Bergsma, A.R.

Bruin, S., de

Chuma, B.

Kooistra, L.

English

Wageningen Yield 

Centre for Geo-information
The development of a dynamic web mapping service for 
vegetation productivity using remote sensing and in situ sensors
in a sensor web based approach
Lammert Kooistra, Aldo Bergsma, Beatus Chuma and Sytze de Bruin
Wageningen University, Centre for Geo#Information
Presentation for workshop Sensing a Changing World, Wageningen University, November 2008
Centre for Geo-information
Why combine in situ and remote sensing sensors?
Opportunities for sensor web based approach?
 multi#sensor 
 Interoperability
 Requirement for scaling in space and time
 Autonomy
In Situ Sensor Measurement
Assimilation Program (ISSMAP) 
(Teillet, 2002)
NEW
Services
Centre for Geo-information
Scaling between user requirements
Source images: ecocast.arc.nasa.gov
T
i
m
e
hour –
day –
week –
month –
quarter –
year –
decade –
Space
– –
–
–
– –
local regional state/country continental global
Up#/Down#
Scaling
Agricultural parcel: precision agriculture (farmer)
Nature reserve: nature management (nature service)
River catchments: flood protection (river & water board)
Nature reserves: fire protection (forest service)
Climate change: carbon accounting (national government)
Invasive species
Drought monitoring
Biodiversity
Climate change
Land use
Structural monitoring
Event monitoring
Centre for Geo-information
Scaling between user requirements
Source images: ecocast.arc.nasa.gov
T
i
m
e
hour –
day –
week –
month –
quarter –
year –
decade –
Space
– –
–
–
– –
local regional state/country continental global
Up#/Down#
Scaling
Agricultural parcel: precision agriculture (farmer)
Nature reserve: nature management (nature service)
River catchments: flood protection (river & water board)
Nature reserves: fire protection (forest service)
Climate change: carbon accounting (national government)
Invasive species
Drought monitoring
Climate change
Land use
Structural monitoring
Event monitoring
Oper
ation
al ea
rth 
bserv
ation
 
syste
ms
e.g., 
MOD
IS pr
oduc
ts
Geo-
sen
sor 
netw
orks 
oper
ated 
by n
 
organ
izatio
n
Centre for Geo-information
Objectives of activities in RGI#project
 Develop a sensor web based approach which 
combines earth observation and in situ sensor data 
to derive daily maps of vegetation productivity for 
regional to national scale
 Implementation in dynamic Web Mapping Service
 Evaluate current limitations and future research 
requirements
Centre for Geo-information
Background
 At the global scale, terrestrial plant productivity (GPP, NPP) is 
one of the most#modeled ecological parameters
 8#day MODIS product (MOD17A2) is available which models 
GPP at a 1 km resolution
 for regional applications (e.g., monitoring crop productivity) 
both spatial and temporal resolution are too coarse
 products developed for a global scale; not taking into 
account the regional heterogeneity of land use and 
meteorological parameters
MODIS land use schematization (left) and high
resolution image (right) with detail of LGN 
database of area around Gendt
Centre for Geo-information
Requirements for service
 Real time information provision
 Easy access to broad range of end#users: from 
farmers to river managers to scientists 
 Automated processing
 Opportunities for scaling
 Use available OGC standards and protocols
Centre for Geo-information
Calculation of vegetation productivity
 GPP = ↓PAR x FPAR x (εg#max x STmin x SVPD)
 GPP = gross primary production (g C m#2 day#1)
 ↓PAR = incoming photosynthetically active radiation
 FPAR = fraction of ↓PAR absorbed by the plant canopy
 εg#max = maximum light use efficiency (land use specific)
 STmin= minimum temperature scalar
 SVPD = vapor pressure deficit scalar
Centre for Geo-information
Calculation of vegetation productivity
 GPP = ↓PAR x FPAR x (εg#max x STmin x SVPD)
 GPP = gross primary production (g C m#2 day#1)
 ↓PAR = incoming photosynthetically active radiation
 FPAR = fraction of ↓PAR absorbed by the plant canopy
 εg#max = maximum light use efficiency (land use specific)
 STmin= minimum temperature scalar
 SVPD = vapor pressure deficit scalar
☻ meteo data
☻remote sensing
Centre for Geo-information
Web Mapping 
Service
(pmapper)
End user
SDI-NL
download 
most actual 
LU & 
processing
LUE
static 
p. year
LUT
Potential LUE
KNMI 
SOS 
serverGet Obser-
vation & 
processing
meteo
time-
series
interpolated PAR
Implementation: automated processing chain
Calculation of 
GPP per 
250 m pixel
(python)
NASA
MODIS
FTP
Automated 
download & 
processing
FPAR 
time-
series
MODIS surface reflectance
FPAR
Centre for Geo-information
RS data # MODIS sensor
 Operated by NASA (start 2000)
 Daily over#flight; spatial res.: 250–1000 m; 36 bands
 Completely automated pre#processing chain
 Daily download MODIS surface reflectance product (MOD09) 
from MODIS ftp download facility1
 Simple cloud screening algorithm
 Calculation of FPAR ( ≈ NDVI = (NIR#RED)/(NIR+RED))
MODIS surface reflectance MODIS NDVI
1 Source: http://edcdaac.usgs.gov/modis/mod09gqv5.asp
Centre for Geo-information
SWE – KNMI set#up
 16 stations (KMDS)
 WMO set#up
 Prec; temp; rel hum; 
glob sol rad; wind dir 
& speed;
 10 minute data
 OGC#SWE 
implementation
 Calculation of daily 
mean
Source: KNMI, Wiel Wauben
Centre for Geo-information
Web Mapping Service Vegetation Productivity
Source: http://webgrs.wur.nl/cgi/projects/sensorweb/pmapper/pmapper_gpp/map.phtml
Centre for Geo-information
Functionality WMS Vegetation Productivity
 Standard: panning, zooming, 
selection of layers, download as 
geotiff etc. 
 Information on most recent 
vegetation productivity
 Time#series of vegetation 
productivity  
arable
grassland
forest
Febr. April June October 2,
2008
Centre for Geo-information
Functionality WMS KNMI Meteo Data
 Query most recent meteo data using KNMI SOS server using 
GetObservation request 
 Example:
Tuesday October 14
14:00:
Leeuwarden (13:30):
14.1 ˚C
Maastricht (13:30):
16.0 ˚C
 Additional functionality:
 trajectories
 other parameters
 geographic selection
 Etc.
Source: http://webgrs.wur.nl/cgi/projects/sensorweb/pmapper/pmapper_gpp/map.phtml
Centre for Geo-information
Limitations and opportunities
 Time delay remote sensing data (5#10 days)
 Direct broadcast (including facility for processing: NL organization)
 SWE standards not used yet
 Limited revisit frequency of RS data (incl. clouds)
 Multi#sensor approach: specific tasking
 Limited spatial resolution of RS data
 Sensor data fusion: combine high (25 m) and low (250 m) resolution sources: e.g., 
Landsat and MODIS
 Multi#sensor approach: specific tasking
 Limited number of point stations available as SOS
 Plug and play services required to include other stations
 However, KNMI evaluation shows not yet the case: incomplete, redundant information, 
security, tools for exploration and finding data
 Interoperability will be key to combine (multi#source) sensors in space and time: 
use of common standards and protocols
Centre for Geo-information
Developments in field of Earth Observation
 OGC # Open Web Service phase 4 # Demonstration
 Service discovery
 EO1 tasking (NASA)
 SPOT Tasking (ESA)
 Web processing:
• threshold filter
• workflow
Source: http://www.opengeospatial.org/pub/www/ows4/demo.html
Example 1: Datafed online EO processing 
Example 2: Wildfire monitoring through EO sensor tasking 
Centre for Geo-information
Conclusions and outlook
 Proof of concept shown
 Combined use of in situ sensor and remote sensing data: 
dynamic continuous maps (as input for SDI)
 Multi#sensor data: extent use of (OGC) standards 
 Further research & implementation to reduce limitations
Outlook
 Extent to other products and applications: 
e.g., actual evapotranspiration


The development of a dynamic web mapping service for vegetation productivity using remote sensing and in situ sensors in a sensor web based approach

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