Spatial Sampling Methods for Environmental Resources

Spatial sampling methods form the backbone of efforts to quantify the distribution and dynamics of environmental resources, ranging from terrestrial biodiversity and soil carbon to freshwater quality and atmospheric chemistry. At their core, these methods employ probability-based designs that control inclusion probabilities and exploit auxiliary information to achieve spatial balance—ensuring that samples are well spread across a study region. Traditional approaches, such as simple random sampling or stratified schemes, have been complemented by systematic grids, adaptive designs that respond to local heterogeneity, and model-based frameworks that integrate geostatistical covariance structures. Recent advances emphasise the use of master samples and multi-scale frameworks to coordinate monitoring across agencies and spatial scales, supporting long-term assessments of change. Computational tools and open-source software now enable practitioners to implement complex algorithms, such as random-tessellation stratification and quasi-random sequences, to generate spatially balanced samples. Coupled with high-resolution remote sensing and machine-learning-driven covariates, modern designs reduce estimator variance, improve the efficiency of field campaigns and enhance the comparability of data across time and space. By marrying robust statistical theory with practical considerations—such as legacy sites, minimum separation distances and rotating panels—spatial sampling continues to evolve as a global framework for evidence-based environmental management.

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Spatial Sampling Methods for Environmental Resources publication trend

The graph below shows the total number of articles in spatial sampling methods for environmental resources across all publications each year (not limited to Nature Index journals).

Technical terms

Spatially balanced sampling: Probability-based selection ensuring sample points are uniformly spread across a study area to reduce estimator variance.

Generalised random tessellation stratified (GRTS): A hierarchical algorithm that partitions space into random tessellations to generate spatially balanced samples with controlled inclusion probabilities.

Balanced acceptance sampling (BAS): A quasi-random sequence approach that yields well-spread sample locations and supports multi-scale and legacy-site designs.

Inclusion probability: The predetermined likelihood that a particular unit in the population will be selected for the sample.

Sample coordination: The deliberate overlap or alignment of sampling units across successive surveys to improve precision in change estimation.

Spatial balance measure: A quantitative metric assessing how evenly sample units are distributed relative to their neighbours within the spatial domain.

References

1. spsurvey: Spatial Sampling Design and Analysis in R. Journal of Statistical Software (2023).
2. Using balanced acceptance sampling as a master sample for environmental surveys. Methods in Ecology and Evolution (2018).
3. A sample coordination method to monitor totals of environmental variables. Environmetrics (2020).
4. How to find the best sampling design: A new measure of spatial balance. Environmetrics (2024).