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Abstract Kelp species provide many ecosystem services associated with their three‐dimensional structures. Among these, fast‐growth, canopy‐forming species, like giant kelpMacrocystis pyrifera, are the foundation of kelp forests across many temperate reefs. Giant kelp populations have experienced regional declines in different parts of the world. Giant kelp canopy is very dynamic and can take years to recover from disturbance, challenging comparisons of standing biomass with historical baselines. The Santa Barbara Coastal LTER (SBC LTER), curates a time series of Landsat sensed surface cover and biomass for giant kelp in the west coast of North America. In the last decade, this resource has been fundamental to understanding the species' population dynamics and drivers. However, simple ready‐to‐use summary statistics aimed at classifying regional kelp decline or recovery are not readily available to stakeholders and coastal managers. To this end, we describe here two simple metrics made available through the R package kelpdecline. First, the proportion of Landsat pixels in decline (PPD), in which current biomass is compared with a historical baseline, and second, a pixel occupancy trend (POT), in which current year pixel occupancy is compared to the time‐series long probability of occupancy. The package produces raster maps and output tables summarizing kelp decline and trends over a 0.25 × 0.25° scale. Using kelpdecline, we show how sensitivity analysis onPPDparameter variation can increase the confidence of kelp decline estimates.
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This content will become publicly available on December 5, 2025
geodl: An R package for geospatial deep learning semantic segmentation using torch and terra
Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer’s accuracies, class-level precisions or user’s accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development.
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- Award ID(s):
- 2046059
- PAR ID:
- 10584439
- Editor(s):
- Sun, Xiaoyong
- Publisher / Repository:
- PLOS One
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 19
- Issue:
- 12
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0315127
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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