Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review
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 an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/19/3870 |
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| author | Hilma S. Nghiyalwa Marcel Urban Jussi Baade Izak P. J. Smit Abel Ramoelo Buster Mogonong Christiane Schmullius |
| author_facet | Hilma S. Nghiyalwa Marcel Urban Jussi Baade Izak P. J. Smit Abel Ramoelo Buster Mogonong Christiane Schmullius |
| author_sort | Hilma S. Nghiyalwa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:52:53Z |
| format | Article |
| id | doaj.art-46bd3c96c865451cac59d2364ff6cf2d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:52:53Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-46bd3c96c865451cac59d2364ff6cf2d2023-11-22T16:42:04ZengMDPI AGRemote Sensing2072-42922021-09-011319387010.3390/rs13193870Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A ReviewHilma S. Nghiyalwa0Marcel Urban1Jussi Baade2Izak P. J. Smit3Abel Ramoelo4Buster Mogonong5Christiane Schmullius6Department for Earth Observation, Friedrich Schiller University Jena, 07743 Jena, GermanyDepartment for Earth Observation, Friedrich Schiller University Jena, 07743 Jena, GermanyDepartment of Physical Geography, Friedrich Schiller University Jena, 07743 Jena, GermanyScientific Services, South African National Parks, Skukuza 0001, South AfricaCentre for Environmental Studies, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0001, South AfricaCentre for African Ecology, School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Johannesburg 2050, South AfricaDepartment for Earth Observation, Friedrich Schiller University Jena, 07743 Jena, GermanyReliable 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.https://www.mdpi.com/2072-4292/13/19/3870spatio-temporalmixed pixel analysissavannafractional coverEarth Observation (EO) |
| spellingShingle | Hilma S. Nghiyalwa Marcel Urban Jussi Baade Izak P. J. Smit Abel Ramoelo Buster Mogonong Christiane Schmullius Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review Remote Sensing spatio-temporal mixed pixel analysis savanna fractional cover Earth Observation (EO) |
| title | Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review |
| title_full | Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review |
| title_fullStr | Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review |
| title_full_unstemmed | Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review |
| title_short | Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review |
| title_sort | spatio temporal mixed pixel analysis of savanna ecosystems a review |
| topic | spatio-temporal mixed pixel analysis savanna fractional cover Earth Observation (EO) |
| url | https://www.mdpi.com/2072-4292/13/19/3870 |
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