The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands

Hill, Michael J.; Guerschman, Juan Pablo

doi:10.3390/rs12030406

Cited by 38 publications

(28 citation statements)

References 69 publications

(97 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whereas on‐the‐ground data collection and monitoring is constrained logistically, satellite remote sensing scales easily, measuring the landscape across space and through time. Satellite measurements are modelled to predict rangeland indicators, providing key information for land managers and practitioners globally (Hill & Guerschman, 2020). Chief among these indicators is vegetation cover at species or functional group levels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

et al. 2021

View full text Add to dashboard Cite

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform ( https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Satellite measurements are modelled to predict rangeland indicators, providing key information for land managers and practitioners globally (Hill & Guerschman, 2020). Chief among these indicators is vegetation cover at species or functional group levels.…”

Section: Introductionmentioning

confidence: 99%

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

et al. 2021

View full text Add to dashboard Cite

show abstract

“…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. EO applications in savannas have also applied traditional hard Human population growth increasingly poses a threat to savanna ecosystems due to land use, land cover changes, and management policies [6] Climate change, such as prolonged droughts and erratic rainfalls, along with government policies for reforestation and afforestation, continue to threaten the resilience of savanna ecosystems [7,8].…”

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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…The intensification of agriculture and forest harvesting are crucial factors shaping biodiversity (Levers et al, 2014, 2016) that require inputs of crop type and vegetation composition within each spatial unit. These ecological considerations of the utility of fractional land cover and land use representations are underpinned by recent advancements in algorithms to produce high resolution mappings of fractional land cover from satellite data (Allred et al, 2020; Hill and Guerschman, 2020).…”

Section: Introductionmentioning

confidence: 99%

A predictive model of fractional land use

Kapitza

Golding

Wintle

2020

Preprint

View full text Add to dashboard Cite

Land use change leads to shifts in species ranges and declines in biodiversity across the world. By mapping likely future land use under projections of socio-economic change, these ecological changes can be predicted to inform conservation decision-making.We present a land use modelling approach that enables ecologists to map changes in land use under various socio-economic scenarios at fine spatial resolutions. Its predictions can be used as a direct input to virtually all existing spatially-explicit ecological models.The most commonly used land use modelling approaches provide binary predictions of land use. However, continuous representations of land use have been shown to improve ecological models. Our approach maps the fractional cover of land use within each grid cell, providing higher information content than discrete classes at the same spatial resolution.When parametrized using data from 1990, the method accurately reproduced land use patterns observed in the Amazon from 1990 until 2018. Predictions were accurate in terms of the fractional amounts allocated across the landscape and the correct identification of areas with declines and increases in different land uses. A small case study showcases the successful application of our model to reproduce patterns of agricultural expansion and habitat decline.The model source code is provided as an open-source R package, making this new, open method available to ecologists to bridge the gap between socio-economic, land use and biodiversity modelling.

show abstract

The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands

Cited by 38 publications

References 69 publications

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

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

A predictive model of fractional land use

Contact Info

Product

Resources

About