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A sensitivity analysis of groundwater-recharge estimates from a water-budget model was completed for the islands of Oahu and Maui, Hawaii (Johnson and others, 2023). Results of the sensitivity analysis were used to quantify the relative importance of selected model parameters to recharge estimates for three moisture zones (dry, mesic, and wet) on Oahu and Maui. These shapefiles contain the boundaries of the moisture zones and boundaries of the model subareas that were used in the model simulations for Oahu and Maui. Attributes in the shapefiles include the names of the land-cover types assigned to model subareas and the mean annual recharge values determined for the model subareas for the baseline scenario of the...
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...
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...
Moloka‘i has great wetland restoration potential in Hawaiʻi, but most remaining sites are highly degraded. The future of several endangered waterbirds and insects relies on restoring coastal wetland habitat that is resilient under sea-level rise and coastal flooding. Currently, managers lack background data on Molokaʻi to prioritize sites for restoration. In this project, Researchers will develop a comprehensive dataset and create a prioritization plan for coastal wetland restoration. The team will work closely with project partners and stakeholders to develop a well-vetted plan to support endangered species and meeting community needs. Existing maps and spatial data about the Molokaʻi landscape will be compiled...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2021,
Birds,
Birds,
CASC,
Data Visualization & Tools,
The Hawaiian Islands are both biologically and ecologically diverse. To better manage and understand this diverse landscape, detailed, reliable projections of future changes in climate are needed by Hawaiʻi resource managers, such as land managers, conservation organizations, and decision makers. Global climate models (or “general circulation models”) produce projections at regional or global scales, however, they are of limited value for managers of small island resources. Currently, large scale projections are commonly “downscaled” to project future climate variations and conditions at the local scale. However, dynamical downscaling models produce huge output datasets that are often difficult to access and use...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2021,
CASC,
Data Visualization & Tools,
Data Visualization & Tools,
Other Landscapes,
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...
This raster integrates the species-specific and community classifications using a hierarchical approach based on classification certainty. A 0.66 probability threshold was applied, with pixels assigned the finest species-specific class as long as the probability exceeded the threshold. Pixels below the threshold were assigned to the broader community class meeting the threshold. This approach displays the most detailed class possible given a minimum confidence, providing a map that balances specificity and certainty. Please note that to reduce the inherent 'salt and pepper' noise in the final land cover classification map, we applied a 3x3 pixel moving window majority filter to the final classification results.
This raster depicts the distribution of broader community-level vegetation classes across Lāna‘i. To generate this map, the species-specific class probabilities were summed to get total probability of membership in each defined community class. Each pixel was then assigned to the community class with the highest probability. This generalized map allows for an assessment of vegetation patterns at a coarser categorical level across the island. Please note that to reduce the inherent 'salt and pepper' noise in the final land cover classification map, we applied a 3x3 pixel moving window majority filter to the final classification results.
This raster stack contains 15 probability layers representing the pixel-level predicted probability of membership in each species-specific vegetation class from 0 to 1. These probability layers can be used to generate class membership uncertainty maps or probabilistic class cover maps from the model outputs. They provide additional information beyond the discrete categorial land cover assignments.
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Conservation,
Hawaiian Islands,
Hawai‘i,
Invasive species,
Land cover,
This data set consists of 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...
Actionable science has evolved rapidly over the last decade, and the Climate Adaptation Science Center (CASC) network has established itself as a leader in the field. The practice of actionable science is generally described as user-focused, action-oriented science that addresses pressing real-world climate adaptation challenges. It is also sometimes referred to as usable science, translational ecology, and coproduction. Successfully carrying out actionable science projects requires a range of skills, mindsets, and techniques in addition to scientific knowledge. Those skills can include mutual learning with stakeholders, attention to social and political context, iterative creative problem-solving, and interdisciplinary...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2021,
Alaska,
CASC,
National CASC,
National CASC,
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...
This raster depicts the distribution of 15 species-specific vegetation classes across the island of Lāna‘i at 2m resolution. It represents the final selected neural network model predictions with expert-adjusted posterior probabilities. Each pixel is assigned to the most likely species-specific class based on the model. Overall and class-specific accuracy assessments indicate this map has generally over 95% accuracy. It provides detailed species-level vegetation mapping to support conservation planning and monitoring. Please note that to reduce the inherent 'salt and pepper' noise in the final land cover classification map, we applied a 3x3 pixel moving window majority filter to the final classification results.
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