Quantitative mapping of pasture biomass using satellite imagery

Edirisinghe, A.; Hill, Michael J.; Donald, G. E.; Hyder, M. W.

doi:10.1080/01431161003743181

Cited by 69 publications

(44 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Vegetation indices of optical satellite imagery such as NDVI (Normalised Differenced Vegetation Index) or EVI (Enhanced Vegetation Index) focus on the green vegetation component. A general relationship between vegetative ground cover and pasture biomass exists for low ground cover areas, but when the ground cover is close to 100% the cover-to-mass relationship saturates and reliable estimates are not possible even at low biomass levels [6][7][8]. Investigating this relationship, Hobbs [5] related four vegetation indices to field data and found a breakdown of biomass levels >1000 kg/ha.…”

Section: Introductionmentioning

confidence: 99%

“…In most savannah systems this approach has significant limitations for pasture biomass estimation due to the presence of senescent dry grass and tree cover. To overcome this direct limitation e.g., Edirisinhe et al [7] and Holm [11] used time series of optical MODIS and AVHRR imagery for a quantitative pasture biomass assessment using cumulative NDVI data. The author of [12] has related MODIS BRDF parameters to pasture biomass, with some success.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

et al. 2016

View full text Add to dashboard Cite

Abstract:Pasture biomass is an important quantity globally in livestock industries, carbon balances, and bushfire management. Quantitative estimates of pasture biomass or total standing dry matter (TSDM) at the field scale are much desired by land managers for land-resource management, forage budgeting, and conservation purposes. Estimates from optical satellite imagery alone tend to saturate in the cover-to-mass relationship and fail to differentiate standing dry matter from litter. X-band radar imagery was added to complement optical imagery with a structural component to improve TSDM estimates in rangelands. High quality paddock-scale field data from a northeastern Australian cattle grazing trial were used to establish a statistical TSDM model by integrating optical satellite image data from the Landsat sensor with observations from the TerraSAR-X (TSX) radar satellite. Data from the dry season of 2014 and the wet season of 2015 resulted in models with adjusted r 2 of 0.81 in the dry season and 0.74 in the wet season. The respective models had a mean standard error of 332 kg/ha and 240 kg/ha. The wet and dry season conditions were different, largely due to changed overstorey vegetation conditions, but not greatly in a pasture 'growth' sense. A more robust combined-season model was established with an adjusted r 2 of 0.76 and a mean standard error of 358 kg/ha. A clear improvement in the model performance could be demonstrated when integrating HH polarised TSX imagery with optical satellite image products.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Several studies used the Normalized Difference Vegetation Index (NDVI) to estimate the LAI of different crop types (such as wheat, grassland, rice, orchard, corn, and maize) or more complex models based on radiative transfer models combined with neural networks [13][14][15]. In addition, several studies have used the NDVI to estimate grassland biomass and height [16][17][18][19]. Schino et al [18] and Payero et al [20] compared different vegetation indices over two different sites in central Italy and northwestern USA and found that NDVI provides the most accurate estimation of grass biomass and height.…”

Section: Introductionmentioning

confidence: 99%

Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data

Hajj¹,

Baghdadi²,

Belaud³

et al. 2014

Remote Sensing

View full text Add to dashboard Cite

Abstract:The objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates . Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°).

show abstract

“…Various vegetation indices from 30 m resolution Landsat Thematic Mapper (TM) imagery were used for pasture biomass in Central Italy, and it was found that NDVI provided the most accurate estimation for grass biomass [5]. Landsat TM images were employed to estimate pasture biomass at the paddock scale in Western Australia [8], but it is difficult to deliver high temporal resolution (weekly and/or daily) information because with low repeating frequency (16 days), and cloud interference, it will be up to one month without data. Therefore, high-resolution Synthetic aperture radar (SAR) and Landsat TM could be combined for pasture biomass estimation in order to deliver information with high temporal and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia

Wang

Liu

2013

Remote Sensing

View full text Add to dashboard Cite

Abstract:Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR), and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) radar signals at horizontally emitted and received polarization (HH) for pasture monitoring at the paddock scale in order to guide farmers for better management. The pasture site is selected, in Otway, Victoria, Australia. The biomass, water content of grass, and soil moisture over this site were analyzed with these three bands of SAR images, through linear relationship between SAR backscattering coefficient, and vegetation indices Normalized Differential Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI)), together with soil moisture index (MI). NDVI, NDWI, and MI are considered as proxy of pasture biomass, plant water content, and soil moisture, respectively, and computed from optical images and climate data. SAR backscattering coefficient and vegetation indices are computed within a grass zone, defined by classification with MODIS data. The grass condition and grazing activities for specific paddocks are detectable, based on SAR backscatter, with all three wavelengths datasets. Both temporal and spatial analysis results show that the X-band SAR has the highest correlation to the vegetation indices. However, its accuracy can be affected by wet weather due to its sensitivity to the OPEN ACCESSRemote Sens. 2013, 5 3612 water on leaves. The C-band HH backscattering coefficient showed moderate reliability to evaluate biomass and water content of grass, with limited influence from rainfall in the dry season. The L-band SAR is the less accurate one for grass biomass measurement due to stronger penetration.

show abstract

Quantitative mapping of pasture biomass using satellite imagery

Cited by 69 publications

References 28 publications

Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data

Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia

Contact Info

Product

Resources

About