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Abel Ramoelo

Researcher at South African National Parks

Publications -  96
Citations -  2393

Abel Ramoelo is an academic researcher from South African National Parks. The author has contributed to research in topics: Rangeland & Vegetation. The author has an hindex of 21, co-authored 74 publications receiving 1852 citations. Previous affiliations of Abel Ramoelo include Université libre de Bruxelles & University of Limpopo.

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An investigation into robust spectral indices for leaf chlorophyll estimation

TL;DR: In this paper, the consistency and robustness of 73 published chlorophyll spectral indices have been assessed, using leaf level hyperspectral data collected from three crop species and a variety of savanna tree species.
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Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system

TL;DR: In this article, the authors investigated the utility of the Carnegie Airborne Observatory (CAO) hyperspectral data, and WorldView-2 and Quickbird multispectral spectral data and 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.
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Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor

TL;DR: The study demonstrated the possibility to map grass nutrients at a regional scale provided there is a spaceborne sensor encompassing the red edge waveband with a high spatial resolution.
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Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa

TL;DR: In this article, the authors validate the MOD16 ET product using data from two eddy covariance flux towers, namely; Skukuza and Malopeni installed in a savanna and woodland ecosystem within the Kruger National Park, South Africa.
Posted Content

Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data

TL;DR: The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability, and is important for rangeland assessment and monitoring.