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.
Highlights
IntroductionSavanna ecosystems are heterogenous landscapes composed of a mixture of discontinuous patches of woody vegetation (i.e., trees and shrubs) and a continuous grass layer, governed by key local and global drivers (Figure 1) [1]
Introduction conditions of the Creative CommonsSavanna ecosystems are heterogenous landscapes composed of a mixture of discontinuous patches of woody vegetation and a continuous grass layer, governed by key local and global drivers (Figure 1) [1]
Estimating mixed pixel parameters in the savanna is essential for long-term understanding of the spatio-temporal dynamics and trends
Summary
Savanna ecosystems are heterogenous landscapes composed of a mixture of discontinuous patches of woody vegetation (i.e., trees and shrubs) and a continuous grass layer, governed by key local and global drivers (Figure 1) [1]. Savannas pivotal and playrecruitment a crucial role in the the grass global rainfall regimen. Browsing areare limitations for tree into carbonwhich cycle;may theylead store of the global carbon contribute about. Whenstock treesand surpass the escape height, the global terrestrial netsuppressed primary productivity [2,3]. At continental level,mature, such asand in Africa, they can no longer be by fire or browsing, making trees this savannasmay are critical wildlifewoody biodiversity immensely to are environmental possibly lead to to increased cover and [27].contribute
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