ndvi_AK10000

NDVI remote sensing data for 10,000 random pixels from Alaska, with rare
land classes removed.

datasets

These tools were created to test map-scale hypotheses about trends in large
remotely sensed data sets but any data with spatial and temporal variation
can be analyzed. Tests are conducted using the PARTS method for analyzing spatially
autocorrelated time series
(Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>).
The method's unique approach can handle extremely large data sets that other
spatiotemporal models cannot, while still appropriately accounting for
spatial and temporal autocorrelation. This is done by partitioning the data
into smaller chunks, analyzing chunks separately and then combining the
separate analyses into a single, correlated test of the map-scale hypotheses.

Clay Morrow

remotePARTS

Spatiotemporal Autoregression Analyses for Large Data Sets

Anthony Ives

ndvi_AK10000 function

data frame with 10,000 rows corresponding to sites and 37 columns:<dl>
 <dt>lng</dt>
<dd>longitude of the pixel</dd> <dt>lat</dt>
<dd>latitude of the pixel</dd> <dt>AR_coef</dt>
<dd>pre-calculated AR REML coefficient standardized by mean ndvi
 values for each pixel</dd> <dt>CLS_coef</dt>
<dd>pre-calculated CLS coefficient standardized by mean ndvi
 values for each pixel</dd> <dt>land</dt>
<dd>dominant land class of the pixel</dd> <dt>land</dt>
<dd>logical: is this land class rare?</dd> <dt>ndvi&lt;t&gt;</dt>
<dd>ndvi value of the pixel during the year &lt;t&gt;</dd>
</dl>

Format

NDVI remote sensing data for 10,000 random pixels from Alaska, with rare
land classes removed. — ndvi_AK10000

data frame with 10,000 rows corresponding to sites and 37 columns:<dl>
 <dt>lng</dt>
<dd>longitude of the pixel</dd>

 <dt>lat</dt>
<dd>latitude of the pixel</dd>

 <dt>AR_coef</dt>
<dd>pre-calculated AR REML coefficient standardized by mean ndvi
 values for each pixel</dd>

 <dt>CLS_coef</dt>
<dd>pre-calculated CLS coefficient standardized by mean ndvi
 values for each pixel</dd>

 <dt>land</dt>
<dd>dominant land class of the pixel</dd>

 <dt>land</dt>
<dd>logical: is this land class rare?</dd>

 <dt>ndvi&lt;t&gt;</dt>
<dd>ndvi value of the pixel during the year &lt;t&gt;</dd>


</dl>

ndvi_AK10000: NDVI remote sensing data for 10,000 random pixels from Alaska, with rare land classes removed.

Description

Usage

Arguments

Format