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
Camargue : Rhône river delta Dynamic system: water and sediment inputs from the Rhône and the sea 90 000 ha of natural habitats mostly wetlands 2/3 on relatively small private estates 2
Socio-economic activities and natural habitats Rice Reed Waterfowl Cattle growing harvesting hunting 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
Main objective Global loss of Proliferation of biodiversity invasive species A fragmented configuration within a large geographical area: monitoring based on Necessity to monitore repeated ground measures the management and difficult the health state of these marshes Remote sensing: good potentialities Reserve managers and for wetlands spatial stakeholders are in needs analysis of management advices Development of reliable and replicable remote sensing tools for wetland monitoring 4
Specific objectives These tools will help to : ► map the vegetation of Camargue marshes (common reed, club- rush, 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
Methodology Sampling Image acquisition Vegetation Estimation of GPS Image processing characterisation water levels (reedbeds, club- for each rush, aquatic beds) image Data image extraction Database Multispectral and Statistical modellings: multitemporal Classification trees index Generalized Linear Models Formulas = maps 6
Sampling Fields campaigns : reedbeds, club-rush, aquatic beds, water levels, GPS Digitalizations : Others 7
Image processing: radiometric normalization 6S atmospheric model vs. pseudo-invariant features (PIF) Similarity index (Euclidian distance): Estimation of radiometric variation of PIF Pine tree Water 16 0.16 Each PIF varies at least once 12 0.12 over the year 8 0.08 Radiometric variation (%) 4 0.04 0 0 Roof Sand 16 0.16 12 0.12 Necessity of different types of PIF 8 0.08 4 0.04 0 0 6S does not take into account this Dec Mar May Jun Jul Sep Dec Mar May Jun Jul Sep variation for the correction 6 Radiometric variation (%) 5 4 Variation significatively lower 3 with 6S 2 1 0 6S PI 8
Spectral variations 0,3 Reedbeds Club-rush 0,25 Aquatic beds Influence of : 0,2 • phenology Reflectance 0,15 • pluviometry • water management 0,1 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 and multitemporal imagery for their monitoring 9
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
Classification tree algorithm Rpart based on the algorithm CART (classification and regression tree) Breiman et al, 1984; implemented in R. Advantages Method Recursive partioning based Hierarchical classification strategy: on gini index easy interpretation of results Binary tree Optimal for presence/absence Cross-validation (k-fold) Small samples and reproducibility Prior parameter Unbalanced samples 11
Recursive partioning A two-dimension example with two variables selected for reedbeds classification 0,7 Split at 0,6 0.04897 Split at 0,5 0.2467 0,4 0,3 other osavi12 aquatic beds 0,2 reedbeds club-rush 0,1 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 c30603 12
Tree: example for reedbeds classification c30603< 0.04897 | 2 672/46 osavi12>=0.2467 1 2 544/0 128/46 ndwif209>=-0.3834 1 2 80/0 48/46 1 2 39/0 9/46 Reedbeds Formula Presence of reedbeds = c30603 ≥ 0.04897 & OSAVI12<0.2467 & NDWIF209<-0.3834 Map 13
Maps resulting from the formula Combination of three maps: reedbeds, club-rush and aquatic beds in Camargue 14
Tree for flooded areas classification c4>=0.1436 | 2 34/181 Scattered ndwif2< -0.5475 1 2 vegetation 29/45 5/136 and high Flooded water levels areas dvw>=-0.5092 1 1 21/12 8/33 Dense vegetation and lower 1 2 water levels 8/22 0/11 Flooded areas Flooded areas = c4 < 0.1436 or (c4 ≥ 0.1436 & NDWIF2 ≥ - 0.5475 et DWV < -0.5092) 15
Classification accuracy and validation Classification accuracy (%) for the 3 types of marsh vegetation in Camargue: 2005 2006 Acquisition in October instead of September + Reedbeds 91,9 92,6 extremely small class ? Club-rush 93 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: All Open Vegetated marshes marshes marshes Best results: first Flooded half of the year and 76 86 70 areas reed height<188 cm 16
Generalized Linear Models (GLM) Equation for p descriptives variables: Y=a1x1+a2x2+…+aixi+…apxp+b Model selection : Coefficient of determination : R² ► R² = 1 → 100 % 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
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
Percentage of explained variance One descriptive Reedbed Best model = variable = one Two dates parameters multidate date Height of 44 54 66 stems Number of dry - 59 61 reeds Panicles - 38 47 number Number of - 35 60 green reeds Ratio dry/green - - 56 Percentage of - 50 60 open areas Best predicted parameter: height of stems 19
Best models : validation in 2006 Purcentage of explained variance (*p=0.05, **p=0.01, ***p=0.001) : 2005 2006 Height of green 66*** 46*** reeds Number of dry 30** 61*** reeds Panicles number 19* 47*** Number of green 1 60*** reeds Ratio dry/green 43*** 56*** Percentage of 17* 60*** open areas Number of panicles: binomial distribution → Rpart? Green reeds: bi-modal distribution → GAM? % of open areas: methodological imprecision 20
Application for monitoring: reedbeds evolution Influence of water management, salinity… 21
Application for monitoring: reedbeds evolution Influence of water management, salinity… 22
Application for monitoring: Birds habitats Great Reed-Warbler reedbeds: height of stems >195 cm 23
Application for monitoring: flooding duration Influence of water management on aquatic beds 24
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
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
Perspectives: other applications ► Rice cultivation: 27
Perspectives: other applications ► Rice cultivation: PNRC: digitalization of rice fields 28
Recommend
More recommend
