On the public citation graph

Mapping Forest Communities, Including Species Composition, Structure, and Carbon, at 10-m Resolution Using Geospatial Embeddings

https://doi.org/10.2139/ssrn.5936862
0 signed · 46 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/eap.1724openalex-snapshot
Current and projected cumulative impacts of fire, drought, and insects on timber volumes across Canada
inferredcites →https://doi.org/10.1002/qj.4803openalex-snapshot
The ERA5 global reanalysis from 1940 to 2022
inferredcites →https://doi.org/10.1007/978-0-387-84858-7openalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1007/s00267-014-0364-1openalex-snapshot
Ecoregions of the Conterminous United States: Evolution of a Hierarchical Spatial Framework
inferredcites →https://doi.org/10.1007/s13280-016-0772-yopenalex-snapshot
When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration
inferredcites →https://doi.org/10.1007/s13280-019-01259-xopenalex-snapshot
The tree species matters: Biodiversity and ecosystem service implications of replacing Scots pine production stands with Norway spruce
inferredcites →https://doi.org/10.1016/j.foreco.2012.02.002openalex-snapshot
A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data
inferredcites →https://doi.org/10.1016/j.foreco.2025.122576openalex-snapshot
Moderate-resolution mapping of aboveground biomass stocks, forest structure, and composition in coastal Alaska and British Columbia
inferredcites →https://doi.org/10.1016/j.isprsjprs.2018.01.006openalex-snapshot
Harmonic regression of Landsat time series for modeling attributes from national forest inventory data
inferredcites →https://doi.org/10.1016/j.jag.2022.103154openalex-snapshot
Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning
inferredcites →https://doi.org/10.1016/j.rse.2007.08.023openalex-snapshot
The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest
inferredcites →https://doi.org/10.1016/j.rse.2007.10.009openalex-snapshot
Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data
inferredcites →https://doi.org/10.1016/j.rse.2011.05.028openalex-snapshot
GMES Sentinel-1 mission
inferredcites →https://doi.org/10.1016/j.rse.2011.08.026openalex-snapshot
The next Landsat satellite: The Landsat Data Continuity Mission
inferredcites →https://doi.org/10.1016/j.rse.2011.11.026openalex-snapshot
Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
inferredcites →https://doi.org/10.1016/j.rse.2013.12.013openalex-snapshot
Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure
inferredcites →https://doi.org/10.1016/j.rse.2014.02.001openalex-snapshot
Landsat-8: Science and product vision for terrestrial global change research
inferredcites →https://doi.org/10.1016/j.rse.2016.08.013openalex-snapshot
Review of studies on tree species classification from remotely sensed data
inferredcites →https://doi.org/10.1016/j.rse.2020.111968openalex-snapshot
Landsat 9: Empowering open science and applications through continuity
inferredcites →https://doi.org/10.1016/j.rse.2020.112165openalex-snapshot
Mapping global forest canopy height through integration of GEDI and Landsat data
inferredcites →https://doi.org/10.1016/j.rse.2021.112743openalex-snapshot
Mapping temperate forest tree species using dense Sentinel-2 time series
inferredcites →https://doi.org/10.1016/j.rse.2022.113205openalex-snapshot
Mapping tree species proportions from satellite imagery using spectral–spatial deep learning
inferredcites →https://doi.org/10.1016/j.rse.2022.113276openalex-snapshot
Mapping the presence and distribution of tree species in Canada's forested ecosystems
inferredcites →https://doi.org/10.1017/cbo9780511623332openalex-snapshot
Multivariate Analysis in Community Ecology
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1029/2004gl019920openalex-snapshot
The gravity recovery and climate experiment: Mission overview and early results
inferredcites →https://doi.org/10.1029/2019ef001348openalex-snapshot
Spatial Simulation of Codesigned Land Cover Change Scenarios in New England: Alternative Futures and Their Consequences for Conservation Priorities
inferredcites →https://doi.org/10.1038/387253a0openalex-snapshot
The value of the world's ecosystem services and natural capital
inferredcites →https://doi.org/10.1046/j.1365-2699.2002.00757.xopenalex-snapshot
The forests of presettlement New England, USA: spatial and compositional patterns based on town proprietor surveys
inferredcites →https://doi.org/10.1046/j.1365-2745.2003.00815.xopenalex-snapshot
Habitat association among Amazonian tree species: a landscape‐scale approach
inferredcites →https://doi.org/10.1093/forestry/cpac002openalex-snapshot
Deep learning for forest inventory and planning: a critical review on the remote sensing approaches so far and prospects for further applications
inferredcites →https://doi.org/10.1093/forestry/cpad024openalex-snapshot
Remote sensing in forestry: current challenges, considerations and directions
inferredcites →https://doi.org/10.1093/jofore/fvac022openalex-snapshot
TreeMap 2016 Dataset Generates CONUS-Wide Maps of Forest Characteristics Including Live Basal Area, Aboveground Carbon, and Number of Trees per Acre
inferredcites →https://doi.org/10.1109/mgrs.2017.2762307openalex-snapshot
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
inferredcites →https://doi.org/10.1111/gcb.16133openalex-snapshot
Will forest dynamics continue to accelerate throughout the 21st century in the Northern Alps?
inferredcites →https://doi.org/10.1111/gcb.16517openalex-snapshot
What is the role of disturbance in catalyzing spatial shifts in forest composition and tree species biomass under climate change?
inferredcites →https://doi.org/10.1111/j.1654-1103.2010.01244.xopenalex-snapshot
Mapping gradients of community composition with nearest‐neighbour imputation: extending plot data for landscape analysis
inferredcites →https://doi.org/10.1126/science.1111772openalex-snapshot
Global Consequences of Land Use
inferredcites →https://doi.org/10.1139/cjfr-2013-0401openalex-snapshot
Mapping attributes of Canada’s forests at moderate resolution through k NN and MODIS imagery
inferredcites →https://doi.org/10.1139/cjfr-2018-0196openalex-snapshot
Applications of the United States Forest Inventory and Analysis dataset: a review and future directions
inferredcites →https://doi.org/10.1139/x02-011openalex-snapshot
Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A.
inferredcites →https://doi.org/10.1177/030913339501900403openalex-snapshot
Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients
inferredcites →https://doi.org/10.1609/aaai.v38i1.27753openalex-snapshot
Neural Embeddings for kNN Search in Biological Sequence
inferredcites →https://doi.org/10.1890/1051-0761(2007)017[0005:ceaseo]2.0.co;2openalex-snapshot
CUMULATIVE ECOLOGICAL AND SOCIOECONOMIC EFFECTS OF FOREST POLICIES IN COASTAL OREGON
inferredcites →https://doi.org/10.3389/fpls.2022.787527openalex-snapshot
Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings
inferredcites →https://doi.org/10.3390/rs13245113openalex-snapshot
Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.