Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017

Jones, Matthew O.; Allred, Brady W.; Naugle, David E.; Maestas, Jeremy D.; Donnelly, John; Metz, Loretta J.; Karl, Jason W.; Smith, Rob; Bestelmeyer, Brandon T.; Boyd, Chad S.; Kerby, Jay D.; McIver, James D.

doi:10.1002/ecs2.2430

Cited by 194 publications

(215 citation statements)

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“…Fractional component maps provide a direct way to quantify surface cover in units both managers and researchers can understand (Jones et al 2018). Fractional component maps provide a direct way to quantify surface cover in units both managers and researchers can understand (Jones et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Long‐term trajectories of fractional component change in the Northern Great Basin, USA

et al. 2019

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The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-yr period, shrub cover declined and bare ground increased. While few pixels had >10% cover change, a large majority had at least some change. All fractional components had significant spatial relationships with water year precipitation (WYPRCP), maximum temperature (WYTMAX), and minimum temperature (WYTMIN) in all years. Shrub and sagebrush cover in particular respond positively to warming WYTMIN, resulting from the largest increases in WYTMIN being in the coolest and wettest areas, and respond negatively to warming WYTMAX because the largest increases in WYTMAX are in the warmest and driest areas. The trade-off of lowering temporal density against removing cloud-contaminated years is justified as temporal density appears to have only a modest impact on trends and climate relationships until n ≤ 6, but multi-year gaps are proportionally more influential. Gradual change analysis is likely to be less sensitive to n than abrupt change. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices.

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Section: Introductionmentioning

confidence: 99%

Long‐term trajectories of fractional component change in the Northern Great Basin, USA

et al. 2019

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“…Unfortunately, RF regression predictions often fail to encompass the full range of observed values (Roy and Larocque 2012) and are also biased against predicting absences (Savage et al 2015). 5 in Jones et al 2018). These predictions under-represent low and high values (see Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Unsurprisingly, those discrepancies were also evident in the stacked GRSG habitat maps (Table 3, Fig. Recent advances in change detection mapping for rangelands at very large spatial scales are very encouraging (Jones et al 2018), but careful consideration of the error and bias structure is vital for determining how maps such as these can inform a planning and regulatory framework. These trade-offs (precision, accuracy, threshold performance, and the capacity for post-modeling variable recombination) hold implications for the utility of vegetation maps for different applications.…”

Section: Discussionmentioning

confidence: 99%

“…Published studies of regional-scale GRSG habitat mapping have relied heavily on classified vegetation maps such as LANDFIRE (Knick et al 2013), on unclassified remotely sensed imagery (Donnelly et al 2016), or on stacked univariate layers (Fedy et al 2014). For example, currently available broadscale data include information on tree and sagebrush cover (Xian et al 2015), 250-m maps of herbaceous annual cover (grass; Boyte and Wylie 2018), and most recently, 30-m maps of percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western United States (Jones et al 2018). For example, currently available broadscale data include information on tree and sagebrush cover (Xian et al 2015), 250-m maps of herbaceous annual cover (grass; Boyte and Wylie 2018), and most recently, 30-m maps of percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western United States (Jones et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In particular, maps of grouse-relevant variables (e.g., sagebrush cover, shrub height, cover of herbaceous vegetation, and bare ground) have been constructed across Wyoming and more recently the sage-steppe habitat of the western United States (Homer et al 2012, Xian et al 2015, Jones et al 2018. In particular, maps of grouse-relevant variables (e.g., sagebrush cover, shrub height, cover of herbaceous vegetation, and bare ground) have been constructed across Wyoming and more recently the sage-steppe habitat of the western United States (Homer et al 2012, Xian et al 2015, Jones et al 2018.…”

Section: Introductionmentioning

confidence: 99%

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Vegetation mapping to support greater sage‐grouse habitat monitoring and management: multi‐ or univariate approach?

2019

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Conservation planning for wildlife species requires mapping and assessment of habitat suitability across broad areas, often relying on a diverse suite, or stack, of geospatial data presenting multidimensional controls on a species. Stacks of univariate, independently developed vegetation layers may not represent relationships between each variable that can be characterized by multivariate modeling techniques, leading to inaccurate inferences on the distribution of suitable habitat. In this paper, we examine the role of variable combining in mapping multiple dimensions of greater sage-grouse (Centrocercus urophasianus, GRSG) habitat as a basis for GRSG conservation in the great basin ecoregion within southeastern Oregon. We compare two modeling approaches: a univariate random forest regression model (RF regression) and a multivariate random forest nearest neighbor (RFNN) imputation model , across an array of variables. These include five GRSG habitat descriptor variables: percent cover of trees, juniper, sagebrush, and GRSG food forbs, and the proportion of grasses that are exotic annuals. We also model species distributions of 51 common species in the sage steppe and combine these predictions to estimate alpha diversity. Our results show that RF regression and RFNN can yield univariate predictions with similar performance, but RF regression predictions tend to contain slightly more bias at broader spatial scales. Stacking univariate predictions from RF regression yields covariance errors that manifest as logical errors (juniper cover > tree cover), biases in estimates of GRSG habitat area, and biases in estimates of alpha diversity. Combining variables from the RFNN model does not introduce covariance errors. We conclude that multivariate modeling approaches are better suited to map multidimensional habitat niches at broader spatial scales, and also better suited to provide information for defining multivariable adaptive management triggers at the population level or above.

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Using big data for evaluating development outcomes: A systematic map

Rathinam

Khatua²,

Siddiqui³

et al. 2021

Campbell Systematic Reviews

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Background Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data—that is digitally generated, passively produced and automatically collected—offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs. Objectives Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts Search Methods A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist. Selection Criteria The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes. Data Collection and Analysis Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only. Main Results The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine‐generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs. Authors' Conclusions This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficaci...

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Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017

Cited by 194 publications

References 53 publications

Long‐term trajectories of fractional component change in the Northern Great Basin, USA

Long‐term trajectories of fractional component change in the Northern Great Basin, USA

Vegetation mapping to support greater sage‐grouse habitat monitoring and management: multi‐ or univariate approach?

Using big data for evaluating development outcomes: A systematic map

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