Integration of EO and in-situ data through expert knowledge for habitats and ecosystems monitoring.

P. Blonda, C. Marangi*, A. Adamo, C. Tarantino, F. Lovergine, V. Tomaselli °

CNR_ISSIA; CNR-IAC*; CNR-IBBR°. Bari-Italy

4th GEOSS S&T Workshop, Norfolk-USA

Multiple temporal and spatial scales are required for monitoring ES and BD state and for understanding system dynamics (Macro-system Ecology, in Heffernan et al., 2014).

Regional/local policy makers requires fine scale measurements for conservation purposes and for satisfying reporting commitments of existing policy instruments at global and Europen level:– The Habitats Directive (EC 2011) and Birds directive (EEA 2011)– Action 5 (Target 2) of the EU Biodiversity Strategy to 2020

Users requirements

Temporal requirements

Credits: Kennedy et al. 2014, 3

file:///media/alma/E29A-8C3C/DISSEMINATION/2015/NORFOLK_VIRGINIA_22MARCH/PRESENTATION_NORFOLK/Blonda_ConnectinGEO_Norfolk_Knowledge.ppt

“The evolution of ecosystems properties over time can be described using simple math. response functions and the better these functions can be described, the grater insight ecologists can draw about ES dynamics”

( Kennedy at al., 2014; Front Ecol. Env. 12 (6))

Actually only abrupt changes of state can be detected at VHR(<3m)as step functions

www.biosos.eu

www.ms-monina.eu

Possible solutions within GEO

Horizon 2020 SC5-16-2014 Ecopotential

ConnectinGEO

Knowledge as input for data interpretation and integration output of the analysis. Knowledge-driven models are able:

• to fill the gaps between different domains: spectral, temporal, spatial relations, geom. attributes• to facilitate data management and multi-source integration;• to monitor limited accessibility or huge extension areas• to facilitate users engagement

Assumptions: habitats with similar morpho-structural characteristics can be differentiated by different vegetative cycles and/or flooding periods, with different spectral signatures in EO images

The FP7-BIO_SOS approach

In-situ component

EO component

Natura 2000 sites in Southern Italy

Lago Salso. WorldView2 imagery:Biomass Peak Image (BPI): June 2010 Pre BPI (PreBPI): Feb. 2011

Le Cesine:BPI: June 2013 Post BPI: Nov. 2013

FP7-SPACE-2010 BIO_SOS (GA.263435)

7

(Tomaselli et al. 2013. Landscape Ecology)

LCCS considered Annex I/EUNIS JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

X/F5.514

X/F6.2C - for

X/F6.2C - back

X/E1.6

6220/E1.313

1210/B1.1

3170/C3.421

1310/A2.51

1410/A2.522

7210/D5.24

X/A2.53C

X/C2

A12/A1.A4.D1.E1

A12/A2.A5.E7

A24/A2.A5.E7

A24/A2.A6.E6

Figure. Visual representation of ccorresponding to a common LC classes in two

Natura 2000 sites: a) IT1 site and b) IT2 site.

Water

Wet or waterlogged soil Dry (at the surface) soil

Visual representation of phenology and water coverage for Le Cesine site (Italy)

Habitat 1310 ( Salicornia)

Similar classes can be discriminated by spatial (topological) relationships

1310 (in S) and 3170 (in L) belong to A24/A2.A5.E7 category, but:

1310 is adjacent to the lagoon (i.e., 1150 in D)

1210 (in A) and E1.6 (in N) belong to A24/A2.A5.E7 category, but:

1210 is adjacent to the sea

LCCS A24/A2.A5.E7 = aquatic vegetation, herbaceous forbs, annualLCCS A12/A2.A5.E7 = terrestrial vegetation, herbaceus forbs, annual

SEA

Featu

res for L

C/L

U to

hab

itat tran

slation

and

discrim

inatio

n

LC/LU

FAO-LCCSAnnex I / Eunis 

 

Topological relations (zonation)

Geometric property

Multi-temporal information

Plant height LIDAR

Adjacency Linearity Phenology CHM

A12/A1.D1.E1

X/F5.514       X

X/F6.2C       X

A12/A1.D2.E1

2250/B1.63 X      

X/G3.F1 X      

A12/A2.A5.E7

X/E1.6 X X X  

6220/E1.313 X X X  

1210/B1.1   X    

 

A24/A2.A5.E7

3170/C3.421 X      

1310/A2.51 X      

A24/A2.A6.E6

1410/A2.522 X X    

7210/D5.24 X X    

X/A2.53C X X    

X/C2 X X    

Decision tree including the pattern zonation rule.

dGreenGRR Re

The GREEN RATIO INDEX (GRR) as

Phenology: Green Ratio Index (GRR) from the Post Biomass Peak Image (PoBPI), Oct. WorldView2;Geometric attribute: instantiated as elongatedness.

ELONGATEDNESS is defined as the object Length/Width Ratio (LWR)

(I)

Geometric attribute: instantiated as elongatednessPattern zonation rules: adjacent rules

A24 aquatic vegetation/ A2 herbaceous. A6 graminoid. E6 perennial

1410/A2.522;X/C2;7210/D5.24;X/A2.53C

(I)

Combined in-situ environmental data and spatial topological rule (adjacency)(II)

Le Cesine site: habitat map

B15A12

B28B16

B15B28 1. PEAK image 20092. POST image 2010

3. PEAK image 20134. POST image 2013

Changes

Conclusions: Habitat maps and LC/LU as EBV.

Kick off meeting. February 18th, 2015. Barcelona 17

Habitats as proxies

LC/LU maps and LIDAR

Bio-physical indices

Conclusions

There is a need to operationalize VHR habitat mapping techniques for the extraction of trends and quantification of pressures: Expert knowledge (prior spectral, ecological modeling at habitat and

landscape levels), with this elicited through ontologies, can be used for new services (knowledge) in large or limited accessibility areas.

VHR EO dense time series for regional policy making as well as LIDAR data for vegetation (ecosystems structure) are not regularly collected on protected sites (e.g., Natura 2000).

In-situ: lack of centralized environmental data bases (e.g., water salinity, lithology, slope) and data for generalization of input expert rules (e.g., pattern zonation changes due to human pressures) and outputs (according to different taxonomies).

• New research in Horizon2020 Ecopotential project• Citizen observatories can help

www.biosos.eu 19

Habitat monitoring from VHR EO data in previous FP7-SPACE-2010 project: BIO_SOS

A24

A1.A4.A12.B3.C2.D3./B10

Aphyllous closed dwarf shrubs on temporarily

flooded land

Annex I 1420

A24

A2.XX.A13.B4.C2.E5/B13.E7

Open annual short herbaceous vegetation on temporarily flooded land

A24A2.A6.A12.B4.C2.E5/B11.E6

Perennial closed tall grasslands on temporarily

flooded land

+environmental attributes

Annex I 1310

Annex I 1410

EUNIS D5.2

CLC3

4.2.1 - Salt marshes

Annex I 7210

ANNEX I Lithology-Parent material

Soil sub-surface aspect

Water quality Floristicattribute

1410 Unconsolid- Clastic sedimentary rock - Sand Solonchaks Brakish/Saline

water

Juncus spp.; Carex spp

7210 Calcareous rock - Calcarenite Histosols Fresh/Brakish

water

Cladium mariscus

Spatial and temporal reasoning

Spatial and temporal reasoning for habitat mapping from LC/LU and multi-source (satellite, aerial LIDAR and in-situ) data integration

Spatial relations concerning vegetation pattern (zonation):• Topological (e.g., adjacency to the seashore, the costal

lagoon). • Non-topological (e.g., close to, distance to)

Multi-temporal information: plant growth stages (phenology) and water regime (i.e., flooding and dray periods)

Plant height information from LIDAR or texture measurements

Assumption: habitats with similar morpho-structural characteristics can be differentiated by different vegetative cycles and/or flooding periods, with different spectral signatures in EO images

21

Knowledge based model of the world model (in a specific domain ) consisting of concepts (objects) and spatial/temporal relations between objects can be represented trough ontologies in a community agreed vocabulary as tools for knowledge description and dissemination of complex categories:

Domain ontologies

Processing ontologies