Quantifying biomass production on rangeland in southern Alberta using SPOT imagery

Grant, K.; Johnson, Dan L.; Hildebrand, David V.; Peddle, Derek R.

doi:10.5589/m12-056

Cited by 14 publications

(11 citation statements)

References 36 publications

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“…While MODIS sensors have lower spatial resolution, the daily coverage allows creation of eight-day composites (employing maximum value compositing and bidirectional reflectance distribution function correction [37]), so errors due to clouds and other effects are reduced. The SPOT sensor can be directed off-nadir to increase the temporal coverage [24]; however, this introduces error due to altered sensor angle [42]. Based on the results of our study, we conclude that the MODIS sensor may actually be more effective than SPOT for mixed prairie yield monitoring and management, particularly when continuous time series are required over large areas.…”

Section: Discussionmentioning

confidence: 73%

“…Some studies [24] have suggested that SPOT is superior to MODIS for rangeland monitoring due to its higher spatial resolution. SPOT satellite imagery has a spatial resolution of 30 m, which resulted in decreased temporal resolution relative to MODIS, as the smaller field of view requires more orbits of the Earth to cover the same position [42].…”

Section: Discussionmentioning

confidence: 99%

“…Another potential source of error in NDVI measurements can be the background contamination from soil, litter, snow, surface wetness, or low-growing vegetation, which can confound satellite-based assessments of prairie productivity and ground cover [24,47]. For instance, Hall-Beyer and Gwyn (1996) determined that the presence of Selaginella densa, a mat-forming member of the fern phylum that is present in dry mixed-grass prairie, can also lead to errors in biomass estimates from optical remote sensing [48].…”

Section: Discussionmentioning

confidence: 99%

“…The increased spatial resolution limits the possible temporal resolution such that SPOT samples a position on the Earth every 26 days, although the sensor can be pointed off nadir at targets not directly below the satellite, increasing the temporal resolution [18]. Commercial satellites offer several potential advantages over MODIS; the finer spatial resolution of commercial satellites offer the potential of providing data products that more closely match the scales of small management units [24]. However, images from commercial systems are often prohibitively expensive for practical application for range management [18].…”

Section: Introductionmentioning

confidence: 99%

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Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

et al. 2016

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Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass "yield"). We evaluated the NDVI-yield relationship for a southern Alberta prairie rangeland, using seasonal trends in NDVI and biomass during the 2009 and 2010 growing seasons, two years with contrasting rainfall regimes. The study compared harvested biomass and NDVI from field spectrometry to NDVI from three satellite platforms: the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l'Observation de la Terre (SPOT 4 and 5). Correlations between ground spectrometry and harvested biomass were also examined for each growing season. The contrasting precipitation patterns were easily captured with satellite NDVI, field NDVI and green biomass measurements. NDVI provided a proxy measure for green plant biomass, and was linearly related to the log of standing green biomass. NDVI phenology clearly detected the green biomass increase at the beginning of each growing season and the subsequent decrease in green biomass at the end of each growing season due to senescence. NDVI-biomass regressions evolved over each growing season due to end-of-season senescence and carryover of dead biomass to the following year. Consequently, mid-summer measurements yielded the strongest correlation (R 2 = 0.97) between NDVI and green biomass, particularly when the data were spatially aggregated to better match the satellite sampling scale. Of the three satellite platforms (MODIS Aqua, MODIS Terra, and SPOT), Terra yielded the best agreement with ground-measured NDVI, and SPOT yielded the weakest relationship. When used properly, NDVI from satellite remote sensing can accurately estimate peak-season productivity and detect interannual variation in standing green biomass, and field spectrometry can provide useful validation for satellite data in a biomass monitoring program in this prairie ecosystem. Together, these methods can be used to identify the effects of year-to-year precipitation variability on above-ground biomass in a dry mixed-grass prairie. These findings have clear applications in monitoring yield and productivity, and could be used to support a rangeland carbon monitoring program.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

et al. 2016

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“…Quanto maior a quantidade de água líquida presente nas células da folha, maior será a absorção e menor a reflectância. Assim, a variação da resposta espectral pela espécie vegetal é muito maior na região do infravermelho, por causa das diferenças na estrutura celular, uma vez que as plantas possuem pigmentos similares, dificultando a distinção na faixa do visível (GRANT et al, 2012).…”

Section: Biunclassified

Quantificação da biomassa e estoque de carbono em diferentes coberturas vegetais por meio de sensoriamento remoto

Nakai¹

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A Deus por me proporcionar a oportunidade de evoluir por meio dos estudos.Aos meus pais, pelo amor incondicional, carinho, apoio e educação.Aos meus irmãos pelo constante apoio.Ao meu orientador, Prof. Dr. Carlos Alberto Vettorazzi, por sua orientação, conversa, por compartilhar seus conhecimentos durante a pesquisa.Aos professores Doutores Peterson Fiorio, Rubens Angulo, Hilton Thadeu, Carlos Pedreira, Sérgio Nascimento e Jarbas pelas conversas, compartilhar seus conhecimentos, ensinamentos que proporcionaram evolução na jornada em busca do conhecimento. Aos colegas de sensoriamento: Rodrigo, Pedro, Juliano, Leila, Raoni, Magda e todos alunos e estagiários pela companhia e cafezinhos.Aos colegas de outros departamentos:

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Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space

Shoko

Mutanga

Dube

2016

ISPRS Journal of Photogrammetry and Remote Sensing

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Quantifying biomass production on rangeland in southern Alberta using SPOT imagery

Cited by 14 publications

References 36 publications

Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

Quantificação da biomassa e estoque de carbono em diferentes coberturas vegetais por meio de sensoriamento remoto

Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space

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