DOI: 10.5061/dryad.34tmpg4xf
title: Data from: Mapping coastal redwoods (<em>Sequoia sempervirens</em>) across their natural range: An updateable and field-validated distribution map using Sentinel satellite data and cloud computing

description: Coast redwood (Sequoia sempervirens) is a uniquely-tall and long-lived
 tree species that occupies a narrow fog-belt along the Pacific coast of
 North America. Despite its ecological and conservation significance,
 existing maps of redwood distribution remain limited in spatial
 resolution, accuracy, and timeliness. In this study, we present an
 updatable and field-validated distribution map of S. sempervirens across
 its entire native range – from southwestern Oregon to central California –
 developed using freely available Sentinel-2 multispectral and Sentinel-1
 SAR data. We compiled a georeferenced canopy classification dataset of 960
 points, combining field surveys conducted in October 2024 with field-based
 ground truth points collected in 2017 from a prior mapping study, and
 externally sourced field-based redwood presence records. This dataset was
 used to train machine learning models (Random Forest and Gradient Boosted
 Trees) within a cloud computing framework to classify redwood presence and
 absence at 10 m spatial resolution. Binary classification models achieved
 high predictive performance, with the best model yielding over 88% overall
 accuracy and an AUC of 0.92 on a 30% hold-out validation set. Ten-fold
 cross-validation on the training data further confirmed model consistency,
 with high true positive rates and low false positive rates across folds. A
 secondary multi-class model differentiated between redwood-dominated and
 mixed-conifer forest types, achieving an overall accuracy of 73.82%.
 Comparison with previous redwood distribution datasets revealed
 substantial agreement but also significant discrepancies, with the new
 model suggesting redwood presences in previously unmapped redwood
 fragments and absences in locations mapped as redwood. Validation against
 field data confirmed higher accuracy in the new map. The resulting
 range-wide redwood map offers a current, accurate, and updateable platform
 for conservation planning, habitat monitoring, and ecological research. It
 also establishes a high-confidence baseline for tracking redwood
 distribution dynamics under ongoing climate and land-use change.
description: # Data from: Mapping coastal redwoods (*Sequoia sempervirens*) across
 their natural range: An updatable and field-validated distribution map
 using Sentinel satellite data and cloud computing Dataset DOI:
 10.5061/dryad.34tmpg4xf ## Description of the data and file structure ###
 Dataset overview This repository provides supporting data and code for a
 Coast Redwood (*Sequoia sempervirens*) distribution mapping study using
 Sentinel-1 and Sentinel-2 satellite data and Google Earth Engine (GEE). To
 minimize potential risks associated with publishing precise locations of
 an **Endangered species**, this archive does **not** include point-level
 geographic coordinates. Instead, it provides a **coordinate-free training
 dataset** containing extracted satellite predictor values and class
 labels, together with spatially explicit raster outputs and a fully
 documented cloud-based reproducibility workflow. Files included: (i) a
 shareable, coordinate-free training table with extracted Sentinel-1/2
 predictors, (ii) the study area boundary, (iii) a binary Coast Redwood
 presence map, (iv) a pixel-wise Coast Redwood presence probability map,
 (v) a Google Earth Engine reproducibility script, and (vi) a run manifest
 and README documenting the analysis. **Note on third-party data** Ground
 reference points supplied by the **Save the Redwoods League (STRL)** were
 used during model development but are **not included** in this archive due
 to data-use restrictions and sensitive-species considerations. Access to
 those original point locations is subject to STRL approval and is not
 required to reproduce the modeling workflow presented here. ## Files and
 descriptions **1) Dryad_Shareable_TrainingTable_960_NoCoordinates.csv**
 **Type/format:** CSV (UTF-8) **Description:** A coordinate-free training
 dataset containing extracted Sentinel-1 and Sentinel-2 predictor values at
 ground reference locations, together with vegetation composition labels, a
 binary Coast Redwood presence indicator, and cross-validation fold
 assignments. This table enables full reproduction of model training,
 evaluation, and validation steps without disclosing point-level locations.
 **Key fields (columns):** * Sentinel-2 spectral bands (B1–B12; resampled
 as described in the manuscript) * Sentinel-1 backscatter bands (VV, VH)
 and VV/VH ratio * Class – categorical vegetation composition class
 (string), representing dominant canopy composition at each reference
 location. The six vegetation composition classes used in the study are: *
 **DOG** – Douglas-fir dominated stands * **SML** – Smaller trees
 (shorter-stature or early successional forest vegetation) * **RED** –
 Coast Redwood (*Sequoia sempervirens*) only * **RDF** – Mixed Coast
 Redwood and Douglas-fir * **ROT** – Coast Redwood mixed with other taller
 tree species (excluding Douglas-fir) * **TAL** – Tall trees other than
 Coast Redwood and Douglas-fir * NumericClass – integer encoding of the six
 vegetation composition classes (0–5), used as the response variable for
 multi-class Random Forest classification in Google Earth Engine. The
 mapping is: * DOG → 0 * SML → 1 * RED → 2 * RDF → 3 * ROT → 4 * TAL → 5 *
 Redwood_Presence – binary response variable (1 = Coast Redwood present, 0
 = absent) * fold – spatial block fold identifier used for cross-validation
 * has_fill – indicator flag (1 = at least one predictor value filled due
 to masking in the Sentinel satellite composite) **Notes on class
 encoding:** NumericClass is a machine-readable representation of
 categorical vegetation composition. The numeric values do **not** imply
 any ordinal or hierarchical relationship among classes and are used solely
 for computational purposes. The mapping between Class and NumericClass is
 defined explicitly in the Google Earth Engine script
 (Redwood_Extent_Mapping_RF_v01.js) and is fully reproducible. **Notes on
 predictor completeness:** Rows with has_fill = 1 indicate locations where
 one or more predictor values were filled due to masking in the Sentinel
 composites. By default, the provided GEE script trains and evaluates
 models using rows where has_fill = 0. **2) Redwood_Extent_Boundary.zip**
 **Type/format:** ESRI Shapefile (zipped; includes .shp, .shx, .dbf, .prj)
 **Description:** Polygon boundary defining the study area (area of
 interest) over which models were trained, applied, and evaluated.
 **Provenance:** Derived from the union of redwood presence extents from
 CALVEG, LEMMA, and a custom map provided by the Save the Redwoods League.
 **Spatial reference:** Defined in the included .prj file. **3)
 Redwood_Presence_Raster.zip** **Type/format:** GeoTIFF (zipped)
 **Description:** Final binary map of Coast Redwood presence across the
 study area. **Value encoding:** * 1 = Redwood presence * NoData = outside
 the analysis boundary **Notes:** Derived by thresholding the probability
 surface using the decision rule described in the manuscript. **4)
 Redwood_Presence_Probability_Map.zip** **Type/format:** GeoTIFF (zipped)
 **Description:** Pixel-wise probability (0–1) of Coast Redwood presence
 across the study area. **Value range:** Continuous 0.0–1.0 (floating
 point). **Notes:** Suitable for alternative thresholding, sensitivity
 analyses, and uncertainty visualization. **5)
 Redwood_Extent_Mapping_RF_v01.js** **Type:** Software (Google Earth Engine
 JavaScript) **Description:** Google Earth Engine script implementing the
 full modeling workflow using the shareable training table and AOI,
 including: * Sentinel-1 and Sentinel-2 composite generation * Random
 Forest model training * Accuracy assessment and cross-validation * Spatial
 block cross-validation * Variable importance analysis * Export of
 classified raster outputs This script reproduces the analysis **without
 requiring point-level geographic coordinates**. **6)
 Dryad_RunManifest_Metadata.csv** **Type/format:** CSV **Description:**
 Metadata file documenting the exact satellite collections, temporal
 windows, spatial resolution, resampling methods, modeling parameters, and
 cross-validation settings used in the analysis. **7)
 README_GEE_Reproducibility.txt** **Type/format:** Text **Description:**
 Step-by-step instructions for reproducing the Google Earth Engine
 analysis, including asset upload procedures and script execution details.
 ### Sensitive species location handling *Sequoia sempervirens* is listed
 as **Endangered**. To reduce the risk of revealing sensitive locations,
 this dataset does not include geographic coordinates of field
 observations. Model reproducibility is ensured through the release of
 extracted satellite predictor values, raster outputs, and a fully
 documented cloud-based analysis workflow. ### Access to finer-resolution
 location data Access to original field coordinates may be considered on a
 case-by-case basis for legitimate research purposes, subject to ethical
 considerations and third-party data permissions. Interested researchers
 may contact the corresponding author.