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Remote Sens. 2018, 10(4), 505; https://doi.org/10.3390/rs10040505

Evaluating the Applications of the Near-Infrared Region in Mapping Foliar N in the Miombo Woodlands

1
Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
2
Department of Physics, Geography and Environmental Science, Great Zimbabwe University, P.O. Box 1235, Masvingo 00263, Zimbabwe
3
Department of Geography & Environmental Science, University of Zimbabwe, P.O. Box MP 167, Mount Pleasant, Harare 00263, Zimbabwe
*
Author to whom correspondence should be addressed.
Received: 23 November 2017 / Revised: 13 January 2018 / Accepted: 20 January 2018 / Published: 23 March 2018
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Abstract

Remote sensing has been widely used to estimate the distribution of foliar nitrogen (N) in a cost-effective manner. Although hyperspectral remote sensing targeting the red edge and shortwave infrared regions has proved successful at estimating foliar N, research has recently shifted to include exploring the benefits of using the near-infrared (NIR) region, especially when using broadband sensing. Bootstrapped random forest regression analysis was applied on Sentinel 2 data to test the significance of using the NIR in foliar N estimation in miombo woodlands. The results revealed a low ranking for individual NIR bands, but the ranking improved when spectral indices were used. In addition, the results indicated a marginal increase in the normalised root mean square error of prediction (nRMSE) from 11.35% N when all bands were used to 11.69% N when the NIR bands were excluded from the model. Bootstrapping results show higher accuracy and better consistency in the prediction of foliar N using combined spectral indices and individual bands. This study therefore underscores the significance of spectral indices to increase the NIR region’s importance in estimating the distribution of foliar N as a key indicator of ecosystem health at the landscape scale in miombo systems. View Full-Text
Keywords: nitrogen; remote sensing; near infrared; miombo; random forest regression nitrogen; remote sensing; near infrared; miombo; random forest regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mutowo, G.; Mutanga, O.; Masocha, M. Evaluating the Applications of the Near-Infrared Region in Mapping Foliar N in the Miombo Woodlands. Remote Sens. 2018, 10, 505.

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