Assessing forest cover change in Mexico from annual MODIS VCF data (2000–2010)

Gao, Yan; Ghilardi, Adrián; Mas, Jean-François; Quevedo, Alexander; Paneque-Gálvez, Jaime; Skutsch, Margaret

doi:10.1080/01431161.2018.1479789

Cited by 11 publications

(1 citation statement)

References 38 publications

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“…However, although the VCF concept does compensate for errors in discrete approaches, the existing VCFs are mainly only available at global scales and are popularly available for low spatial resolution EO data and focused mainly to estimate tree cover. Global VCFs products are generated from MODIS [56][57][58][59][60][61][62]; AVHRR [44,53,[63][64][65][66]; Landsat [59,[67][68][69]; Visible Infrared Imaging Radiometer Suite (VI-IRS) [58,70]; or from a combination of some of these EO data [65]. VCF are widely applied in environmental change, forestry monitoring and estimation, such as deforestation and forest degradation [71,72], atmosphere-biosphere models [73], prediction of forests vegetation biodiversity and species diversity [74], and environmental resources monitoring, such as estimating stock volume [56].…”

Section: Introductionmentioning

confidence: 99%

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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