free gis software meets zoonotic diseases: from raw data to ecological indicators

FOSS4G 2008
Open Source Geospatial: an option for Developing Nations
29 Sep-3 Oct 2008, Cape Town, South Africa

Free GIS Software meets zoonotic diseases: From raw data to ecological indicatorsM. NetelerFondazione Mach - Centre for Alpine Ecology
38100 Viote del Monte Bondone (Trento), Italyhttp://www.cealp.it
http://www.grassbook.orgneteler * cealp.it

Focus on zoonotic diseases

They are able to be transmitted from animals to humans,
usually by a vector (e.g., ticks, mosquitoes)

Both wildlife (e.g., roe and red deer, rodents) and
domestic animals are reservoir hosts

Zoonoses involve all types of agents (bacteria, parasites, viruses
and others)

Zoonotic diseases
cause major health
problems

in many countries.

They are driven by
environmental and
pathogen changes
as well as political
and cultural changes.

The problem: Emerging infectious diseases in Europe

The problem: Emerging infectious diseases in Europe

Two related research projects at FEM-CEA:

1) EDEN (Emerging Diseases in a changing European eNvironment)
is an FP6 Integrated Project (2004-2009) that aims to identify and
catalog those European ecosystems and environmental conditions
which can influence the spatial and temporal distribution and
dynamics of human pathogenic agents.
EDEN consortium: 48 research institutes from 24 countries
http://www.eden-fp6project.net

EDEN at FEM-CEA:

Tick-borne diseases: Lyme borreliosis, Tick-b. Encephalitis

Rodent-borne diseases: Hantavirus, Arenavirus

2) RISKTIGER: Risk assessment of the emergence of new
arboviruses diseases transmitted by the tiger mosquito
Aedes albopictus (Diptera: Culicidae) in the Autonomous
Province of Trento.

Potential disease transmission: Chikungunya, Dengue, ...

CDC

J. Lindsey

Why using satellite data?

Sparse meteo stations versus dense LST maps from MODISData enhancements in complex Alpine terrain:... interpolating meteo data?

Vallarsa near Rovereto (Northern Italy)

Lagorai

Trento

Why using satellite data?

Sparse distribution of meteo stations versus
dense Land Surface Temperature maps from MODIS+ Provincial & private meteo stations

Temperature trends
from meteo stationLST Day 28 Aug 2001
in Deg. Celsius

Data enhancements in complex Alpine terrain

What is the MODIS sensor?

Approach: replace climatic station data with satellite data

MODIS sensor on Terra and Aqua satellites

Typical MODIS
overpass: data
coverage

Sensor with 36-channels from visible
to thermal-infrared

Delivers data at 250m, 500m and
1km pixel resolution

MODIS/Terra (EOS-AM):- launched Dec. 1999- passes at approx 10:30 + 22:30
local time

MODIS/Aqua (EOS-PM):- launched May 2002
- passes at approx 13:30 + 01:30
local time

4 overpasses per 24h

Tile h18_v04

MODIS products and processing

MODIS sensor on Terra and Aqua satellites

Data freely available from NASA/USGS

Delivered in HDF format, in SIN projection (2008: product. level V005)

Series of products is made available by NASA:

Land surface temperature (LST)

Vegetation indices (NDVI and EVI)

Snow cover maps

LAI/FPAR ... and 40 further products

Data preprocessing

Each map comes with a corresponding Quality Assessment map

It is essential to apply these quality maps pixelwise (bit-pattern encod.)

Reprojection from SIN to common map projections

MODIS processing chain implemented in GRASS GIS (http://grass.osgeo.org)

Refs: Neteler, 2005. Time series proc. MODIS..., Intl J Geoinformatics
Rizzoli et. al., 2007, TBE. Geospatial Health
Carpi et al., 2008, TBE. Epidem. & Infect.

Linux clusterBatch
processingon PBS and
SGE: 1460 LST
maps/year

Comparing MODIS LST and meteorological data

Minimum/maximum temperatures [C]

- Meteo: 2 values per day (min/max)- MODIS: 4 values per day (2*day, 2*night)

Temperature dynamics: daily min/max temperatures

Station/pixel:

Temperature [C]

Aggregation needed

Searching for patterns

Speccheri (860m; GB 1666033E 5070563N)

Comparing MODIS LST and meteorological data

10 days aggregates: time series processing (GRASS GIS)
Comparison of meteo station and MODIS

Note: Land surface temperature != air temp.

Station/pixel:

Temperature [C]

10-days period

Station/pixel:

Temperature [C]

10-days period

Wilcox.test: W = 679, p-value = 0.9572

minimum/maximum
temperatures

mean temperatures

Station Speccheri (860m;
GB 1666033E 5070563N)

Raw MODIS Land Surface Temperature map

C

MODIS LST reconstruction 1/5

C

Approach (simpified)

Temperature gradient from MODIS LST image statistics

If too few pixel, use seasonal gradient

Interpolate with Volume Splines in GRASS using
elevation as auxiliary variable

Correction for south/north exposed slopes


Reconstructed MODIS LST map

C


Difference map: filtered MODIS LST RST3D interpolated MODIS LST

n: 448514minimum: -16.104maximum: 10.111range: 26.215mean: -0.388mean of abs. values: 1.469standard deviation: 2.037variance: 4.149


MODIS sensors night data (22:30, 01:30)

Todo: - more fine grain
seasonal model- avoid winter outliers

Continuous time series


MODIS sensors day data (10:30, 13:30)

Continuous time series

Todo: - more fine grain
seasonal model- avoid winter outliers

Indicators from MODIS sensor 1/5

Base product: Land surface temperature (LST)

LST derived indices relevant for disease monitoring and risk modeling:

(through time series analysis in GIS)

late frost periods: relevant for masting of trees and seed production

growing degree days (GDD) for phenological status

hot/cold summers through mean temperature differences

autumnal temperature decrease, spring warming gradient

annual/monthly temperature minima/maxima

Land Surface Temperature [C]

Trentino LST map
28 June 2006from Aqua satelliteat ~13:30 local time
(Deg. Celsius)

Enhanced Vegetation Index (EVI)

EVI tends to perform better than Norm. Differences Veg. Index (NDVI):

less prone to saturation

less sensitive to haze

Derived indices:

seasonal differences
by simple pixel-wise
map substraction

in a localized way:

spring/autumn
detection

length of growing
season


Enhanced Vegetation Index (EVI)

Spring/autumn detection: Trentino 2003

Effect of valley orientation and exposition

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Cavedine (570m a.s.l)

Val di Non (610m a.s.l)

Levico (760m a.s.l)

April

EVI

Vegetation
greening

10km

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Maximum snow extent map: accumulated over 8 days

Example: Early snow event in October 2004

MODIS sensor based map(satellite, every 8 days)
=> the easy way

Situation 24th Oct. 2004Pergine, Valsugana (Trentino), Italy

Endrizzi, Bertoldi, Neteler,
Rigon, 2005. EGU


GEOtop snow-model based map
(using climate data)

Situation 17th Nov. 2004Pergine, Valsugana (Trentino), Italy

Endrizzi, Bertoldi, Neteler,
Rigon, 2005. EGU

MODIS sensor based map(satellite, every 8 days)
=> the easy way

Maximum snow extent map: accumulated over 8 days

Example: Early snow event in October 2004


GEOtop snow-model based map
(using climate data)

Ongoing...

Use of new remote sensing variables in machine learning

LST

EVI

Snow

Use of Machine Learningalgorithms(ensemblemethods)

to create
spatio-temporal
risk models

Rizzoli et al. 2002: Bagging of TreesFurlanello et al. 2003: RandomForest, DSCBenito Garzn et al. 2006: Predicting habitat
suitability. Ecol. Mod.Rizzoli et al. 2007, Geospatial Health

ABIOTIC

BIOTIC

GIS data

Ticks and host density maps

t1t2t3Web Map Service (WMS1.3) Provides three operations protocols (GetCapabilities, GetMap, and GetFeatureInfo) in support of the creation and display of registered and superimposed map-like views of information that come simultaneously from multiple sources that are both remote and heterogeneous.

Web Coverage Service (WCS) Extends the Web Map Server (WMS) interface to allow access to geospatial "coverages" that represent values or properties of geographic locations, rather than WMS generated maps (pictures).

Web Feature Service (WFS) The purpose of the Web Feature Server Interface Specification (WFS) is to describe data manipulation operations on OpenGIS Simple Features (feature instances) such that servers and clients can 'communicate' at the feature level.

Web Map Context Documents (WMC) Create, store, and use "state" information from a WMS based client application

Conclusions

Rich archive of remote sensing data available
(thanks to the US legislation)

Data processing is completely based on FOSS4G software

Time series permit for extraction of time series
-> seasonality patterns

TBE in goats: synchronous activity of larvae and nymphs
driven by climatic condition (autumnal cooling), captured
by satellite data derived maps:
Early warning system for TBE

New satellite systems provide a wealth of data from which
epidemiologically relevant indicators can be derived

Markus NetelerFondazione Mach - Centre for Alpine Ecology38100 Viote del Monte Bondone (Trento), Italyhttp://www.cealp.it/ - neteler AT cealp.it

Web Map Service (WMS1.3) Provides three operations protocols (GetCapabilities, GetMap, and GetFeatureInfo) in support of the creation and display of registered and superimposed map-like views of information that come simultaneously from multiple sources that are both remote and heterogeneous.

Web Coverage Service (WCS) Extends the Web Map Server (WMS) interface to allow access to geospatial "coverages" that represent values or properties of geographic locations, rather than WMS generated maps (pictures).

Web Feature Service (WFS) The purpose of the Web Feature Server Interface Specification (WFS) is to describe data manipulation operations on OpenGIS Simple Features (feature instances) such that servers and clients can 'communicate' at the feature level.

Web Map Context Documents (WMC) Create, store, and use "state" information from a WMS based client application

TBE in Trentino: case study

TBE human cases, autumnal cooling and trapping sites

Identification of TBE foci

Red spots: cross-sectional and
longitudinal rodent trapping grids
operated in 2002

large and small grey circles
indicate locations of old and new
TBE foci according to the number
of human cases recorded

Autumnal cooling of previous year

Map of 2001's autumnal cooling
colored from red (rapid cooling)
to green (slow cooling)

white patches are high cloud
contamination (cooling estimates
could not be obtained)

TBE in Trentino (Italy)

Serological survey in goats: Risk of TBE
transmision in raw milk/cheese

Mean TBE seroprevalence 7.87 0.93 (PRNT)Rizzoli et al. 2007,
Geospatial Health

Significant correlation of TBE SEROPOSITIVE with COOLING RATE of previous year (GLM with binom. error, p

free gis software meets zoonotic diseases: from raw data to ecological indicators

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