {Izak P.J.} and Barend Erasmus",

note = "Funding Information: We thank the Council for Scientific and Industrial Research (CSIR), South Africa for proving the funding for the study. Hyperspectral and LiDAR imagery were supplied by the Carnegie Airborne Observatory, which is funded by the Andrew Mellon Foundation . The CAO system is further supported by the W.M. Keck Foundation , the Gordon and Betty Moore Foundation and William Hearst III . Data were pre-processed by T. Kennedy-Bowdoin, D. Knapp, J. Jacobson and R. Emerson at the Carnegie Institution for Science. Lastly, we appreciate the very useful inputs of the anonymous reviewers.",

year = "2012",

month = oct,

doi = "10.1016/j.rse.2012.07.010",

language = "English (US)",

volume = "125",

pages = "214--226",

journal = "Remote Sensing of Environment",

issn = "0034-4257",

publisher = "Elsevier Inc.",

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TY - JOUR

T1 - Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system

AU - Cho, Moses Azong

AU - Mathieu, Renaud

AU - Asner, Gregory P.

AU - Naidoo, Laven

AU - van Aardt, Jan

AU - Ramoelo, Abel

AU - Debba, Pravesh

AU - Wessels, Konrad

AU - Main, Russell

AU - Smit, Izak P.J.

AU - Erasmus, Barend

N1 - Funding Information: We thank the Council for Scientific and Industrial Research (CSIR), South Africa for proving the funding for the study. Hyperspectral and LiDAR imagery were supplied by the Carnegie Airborne Observatory, which is funded by the Andrew Mellon Foundation . The CAO system is further supported by the W.M. Keck Foundation , the Gordon and Betty Moore Foundation and William Hearst III . Data were pre-processed by T. Kennedy-Bowdoin, D. Knapp, J. Jacobson and R. Emerson at the Carnegie Institution for Science. Lastly, we appreciate the very useful inputs of the anonymous reviewers.

PY - 2012/10

Y1 - 2012/10

N2 - Mapping savanna tree species is of broad interest for savanna ecology and rural resource inventory. We investigated the utility of (i) the Carnegie Airborne Observatory (CAO) hyperspectral data, and WorldView-2 and Quickbird multispectral spectral data and (ii) a combined spectral. +. tree height dataset (derived from the CAO LiDAR system) for mapping seven common savanna tree species or genera in the Sabi Sands Reserve and communal lands adjacent to Kruger National Park, South Africa. We convolved the 72 spectral bands of the CAO imagery to eight and four multispectral channels available in the WorldView-2 and Quickbird satellite sensors, respectively. A combination of the simulated WorldView-2 data and LiDAR tree height imagery was also assessed for species classification. First, the simulated WorldView-2 imagery provided a higher classification accuracy (77% ± 3.1 (mean ± standard deviation)) when compared to the simulated Quickbird (65% ± 1.9) and CAO (65% ± 1.2) data. Secondly, the combined spectral. +. height dataset provided a slightly higher overall classification accuracy (79% ± 1.8) when compared to the WorldView-2 spectral only dataset. The difference was however, statistically significant (p<0.001; one-way analysis of variance for 30 bootstrapped replicates (n=100) of the independent validation dataset). Higher classification accuracies were observed for trees with large crowns such as S. birrea, S. africana and A. nigrescens as compared to trees with small crowns. Species composition and diversity maps of trees with large crowns were consistent with established knowledge in the area. For example, the results showed higher tree diversity (number of different species per ha) in the Sabi Sands game reserve than in the communal areas. This study highlights the feasibility of remote sensing of tree species at the landscape scale in African savannas and the potential applicability of WorldView-2 sensor in mapping savanna tree species with a large crown.

AB - Mapping savanna tree species is of broad interest for savanna ecology and rural resource inventory. We investigated the utility of (i) the Carnegie Airborne Observatory (CAO) hyperspectral data, and WorldView-2 and Quickbird multispectral spectral data and (ii) a combined spectral. +. tree height dataset (derived from the CAO LiDAR system) for mapping seven common savanna tree species or genera in the Sabi Sands Reserve and communal lands adjacent to Kruger National Park, South Africa. We convolved the 72 spectral bands of the CAO imagery to eight and four multispectral channels available in the WorldView-2 and Quickbird satellite sensors, respectively. A combination of the simulated WorldView-2 data and LiDAR tree height imagery was also assessed for species classification. First, the simulated WorldView-2 imagery provided a higher classification accuracy (77% ± 3.1 (mean ± standard deviation)) when compared to the simulated Quickbird (65% ± 1.9) and CAO (65% ± 1.2) data. Secondly, the combined spectral. +. height dataset provided a slightly higher overall classification accuracy (79% ± 1.8) when compared to the WorldView-2 spectral only dataset. The difference was however, statistically significant (p<0.001; one-way analysis of variance for 30 bootstrapped replicates (n=100) of the independent validation dataset). Higher classification accuracies were observed for trees with large crowns such as S. birrea, S. africana and A. nigrescens as compared to trees with small crowns. Species composition and diversity maps of trees with large crowns were consistent with established knowledge in the area. For example, the results showed higher tree diversity (number of different species per ha) in the Sabi Sands game reserve than in the communal areas. This study highlights the feasibility of remote sensing of tree species at the landscape scale in African savannas and the potential applicability of WorldView-2 sensor in mapping savanna tree species with a large crown.

KW - Carnegie Airborne Observatory

KW - Hyperspectral remote sensing

KW - Land use

KW - LiDAR WorldView-2

KW - Savanna tree species

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SN - 0034-4257

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SP - 214

EP - 226

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

ER -

Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system

Abstract

Keywords

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