matrices

mat_quant

mat_cov

mat_covNB

Create covariate and sample matrices

internal

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.

Tristan RH Goodbody

sgsR

Structurally Guided Sampling

Nicholas C Coops

Martin Queinnec

matrices function

<dl><dt>vals</dt>
<dd>Covariate / sample data</dd>
<dt>nQuant</dt>
<dd>Number of quantiles</dd>
<dt>nb</dt>
<dd>Number of bands</dd>
<dt>matQ</dt>
<dd>Quantile matrix</dd></dl>

Arguments

Matrices — matrices

<dl>

<dt>vals</dt>
<dd>Covariate / sample data</dd>


<dt>nQuant</dt>
<dd>Number of quantiles</dd>


<dt>nb</dt>
<dd>Number of bands</dd>


<dt>matQ</dt>
<dd>Quantile matrix</dd>

</dl>

matrices: Matrices

Description

Usage

Value

Arguments