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Estimation and monitoring of grass nitrogen and phosphorus using Sentinel-2 derived spectral information and vegetation indices in a Savanna ecosystem
Dissertation (MSc (Geography))--University of Pretoria, 2023.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613517457129472 |
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| access_status_str | Open Access |
| author2 | Ramoelo, Abel |
| author_browse | Ramoelo, Abel |
| author_facet | Ramoelo, Abel |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Geography))--University of Pretoria, 2023. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/91705 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:24.530Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/91705 Estimation and monitoring of grass nitrogen and phosphorus using Sentinel-2 derived spectral information and vegetation indices in a Savanna ecosystem Ramoelo, Abel u11059126@tuks.co.za Tsele, Philemon Tseka, Charity Lehlabule UCTD Sentinel-2 Red edge Vegetation indices Cross-validation Random Forest Dissertation (MSc (Geography))--University of Pretoria, 2023. Grass quality as measured by leaf nitrogen (P) and phosphorus (N) plays a major role in understanding the distribution, densities and population dynamics of herbivores including livestock and wild herbivores. The aim of this study was to estimate and monitor grass N and P using Sentinel-2 derived spectral information and vegetation indices during the wet season for the selected period between 2017 and 2022 spanning a savanna ecosystem in the Kruger National Park area. Sentinel-2 satellite images were used as they provide images with high spatial, spectral and temporal resolutions. Field data were used where grass samples were collected, and spectral reflectance measurements (400 and 2350 nm) were undertaken. Three analysis scenarios were employed to estimate grass N and P in conjunction with classical regression and machine learning techniques. The scenarios included: (i) specific spectral bands, (ii) conventional and red edge-based vegetation indices (VIs) and (iii) a combination of VIs and spectral bands using the Stepwise Multiple Linear Regression (SMLR), Random Forest (RF) and Support Vector Machine (SVM) statistical models. Results showed that SMLR yielded the highest estimation accuracy based on a combination of bands and VIs for leaf N (i.e. Coefficient of determination (R2) = 0.69 and Root Mean Square Error (RMSE) = 0.14%, Relative Root Mean Square Error (RRMSE) = 6.73%, Mean Absolute Error (MAE) = 0.11) and for P based on a combination of VIs only (i.e. R2= 0.40 and RMSE= 0.04%, RRMSE = 34.322% and MAE = 0.04). This study confirms that the combination of red edge bands and VIs of Sentinel-2 data are crucial for accurately estimating biochemical concentrations in a savanna ecosystem. This study has significant implications for mapping and monitoring grass quality over large spatial extents. SANSA Geography, Geoinformatics and Meteorology MSc (Geography) Unrestricted 2023-07-31T10:22:58Z 2023-07-31T10:22:58Z 2023-09 2023 Dissertation * http://hdl.handle.net/2263/91705 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sentinel-2 Red edge Vegetation indices Cross-validation Random Forest Estimation and monitoring of grass nitrogen and phosphorus using Sentinel-2 derived spectral information and vegetation indices in a Savanna ecosystem |
| title | Estimation and monitoring of grass nitrogen and phosphorus using Sentinel-2 derived spectral information and vegetation indices in a Savanna ecosystem |
| title_full | Estimation and monitoring of grass nitrogen and phosphorus using Sentinel-2 derived spectral information and vegetation indices in a Savanna ecosystem |
| title_fullStr | Estimation and monitoring of grass nitrogen and phosphorus using Sentinel-2 derived spectral information and vegetation indices in a Savanna ecosystem |
| title_full_unstemmed | Estimation and monitoring of grass nitrogen and phosphorus using Sentinel-2 derived spectral information and vegetation indices in a Savanna ecosystem |
| title_short | Estimation and monitoring of grass nitrogen and phosphorus using Sentinel-2 derived spectral information and vegetation indices in a Savanna ecosystem |
| title_sort | estimation and monitoring of grass nitrogen and phosphorus using sentinel 2 derived spectral information and vegetation indices in a savanna ecosystem |
| topic | UCTD Sentinel-2 Red edge Vegetation indices Cross-validation Random Forest |
| url | http://hdl.handle.net/2263/91705 |