Near real-time Feed On Offer (FOO) from MODIS for early season grazing management of Mediterranean annual pastures

Smith, Richard C.; Adams, Merrin S.; Gittins, S. P.; Gherardi, SG; Wood, D. A.; Maier, Stefan W.; Stovold, Richard; Donald, G. E.; Khohkar, Sarfraz; Allen, Adrian

doi:10.1080/01431161.2010.487078

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…Previous studies have reported good agreement between field measurements of pasture biomass and information extracted from satellite data [13][14][15]. However, the temporal frequency of satellite data and the spatial resolution needed to capture the forage production variation between or within paddocks have been an obstacle to achieve effective pasture monitoring using the commonly used satellite data, such as Terra/Aqua/MODIS (Moderate Resolution Imaging Spectroradiometer) (250 m near daily since 2000) [16,17] and Landsat 5/TM, 7/ETM+, 8/OLI (30 m every 16 days from 1984) [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

et al. 2020

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Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

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Section: Introductionmentioning

confidence: 99%

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

et al. 2020

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“…The EVI (15%), the SAVI (9%) and the Leaf Area Index (LAI) (8%) were also utilized often within satellite data-based biomass models. The empirical relationship was mostly created by using a simple linear or multiple linear regression (60% of studies using biomass samples) [72][73][74]. In addition, in some cases machine learning-based regression methods were tested to estimate biomass, such as Random Forest [75][76][77][78], Support Vector Machines [79], Generalized Linear Models [80], Gaussian process regression [81], Artificial Neural Networks [82][83][84][85][86][87][88] and Adaptive Neuro-Fuzzy Inference Systems [83].…”

Section: Mapping Grassland Production Using a Vegetation Index And Grmentioning

confidence: 99%

Remote Sensing of Grassland Production and Management—A Review

2020

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Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

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“…It also assumes that sustainable production may not be achieved because of the lack of information to make sound management decisions on feed resources. Pastures from Space uses remotely sensed data to provide estimates of pasture production during the growing season (Hill et al, 2004;Edirisinghe et al, 2011;Smith et al, 2011). In recent years, farmers have accounted for about 70% of total users logging into the Commonwealth Scientific and Industrial Research Organisation (CSIRO)'s systems seeking estimates of pasture biomass and growth rates.…”

Section: Lucianamentioning

confidence: 99%

Productivity and Biomass of Australia’s Rangelands: Towards a National Database

Porfirio

Antille²,

Watson

et al. 2020

The Proceedings of the Royal Society of Queensland

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This paper reviews information about field observations of vegetation productivity in Australia's rangeland systems and identifies the need to establish a national initiative to collect net primary productivity (NPP) and biomass data for rangeland pastures. Productivity data are needed for vegetation and carbon model parameterisation, calibration and validation. Several methods can be used to estimate pasture productivity at various spatial and temporal scales, ranging from in situ measurements to satellite-based approaches and biogeochemical modelling. However, there is a barrier to implementing national vegetation and carbon modelling schemes because of the lack of digitised and readily available data derived from field observations, not because of the lack of modelling expertise. Our main goal in this paper is to explore the potential for consolidation of existing NPP and biomass databases for Australian rangelands. A protocol structure was proposed to establish a productivity database for Australia. The TERN (Terrestrial Ecosystems Research Network) national field data network for rangeland pasture productivity monitoring and modelling team could potentially coordinate the database. Government agencies and national and international research institutions could use the outputs from productivity models to inform greenhouse gas emissions and in measuring mitigation activities relevant for reporting against the United Nations' Sustainable Development Goals and other international obligations. Other applications include monitoring fire danger, tracking ecological restoration and protection, and estimating fodder availability. Australian researchers have the tools needed to succeed in creating such a national database and a robust community of practice to curate it, enhance it and benefit from its availability.

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Near real-time Feed On Offer (FOO) from MODIS for early season grazing management of Mediterranean annual pastures

Cited by 8 publications

References 20 publications

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

Remote Sensing of Grassland Production and Management—A Review

Productivity and Biomass of Australia’s Rangelands: Towards a National Database

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