Structurally Guided Sampling
Allocating strata: existing
Sample allocation type and count
Sample allocation type and count
coobs algorithm sampling
Distance to access layer
Analyze optimal Latin hypercube sample number
Raster principal components
Population descriptors
Compare sample representation within sraster strata
Sample size determination
Check existing sample data against requirements
Get existing and XY coordinates
Extract metrics
Extract strata
Masking
Matrices
Plot
Prepare existing sample data
Sampling rules
Adapted Hypercube Evaluation of a Legacy Sample (ahels)
Balanced sampling
Conditioned Latin Hypercube Sampling
Sample Existing Data Using Balanced Sampling
Sub-sample using the conditional Latin hypercube sampling (CLHS)
Randomly sample from an existing dataset
Sample Existing Data Based on Strata
Sample existing
Nearest centroid (NC) sampling
Simple random sampling
Stratified sampling
Systematic stratified sampling
Systematic sampling
sgsR: Structurally Guided Sampling
Breaks stratification
k-means stratification
Map a raster stack of a list of rasters
Stratify using polygons
Quantiles stratification
Take Samples Based on Strata
Vectorization helpers
Write
Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches.
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