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An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem

Dissertation (MSc (Geoinformatics))--University of Pretoria 2023.

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Other Authors: Ramoelo, Abel
Format: Thesis
Language:English
Published: University of Pretoria 2023
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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 (Geoinformatics))--University of Pretoria 2023.
format Thesis
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institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:38:27.571Z
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
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source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/91719 An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem Ramoelo, Abel u21753742@tuks.co.za Tsele, Philemon Ngcoliso, Nasiphi Nutrient limitation N:P ratio Spectral simulation Sentinel-2 Model inversion UCTD Dissertation (MSc (Geoinformatics))--University of Pretoria 2023. Nutrient limitations may impact the ecosystem services the savanna biome provides. It may lead to degradation and, consequently, reduce the grazing capacity of the savannas if the necessary control measures are not implemented in time. The key indicator of the growth-limiting nutrients is the Nitrogen to Phosphorus (N:P) ratio. Grass foliar phosphorus content had rarely been investigated in African savannas, especially with remote sensing. Hence, information on the distribution of nutrient limitation is very limited. This study aimed to develop a Sentinel-2-based N:P predicting model and map the spatiotemporal variations of the N:P ratio in the Kruger National Park (KNP) area in the Northern part of the South African savanna biome. This was achieved by simulating the Analytical Spectral Device (ASD) reflectance data from 49 sampling points to Sentinel-2 MultiSpectral Instrument (MSI) configuration dataset. Laboratory-based chemical analysis was conducted to extract the concentrations of N and P from the grass samples. Partial least squares regression (PLSR) and random forest regression (RFR) techniques were used to develop the N:P prediction models from the simulated Sentinel-2 datasets. Results show that the best predicting RFR model explained over 80% of N:P variability with the lowest relative root mean square error (RRMSE) of 14%, with a p-value of less than 0.05. The optimal-predicting model was used to map the distribution of nutrient limitation using Sentinel-2 images across KNP and surroundings. Different parts of the KNP area are either N-limited or co-limited. The observed variations may result from varying environmental factors and anthropogenic activities. The Sentinel-2 N:P ratio estimation accuracies were then compared to the ratio of N:P of data from commercial multispectral (RapidEye and WorldView-2) and hyperspectral (Hyperion and EnMap) sensors. There is no vast difference between the estimation accuracy of these commercial sensors and that of the freely available Sentinel-2 when using RFR. However, when using PLSR, Sentinel-2 produced improved N:P ratio estimation accuracy than the commercial sensors with the highest R2 value of 0.66 and an RRMSE of 20.696%. This makes Sentinel-2 a cost-effective means for estimating nutrient limitation in a heterogeneous savanna landscape. This study provides decision-makers with a cost-effective tool for managing, sustaining, and restoring the savanna biome. The inclusion of textural information is recommended for future research. National Research Foundation (NRF) postgraduate scholarship. Geography, Geoinformatics and Meteorology MSc (Geoinformatics) Unrestricted 2023-07-31T12:25:30Z 2023-07-31T12:25:30Z 2023-09-05 2023 Dissertation Ngcoliso, N 2022, An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem, Masters thesis, University of Pretoria. http://hdl.handle.net/2263/91719 10.25403/UPresearchdata.23799000 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 Nutrient limitation
N:P ratio
Spectral simulation
Sentinel-2
Model inversion
UCTD
An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem
title An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem
title_full An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem
title_fullStr An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem
title_full_unstemmed An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem
title_short An application of multi-scale remote sensing in estimating grass nutrient limitation as measured by a ratio of Nitrogen and Phosphorus in a savanna ecosystem
title_sort application of multi scale remote sensing in estimating grass nutrient limitation as measured by a ratio of nitrogen and phosphorus in a savanna ecosystem
topic Nutrient limitation
N:P ratio
Spectral simulation
Sentinel-2
Model inversion
UCTD
url http://hdl.handle.net/2263/91719