A General-Purpose Spatial Survey Design for Collaborative Science and Monitoring of Global Environmental Change: The Global Grid

Theobald, David M.

doi:10.3390/rs8100813

Cited by 15 publications

(13 citation statements)

References 41 publications

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“…We estimated human intrusion (H i ) using a method that builds on and extends accessibility modeling (Nelson, 2008;Theobald, 2008Theobald, , 2013Theobald et al, 2010;Weiss et al, 2018;Nelson et al, 2019). Human intrusion (a.k.a.…”

Section: Human Intrusionmentioning

confidence: 99%

Earth transformed: detailed mapping of global human modification from 1990 to 2017

Theobald

Kennedy

Чэн

et al. 2020

Earth Syst. Sci. Data

142

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Abstract. Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 2000, 2010, and 2015 to evaluate temporal and spatial trends of land use modification of terrestrial lands (excluding Antarctica). We found that the expansion of and increase in human modification between 1990 and 2015 resulted in 1.6 M km2 of natural land lost. The percent change between 1990 and 2015 was 15.2 % or 0.6 % annually – about 178 km2 daily or over 12 ha min−1. Worrisomely, we found that the global rate of loss has increased over the past 25 years. The greatest loss of natural lands from 1990 to 2015 occurred in Oceania, Asia, and Europe, and the biomes with the greatest loss were mangroves, tropical and subtropical moist broadleaf forests, and tropical and subtropical dry broadleaf forests. We also created a contemporary (∼2017) estimate of human modification that included additional stressors and found that globally 14.6 % or 18.5 M km2 (±0.0013) of lands have been modified – an area greater than Russia. Our novel datasets are detailed (0.09 km2 resolution), temporal (1990–2015), recent (∼2017), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We believe these datasets support an improved understanding of the profound transformation wrought by human activities and provide foundational data on the amount, patterns, and rates of landscape change to inform planning and decision-making for environmental mitigation, protection, and restoration. The datasets generated from this work are available at https://doi.org/10.5281/zenodo.3963013 (Theobald et al., 2020).

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Section: Human Intrusionmentioning

confidence: 99%

Earth transformed: detailed mapping of global human modification from 1990 to 2017

Theobald

Kennedy

Чэн

et al. 2020

Earth Syst. Sci. Data

142

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“…Open Access human modification from visual interpretation of high resolution aerial or satellite imagery across the world. We selected plots using the Global Grid sampling design (Theobald 2016), a spatially-balanced and probability-based random sampling that was stratified on a five-class rural to urban gradient using "stable nighttime-lights" 2013 imagery (Elvidge et al , 2001). Within each of 1,000 ~1 km 2 plots, we selected 10 simple-random locations to capture rare features and heterogeneity in land use and land cover (for a total of 10,000 sub-plots), which were separated by a minimum distance of 100 m. The spatial-balanced nature of the design maximizes statistical information extracted from each plot because it increases the number of samples in relatively rare areas that are likely of interest (in contrast to simple random sampling) --especially for urbanized and growing cities (Theobald, 2016).…”

Section: Uncertainty and Validation Analysesmentioning

confidence: 99%

Earth transformed: detailed mapping of global human modification from 1990 to 2017

Theobald¹,

Kennedy²,

Chen³

et al. 2020

Preprint

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Abstract. Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 2000, 2010, and 2015 to evaluate temporal and spatial trends of land use modification of terrestrial lands (excluding Antarctica). We found that the expansion and increase of human modification between 1990 and 2015 resulted in 1.6 M km2 of natural land lost. The percent change between 1990 and 2015 was 15.2 % or 0.61 % annually – roughly 178 km2 daily. Over the pause of a deep breath, over 8 football pitches of natural lands were lost (~ 17 per minute). Worrisomely, we found that the global rate of loss has increased over the past 25 years. The greatest loss of natural lands from 1990–2015 occurred in Oceania, Asia, and Europe, and the biomes with the greatest loss were mangroves, tropical & subtropical moist broadleaf forests, and tropical & subtropical dry broadleaf forests. We also created a contemporary (~ 2017) estimate of human modification that included additional stressors and found that globally 14.5 % or 18.5 M km2 of lands have been completely modified – an area greater than Russia. Our novel datasets are detailed (0.09 km2 resolution), temporal (1990–2015), recent (~ 2017), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We believe these datasets will support better understanding of the profound transformation wrought by human activities and provide foundational data on the amounts, patterns, and rates of change to inform planning and decision making for environmental mitigation, protection, restoration, and adaptation to climate change. The datasets generated from this work are available at https://doi.org/10.5061/dryad.n5tb2rbs1 (Theobald et al., 2020).

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“…A master sample can be a useful tool to organise environmental monitoring at different spatial scales as previously done using GRTS or RRQRR (Larsen et al, 2008;Theobald, 2016). Using BAS instead of GRTS gives better spatial balance (Robertson et al, 2013) and no need for an oversample.…”

Section: Discussionmentioning

confidence: 99%

“…However, once an oversample is chosen, it is not possible to generate additional points and this needs to be accounted for at the planning stage. Theobald () also uses an adaptation of GRTS, Reversed Randomised Quadrant‐Recursive Raster (RRQRR), implemented in ArcGIS software (Theobald et al., ) to coordinate monitoring effort. The authors’ are not aware of an ordering strategy for the LPM methods and hence, it is not clear how these methods could be used for oversampling.…”

Section: Introductionmentioning

confidence: 99%

Using balanced acceptance sampling as a master sample for environmental surveys

Dam‐Bates¹,

Gansell²,

Robertson

2018

Methods Ecol Evol

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Well‐designed environmental monitoring programmes for management organisations are important for evidence‐based decision making. However, many environmental problems are not single agency issues that require intervention or monitoring at one spatial scale. A master sample can be used to coordinate and scale monitoring designs to ensure consistency in information gathered and robustness of estimators at the different spatial scales. We propose using balanced acceptance sampling (BAS) to generate a master sample. In this context, practical applications and justification of BAS as a master sample are addressed. These include sample generation, stratification, unequal probability sampling, rotating panel designs, and regional intensification. A method for incorporating legacy sites is also provided. Using BAS as a master sample is conceptually simple, gives good spatial balance over different spatial scales, and is computationally efficient to generate. An example for terrestrial biodiversity monitoring in New Zealand is provided. Environmental monitoring can benefit from increased coordination between agencies. A master sample is an excellent way to incorporate coordination directly into the sample design. BAS improves on methods previously described and provides an effective method to monitor populations at multiple spatial scales.

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A General-Purpose Spatial Survey Design for Collaborative Science and Monitoring of Global Environmental Change: The Global Grid

Cited by 15 publications

References 41 publications

Earth transformed: detailed mapping of global human modification from 1990 to 2017

Earth transformed: detailed mapping of global human modification from 1990 to 2017

Earth transformed: detailed mapping of global human modification from 1990 to 2017

Using balanced acceptance sampling as a master sample for environmental surveys

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