418 research outputs found
A ruin model for trees
No full textA stochastic model of ruin for trees
by P. Yiou and N. Viovy (LSCE, Oct. 2020)
This archive contains the code (in R and sh) to simulate a ruin model for trees. The parameters of the ruin model are based on meteorological observations, from ECA&D (temperature and precipitation).
The R code is provided with an sh script to run in batch on parallel computers.
The code is provided "as is" under a CeCill free licence. It is meant only for use for academic research. For any commercial use, please contact Pascal Yiou ([email protected]).
The archive contains:
- R file (ruin_arbre-v2.R) that describes the model
- sh script (ruine_arbre.sh): to run the R code on a parallel computer
- R file (drought_indice.R): to compute a drought index and plot it
- netcdf file with a land surface model simulation forced by ERA5
- two .txt files with temperature and precipitation daily observations
- this README file
P. Yiou & N. Viov
Interannuality and CO2 sensitivity of the SECHIBA-BGC coupled SVAT-BGC model
No full textInternational audienc
Optimizing a process‐based ecosystem model with eddy‐covariance flux measurements: A pine forest in southern France
No full textInternational audience[1] We design a Bayesian inversion method (gradient-based) to optimize the key functioning parameters of a process-driven land surface model (ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE)) against the combination of prior information upon the parameters and eddy covariance fluxes. The model calculates energy, water, and CO 2 fluxes and their interactions on a half-hourly basis, and we carry out the inversion using measurements of CO 2 , latent heat, and sensible heat fluxes as well as of net radiation over a pine forest in southern France. The inversion method makes it possible to assess the reduction of uncertainties and error correlations of the parameters. We designed an ensemble of inversions with different set ups using flux data over different time periods, in order to (1) identify well-constrained parameters and loosely constrained ones, (2) highlight some model structural deficiencies, and (3) quantify the overall information gained from assimilating each type of CO 2 or energy fluxes. The sensitivity of the optimal parameter values to the initial carbon pool sizes and prior parameter values is discussed and an analysis of the posterior uncertainties is performed. Assimilating 3 weeks of half-hourly flux data during the summer improves the fit to diurnal variations, but merely improves the fit to seasonal variations. Assimilating a full year of flux data also improves the fit to the diurnal cycle more than to the seasonal cycle. This points out to the key importance of timescales when inverting parameters from high-frequency eddy-covariance data. We show that photosynthetic parameters such as carboxylation rates are well-constrained by the carbon and water fluxes data and get increased from their prior values, a correction that is corroborated by independent measurements at leaf scale. In contrast, the parameters controlling maintenance, microbial and growth respirations, and their temperature dependencies cannot be robustly determined. The CO 2 flux data could not discriminate between the different respiration terms. At face value, all the parameters controlling the surface energy budget can be safely determined, leading to a good model-data fit on different timescales. Citation: Santaren, D., P. Peylin, N. Viovy, and P. Ciais (2007), Optimizing a process-based ecosystem model with eddy-covariance flux measurements: A pine forest in southern France, Global Biogeochem. Cycles, 21, GB2013
Phenological metrics for Protected Area "CuronianLagoon", MODIS aqua tile h19v03
No full textPhenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include:
(1) Start of season / green-up (GU);
(2) End of season / senescence (SEN);
(3) Length of vegetation period (VP);
(4) GPP proxy (integral over vegetation period, GSIVI);
(5) minimum NDVI (MinNDVI);
(6) maximum NDVI (MaxNDVI);
(7) day of minimum NDVI as julian date (MinDOY);
(8) day of maximum NDVI as julian date (MaxDOY);
(9) r-square of modelled NDVI curve;
(1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)).
(1)-(6) include mean and standard deviation;
References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590;
[Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie;
[Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29
Phenological metrics for Protected Area "PenedaGeres", MODIS terra tile h17v04
No full textPhenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include:
(1) Start of season / green-up (GU);
(2) End of season / senescence (SEN);
(3) Length of vegetation period (VP);
(4) GPP proxy (integral over vegetation period, GSIVI);
(5) minimum NDVI (MinNDVI);
(6) maximum NDVI (MaxNDVI);
(7) day of minimum NDVI as julian date (MinDOY);
(8) day of maximum NDVI as julian date (MaxDOY);
(9) r-square of modelled NDVI curve;
(1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)).
(1)-(6) include mean and standard deviation;
References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590;
[Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie;
[Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29
Automatic Classification of Time Series (ACTS): A new clustering method for remote sensing time series
No full textInternational audienc
Phenological metrics for Protected Area "SierraNevadaEcosystem", MODIS aqua tile h17v05
No full textPhenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include:
(1) Start of season / green-up (GU);
(2) End of season / senescence (SEN);
(3) Length of vegetation period (VP);
(4) GPP proxy (integral over vegetation period, GSIVI);
(5) minimum NDVI (MinNDVI);
(6) maximum NDVI (MaxNDVI);
(7) day of minimum NDVI as julian date (MinDOY);
(8) day of maximum NDVI as julian date (MaxDOY);
(9) r-square of modelled NDVI curve;
(1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)).
(1)-(6) include mean and standard deviation;
References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590;
[Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie;
[Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29
Phenological metrics for Protected Area "HighTatra", MODIS terra tile h19v04
No full textPhenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include:
(1) Start of season / green-up (GU);
(2) End of season / senescence (SEN);
(3) Length of vegetation period (VP);
(4) GPP proxy (integral over vegetation period, GSIVI);
(5) minimum NDVI (MinNDVI);
(6) maximum NDVI (MaxNDVI);
(7) day of minimum NDVI as julian date (MinDOY);
(8) day of maximum NDVI as julian date (MaxDOY);
(9) r-square of modelled NDVI curve;
(1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)).
(1)-(6) include mean and standard deviation;
References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590;
[Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie;
[Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29
Phenological metrics for Protected Area "Hardangervidda-h18v03", MODIS aqua tile h18v03
No full textPhenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include:
(1) Start of season / green-up (GU);
(2) End of season / senescence (SEN);
(3) Length of vegetation period (VP);
(4) GPP proxy (integral over vegetation period, GSIVI);
(5) minimum NDVI (MinNDVI);
(6) maximum NDVI (MaxNDVI);
(7) day of minimum NDVI as julian date (MinDOY);
(8) day of maximum NDVI as julian date (MaxDOY);
(9) r-square of modelled NDVI curve;
(1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)).
(1)-(6) include mean and standard deviation;
References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590;
[Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie;
[Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29
Phenological metrics for Protected Area "DanubeDelta", MODIS terra tile h20v04
No full textPhenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include:
(1) Start of season / green-up (GU);
(2) End of season / senescence (SEN);
(3) Length of vegetation period (VP);
(4) GPP proxy (integral over vegetation period, GSIVI);
(5) minimum NDVI (MinNDVI);
(6) maximum NDVI (MaxNDVI);
(7) day of minimum NDVI as julian date (MinDOY);
(8) day of maximum NDVI as julian date (MaxDOY);
(9) r-square of modelled NDVI curve;
(1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)).
(1)-(6) include mean and standard deviation;
References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590;
[Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie;
[Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29
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