This data set comprises a 30 m resolution GeoTIFF raster stack containing multiple topographical variables for the Hawaiian Islands (elevation, aspect, slope, geomorphon landform classification, and hillshade). This 30 meter resolution 5-band GeoTIFF includes the following topographical layers: • Elevation (m) • Slope (degrees) • Aspect (degrees) • Geomorphon (integer values) landform classification (1-flat, 2-peak, 3-ridge, 4-shoulder, 5-spur, 6-slope, 7-hollow, 8-footslope, 9-valley, 10-pit) • Hillshade (integer values)

This data set comprises a 10m resolution GeoTIFF raster stack containing multiple topographical variables for the Hawaiian Islands (elevation, aspect, slope, geomorphon landform classification, and hillshade). 10-meter resolution 5 band GeoTIFF includes the following topographical layers: • Elevation (m) • Slope (degrees) • Aspect (degrees) • Geomorphon (integer values) landform classification (1-flat, 2-peak, 3-ridge, 4-shoulder, 5-spur, 6-slope, 7-hollow, 8-footslope, 9-valley, 10-pit) • Hillshade (integer values)

This data release comprises a collection of high-resolution environmental raster data for the Hawaiian Islands, developed to support conservation planning and ecological research. The collection includes both 30-meter and 10-meter resolution GeoTIFFs with topographical variables (elevation, aspect, slope, hillshade, and geomorphon landform classification), as well as complementary ecological variables (vegetation height, habitat quality, and mean annual temperature and rainfall). All rasters have been processed to share consistent resolution, extent, and projection (WGS84), making them readily integrated into spatial analyses and tool development. The primary source data for the topographical variables was the USGS National Map. The dataset provides standardized environmental layers that can be used to identify suitable microhabitats for species conservation, restoration site selection, and ecological modeling across the Hawaiian archipelago. This data release is divided into 3 files: -a 10m resolution GeoTIFF raster stack containing multiple topographical variables for the Hawaiian Islands (elevation, aspect, slope, hillshade, and geomorphon landform classification). -a 30m resolution GeoTIFF raster stack containing multiple topographical variables for the Hawaiian Islands (elevation, aspect, slope, hillshade, and geomorphon landform classification). -a 30m resolution GeoTIFF raster stack containing multiple ecological/climatic variables that describe natural habitats across the Hawaiian Islands (vegetation height, habitat quality, mean annual temperature and rainfall).

We integrated recent climate model projections developed for the State of Hawai’i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used observation-based data for the baseline bioclimatic variables from the Rainfall Atlas of Hawai’i. We used the most up-to-date dynamically downscaled future projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two projections into a suite of 19 bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions specifically for the Hawaiian Islands. These bioclimatic variables are available state-wide for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under RCP 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections). As Hawai’i is characterized by two 6-month seasons, we also provide mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate.

We integrated recent climate model projections developed for the State of Hawai’i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used observation-based data for the baseline bioclimatic variables from the Rainfall Atlas of Hawai’i. We used the most up-to-date dynamically downscaled future projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two projections into a suite of 19 bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions specifically for the Hawaiian Islands. These bioclimatic variables are available state-wide for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under RCP 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections). As Hawai’i is characterized by two 6-month seasons, we also provide mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate.

We integrated recent climate model projections developed for the State of Hawai’i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used observation-based data for the baseline bioclimatic variables from the Rainfall Atlas of Hawai’i. We used the most up-to-date dynamically downscaled future projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two projections into a suite of 19 bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions specifically for the Hawaiian Islands. These bioclimatic variables are available state-wide for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under RCP 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections). As Hawai’i is characterized by two 6-month seasons, we also provide mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate.

We integrated recent climate model projections developed for the State of Hawai’i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used observation-based data for the baseline bioclimatic variables from the Rainfall Atlas of Hawai’i. We used the most up-to-date dynamically downscaled future projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two projections into a suite of 19 bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions specifically for the Hawaiian Islands. These bioclimatic variables are available state-wide for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under RCP 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections). As Hawai’i is characterized by two 6-month seasons, we also provide mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate.

This dataset provides high-resolution, species-specific land cover maps for the Hawaiian island of Lāna'i based on 2020 WorldView-2 satellite imagery. Machine learning models were trained on extensive ground control polygons and points. The land cover maps capture the distribution and diversity of vegetation with high accuracy to support conservation planning and monitoring. This data release consists of two child items, one containing the field and expert collected ground control data used to train our models, and another consisting of resulting land cover maps for the island of Lāna‘i. The research effort that generated these input data, and products are carefully described in the associated manuscript Berio Fortini et al. 2024. Full citation is listed in the larger work section of this XML file. Inputs included in this page include: Ground control polygons used for model training and evaluation (ground_control_polygons.gpkg): This dataset consists of refined vegetation polygons digitized across the island of Lāna‘i representing the 15 land cover classes of interest. High-resolution aerial imagery and extensive field experience were used to iteratively collect and improve the polygons through expert review and interpretation. The polygons were divided into a 250m grid overlaying the island to balance sample size and spatial resolution while reducing spatial autocorrelation, resulting in 1,754 smaller polygons. These polygon data served as the primary dataset used to train, validate, and evaluate the classification models through cross-validation. An iterative collection process aimed to achieve satisfactory model accuracy across all classes prior to final model selection and island-wide mapping. Ground control points used for independent pixel-level model validation (ground_control_points.gpkg): This dataset consists of 313 points distributed across the 15 vegetation classes on the island of Lāna‘i. The points were randomly generated from the final species-specific land cover classification map and stratified by class to ensure representation across all classes. The dataset provides species-specific land cover labels for the 313 points, with the spatial location corresponding to the pixel coordinate location on the 2m resolution land cover map. Comparing modeled class assignments to these expert-validated classes enables an independent accuracy assessment supplemental to the polygon-based cross-validation accuracy evaluation.

This data set consists of ground control points used for independent pixel-level model validation (ground_control_points.gpkg): This dataset consists of 295 points distributed across the 15 vegetation classes on the island of Lāna‘i. The points were randomly generated from the final species-specific land cover classification map and stratified by class to ensure representation across all classes. The dataset provides species-specific land cover labels for the points, with the spatial location corresponding to the pixel coordinate location on the 2m resolution land cover map. Comparing modeled class assignments to these expert-validated classes enables an independent accuracy assessment supplemental to the polygon-based cross-validation accuracy evaluation.

This dataset comprises high-resolution geotif files representing various aspects of the ʻākohekohe (Palmeria dolei) potential habitat on the Island of Hawaiʻi. It includes a habitat suitability map showing average suitability scores, a map of homogenous forested areas (HFAs) depicting clusters with consistent suitability scores, and a map of pixel-wise standard deviation across habitat suitability models. These maps were generated through a comprehensive analysis using lidar-based metrics, offering detailed insights into the habitat preferences of ʻākohekohe.