Research graph
References from Grassland ecosystem assessments: Integrating UAV-derived features for aboveground biomass estimation. Local targets link to admitted publications; unresolved targets remain external evidence.
Grasslands—more important for ecosystem services than you might think
10.1002/ecs2.2582 · 2019 · External reference
10.4324/9781315775302-37
10.4324/9781315775302-37 · External reference
Future wet grasslands: ecological implications of climate change
2016 · External reference
Influence of cutting regime and fertilizer application on the botanical composition, yield and nutritive value of herbage of wet grasslands in Central Europe
10.1111/j.1365-2494.2009.00713.x · 2009 · External reference
Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches
10.1016/j.rse.2014.05.018 · 2014 · External reference
Carbon sequestration in temperate grassland ecosystems and the influence of management, climate and elevated CO2
10.1111/j.1469-8137.2004.01201.x · 2004 · External reference
Assessment of spatial variability of multiple ecosystem services in grasslands of different intensities
2019 · External reference
Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of Grassland habitats
10.1080/01431161.2010.532172 · 2011 · External reference
Productivity and sustainability influenced by biodiversity in grassland ecosystems
10.1038/379718a0 · 1996 · External reference
Canopy height measurements and non-destructive biomass estimation of Lolium perenne swards using UAV imagery
10.1111/gfs.12439 · 2019 · External reference
Replacing manual rising plate meter measurements with low-cost UAV-derived sward height data in grasslands for spatial monitoring
2018 · External reference
Remote sensing of wetlands: case studies comparing practical techniques
10.2112/jcoastres-d-10-00174.1 · 2011 · External reference
Spectral discrimination of vegetation types in a coastal wetland
10.1016/s0034-4257(02)00196-7 · 2003 · External reference
10.1155/2017/1353691
10.1155/2017/1353691 · External reference
All models of satellite-derived phenology are wrong, but some are useful: a case study from northern Australia
2021 · External reference
Improving accuracy of herbage yield predictions in perennial ryegrass with UAV-based structural and spectral data fusion and machine learning
10.3390/rs13173459 · 2021 · External reference
Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands
10.1111/gfs.12312 · 2018 · External reference
Can low-cost unmanned aerial systems describe the forage quality heterogeneity? Insight from a timothy pasture case study in southern Belgium
10.3390/rs12101650 · 2020 · External reference
Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices
10.1117/1.jrs.13.034525 · 2019 · External reference
10.5194/isprsarchives-xli-b1-991-2016
10.5194/isprsarchives-xli-b1-991-2016 · External reference
Herbage mass, N concentration, and N uptake of temperate grasslands can adequately be estimated from UAV-based image data using machine learning
10.3390/rs14133066 · 2022 · External reference
Prediction of biomass and N fixation of legume–grass mixtures using sensor fusion
10.3389/fpls.2020.603921 · 2021 · External reference
Fusion of ultrasonic and spectral sensor data for improving the estimation of biomass in grasslands with heterogeneous sward structure
10.3390/rs9010098 · 2017 · External reference
Evaluation of grass quality under different soil management scenarios using remote sensing techniques
10.3390/rs11151835 · 2019 · External reference
Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices
10.1016/j.isprsjprs.2019.02.022 · 2019 · External reference
Textural features for image classification
10.1109/tsmc.1973.4309314 · 1973 · External reference
Monitoring pasture aboveground biomass and canopy height in an integrated crop–livestock system using textural information from planetscope imagery
10.3390/rs12162534 · 2020 · External reference
The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures
10.1371/journal.pone.0234703 · 2020 · External reference
A systematic review of the factors influencing the estimation of vegetation aboveground biomass using unmanned aerial systems
10.3390/rs12071052 · 2020 · External reference
Estimating biomass of native grass grown under complex management treatments using worldview-3 spectral derivatives
10.3390/rs9010055 · 2017 · External reference
A review of estimation methods for aboveground biomass in grasslands using UAV
10.3390/rs15030639 · 2023 · External reference
Dynamic C and N stocks - key factors controlling the C gas exchange of maize in heterogenous peatland
10.5194/bg-12-2737-2015 · 2015 · External reference
A novel machine learning method for estimating biomass of grass swards using a photogrammetric canopy height model, images and vegetation indices captured by a drone
2018 · External reference
Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability
10.1007/s11119-004-5319-8 · 2004 · External reference
Estimating canopy chlorophyll concentration from field and airborne spectra
10.1016/s0034-4257(98)00113-8 · 1999 · External reference
A broad-band leaf chlorophyll vegetation index at the canopy scale
10.1007/s11119-008-9075-z · 2008 · External reference
A comparison of vegetation indices over a global set of TM images for EOS-MODIS
10.1016/s0034-4257(96)00112-5 · 1997 · External reference
10.13031/2013.27838
10.13031/2013.27838 · External reference
Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves
10.1078/0176-1617-00887 · 2003 · External reference
Use of a green channel in remote sensing of global vegetation from EOS-MODIS
10.1016/s0034-4257(96)00072-7 · 1996 · External reference
Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance
10.1016/s0034-4257(00)00113-9 · 2000 · External reference
A modified soil adjusted vegetation index
10.1016/0034-4257(94)90134-1 · 1994 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Red and photographic infrared linear combinations for monitoring vegetation
10.1016/0034-4257(79)90013-0 · 1979 · External reference
Optimization of soil-adjusted vegetation indices
10.1016/0034-4257(95)00186-7 · 1996 · External reference
Estimating PAR absorbed by vegetation from bidirectional reflectance measurements
10.1016/0034-4257(94)00114-3 · 1995 · External reference
Derivation of leaf-area index from quality of light on the forest floor
10.2307/1936256 · 1969 · External reference
Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry
10.1016/j.rse.2020.111830 · 2020 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Estimation of herbage biomass and nutritive status using band depth features with partial least squares regression in Inner Mongolia grassland, China
10.1111/grs.12112 · 2016 · External reference
Mapping foliar functional traits and their uncertainties across three years in a grassland experiment
10.1016/j.rse.2018.11.016 · 2019 · External reference
The fusion of spectral and structural datasets derived from an airborne multispectral sensor for estimation of pasture dry matter yield at paddock scale with time
10.3390/rs12122017 · 2020 · External reference
Mapping and monitoring of biomass and grazing in pasture with an unmanned aerial system
10.3390/rs11050473 · 2019 · External reference
Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment. PFG—Journal of Photogrammetry
2020 · External reference
Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks
10.3390/rs14112692 · 2022 · External reference
