Remote sensing capabilities to estimate pasture production in France

Bella, Di; Faivre, Robert; Ruget, Françoise; Séguin, Bernard; Guérif, Martine; Combal, B.; Weiss, Marie; Rebella, C.

doi:10.1080/01431160410001719849

Cited by 48 publications

(25 citation statements)

References 14 publications

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“…-STICS has been coupled with various remote sensing data in the past, for different applications (Di Bella et al, 2004;Guerif et al, 2007;Varella et al, 2010) and -it has been developed and validated for grasslands by our team (Ruget et al, 2002(Ruget et al, , 2008(Ruget et al, , 2009.…”

Section: Was Chosen Becausementioning

confidence: 99%

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Courault

Hadria²,

Ruget³

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. The aim of this study is to propose methods to improve crop and water management in Mediterranean regions. At landscape scale, there is a spatial variability of agricultural practices, particularly for grasslands irrigated by flooding. These grasslands are harvested three times per year and produce high quality hay, but their productions decreased significantly during the last few years because of the water scarcity. It is therefore important to assess the real water requirement for crops in order to predict productions in the case of agricultural practice modifications. Until now, the spatial variability of agricultural practices was obtained through surveys from farmers, but this method was tedious to describe an entire region. Thus, the specific aim of the study is to develop and assess a new method based on a crop model for estimating water balance and crop yield constrained by products derived from optical remote sensing data with high spatio-temporal resolution.A methodology, based on the combined use of FORMOSAT-2 images and the STICS crop model, was developed to estimate production, evapotranspiration and drainage of irrigated grasslands in "the Crau" region in the South Eastern France. Numerous surveys and ground measurements were performed during an experiment conducted in 2006. Simple algorithms were developed to retrieve the dynamic of Leaf Area Index (LAI) for each plot and the main agricultural practices such as mowing and irrigation dates. These variables computed from remote sensing were then used to parameterize STICS, applied at region scale to estimate the spatial variability of water budget associated with the biomass productions. Results are displayed at the farm Correspondence to: D. Courault (courault@avignon.inra.fr) scale. Satisfactory results were obtained when compared to ground measurements. The method for the extrapolation to other regions or crops is discussed as regard to data available.

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Section: Was Chosen Becausementioning

confidence: 99%

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Courault

Hadria²,

Ruget³

et al. 2010

Hydrol. Earth Syst. Sci.

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“…This is confirmed by results from the other levels of analysis. This type of approach has already been considered by di Bella et al (2004) and Courault et al (2010) through coupling with a vegetation model (simulateur multidisciplinaire pour les cultures standard-Grasslands type). Results for the efficiency index are not better than the FPI computed only with fCover or fAPAR.…”

Section: Evolution Of Fpi Computationmentioning

confidence: 99%

Development of an index-based insurance product: validation of a forage production index derived from medium spatial resolution fCover time series

Roumiguié

Jacquin

Sigel

et al. 2015

GIScience & Remote Sensing

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An index-based insurance is being developed to estimate and monitor forage production in France in near real-time based on a forage production index (FPI) derived from the fraction of green vegetation cover (fCover) integral, obtained from medium spatial resolution time series. This article presents the first step of the scientific validation implemented. The grassland parcels, the field protocol established to collect biomass production data, and the method used to get the fCover are described. Local ground measurements of biomass production are compared with FPI values obtained from high-resolution space-based images. Discrepancies between the two variables are quantified by the coefficient of determination, the mean square error and the normalised root mean square error. First, fCover derived from the four sensors are coherent demonstrating the ability of the algorithm used to provide a consistent way of calculating fCover. Second, for the whole data set, the scatter plot between FPI and biomass shows an acceptable correlation (R 2 = 0.75) improved when only taking into account data recorded up until the production maximum (R 2 = 0.81). Third, the analysis carried out on the scale of the parcels, grass species, period of mowing or climatic conditions reveals variability on the regression coefficients indicating that other explanatory variables should be integrated to better compute the FPI.

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“…For example, Skowronski et al (2007) estimated fuel load in the Pinelands of New Jersey using light detection and Downloaded by [Selcuk Universitesi] at 02:51 09 February 2015 ranging (lidar) data. In grasslands, live vegetation biomass has been found to correlate with the normalized difference vegetation index (NDVI) (Anderson, Hanson, and Haas 1993), the sum of NDVIs during the growth season ( NDVI) (Wessels et al 2006), the shortwave vegetation index (SWVI) (Di Bella et al 2004), or band ratio (TM4/TM5) (Ikeda, Okamoto, and Fukuhara 1999). In the Xilin Gol steppe region, Kawamura et al (2005) collected 30 biomass samples in June, July, and August in meadow regions and typical steppe areas.…”

Section: Live Fuel Loadmentioning

confidence: 99%

Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression

Cao

Cui

Miao

et al. 2013

International Journal of Remote Sensing

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This research simulated wildfire propagation susceptibility based on multivariate logistic regression. Moderate Resolution Imaging Spectrometer (MODIS)-derived fuel indicators and topographic factors were the independent variables, and burnt areas served as the dependent variable. MODIS data were collected daily during the wildfire seasons of to acquire information about live and dead fuel in the Mongolia-China grasslands. The inputs for the independent parameters for wildfire propagation susceptibility modelling were the normalized difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), moisture stress index (MSI), global vegetation moisture index (GVMI), dead fuel INDEX (DFI), elevation, slope, and aspect. Multivariate logistic regression ranking indicates that DFI, MSI, DEM, and OSAVI are the top four factors, with an overall accuracy of 80%. 'Leave one out' cross-validation demonstrated that the overall accuracy of the propagation susceptibility modelling ranged from 65% to 87%. Finally, the model was used to produce 10 day average wildfire propagation susceptibility maps during the wildfire seasons of 2001-2007 and to predict the location of burned areas. This research will be useful for understanding the propagation susceptibility of wildfires in grassland areas and for creating policies for preventing wildfire spread.

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Remote sensing capabilities to estimate pasture production in France

Cited by 48 publications

References 14 publications

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Development of an index-based insurance product: validation of a forage production index derived from medium spatial resolution fCover time series

Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression

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