On the public citation graph

A Continental-Scale Approach to Ecosystem Condition Monitoring Using Satellite Imagery and Deep Learning

0 signed · 63 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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.1970openalex-snapshot

Modeling biodiversity benchmarks in variable environments

inferredcites →https://doi.org/10.1002/ece3.5376openalex-snapshot

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition

inferredcites →https://doi.org/10.1002/rse2.59openalex-snapshot

Satellite remote sensing of ecosystem functions: opportunities, challenges and way forward

inferredcites →https://doi.org/10.1007/s10980-021-01221-xopenalex-snapshot

Extending vegetation site data and ensemble models to predict patterns of foliage cover and species richness for plant functional groups

inferredcites →https://doi.org/10.1007/s10994-020-05942-zopenalex-snapshot

A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping

inferredcites →https://doi.org/10.1016/j.compag.2021.106090openalex-snapshot

A new attention-based CNN approach for crop mapping using time series Sentinel-2 images

inferredcites →https://doi.org/10.1016/j.ecolind.2012.09.014openalex-snapshot

Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats

inferredcites →https://doi.org/10.1016/j.ecolind.2015.03.021openalex-snapshot

Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review

inferredcites →https://doi.org/10.1016/j.ecolmodel.2009.01.037openalex-snapshot

Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

inferredcites →https://doi.org/10.1016/j.envsoft.2022.105467openalex-snapshot

Multi-modal temporal CNNs for live fuel moisture content estimation

inferredcites →https://doi.org/10.1016/j.gecco.2020.e01299openalex-snapshot

Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982–2014

inferredcites →https://doi.org/10.1016/j.isprsjprs.2021.07.010openalex-snapshot

Live fuel moisture content estimation from MODIS: A deep learning approach

inferredcites →https://doi.org/10.1016/j.jnc.2017.08.004openalex-snapshot

Review of native vegetation condition assessment concepts, methods and future trends

inferredcites →https://doi.org/10.1016/j.rse.2009.01.006openalex-snapshot

Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

inferredcites →https://doi.org/10.1016/j.rse.2012.02.021openalex-snapshot

Separating grazing and rainfall effects at regional scale using remote sensing imagery: A dynamic reference-cover method

inferredcites →https://doi.org/10.1016/j.rse.2015.11.032openalex-snapshot

The global Landsat archive: Status, consolidation, and direction

inferredcites →https://doi.org/10.1016/j.rse.2015.12.024openalex-snapshot

Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity

inferredcites →https://doi.org/10.1016/j.rse.2017.03.015openalex-snapshot

The Australian Geoscience Data Cube — Foundations and lessons learned

inferredcites →https://doi.org/10.1016/j.rse.2019.111259openalex-snapshot

Detecting hotspots of interactions between vegetation greenness and terrestrial water storage using satellite observations

inferredcites →https://doi.org/10.1016/j.rse.2020.111797openalex-snapshot

SAR-enhanced mapping of live fuel moisture content

inferredcites →https://doi.org/10.1016/j.rse.2024.114311openalex-snapshot

Quantifying how topography impacts vegetation indices at various spatial and temporal scales

inferredcites →https://doi.org/10.1016/j.scitotenv.2016.05.170openalex-snapshot

The Australian SuperSite Network: A continental, long-term terrestrial ecosystem observatory

inferredcites →https://doi.org/10.1016/j.scitotenv.2021.146716openalex-snapshot

Future land-use changes and its impacts on terrestrial ecosystem services: A review

inferredcites →https://doi.org/10.1029/2005rg000183openalex-snapshot

The Shuttle Radar Topography Mission

inferredcites →https://doi.org/10.1038/nature11118openalex-snapshot

A global synthesis reveals biodiversity loss as a major driver of ecosystem change

inferredcites →https://doi.org/10.1038/s41467-017-01530-3openalex-snapshot

Mapping functional diversity from remotely sensed morphological and physiological forest traits

inferredcites →https://doi.org/10.1038/s41467-021-21266-5openalex-snapshot

Implications of the 2019–2020 megafires for the biogeography and conservation of Australian vegetation

inferredcites →https://doi.org/10.1038/s41559-021-01451-xopenalex-snapshot

Priority list of biodiversity metrics to observe from space

inferredcites →https://doi.org/10.1038/s41559-021-01538-5openalex-snapshot

Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework

inferredcites →https://doi.org/10.1038/s41586-020-2531-2openalex-snapshot

Ecosystem decay exacerbates biodiversity loss with habitat loss

inferredcites →https://doi.org/10.1038/s41586-021-04179-7openalex-snapshot

Do not downplay biodiversity loss

inferredcites →https://doi.org/10.1071/sr14271openalex-snapshot

Derivation of terrain covariates for digital soil mapping in Australia

inferredcites →https://doi.org/10.1071/wf22188openalex-snapshot

Projecting live fuel moisture content via deep learning

inferredcites →https://doi.org/10.1080/17538947.2022.2130461openalex-snapshot

Operational continental-scale land cover mapping of Australia using the Open Data Cube

inferredcites →https://doi.org/10.1080/20964471.2017.1402490openalex-snapshot

Digital earth Australia – unlocking new value from earth observation data

inferredcites →https://doi.org/10.1080/20964471.2021.1964879openalex-snapshot

Deep learning for processing and analysis of remote sensing big data: a technical review

inferredcites →https://doi.org/10.1093/biosci/biw037openalex-snapshot

Measuring Ecological Integrity: History, Practical Applications, and Research Opportunities

inferredcites →https://doi.org/10.1109/icsccc.2018.8703316openalex-snapshot

Convolutional Neural Network (CNN) for Image Detection and Recognition

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/10.1109/mgrs.2024.3393010openalex-snapshot

Deep Learning for Satellite Image Time-Series Analysis: A review

inferredcites →https://doi.org/10.1111/2041-210x.12579openalex-snapshot

Habitat Condition Assessment System: a new way to assess the condition of natural habitats for terrestrial biodiversity across whole regions using remote sensing data

inferredcites →https://doi.org/10.1111/2041-210x.13018openalex-snapshot

Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

inferredcites →https://doi.org/10.1111/conl.12295openalex-snapshot

Persistent Disparities between Recent Rates of Habitat Conversion and Protection and Implications for Future Global Conservation Targets

inferredcites →https://doi.org/10.1111/conl.12822openalex-snapshot

Toward monitoring forest ecosystem integrity within the post‐2020 Global Biodiversity Framework

inferredcites →https://doi.org/10.1111/ecog.06426openalex-snapshot

Patterns and drivers of plant diversity across Australia

inferredcites →https://doi.org/10.1111/gcb.15383openalex-snapshot

Reference state and benchmark concepts for better biodiversity conservation in contemporary ecosystems

inferredcites →https://doi.org/10.1111/j.1365-2486.2008.01746.xopenalex-snapshot

Climate‐related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006

inferredcites →https://doi.org/10.1111/j.1442-8903.2006.293_2.xopenalex-snapshot

Vegetation condition mapping at a landscape‐scale across Victoria

inferredcites →https://doi.org/10.1111/j.1461-0248.2010.01457.xopenalex-snapshot

Habitat fragmentation causes immediate and time‐delayed biodiversity loss at different trophic levels

inferredcites →https://doi.org/10.1126/sciadv.1400253openalex-snapshot

Accelerated modern human–induced species losses: Entering the sixth mass extinction

inferredcites →https://doi.org/10.1126/sciadv.abm9982openalex-snapshot

The direct drivers of recent global anthropogenic biodiversity loss

inferredcites →https://doi.org/10.1126/science.aam9317openalex-snapshot

Biodiversity losses and conservation responses in the Anthropocene

inferredcites →https://doi.org/10.1126/science.adg7870openalex-snapshot

Loss of terrestrial biodiversity in Australia: Magnitude, causation, and response

inferredcites →https://doi.org/10.1126/science.adn3441openalex-snapshot

Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050

inferredcites →https://doi.org/10.1146/annurev-environ-120120-054300openalex-snapshot

Biodiversity: Concepts, Patterns, Trends, and Perspectives

inferredcites →https://doi.org/10.1890/070176openalex-snapshot

Monitoring and evaluating the ecological integrity of forest ecosystems

inferredcites →https://doi.org/10.25080/majora-7b98e3ed-013openalex-snapshot

Dask: Parallel Computation with Blocked algorithms and Task Scheduling

inferredcites →https://doi.org/10.3390/rs11050523openalex-snapshot

Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

inferredcites →https://doi.org/10.3390/rs11171971openalex-snapshot

Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives

inferredcites →https://doi.org/10.3390/rs14164101openalex-snapshot

Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.5555/3026877.3026899openalex-snapshot

TensorFlow: A system for large-scale machine learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.3389/fevo.2026.1856707openalex-snapshot

Plant diversity and community characteristics in the intake area of the South-to-North water diversion project canal in Nanyang City

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.