Remote Sensing of Savannas and Woodlands: Editorial

Hill, Michael J.

doi:10.3390/rs13081490

Cited by 3 publications

(8 citation statements)

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“…11% use compared to ~2.5% application of non-parametric methods there. Due to Africa having a range of savanna ecosystems with high cover variability due to variable fire regimes and land uses (e.g., livestock grazing and wildlife conservation), the high variability in cover may prompt researchers to opt for methods which are capable of dealing with high spatial heterogeneity in land cover [15]. When we observed the type of methods from VHR to low spatial resolution in Figure 9, we found non-parametric methods to be the most frequently applied to VHR and HR EO systems with 60% and 77%, respectively.…”

Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

“…Additionally, C4 grasses are intolerant to shading of closed canopies, and the absence of understory grass can lead to fire suppression [26]. Using EO information for savanna monitoring has advanced in previous decades, and several approaches have been used to characterize the savanna [15]. Remote sensing approaches for savanna monitoring include utilization of vegetation indices, such as the Normalized Difference Vegetation Indices (NDVI) based on phenological differences between trees and grasses, photosynthetic and non-photosynthetic differences of the vegetation using low spatial resolution EO data [11,28], time series methods, combinations of vegetation indices with Spectral Mixture Analysis (SMA) methods, or SMA methods alone [29,30,31,32,33], to derive fractional cover for spatial temporal dynamics of the savanna.…”

Section: Introductionmentioning

confidence: 99%

“…Earth Observation (EO) information provide a suitable tool for the monitoring of savanna ecosystems [11][12][13][14]. However, due to the nature of savanna, it is one of the challenging biome to monitor using EO data [11,15], particularly because of the difficulty to distinguish between woody and herbaceous components. The savanna landscape is shaped by complex interactions of top-down and bottom-up processes, resulting in heterogenous woody and herbaceous layers with plant density, height, and canopy cover varying over space and time [1,2,15].…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the nature of savanna, it is one of the challenging biome to monitor using EO data [11,15], particularly because of the difficulty to distinguish between woody and herbaceous components. The savanna landscape is shaped by complex interactions of top-down and bottom-up processes, resulting in heterogenous woody and herbaceous layers with plant density, height, and canopy cover varying over space and time [1,2,15]. Rainfall, fires, herbivory, and human activities are major drivers of the complex savanna landscape [1,2,16].…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

et al. 2021

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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.

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Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

et al. 2021

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Towards accurate mapping of forest in tropical landscapes: A comparison of datasets on how forest transition matters

Velasco

Lippe

Torres

et al. 2022

Remote Sensing of Environment

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Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.

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Remote Sensing of Savannas and Woodlands: Editorial

Abstract: Savannas and woodlands represent one of the most challenging targets for remote sensing [...]

Cited by 3 publications

References 30 publications

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

Towards accurate mapping of forest in tropical landscapes: A comparison of datasets on how forest transition matters

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

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