USE OF REMOTE SENSING FOR MONITORING WETLAND PARAMETERS RELEVANT TO BIRD CONSERVATION

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1 USE OF REMOTE SENSING FOR MONITORING WETLAND PARAMETERS RELEVANT TO BIRD CONSERVATION AURELIE DAVRANCHE TOUR DU VALAT ONCFS UNIVERSITY OF PROVENCE AIX-MARSEILLE 1 UFR «Sciences géographiques et de l aménagement» University - CNRS 6012 E.S.P.A.C.E

2 Camargue : Rhône river delta Dynamic system: water and sediment inputs from the Rhône and the sea ha of natural habitats mostly wetlands 2/3 on relatively small private estates 2

3 Socio-economic activities and natural habitats Rice growing Reed harvesting Waterfowl hunting Cattle grazing Water management input of freshwater in brackish marshes modification of the hydroperiod division of the marshes into smaller dyked units Influence on floristic composition and vegetation biomass Changes in bird habitat 3

4 Main objective Global loss of biodiversity Proliferation of invasive species Necessity to monitore the management and the health state of these marshes Reserve managers and stakeholders are in needs of management advices A fragmented configuration within a large geographical area: monitoring based on repeated ground measures difficult Remote sensing: good potentialities for wetlands spatial analysis Development of reliable and replicable remote sensing tools for wetland monitoring 4

5 Specific objectives These tools will help to : map the vegetation of Camargue marshes (common reed, clubrush, aquatic beds) to follow their spatial evolution over time map flooded areas irrespective of vegetation density to follow their spatial evolution monthly map vegetation parameters that are associated with ecological requirements of vulnerable birds in reed marshes 5

6 Methodology Image acquisition Sampling Image processing Data image extraction GPS Vegetation characterisation (reedbeds, clubrush, aquatic beds) Database Estimation of water levels for each image Multispectral and multitemporal index Statistical modellings: Classification trees Generalized Linear Models Formulas = maps 6

7 Sampling Fields campaigns : reedbeds, club-rush, aquatic beds, water levels, GPS Digitalizations : Others 7

8 Image processing: radiometric normalization 6S atmospheric model vs. pseudo-invariant features (PIF) Similarity index (Euclidian distance): Estimation of radiometric variation of PIF Radiometric variation (%) Water Roof Pine tree Sand Each PIF varies at least once over the year Necessity of different types of PIF Dec Mar May Jun Jul Sep Radiometric variation (%) S Dec Mar May Jun Jul Sep PI 0 6S does not take into account this variation for the correction Variation significatively lower with 6S 8

9 Spectral variations 0,3 Reedbeds Reflectance 0,25 0,2 0,15 0,1 Club-rush Aquatic beds Influence of : phenology pluviometry water management 0,05 0 B1 B2 B3 MIR B1 B2 B3 MIR B1 B2 B3 MIR B1 B2 B3 MIR B1 B2 B3 MIR B1 B2 B3 MIR December March May June July September Natural and artificial phenomena characterizing Camargue wetlands require a multispectral imagery for their monitoring and multitemporal 9

10 Statistical modelling : two approaches 1 - Qualitative approach : presence/absence Presence of reed, club-rush and aquatic beds Presence of water in differing conditions of vegetation density Classification trees 2 - Quantitative approach : prediction of continuous variables Diagnostic parameters of reedbeds Quality for reed harvesting Suitability for vulnerable reed birds species (passerines, Purple heron, Eurasian bitterns) Generalized Linear Models 10

11 Classification tree algorithm Rpart based on the algorithm CART (classification and regression tree) Breiman et al, 1984; implemented in R. Method Advantages Recursive partioning based on gini index Hierarchical classification strategy: easy interpretation of results Binary tree Optimal for presence/absence Cross-validation (k-fold) Prior parameter Small samples and reproducibility Unbalanced samples 11

12 Recursive partioning A two-dimension example with two variables selected for reedbeds classification 0,7 Split at ,6 0,5 0,4 Split at osavi12 0,3 0,2 0,1 other aquatic beds reedbeds club-rush 0-0,2-0,15-0,1-0,05 0 0,05 0,1 0,15 0,2 0,25-0,1-0,2-0,3 c

13 Tree: example for reedbeds classification c30603< / /0 osavi12>= / /0 ndwif209>= / /0 2 9/46 Formula Reedbeds Presence of reedbeds = c & OSAVI12< & NDWIF209< Map 13

14 Maps resulting from the formula Combination of three maps: reedbeds, club-rush and aquatic beds in Camargue 14

15 Tree for flooded areas classification c4>= / /12 ndwif2< /45 1 8/22 dvw>= /33 Scattered vegetation and high water levels 2 0/11 Flooded areas 2 5/136 Flooded areas Dense vegetation and lower water levels Flooded areas = c4 < or (c & NDWIF et DWV < ) 15

16 Classification accuracy and validation Classification accuracy (%) for the 3 types of marsh vegetation in Camargue: Reedbeds Club-rush , ,6 Acquisition in October instead of September + extremely small class? Aquatic beds 88,3 84,9 Aquatic beds in brackish marshes mixed with Club-rush + acquisition in October? Classification accuracy (%) for flooded areas in 2006: Flooded areas All marshes 76 Open marshes 86 Vegetated marshes 70 Best results: first half of the year and reed height<188 cm 16

17 Generalized Linear Models (GLM) Equation for p descriptives variables: Y=a1x1+a2x2+ +aixi+ apxp+b Model selection : Coefficient of determination : R² R² = % variance explained R² increases with the number of variables Best model : maximum R² with minimum number of variables Variable selection : Forward stepwise (FSW) Sequence of F-tests (Fischer statistic) : inclusion and exclusion of «statistically significant» descriptive variables End: when no additional variable contribute to increase significantly the variance explained Problem : the first variables selected have a big influence on the resulting model Pre-selection of descriptive variables necessary 17

18 Variables pre-selection Criterions for pre-selection : stability Spectral response: correlation between two consecutive years Mean spectral response : no significant difference between two consecutive years 20 of the 90 variables are pre-selected! 1 - What is the efficiency of these variables for modelling reedbed parameters? 2 - What is the minimum number of images required for modelling reedbed parameters? 18

19 Percentage of explained variance Reedbed parameters One descriptive variable = one date Two dates Best model = multidate Height of stems Number of dry reeds Panicles number Number of green reeds Ratio dry/green Percentage of open areas Best predicted parameter: height of stems 19

20 Best models : validation in 2006 Purcentage of explained variance (*p=0.05, **p=0.01, ***p=0.001) : Height of green reeds Number of dry reeds Panicles number Number of green reeds Ratio dry/green Percentage of open areas *** 61*** 47*** 60*** 56*** 60*** *** 30** 19* 1 43*** 17* Number of panicles: binomial distribution Rpart? Green reeds: bi-modal distribution GAM? % of open areas: methodological imprecision 20

21 Application for monitoring: reedbeds evolution Influence of water management, salinity 21

22 Application for monitoring: reedbeds evolution Influence of water management, salinity 22

23 Application for monitoring: Birds habitats Great Reed-Warbler reedbeds: height of stems >195 cm 23

24 Application for monitoring: flooding duration Influence of water management on aquatic beds 24

25 Conclusion Remote sensing and statistical modelling for wetland monitoring : sustainability, precision, affordablility SPOT 5: multispectral and multitemporal modes optimal for wetland monitoring on large areas Roles reversed : field campaigns as a complementary tool for wetland monitoring with satellite remote sensing 25

26 Perspectives: improvements More descriptive variables : TC wetness, index differences Additional field campaigns to monitor reed harvesting Monitoring of water levels with the IME Number of panicles and green reeds : Rpart? GAM? Automatization of the methodology: simplicity for managers 26

27 Perspectives: other applications Rice cultivation: 27

28 Perspectives: other applications Rice cultivation: PNRC: digitalization of rice fields 28

29

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