remote sensing-based spatial modelling for natura 2000 ...lvarez-martínez-jm-an… ·...

Remote sensing-based spatial modelling forNatura 2000 management at large scale:

from habitat monitoring to site selection

JM Álvarez-Martí[email protected]

J.M. Álvarez-Martínez, S. García, B. Jiménez-Alfaro, A. Silió-Calzada, M. Recio, J. Barquín, B. Ondiviela, I. Pérez Silos, and J. Juanes

A need for spatial data: patterns, process, dynamics and functioning

of natural and seminatural systems (N2000)

20122010

SIOSE: Sistema de Información sobre Ocupación del Suelo de España (CNIG)

CLC (CORINE): CoORdinationof INformation of the Environment (EEA)

Land use-land cover typologies

Vectorial format: user-defined

Not homogeneous land cover patches: mapping habitats?

Limited or no temporal resolution: monitoring?

Traditional inventory: visual interpretation and digitalization

LAND COVER MAPPING

§ High cost (time, people)

§ 5 to 7 years (pilot area)

§ Difficult to update (no temporal resolution)

Traditional inventory

§ Lack of spatial data

§ Need for trainedsurveiers

§ Mixed patches

LAND COVER MAPPING

A cost-effective solution for

large scale mapping based

on optimal field surveys

(adaptive sampling), remote

sensing and habitat and

species modelling

LAND COVER MAPPING

Remote sensing-based spatial modelling

CASE STUDY: MONITORING

Management plan

Mapping broad-scale vegetation patterns in complex mountainous territoriesHabitat maps using modelling techniques in SCIàSACof Natura 2000 Network in Cantabria (NW Spain)26% of Cantabria. 25 hábitats…

3. Local actions,

cost, monitoring

1. Spatial

distribution

2. Conservation

Status

Annex I

Atlantic biogeographical

region (NW Spain)

Vegetation DB:

National maps

Regional programs

Fieldwork

Predictor layers

Limiting factors

Remote sensing

Monitoring and reporting

Conservation status

CASE STUDY: SITE SELECTION

Álvarez-Martínez et al, 2017

3. SAC definition

1. Spatial

distribution

2. Conservation

Status

Annex I, II

SWOT ANALYSIS

Cost-effective solution for large scale mapping

We have to get information in a quick, effective, homogeneous and dynamic manner

Álvarez-Martínez et al, 2017

We do not end up with available tools (year 2017 and so on) and outputs…

1] CLASSIFICATION TYPOLOGYLand use-land cover (LULC)

Vegetation types

2] OCCURRENCE DATATraining

Validation

3] PREDICTOR LAYERSEnvironmental limiting factorsRemote sensing: resolution

4] MODELLING PROCEDUREEnsemble, sensitivity analyses

Data mining tools…

HABITAT MAPPING

New modelling tools and remote sensing for vegetation mapping:

what actually matters?


Vegetation types


Validation

3] PREDICTOR LAYERSEnvironmental limiting factorsRemote sensing: resolution



HABITAT MAPPING

Expert-based methods: a review of a estandard database

EUNIS (level 1): Heathland, scrub and tundraEUNIS (level 2): Temperate shrub heathlandEUNIS (level 3): Dry heathsEUNIS (level 4): Sub-Atlantic [Calluna] - [Genista] heaths F4.22

HABITATS DIRECTIVE ANNEX I: 4030-EU dry heathlands

EUNISF4.22

1] CLASSIFICATION SYSTEM

Patron List of Spanish Habitat types Download from MITECOResolución de 17 de febrero de 2017, de la Secretaría de Estado de Medio Ambiente, por la que se establecen tres listas patrón: la de las especies terrestres, la de las especies marinas y la de los hábitats terrestres, presentes en España - Texto de la Resolución

Borja Jiménez-Alfaro (U. de Oviedo)

https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/BDN_listas_patron.aspx

http://www.boe.es/diario_boe/txt.php?id=BOE-A-2017-2401

EUNIS 3-5 level habitat types



EUNIS typologies in Cantabria





EUNIS typologies

in Central Anatolia

EUNIS 4 (6)

Patron List of Spanish Habitat typesDownload from MITECO

https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/BDN_listas_patron.aspx


Vegetation types


Validation

3] PREDICTOR LAYERSEnvironmental limiting factors

Remote sensing: satellite and LiDAR



HABITAT MAPPING

Vectorial LULC databases simplify complex landscapes mosaics by creating “homogeneous”

Landscapes patches inclufding different communities and environmental gradients…

There is a need for defining detailed (EUNIS, MAES…) typologies for large territories in order to

accomplish landscape complexity, temporal variability and locally-tailored management practices

2] OCURRENCE DATA

Occurrence data obtained from field surveys with (almost) no uncertainty:

Field campaigns (botanists)

Point-based sampling surveys

“Tablet System” H2020 Working Group of Turkey

Remotely connected to a central DB that maximizes data security and accuracy

Species caract., indicators of structure and function and dynamic update

2] OCURRENCE DATA

2] OCURRENCE DATA

İç Anadolu Pilot Bölgesi

117 449 pointsHabitats Directive

(Annex I)

224 770 points

2] OCURRENCE DATA


Vegetation types


Validation





What actually matters?

Remote Sensing (RS)

Satellite imagery:

Landsat 5TM and 8OLI 30m

Sentinel 2 A and B, 10-20m

DEIMOS-2, 4m

LiDAR derived data, 5-30m

Env. Limiting factors

topography, climate, soil

(digital soil mapping *)

3] PREDICTOR LAYERS

REGIONAL CASE STUDIES

2015-2018, MVC

LARGE-SCALE PROJECTS

35 Sentinel2A sub-scenes (20m). Sensitivity analyses with

Landsat data (30m). Final modelling: combined products (120m)


Vegetation types


Validation





What actually matters?

A DATA MINING method or modelling algorithm for habitat mapping relates

occurrence data and the process-based environmental and RS predictors

MaxEnt: SWD format, Tunning parameters, Phillips et al (2006)SDM: Multiple algorithms, Bootstraping, Naimi and Araújo (2016)

1

2 SPATIAL MODELLING

OCCURRENCE DATA

PREDICTORS

SPATIAL PREDICTIONS

MAPS

3

4] MODELLING

4030 –European dry

heathlands

0 1E 1:50 000 Local AOO

4] MODELLING RESULTS

9120 – Atlantic

acidophilous beech

forests with Ilex and

sometimes also Taxus

in the shrublayer

0 1E 1:25 000 Local AOO


E 1:50 000

Automatic and objective: depends on the models

n habitats with good quality data

Realized AOO


Teselado de la

vegetación en unidades

fisionómicas (manchas

homogéneas mayores

de 5hectáreas)

Automatic and objective: depends on the models

E 1:25 000 UNCERTAINTYDOMINANCE +


VALIDATION - Confusion matrices


Spectral uncertainty and unmixing

Landsat 8 OLI (30 m)

VALIDATION - Confusion matrices


Landsat 8 MVC Landsat8 x2Sentinel2 x2Deimos2 x2+LiDAR +MDT

High suitability

Low suitability


SPECTRAL SIGNATURES

Hyperspectralmeausrements

SPECTRAL SIGNATURES

Hábitat 4030 (b)

Vera

noOt

oño

C

D

E

F

Hábitat 6510 (a)

B

A

Hábitat 4020Spectral library: HABITAT TYPES

Spectral library: PHENOLOGY

SPECTRAL SIGNATURES

Hábitat 9120(F. sylvatica)

Hábitat 9230(Q. pirenaica)

Verano OtoñoVerano OtoñoB C DA

Whole TURKEY(example)

Traditional mapping system

Modelling

Economic cost

Time

Number of fıeld-

workers (2 years)

Resolution

Accuracy of mapping

products

% of habitats mapped

Monitoring

capabilities

(1) Could improve with

photo-interpretation

refinement of model

outcomes

(2) This % could easily

improve with further

research and data

(1)

7.000.000 € 2.500.000 €

5 years 2 years

486 162

< 1:50.000 < 1:50.000

80-90% 70-80%

70% 70%

Low, sampling Real-time

Good

Medium

Bad

(2)

HABITAT MAPPING: SWOT

APPLICATIONS

Monitoring the conservation status

Sites selection at the national level

Area of Occupancy (AOO)Estructural and functionalindicators

Early warning system: identification of drivers and pressures

Common cost-effectiveindicators of ConservationStatus through remote sensing

Non dependent of MemberState data (validation!!!)

PASTURES

CONSERVATION STATUS


FORESTS

0 50 meters high Vegetation structure (LiDAR derived data)

LiDAR PNOA: 0.5 p/m2, <0.5m=NoData

CONSERVATION STATUS

1985 1990 1995 2000 2005 2010 2015 2020 … 2030 2040 2050

FOREST FIRES

VEG. RECOVERY

CONSERVATION STATUS

2030

2040

2050

Future scenarios

TIME

NATURA 2000 SELECTION

Systematic Conservation Planning principles+

Requirements of the Habitats Directive

METHODOLOGY FOR THE SELECTION OF NATURA 2000 SITES

Europe Aid project – Turkey

What – how much - where

44 habitats (EUNIS) à concurrence map (dominance+uncertainty)

WHAT TO CONSERVE?

High resolution maps: 22 Habitats (Annex I) in Central Anatolia

HOW MUCH TO CONSERVE?

Target definition by following objective criteria

WHERE TO CONSERVE?

Social and environmental costs

OPPORTUNITIES

Connectivity / Vegetation Sensitivity Index / Net primary productivity

Pollination / Evapotranspiration / Soil erosion protection

Integration of ecological processes

CONSTRAINTS

Integration of climate change

Area Lost àCentral Anatolian

gypsum steppes (E12B)

Future RCP 6.0 2060-2080

Actual

1

Actual distribution

Future (Potential)

0,5

DECISON SUPPORT SOFTWARE

Allows managing lots of spatial data in a same process to:

§ Identify the smaller number of planning units (that

means the optimal solution),

§ That are necessary to meet at the same time targets

for all the biodiversity,

§ Maximazing connectivity and coherence of the system

§ With the minimal cost

MARXAN

MARXAN PROPOSAL

Data-driven proposal of N2000 sites

Expert judgement usign a

Delphy methodology, asisted

by GIS information

Group AHABITAT THREATS

EXPERT KNOWLEDGE

Expert-driven refinement of N2000 sites

Connectivity at a regional

and interregional scale

Replication and

geographic

distribution

Closer to other

protection figuresSize (better bigger

than smaller)

Socioeconomic features

without spatial data for all

CAPA (present and future)

Compactness

(better round than

irregular-long areas)

EXPERT KNOWLEDGE

Natura 2000 potential sites in Central Anatolia

MANAGEMENT AND REPORTING

We need to get an approach that allows:

§ Information flow for updated data

§ Query system for adaptative management

A management system for N2000:

§ Automatic reports: sexenial reports (art 17)

§ Prioritised Action Frameworks (PAF)

MANAGEMENT AND REPORTING

Standard Data Form

ConservationStatus

Managementplans

Reportsproduction

Assesmentof effects

• Art. 17• PAF• Non Effects

DRIADE

Close

ZEC 00003

Fecha de actualización

Conservation status

Documento Legislativo de designación

HabitatCode

Documento Legislativo de designación

Fecha de actualización

Distribution area Structure and Function Global EvaluationFuture perspectives

Area: Ha

Mapa: Place

Region

Turkey

UE

Word

Quality of the data

Last update:

Tendency:

Observaciones:

Save

Impacto negativoR Código Presión/Amenaza OA A04.03 Abandono de los sistemas de pastoreoA A08 Uso de fertilizantes M D01.01 Sendas, pistas, carriles para bicicletas M

B02.04Eliminación de árboles muertos o

deterioradosB G091.02.03 Conducción motorizada todoterreno RB G05.01 Pisoteo, uso excesivoA

J03.01

Disminución o pérdida de las

características específicas de un

hábitat B K01.01 Erosión M

K02.01Cambios en la composición de especies

(sucesión)

R

MI01

Especies invasoras y especies

alóctonasM M02.03 Declive o extinción de especies

Impacto positivo R Código Actividad/Gestión OA A04.02.02 Pastoreo de ovejas no intensivo RM A04.02.03 Pastoreo de caballos no intensivo RA

A06.01.02Cultivos anuales no intensivos

para

producción de alimento

R

MB02.05

Sacas no intensivas (dejando

madera muerta/ árboles viejos

intactos )

R

F I M D

Área de distribución

Estructura y

funciónPerspectivas

futuras

Evaluación Global

F: FavorableI: InadecuadaM: MalaD: Desconocida

I: Intensidad; O: Observaciones

Obs:

Dry heathlands4210

Bibliography:- Sanz-Azkue I. & I. Olariaga. Uribe-Echebarría P. M., I. Zorrakin, J. A. Campos & A. Domínguez.

pq

pq

Code TR610002

Driade: N2K management system

Management plan

¡Gracias!

Thanks!

Jose Manuel Álvarez-Martí[email protected]

remote sensing-based spatial modelling for natura 2000 ...lvarez-martínez-jm-an… ·...

Documents