Spatio-temporal mixed pixel analysis of savanna ecosystems : a review

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dc.contributor.author Nghiyalwa, Hilma S.
dc.contributor.author Urban, Marcel
dc.contributor.author Baade, Jussi
dc.contributor.author Smit, Izak P.J.
dc.contributor.author Ramoelo, Abel
dc.contributor.author Mogonong, Buster
dc.contributor.author Schmullius, Christiane
dc.date.accessioned 2022-05-24T11:22:34Z
dc.date.available 2022-05-24T11:22:34Z
dc.date.issued 2021-09
dc.description.abstract Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing. en_US
dc.description.department Geography, Geoinformatics and Meteorology en_US
dc.description.librarian pm2022 en_US
dc.description.sponsorship The Deutscher Akademischer Austauschdienst and the Federal Ministry of Education and Research (BMBF) within the framework of the Strategy “Research for Sustainability” (FONA). en_US
dc.description.uri http://www.mdpi.com/journal/remotesensing en_US
dc.identifier.citation Nghiyalwa, H.S.; Urban, M.; Baade, J.; Smit, I.P.J.; Ramoelo, A.; Mogonong, B.; Schmullius, C. Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review. Remote Sensing 2021, 13, 3870. https://doi.org/10.3390/rs13193870. en_US
dc.identifier.issn 2072-4292 (online)
dc.identifier.other 10.3390/rs13193870
dc.identifier.uri https://repository.up.ac.za/handle/2263/85660
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.rights © 2021 by the authors. Licensee: MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. en_US
dc.subject Spatio-temporal en_US
dc.subject Mixed pixel analysis en_US
dc.subject Savanna en_US
dc.subject Fractional cover en_US
dc.subject Earth Observation (EO) en_US
dc.title Spatio-temporal mixed pixel analysis of savanna ecosystems : a review en_US
dc.type Article en_US


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