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Predicting Plants in the Wild: Mapping Arctic and Boreal Plants with Uas-Based Visible and Near Infrared Reflectance Spectra

https://doi.org/10.2139/ssrn.4827178
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Submeter-level land cover mapping of Japan
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Reflectance spectra of subarctic lichens between 400 and 2400 nm
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Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes
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Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska
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Arctic tundra shrubification can obscure increasing levels of soil erosion in NDVI assessments of land cover derived from satellite imagery
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Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales
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Detection of Vegetation Stress Via a New High Resolution Fluorescence Imaging System
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Cell wall structure and composition of cultured mycobionts from the lichens Cladonia macrophylla, Cladonia caespiticia, and Physcia stellaris (Lecanorales, Ascomycetes)
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Are vegetation indices useful in the Arctic?
inferredcites →https://doi.org/10.1029/2021jg006697openalex-extracted
Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges
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Predicting species distributions and community composition using satellite remote sensing predictors
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A new method for non-parametric multivariate analysis of variance
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Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite
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Remote Sensing of Water Content in Eucalyptus Leaves
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Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery
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Red edge spectral measurements from sugar maple leaves
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A New U-Net Based Convolutional Neural Network for Estimating Caribou Lichen Ground Cover from Field-Level RGB Images
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Scale-dependency of Arctic ecosystem properties revealed by UAV
inferredcites →https://doi.org/10.1088/1748-9326/abf464openalex-extracted
Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data
inferredcites →https://doi.org/10.1088/1748-9326/ac6965openalex-extracted
Time-series maps reveal widespread change in plant functional type cover across Arctic and boreal Alaska and Yukon
inferredcites →https://doi.org/10.1109/tpami.2024.3362475openalex-extracted
SpectralGPT: Spectral Remote Sensing Foundation Model
inferredcites →https://doi.org/10.1111/1365-2745.13976openalex-extracted
Remote sensing from unoccupied aerial systems: Opportunities to enhance Arctic plant ecology in a changing climate
inferredcites →https://doi.org/10.1111/j.1442-9993.2001.01070.pp.xopenalex-extracted
A new method for non‐parametric multivariate analysis of variance
inferredcites →https://doi.org/10.1111/j.1469-8137.2010.03284.xopenalex-extracted
Remote sensing of plant functional types
inferredcites →https://doi.org/10.1139/as-2021-0044openalex-extracted
Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles
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Effects of Mismatches of Scale and Location between Predictor and Response Variables on Forest Structure Mapping
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Building Predictive Models in R Using the caret Package
inferredcites →https://doi.org/10.18637/jss.v077.i01openalex-extracted
ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
inferredcites →https://doi.org/10.18637/jss.v089.i12openalex-extracted
Hyperspectral Data Analysis in R : The hsdar Package
inferredcites →https://doi.org/10.2747/1548-1603.48.3.324openalex-extracted
Mapping Plant Functional Types at Multiple Spatial Resolutions Using Imaging Spectrometer Data
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Regional Quantitative Cover Mapping of Tundra Plant Functional Types in Arctic Alaska
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