remote sensing for essential biodiversity variables (rs4ebv) · vrieling; marc paganini. aim and...
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Remote Sensing for Essential Biodiversity Variables (RS4EBV)Brian O'Connor; Andrew Skidmore; Roshanak
Darvishzadeh; Tiejun Wang; Chris McOwen; Anton Vrieling; Marc Paganini
Aim and Objectives
• To develop methods to map and monitor EBVs from Sentinel-2 in support of biodiversity conservation, e.g. CBD Aichi Targets (5 and 15)
• Objectives:
– map direct EBVs at high temporal and spatial res
– develop a model for indirect EBV from in-situ and RS
– pilot the model and validate on 3 pilot sites across EU
Pilot sites
Lowland grasslandNorth Wyke Farm Platform,UK
SaltmarshSchiermonikoog Island,the Netherlands
Temperate ForestBavaria NP,Germany
Essential Biodiversity Variables• Direct EBVs:
– Leaf Area Index (LAI)
– Leaf chlorophyll
– Phenological metrics (SOS, EOS, Amp, Peak etc.)
• Indirect EBV:
– Functional Diversity (FD)
What is Functional Diversity
• Species richness has long been used by ecologists but is problematic in describing species function
• FD represents functional differences among the species in a community through analysis of their range of traits, e.g. leaf area for productivity
• Various FD metrics reported from an analysis of species trait values in functional trait space
The concept of functional space Functional space is a multidimensional euclidean space where axes are ecologically relevant traits Trait 1
Various metrics are computed to quantify FD (similar to PCA)
FD in practice
Same richness however community 2 (right) is clearly more functionally diverse
EBV Validation
• Phase 1: Fieldwork:
– 40 plots & 14 RGB cameras at Bavaria (July, 2015)
– 30 plots & 10 RGB cameras at Schiermonnikoog (July, 2015)
– 10 plots & 5 RGB cameras at North Wyke Farm Platform (June, 2015)
– 8 plots at Dartmoor National Park (June, 2015)
Botanical data
For each 20x20m plot:
– Floral species identification and % abundance
– In-situ traits:
• leaf chl, canopy height, LAI, fresh mass, dry mass..
– Generalised traits from the TRY database
The TRY database
Coverage of trait data
• How many and what traits can we reliably use?
• A balance is needed between traits for FD calculation and coverage of three pilot sites
– Traits selected based on their ecological relevance
– 85% threshold for coverage of trait values per species
Building a trait database
Assessment of species at sites
Compilation of species list
Checks on compatibility
Querying major databases
Inclusion of data in a species/trait matrix
Phase 1 satellite imageryImagery No. of images NL DE UK
Rapid Eye Total images acquired 9 9 9
Satisfactory quality 5 4 9
SPOT 5 Total images acquired 17 16 14
Satisfactory quality 9 10 4
Phenological analysis
Phenological model
Schiermonnikoog Bavaria
no observations!
Phenological results: SOS
Schiermonnikoog
Bavaria National ParkColor scale from:Vrieling et al. (2016): RSE 174, 44-55.
RT modelling for plant traits• Options considered:
INFORM (Atzberger 2000)
a physical based hybrid radiative transfer model for forests
PROSAIL (Verhoef, 1984, Jacquemoud & Baret, 1990)
PROSPECT leaf optical and SAILH models
Field measures in 2015 were used to parameterise the model: leaf dry matter, water and chlorophyll content, leaf area index, canopy diameter and stand height …
RT model inversionReflectance correctionValidation
INFORM results for LAI and Chl.
RGB imageRapid Eye for BNP
RGB image+ forest mask
5
10
15
20
25
30
35
40
45
50
55
LAI map 17/07/2015
Chlorophyll map 17/07/2015
Phenology metrics:
• SOS: start of season (20% of amplitude reached)
• PS: peak season (90% of amplitude reached)
• LG = PS-SOS
• AMP: amplitude between 31 August and 1 March
RS-EBV FRic FEve FDiv FDis
SOS -0.047 0.060 -0.111 -0.350
PS -0.240 -0.289 0.011 -0.476
LGS -0.221 -0.367 0.108 -0.184
max.NDVI 0.134 0.264 -0.504 -0.265
AMP 0.278 0.402 -0.266 0.051
RS-EBV FRic FEve FDiv FDis
SOS 0.819 0.771 0.589 0.058
PS 0.237 0.152 0.958 0.008
LGS 0.278 0.065 0.599 0.331
max.NDVI 0.513 0.193 0.009 0.156
AMP 0.169 0.042 0.189 0.791
Linking direct and indirect RS-EBVPearson’s r
P-value
Next steps• Apply RS algorithms to Sentinel 2 data in 2016
• Develop plot-FD-EBV relationships further
• Build (GLMM) model to predict FD from a combination of RS-EBVs as well as abiotic data (met, soil etc.)
• Apply the model at coarser spatial units, e.g. land cover, and scale up
Botanical diversity
BFNP
North WykeSchierm.
174
3026
11 18
1
Festuca rubra
>15% of European botanical species listed in TRY are present across the pilot sites
FD metrics
Functional spaceFunctional evenness: some parts of niche space are under utilised
Functional divergence : little niche differentiation
Functional dispersion: environmental filters determine community assemblages
Functional richness: resources potentially unused