Accuracy assessment and land cover mapping have been inexorably linked throughout the
first 50 years of publication of Remote Sensing of Environment. The earliest developers of …

Abstract spsurvey is an R package for design-based statistical inference, with a focus on
spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) …

Some environmental studies use non-probabilistic sampling designs to draw samples from
spatially distributed populations. Unfortunately, these samples can be difficult to analyse …

High-fidelity characterization and effective monitoring of spatial and spatiotemporal
processes are crucial for high-performance quality control of many manufacturing processes …

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic
heterogeneity. Machine learning approaches have previously been utilised for patient …

The design‐based and model‐based approaches to frequentist statistical inference rest on
fundamentally different foundations. In the design‐based approach, inference relies on …

Spatial predictive methods are increasingly being used to generate predictions across
various disciplines in environmental sciences. Accuracy of the predictions is critical as they …

The units observed in a biological, agricultural, and environmental survey are often
randomly selected from a finite population whose main feature is to be geo‐referenced thus …

A spatial sampling design determines where sample locations are placed in a study area so
that population parameters can be estimated with relatively high precision. Spatially …

Global and local land-cover mapping products provide important data on land surface.
However, the accuracy of land-cover products is the key issue for their further scientific …