Estimating taxonomic diversity and functional types of perennial forage grasses in mountain meadows: potentialities of Pléiades imagery

Balent, Gérard

Bertoni, Georges

Fauvel, Mathieu

Sheeren, David

Balent, Gerard

Hal-Diderot

HAL Descartes

ProdInra

Open Archive Toulouse Archive Ouverte

  Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers andmakes it freely available over the web where possible. This  is  an author-deposited  version published in  :  http://oatao.univ-toulouse.fr/Eprints ID : 19602To link to this article : URL https://prodinra.inra.fr/?locale=fr#!ConsultNotice:422148To cite this version :  Sheeren, David and Fauvel, Mathieu and Bertoni, Georges and Balent, Gérard : Estimating taxonomic diversity and functional types of perennial forage grasses in mountain meadows: potentialities of Pléiades imagery (2014)Any correspondance concerning this service should be sent to the repositoryadministrator: staff-oatao@listes-diff.inp-toulouse.frD. Sheeren, M. Fauvel, G. Bertoni, G. BalentDYNAFOR Lab.UMR 1201 INP-ENSAT / INRA / INP – EI PurpanBP 32607, 31326 Castanet Tolosan Cedex, FrancePléiades image dataConclusions Project funded by the Centre National d’Etudes Spatiales (CNES)& the Agence Nationale de la Recherche  – MOUVE Project (ANR-10-STRA-0005) Sem (191O) Sem (1997) Results- No significant relationships between plant species response variables and remote sensing predictors- Results are contradictory to similar studies (e.g. Levin et al. 2007, Parviainen et al. 2010)- The acquisition date of the Pléiades image (August 10th, 2012) could explain the non-conclusive results: time delay with the plant survey, mowing of meadows- In the next step, the study will evaluate the effect of the date of the image by using reflectance spectra obtained during the vegetation surveys. Relationships with other vegetation indices willalso be explored.Introduction Study areaMeadows play a major role in agro-pastoral systems. Theyare used as a fodder resource for animals. They are alsoamong the most species rich plant communities dependingon the nature and the intensity of management practices.Remote sensing has shown considerable potential toestimate the spatial patterns of biodiversity. In particular,NDVI-based variables have been found to be usefulpredictors of plant species richness since it responds tovariation in primary productivity and habitats (e.g. Levin etal. 2007).The objective of this study is to investigate the potential ofPléiades imagery to explain (1) the richness patterns in plantand (2) the functional types of forage grasses in mountainmeadows.Villelongue municipality, in the peripheral area of the National Park ofthe Pyrenees, France (00°03’W and 42°57’N). This municipality islocated in a small valley of medium altitudinal range (450 – 1800ma.s.l.).FranceField sampling of plantsSelection of 30 meadows:- Derived from the same production system- Chosen according to an altitudinal gradient- Distinguished by their land use (grazing alone or grazing and mowing)Recording botanical composition:- Period: between May and June 2012 (time of peak vegetation in each elevation)- Point quadrat analysis method- 50 punctual observations along a 20 m long transect (one observation every 40 cm)- Recording of each plant species when the foliage contacts a steel needle stuck vertically- Acquisition date: August 10th, 2012- Spatial resolution: 2 meters- Spectral resolution: 4 bands (NIR, R, G, B)- Product level: ortho-imagery (level 2)!!! !!!!!!!!!!!!!!!!!!!!!!!!!!3433383929393740334245445435454132404337483538344138394334-9990 0,5 KmºIndicator of taxonomic diversity Species richness (SR) per meadowSum of all the different speciesFunctional types of perennial forage grasses per meadowClassification of plant communities based on functional traitsIndicators of functionnal diversity Type A : - high growth rate,- high growth speed- low biomass accumulation- high leaf area with short lifeEx. Lolium perenne, Poa trivialisType C : - low growth rate,- low growth speed- low biomass accumulation- low leaf area with long lifeEx. Festuca rubra, Agrostis capillarisType B : - high growth rate,- low growth speed- high biomass accumulation- high leaf area with long lifeEx. Dactylis glomerataType D : - low growth rate,- very low growth speed- medium to high biomass accum.- low leaf area with very long lifeEx. Brachypodium pinnatumSpecies of rich environnement Species of poor environnement(Source : Cruz et al. 2002)Dominant species :Plantago lanceolata, Rhinanthus minor, Anthoxanthumodoratum, Lolium perenne, Holcus lanatus, Festucarubra, Dactylis glomerata, Ranunculus bulbosus,Trifolium reprens, Trifolium pratense.NDVI-based variablesmean, max, range, std dev. per meadowIndicators of spectral responses of meadows 414537355434©CNES distribution Astrium ServicesStatistical analysesPlant species response variable(Poisson or binomial distribution) Generalized Linear Models (GLM):Productivity models:3.Heterogeneity models: Hybrid models:Remote Sensing predictor variables Productivity Models (GLM)Response variable %D² Sign. RMSE1. Species Richness 16.98 n.s. 3.052. % grasses of Type A 30.79 n.s. 12.193. % grasses of Type B 35.44 n.s. 5.704. % grasses of Type C 15.38 n.s. 12.095. % grasses of Type D 2.60 n.s. 2.37Hybrid Models (GLM)Response variable %D² Sign. RMSE11. Species Richness 17.28 n.s. 3.0412. % grasses of Type A 15.26 n.s. 13.5313. % grasses of Type B 20.49 n.s. 6.4914. % grasses of Type C 16.36 n.s. 11.9815. % grasses of Type D 1.61 n.s. 2.37Predicted vs Observed (Type A) Observed %grasses of Type APredicted %grasses of Type B2030405010 20 30 40 50 60Predicted vs Observed (Species Richness) Observed species richnessPredicted species richness333435363730 35 407.11.Heterogeneity Models (GLM)Response variable %D² Sign. RMSE6. Species Richness 13.69 n.s. 3.157. grasses of Type A 37.16 n.s. 11.858. % grasses of Type B 9.46 n.s. 6.889. % grasses of Type C 23.78 n.s. 11.2510. % grasses of Type D 0.5 n.s. 2.38Predicted vs Observed (Type B)Observed %grasses of Type BPredicted %grasses of Type B152025303510 20 30 40Summary statistics of plant species variables Summary statistics of remote sensing variablesEstimating taxonomic diversity and functional types of perennialforage grasses in mountain meadows Potentialities of Pléiades imageryApril 1-3, 2014Pléiades Days 

http://oatao.univ-toulouse.fr/19602/1/Sheeren-19602.pdf

Estimating taxonomic diversity and functional types of perennial forage grasses in mountain meadows: potentialities of Pléiades imagery

Abstract

Similar works

Full text

Available Versions

Hal-Diderot

HAL Descartes

ProdInra

Open Archive Toulouse Archive Ouverte