dc.contributor.author |
Ramoelo, Abel
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dc.contributor.author |
Cho, Moses A
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dc.contributor.author |
Mathieu, Renaud SA
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dc.contributor.author |
Skidmore, AK
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dc.contributor.author |
Schlerf, M
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dc.contributor.author |
Heitkonig, IMA
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dc.contributor.author |
Prins, HHT
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dc.date.accessioned |
2011-05-17T09:38:56Z |
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dc.date.available |
2011-05-17T09:38:56Z |
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dc.date.issued |
2011-04 |
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dc.identifier.citation |
Ramoelo, A., Cho, M.A., Mathieu, R. et al. 2011. Integrating environmental and in situ hyperspectral remote sensing variables for grass nitrogen estimation in savannah ecosystems. The International Symposium on Remote Sensing of Environment (ISRSE 2011)-34th ISRSE, Sydney, Australia, 10-15 April 2011 |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10204/5004
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dc.description |
The International Symposium on Remote Sensing of Environment (ISRSE 2011)-34th ISRSE, Sydney, Australia, 10-15 April 2011 |
en_US |
dc.description.abstract |
Information about the distribution of grass nitrogen (N) concentration is crucial in understanding rangeland vitality and facilitates effective management of wildlife and livestock. A challenge in estimating grass N concentration using remote sensing in savannah ecosystems is that these areas are characterised by heterogeneity in edaphic, topographic and climatic factors. The objective is to test the utility of integrating environmental variables and in situ hyperspectral remote sensing variables for predicting grass N concentration along a land use gradient in the greater Kruger National Park. Data used include i) environmental variables, ii) measured grass N concentration and iii) in situ measured hyperspectral spectra. Non-linear partial least square regression was used. Results showed that several environmental variables were important for N estimation. Integrating environmental variables with in situ hyperspectral variables increased grass N estimation accuracy. The study demonstrated the importance of integrated modelling for savannah ecosystem state assessment. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Workflow request;6483 |
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dc.subject |
Hyperspectral |
en_US |
dc.subject |
Savannah ecosystems |
en_US |
dc.subject |
Foliar nitrogen concentration |
en_US |
dc.subject |
Grass nitrogen (N) |
en_US |
dc.subject |
Wildlife management |
en_US |
dc.subject |
Livestock management |
en_US |
dc.subject |
Kruger National Park |
en_US |
dc.subject |
Environmental remote sensing |
en_US |
dc.subject |
Remote sensing variables |
en_US |
dc.subject |
Rangeland vitality |
en_US |
dc.title |
Integrating environmental and in situ hyperspectral remote sensing variables for grass nitrogen estimation in savannah ecosystems |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Ramoelo, A., Cho, M. A., Mathieu, R. S., Skidmore, A., Schlerf, M., Heitkonig, I., & Prins, H. (2011). Integrating environmental and in situ hyperspectral remote sensing variables for grass nitrogen estimation in savannah ecosystems. http://hdl.handle.net/10204/5004 |
en_ZA |
dc.identifier.chicagocitation |
Ramoelo, Abel, Moses A Cho, Renaud SA Mathieu, AK Skidmore, M Schlerf, IMA Heitkonig, and HHT Prins. "Integrating environmental and in situ hyperspectral remote sensing variables for grass nitrogen estimation in savannah ecosystems." (2011): http://hdl.handle.net/10204/5004 |
en_ZA |
dc.identifier.vancouvercitation |
Ramoelo A, Cho MA, Mathieu RS, Skidmore A, Schlerf M, Heitkonig I, et al, Integrating environmental and in situ hyperspectral remote sensing variables for grass nitrogen estimation in savannah ecosystems; 2011. http://hdl.handle.net/10204/5004 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Ramoelo, Abel
AU - Cho, Moses A
AU - Mathieu, Renaud SA
AU - Skidmore, AK
AU - Schlerf, M
AU - Heitkonig, IMA
AU - Prins, HHT
AB - Information about the distribution of grass nitrogen (N) concentration is crucial in understanding rangeland vitality and facilitates effective management of wildlife and livestock. A challenge in estimating grass N concentration using remote sensing in savannah ecosystems is that these areas are characterised by heterogeneity in edaphic, topographic and climatic factors. The objective is to test the utility of integrating environmental variables and in situ hyperspectral remote sensing variables for predicting grass N concentration along a land use gradient in the greater Kruger National Park. Data used include i) environmental variables, ii) measured grass N concentration and iii) in situ measured hyperspectral spectra. Non-linear partial least square regression was used. Results showed that several environmental variables were important for N estimation. Integrating environmental variables with in situ hyperspectral variables increased grass N estimation accuracy. The study demonstrated the importance of integrated modelling for savannah ecosystem state assessment.
DA - 2011-04
DB - ResearchSpace
DP - CSIR
KW - Hyperspectral
KW - Savannah ecosystems
KW - Foliar nitrogen concentration
KW - Grass nitrogen (N)
KW - Wildlife management
KW - Livestock management
KW - Kruger National Park
KW - Environmental remote sensing
KW - Remote sensing variables
KW - Rangeland vitality
LK - https://researchspace.csir.co.za
PY - 2011
T1 - Integrating environmental and in situ hyperspectral remote sensing variables for grass nitrogen estimation in savannah ecosystems
TI - Integrating environmental and in situ hyperspectral remote sensing variables for grass nitrogen estimation in savannah ecosystems
UR - http://hdl.handle.net/10204/5004
ER -
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en_ZA |