build_ee_forest

Train a regression forest in Earth Engine

Provides an end-to-end workflow for NASA Global Ecosystem
Dynamics Investigation (GEDI) Level 1B, Level 2A, Level 2B, Level 3, Level
4A, and Level 4B data. Functions search NASA catalogues, download granules,
stream HDF5 products from Earthdata Cloud, and extract geolocations,
waveforms, elevation and relative-height metrics, canopy cover, plant area
profiles, and aboveground biomass. Methods quality-filter and spatially clip
HDF5, tabular, and raster products; calculate footprint and polygon
statistics; create gridded summaries; and visualize orbital tracks and
measurements. The package also simulates GEDI-like waveforms from airborne
laser-scanning point clouds and supports spatial sampling, predictive
modelling, ancillary-data extraction, and wall-to-wall upscaling with
optional Google Earth Engine workflows.

Carlos Alberto Silva

rGEDI

NASA's Global Ecosystem Dynamics Investigation (GEDI) Data
Visualization and Processing

Caio Hamamura

build_ee_forest function

<dl><dt>training</dt>
<dd>Earth Engine <code>FeatureCollection</code> containing predictors and
the response.</dd>
<dt>response</dt>
<dd>Response property name, such as <code>"agbd"</code> or <code>"rh98"</code>.</dd>
<dt>predictors</dt>
<dd>Predictor property names.</dd>
<dt>trees</dt>
<dd>Number of trees.</dd>
<dt>seed</dt>
<dd>Random seed.</dd></dl>

Arguments

Train a regression forest in Earth Engine — build_ee_forest

<dl>

<dt>training</dt>
<dd>Earth Engine <code>FeatureCollection</code> containing predictors and
the response.</dd>


<dt>response</dt>
<dd>Response property name, such as <code>"agbd"</code> or <code>"rh98"</code>.</dd>


<dt>predictors</dt>
<dd>Predictor property names.</dd>


<dt>trees</dt>
<dd>Number of trees.</dd>


<dt>seed</dt>
<dd>Random seed.</dd>

</dl>

build_ee_forest: Train a regression forest in Earth Engine

Description

Usage

Value

Arguments