typhoon lupit - our planet€™s probable path and whether it would ... 500 1,000 km 250 500 miles...

Information for decision making: weather 85

In 2003, the Fourteenth Congress of the World MeteorologicalOrganization (WMO) established The Observing System Research andPredictability Experiment (THORPEX). This international programmeseeks to acceler ate improvements in the accuracy and utility of high-impact weather forecasts up to two weeks ahead of an event.

Today, 10 of the world’s leading weather forecast centres regularlycontribute ensemble forecasts to the THORPEX Interactive Grand GlobalEnsemble (TIGGE) project in order to support the development of prob -abilistic forecasting techniques. Ensemble prob abilistic forecasting is anumerical prediction method that uses multiple simulations, sometimes

20 or more, in a given time frame to generate a representative sample ofthe possible future states of weather systems.

Within the realm of numerical weather prediction, ensembleprob abilistic forecasting is a major new tool for improving early warningof such high-impact events. This is particularly important for predictingsevere tropical cyclones, also known as hurricanes and typhoons, whichare the most powerful and destructive weather systems on the planet.The photo above shows damage caused by Typhoon Parma in thePhilippines in September 2009.

Source: J. Van de Keere/Bloggen.be

TYPHOON LUPIT

86 Crafting geoinformation

NCAR

CMC

NCEP

NCDC

UKMO

ECMWF

Météo-

France

CPTEC

CMA

KMA

BoM

JMA

Archive centre

Data provider

LDM

FTP

HTTP

In situ, airborne and satellite observations are used to initialize TIGGEEnsemble Prediction Systems (EPS1, EPS2, etc.). The EPS outputs can inturn be combined to generate weather prediction products. These can thenbe distributed via regional centres to national centres and end users. Tenof the world’s leading weather forecasting centres regularly contribute

ensemble forecasts to the TIGGE project. The map below shows how dataare transferred from the forecast centres to three archiving centres.

Source: WMO.

TIGGE Ensemble Prediction System


In October 2009, Typhoon Lupit approachedthe Philippines from the Pacific Ocean.Forecasters wanted to determine thestorm’s probable path and whether it wouldstrike the Philippines, adding to thedestruction recently brought by TyphoonParma, or veer off in another direction. Thisis an example of how TIGGE can help withforecasts of tropical cyclones.

Source: Hurricane Lupit 17 October 2009 MTSAT-1Rprocessed by Japan’s National Institute of Informatics.

Typhoon Lupit

Forecasting Typhon Lupit's path

The US/Japan Tropical Rainfall MeasuringMission (TRMM) observed that some ofLupit’s towering thunder storms reachedas high as 14 kilometres (more than 8.5miles), indicating very powerful stormswith heavy rainfall threat.

Source: TRMM.

TRMM Precipitation Radar

200km

Light rain Moderate rain Heavy rain

15 20 25 30 35 40 45 50 50 55 dBZ

10/17/2009 1629Z LUPIT West Pacific

15

10

5

0km



Forecast paths of Typhoon Lupit, 18 October 2009, 12:00 UTC, from six of the TIGGE data providers, as displayed on the JapaneseMeteorological Research Institute’s tropical cyclone forecast website.

The colour changes every 24 hours along each forecast track. The blackline is the storm’s actual path (as recorded a posteriori ).

115ºE 120ºE 125ºE 130ºE 135ºE

20ºN

25ºN

10ºN

15ºN

115ºE 120ºE 125ºE 130ºE 135ºE

20ºN

25ºN

10ºN

15ºN

21 members 24 members 15 members

115ºE 120ºE 125ºE 130ºE 135ºE

20ºN

25ºN

10ºN

15ºN

Four-day forecast of Typhoon Lupit’s path, 18 October

11 members 51 members 51 members

115ºE 120ºE 125ºE 130ºE 135ºE

20ºN

25ºN

10ºN

15ºN

115ºE 120ºE 125ºE 130ºE 135ºE

20ºN

25ºN

10ºN

15ºN

115ºE 120ºE 125ºE 130ºE 135ºE

20ºN

25ºN

10ºN

15ºN

Source: JMA.


100ºE 110ºE 120ºE 130ºE 140ºE 150ºE 160ºE 100ºE 110ºE 120ºE 130ºE 140ºE 150ºE 160ºE

Ensemble forecast tracks Strike probabilitiesLupit 18 Oct 2009

Colour change each 24 hours in forecast

Probability the storm will pass within 75 miles

5-19% 20-39% 40-59% 60-79% 80-100%

T0-24

T24-48

T48-72

T72-96

T96-120

T120-144

T144-168

T168-192

T192-216

These images show the UK Met Office’s early forecasts for Lupit (left).Based on these tracks, it was forecast (right) that there was a highprobability of the typhoon striking the northern end of the Philippines– but there is also a hint that the hurricane could instead turn towardthe northeast.

Source: UK Met Office.

Six-day forecast of Typhoon Lupit’s path, 18 October


SouthKorea

Japan

Taiwan

Hong Kong

Macau

Philippines

Vietnam

Lupit

21 October 2009 12ZECMWF (51) members)UKMO (51 members)NCEP (21 members)

Laos

Cambodia

Thailand

China

Later ensemble forecasts showed increasing probability that Lupitwould turn north-eastward, as shown on this web site display from theUS National Oceanic and Atmospheric Administration (NOAA). Forecasttracks are shown in colour, with the actual track in black. Lupit is shownclearly turning north, sparing the Philippines this time.

Source: NOAA.

Forecasting Typhoon Lupit’s path: 21 October

Ecosystems provide many valuable products and services, from food,fuel and fibre to purification of water, maintenance of soil fertility andpollination of plants. To sustain these societal benefits, managers needto fully understand the types, spatial patterns, scales and dis tributionsof the ecosystems under their care. Some of the critical tools they relyon are ecosystem classifications and maps at scales ranging from globalto local. The ecosystem mapping activities described in the followingpages are being carried out by the US Geological Survey and its partners. Source: USGS.

ECOSYSTEM SERVICES


Information for decision making: ecosystems 93

EC

OSY

STE

M

Macroclimate

Topoclimate

Biota

Landform

Surface water

Soils

Groundwater

Bedrock

An ecosystem can be viewed as a spatial integration of itslandforms, climate regime, vegetation and other features thatoccur in response to the physical environment.

Ecosystem structure

Source R. Bailey/US Forest Service


Tropical pluvial

Tropical pluvial/seasonal

Tropical xeric

Tropical desertic/hyperdesertic

Mediterranean pluvial/seasonal oceanic

Mediterranean xeric oceanic

Mediterranean desertic/hyperdesertic

Temperate hyperoceanic/oceanic

Temperate xeric

Boreal hyperoceanic

N

0

0

500 1,000 km

250 500 miles

Bioclimate maps

Satellite and in situ observations are the building blocks for creatingthe elements of ecosystem structure. The observations are processedusing a range of techniques; for example, bioclimate is modelled fromin situ and automated weather station observations.

Key environmental data layers are first developed for large regions or continents. They include landforms, geology, bioclimate regions and land cover. These data layers serve as input data into models to map ecosystems.

Source: TNC/NatureServe.


Tree cover, broadleaf evergreen forest

Bamboo dominated forest

Tree cover, broadleaf deciduous forest

Mangrove

Freshwater flooded forest

Permanent swamp forest

Temperate evergreen forest

Temperate mixed forest

Temperate deciduous broadleaf forest

Converted vegetation

Degraded vegetation

Herbaceous cover

Shrub savannah

Periodically flooded savannah

Closed shrubland

Sparse herbaceous

Periodically flooded shrubland

N

Moorland/heathland

Closed steppe grassland

Grassland

Barren

Desert

Salt pan

Water

Montane transitional forest

Montane flooded forest

0

0

500 1,000 km

250 500 miles

Land cover maps

Land cover maps are produced by using statistics to categorize regionsin multispectral satellite images as particular land cover types.



N

Forest

Flooded forest

Savannah

Flooded savannah

Shrubland

Flooded shrubland

Grassland

Desert

Barren

Salt

Water

Converted

0

0

500 1,000 km

250 500 miles

Terrestrial ecosystems

Towards a global ecosystems map: South America

The ecosystem structure approach to ecosystem mappinginvolves mapping the major attributes of the physicalenvironment that contribute to the ecosystem’s structure.Satellite imagery, field observations and other data are usedto characterize the biological and physical environment.These data are then integrated and interpreted in order topresent a coherent picture of each ecosystem.

The resulting ecosystem map can be used for a variety of applications, such as climate change assessments, eco -system services evaluations, conservation applications andresource management.



0

0

500 1,000 km

250 500 miles

Forest and woodland ecosystems

Herbaceous ecosystems

Shrubland ecosystems

Steppe/savannah ecosystems

Woody wetland ecosystems

Herbaceous wetland ecosystems

Sparsely vegetated ecosystems

Water

Terrestrial ecosystems

N

Towards a global ecosystems map: United States of America

The methodology for producing ecosystems maps using Earthobservation data has been imple mented for several continentalregions and a global ecosystem map is in development.

Source: USGS.


Kalahari camel thorn woodland and savannah

Limpopo mopane

Lower Karoo semi-desert scrub and grassland

Lowveld-Limpopo salt pans

Makarenga swamp forest

Moist Acacia (-Combretum) woodland and savannah

Moist highveld grassland

Namaqualand Hardeveld

Namibia-Angola mopane

North Sahel steppe herbaceous

North Sahel treed steppe and grassland

Pro-Namib semi-desert scrub

Southern Indian Ocean coastal forest

Southern Kalahari dunefield woodland and savannah

Southern Namib Desert

Southern Namibian semi-desert scrub and grassland

Sudano-Sahelain herbaceous savannah

Sudano-Sahelain shrub savannah

Sudano-Sahelain treed savannah

Upper Karoo semi-desert scrub and grassland

Wet miombo

Zambezian Cryptocepalum dry forest

Zambezi mopane

Zululand-Mozambique coastal swamp forest

African temperate dune vegetation

African tropical freshwater marsh (dembos)

Antostema-Alstoneia swamp forest

Baikiaea woodland and savannah

Bushmanland semi-desert scrub and grassland

Central Congo Basin swamp forest

Central Indian Ocean coastal forest

Drakensberg grassland

Dry Acacia woodland and savannah

Dry Acacia-Terminalia-Combretum woodland and savannah

Dry Combretum-mixed woodland and savannah

Dry miombo

Eastern Africa Acacia woodland

Eastern Africa Acacia-Commiphora woodland

Eastern Africa bushland and thicket

Etosha salt pans

Gabono-Congolian mesic woodland and grassland

Gariep desert

Guineo-Congolian evergreen rainforest

Guineo-Congolian littoral rainforest

Guineo-Congolian semi-deciduous rainforest

Guineo-Congolian semi-evergreen rainforest

Indian Ocean mangroves

Sub-Saharan ecosystems

Towards a global ecosystems map: Sub-Saharan Africa

Source: USGS.

Information for decision making: agriculture 99

The GEO Global Agricultural Monitoring Community of Practice is leadingthe effort to develop a global agricultural monitoring system of systems.Based on existing national and international agricultural monitoringsystems, this comprehensive network will improve the coordination ofdata and indicators on crop area, soil moisture, temperature, precipitation,crop condition, yield and other agricultural parameters. The end resultwill be better forecasting of agricultural yields and enhanced food security.

FOOD SECURITY


Multiple spatial and temporal scales of Earth observation data are needed for monitoring agriculture because cropping systems vary widely in terms offield size, crop type, cropping intensity and complexity, soil type, climate, and growing season. This global crop land distribution map (top), based on 250 metre resolution data from the MODIS Earth Observing Satellite sensor,is useful for monitoring global vegetation conditions and identifyinganomalies.

Source: MODIS.

The image of the Indian state of Punjab (right) is based on 30-65 metreresolution images. Even finer resolutions, down to 6 metres, are used formonitoring at the district and village levels.

Source: AWiFS/NASA.

Crop mapping

Probability0

100

Crop rotation in Punjab State, 2004-05

Rice / wheatCotton / wheatMaize-basedSugar cane-basedCotton / other cropsRice / other cropsOther crops / wheatTriple cropping

Other rotationsNon-agricultureDistrict boundary

0 30 60 km

N

MuktsarBathinda

Mansa

Sangrur

Moga

Barnala

Faridkot

LudhianaFirozpur

Amritsar

Gurdaspur

Taran Taran Kapurthala

Jalandhar

Hoshiarpur

FatehgarhSahib

Patiala

RupnagarNawan Shahar

Nagar

PAKISTA

N

PAKISTA

N

Rajasthan HaryanaHarya

na

Him

achal Pradesh

J&K

Ch

andig

arh


Crop yield models are a critical tool for decision making on agricultureand food security, and daily weather data are a critical input for thesemodels. These data are gathered by weather stations and coordinatedon a global basis. This image depicts zones in Europe that suffered from

high temperatures throughout June and July 2010 and where the cropmodel depicts soil moisture values for spring barley 20 per cent belowthe average.

Crop yield models

Dry and hot regions

Number of days1 - 3

4 - 6

7 - 9

10 - 12

13 - 15

16 - 18

19 - 21

21 +

Crop analysed: spring barley soil moisture/soil moisture 20% below the average

Period of analysis: 11 June 2010 - 20 July 2010

Data source: MARS agrometeorological database

Number of days with temperature over 30ºC in areas with low soil moisture


Another example based on observed meteorological data shows areaswhere crops are under stressing conditions due to consecutive days withhigh temperatures.

Number of heat waves

Number of occurrences

0

> = 1- < 2

> = 2- < 3

> = 3- < 4

> = 4

Source: National Meteorological Services

Processed by Alterra Consortium on behalf

of AGRI4CAST Action - MARS Unit

> = 2 consecutive days where TMAX > 30, cumulated valuesFrom 11 June 2010 to 10 July 2010

13/07/2010

Interpolated grid

of 25x25km

Modelling heat stress


Clustering: arable landbased on NDVI actual data

SPOT-Vegetation (P) from 1 October to 30 April 2010

Clusters11%

10%

14%

15%

15%

14%

19%

Masked

No data

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Produced by VITO (BE)

on behalf of the

AGRI4CAST Action

AGRICULTURE Unit

on 02 May 2010

Oct Nov Dec Jan Feb Mar Apr

2009/2010

10-daily NDVI [-]

Besides crop models which are used for qualitative forecasts, low-resolution data are used to monitor agricultural areas. Below is anexample from the JRC MARS Remote Sensing database. The map displaysthe results of a cluster analysis of NDVI values throughout the season fromMarch to June. The NDVI (Normalized Difference Vegetation Index), a

"greenness index", is an indicator of green biomass derived from satelliteobservations and widely used for vegetation monitoring. The diagramdisplays the early start of the season around the Mediterranean Basin andthe winter dormancy of most crops in central Europe.

Monitoring agricultural areas


Daily rainfall is also a key input for crop yield models. It is estimated byintegrating satellite-derived precipitation estimates with weather-station

observations, as presented in the example below from the Famine EarlyWarning System Network (FEWS-NET) system.

Rainfall estimates

8 August 2010

9 August 2010

7 August 20106 August 2010

10 August 2010 11 August 2010

Rainfall estimates 6 - 11 August 2010

Data:

NOAA-RFE 2.0

0 - 0.10.1 - 11 - 22 - 55 - 1010 - 1515 - 2020 - 3030 - 4040 - 5050 - 75> 75No data

Daily totals (mm)


Effective early warning of famine is vital for quickly mobilizing foodaid and other support. Areas of maize crop failure due to droughtin the Greater Horn of Africa in August 2009 are here indicated in pink and red, based on the Water Requirement SatisfactionIndex (WRSI).

Source: FEWS-NET.

Crop water requirement

Crop WRSIGrains: 2010-08-1

< 50 failure50 - 60 poor60 - 80 mediocre80 - 95 average95 - 99 good99 - 100 very goodNo start (late)Yet to start

Famine early warning


Calculating NDVI anomalies

Central America - eMODIS 250m NDVI Anomaly Period 2, 1-10 January 20102010 minus average (2001-2008)

NDVI anomaly< -0.3-0.2-0.2-0.05-9.02No difference0.020.050.10.2> 0.3Water

NDVI anomalies can be calculated on aregular basis to identify vegetationstress during critical stages of cropgrowth. An example below shows howdrought effects on crops were trackedduring 2010 over Central America usingvegetation index data. The image fromthe Moderate Resolution Imaging Spec -tro radiometer (MODIS) contrasts theconditions between data collected from2000 to 2009 (average conditions) andthe conditions under the drought of 2010.The brown and red areas on the Mexico–Guatemala border indicate the areasaffected by the drought where thevegetation index is lower than average,meaning that less photosynthesis wasoccurring.

Source: MODIS.


Significantly improved

Improved

Normal

Worse

Significantly worse

No data

Crop-growing profile - Shandong

Crop-growing profile - Henan

Crop-growing profile - Anhui

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.005 05 05 05 05 05 05 05

Jan Feb Mar Apr May Jun Jul Aug

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.005 05 05 05 05 05 05 05


1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.005 05 05 05 05 05 05 05


Max 2005-09Average 2005-0920092010

Max 2005-09Average 2005-0920092010

Max 2005-09Average 2005-0920092010

Crop condition in China, April 2009

The timely and accurate assessment of crop condition is a determiningfactor in the process of decision making in response to crop stress. Cropcondition maps and crop growth profile charts of several provinces inChina in mid-April 2009, retrieved from the global Crop Watch System,show the crop condition in drought-affected areas relative to the

previous year. The crop growth profile charts of three selected provincesillustrate how crop growth responds to drought conditions.

Source: China CropWatch System.

Crop assessment


Protected areas are often seen as a yardstick for evaluating conser -vation efforts. While the global value of protected areas is not in dispute,the ability of any given area to protect biodiversity needs to be evaluatedon the basis of rigorous monitoring and quantitative indicators.

The GEO Biodiversity Observation Network (GEO BON) AfricanProtected Areas Assessment demonstrates how field observationscombined with satellite imaging can be combined to assess the value ofprotected areas.

Data for 741 protected areas across 50 African countries have beenassembled from diverse sources to establish the necessary informationsystem. The data cover 280 mammals (including the African wild dogpictured here), 381 bird species and 930 amphibian species as well as alarge number of climatic, environmental and socioeconomic variables.

Source: P. Becker and G. Flacke.

PROTECTED AREAS

Information for decision making: biodiversity 109

Dat

a in

tegr

atio

nD

ata

pres

enta

tion

Mammal

Bir

d

Agriculture Population

Amphibian

Ha

bita

t

Irreplaceabilit

y

Protected areas

Species maps

Assessing pressures on biodiversity

Indicators of Protected Areas Irreplaceability (where the loss of uniqueand highly diverse areas may permanently reduce global biodiversity)and Protected Areas Threats are developed as practical and simplifiedestimates of the highly complex phenomenon of biodiversity. They areestablished using a wide range of geographic, environmental and

species data from the World Database on Protected Areas and othersources. The habitat of each protected area is characterized on the basis of its climate, terrain, land cover and human population. The datalayers are then integrated and the multiple pressures on biodiversityare assessed.


Ia Science

Ib Wilderness protection

II Ecosystem protection and recreation

III Conservation of specific natural features

IV Conservation through management intervention

Convention on wetlands of international importance

UNESCO World Heritage Convention

Other national parks

Categories of protected area management

This map shows the protected areas in Africa.The colour code indicates their protectionstatus. The information is gathered fromnational governments and internationalagencies and is used by the assessment teamas its starting point.

Source: GEO BON.

Protection status

The following three continent-scale mapshave been processed to show the vegetationindex, the percentage of land covered by treesand crops, and the land elevation.

Source: NASA.

Vegetation index


0

0.3

0.6

0.9

Vegetation index

Cropland and tree cover

30 - 40

40 - 60

> 60

Per cent cropland

< 10

10 - 30

30 - 60

> 60

Per cent tree cover


Source: NASA.

Elevation


Source: NASA.


African protected areas: Value compared

to pressure

VALUE

PR

ESSU

RE

High

Low

Low High

G200 Ecoregions

30ºN

20ºN

10ºN

0º

10ºS

20ºS

30ºS

20ºW 10ºW 0º 10ºE 20ºE 30ºE 40ºE 50ºE

Based on the previous maps of pro -tection status, vegetation coverage,elevation and other variables, indic -ators have been dev eloped to scoreeach pro tected area for the value of itsbio diversity and the threats that it faces.

Source: GEO BON.

Assessing protected areas


Visual products that can be understood and interpreted by a wide rangeof end users can also be created and used to inform decision making onconservation actions and funding priorities. For example, protected areasin Ghana (left) can be contrasted with all protected areas in Africa (right)to determine their relative status. The coloured sectors of the graphdepict indicators of biodiversity and habitat value (increasing to the right)

and indicators of pressure (increasing to the top). Ghana’s protectedareas are represented by the square symbols. The upper-right portionof the graphic identifies the protected areas – including several in Ghana –that have high biodiversity value and are also under high pressure.

Source: GEO BON.

Informing decision making

Mole NP

Bui NP

Digya NP

Nini-Suhien NP

Kumasi

Accra

Ankasa FRKakum NP

Semi-arid

Dry sub-humid

Moist sub-humid

Humid

Very humid

GHANA

Mole NP

Bui NP

Digya NP

Protected areas in Ghana

Kumasi

Index of value (biodiversity and habitat)

High value/low pressure

Low value/high pressure

High value/high pressure

High value/pressure

Others

All protected areas in Africa

Inde

x of

pre

ssur

e (p

opul

atio

n an

d ag

ricu

ltur

e)

1.00

0.75

0.50

0.25

0.000.00 0.25 0.50 0.75 1.00

Low value/low pressure

Average value/average pressure


CONCLUSION: GLOBAL CHANGE AND TRENDS

The nine stories in this book have described how geoinformation canbe used to support decision making in nine separate societal benefitareas. None of these issues, of course, exists in isolation. They areall interrelated: water supplies affect agriculture, ecosystems affecthealth, climate affects biodiversity, and so forth. Drawing theselinkages together in order to monitor and understand the Earthsystem as an integrated system of systems is essential foraddressing today’s complex global challenges.

The Global Earth Observation System of Systems makes itpossible to do this by assembling a large number of consistent,validated and interoperable data sets of Earth observations. Thesediverse data sets can be used to generate a snapshot of the Earth ata given moment in time. This snapshot can serve as a comprehensivebaseline against which to measure global change over the years anddecades to come. It can provide an essential point of departure forboth retrospective analysis and ongoing monitoring.

The individual baselines presented in the following pagesinclude parameters that do not change substantially over shortperiods of time but are fundamental for understanding globalchange. These relatively static data sets include elevation, soils andgeology. Also featured are data sets for continuously changingvariables that must be gathered at regular intervals. These data sets include surface reflectance, temperature, precipitation andvegetation.

The establishment of a comprehensive 2010 baseline for theEarth and its oceanic, atmospheric and terrestrial componentswould serve as a lasting contribution of the Earth observationcommunity to international efforts to protect and manage the planetfor future generations.

Source: NASA.

Topography

Global digital elevation models (DEMs) are created through the stereo -scopic analysis of multiple satellite images, in this case from theAdvanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER).

Digital elevation models are used to extract terrain parameterssuch as slope, aspect and elevation. They can be used as inputs for floodprediction models, ecosystem classifications, geomorphology studies andwater-flow models.

Source: ASTER NASA.

Conclusion: global change and trends 117

The OneGeology initiative is working to make geological maps morewidely available. It has assembled maps from geological surveys aroundthe world.

Geological maps are used to identify natural resources,understand and predict natural hazards, and identify potential sites forcarbon sequestration. Source: OneGeology .

Geology


Surface reflectance

Surface reflectance images such as the map above provide an estimateof the surface spectral reflectance as it would be measured at groundlevel without the distortion of atmospheric effects. To achieve this, rawsatellite data are corrected for the effects of atmospheric gases andaerosols and the positions of the satellite and the sun.

Surface reflectance data can be used for improving land-surface

type classification, monitoring land change and estimating the Earth’sradiation budget. These data can also serve as building blocks for otherprocessed data such as vegetation indices and land cover classification.

Source: NASA/MODIS.



Vegetation index

Vegetation indices are created from surface reflectance data. Bycombining spectral bands that are sensitive to chlorophyll absorptionand cellular structure, it is possible to highlight variations in the typeand density of forests, fields and crops.

Vegetation index data are used for a wide variety of applications,including agricultural assessment, land management, forest-fire

danger assessment and drought monitoring. The data are also used askey inputs for land cover mapping, phenological characterization andmany other applications.

Source: ESA/MERIS.

Cultivated and managed areas/rainfed cropland

Post-flooding or irrigated croplands

Mosaic cropland (50-70%)/vegetation (grassland/shrubland/forest) (20-50%)

Mosaic vegetation (grassland/shrubland/forest) (50-70%)/cropland (20-50%)

Closed to open (>15%) broadleaved evergreen and/or semi-deciduous forest (>5m)

Closed (>40%) broadleaved deciduous forest (>5m)

Open (15-40%) broadleaved deciduous forest/woodland (>5m)

Closed (>40%) needle-leaved evergreen forest (>5m)

Closed (>40%) needle-leaved deciduous forest (>5m)

ESA GlobCover Version 2 - 300mDecember 2004/June 2006 [ENVISAT MERIS]

Open (15-40%) needle-leaved deciduous or evergreen forest (>5m)

Closed to open (>15%) mixed broadleaved and needle-leaved forest

Mosaic forest or shrubland (50-70%) and grassland (20-50%)

Mosaic grassland (50-70%) and forest or shrubland

Closed to open (>15%) shrubland (<5m)

Closed to open (>15%) grassland

Sparse (<15%) vegetation

Closed (>40%) broadleaved semi-deciduous and/or evergreen forest

regularly flooded, saline water

Closed (>40%) broadleaved forest regularly flooded, fresh water

Closed to open (>15%) grassland or shrubland or woody vegetation on

regularly flooded or waterlogged soil, fresh, brackish or saline water

Artificial surfaces and associated areas (urban areas >50%)

Bare areas

Water bodies

Permanent snow and ice

No data

Land cover data are produced from relevant data sets such as surfacereflectance, temperature, vegetation indices, and other satelliteproducts. Land cover data are created by statistically clusteringtogether pixels with similar spectral and/or temporal patterns and thenlabelling them accordingly. Large-area land cover data are used for

many applications, including change detection studies, agricultural andforest monitoring, and input to global circulation models and carbonsequestration models.

Source: ESA.

Land cover



Tropical rainfall

The Tropical Rainfall Measuring Mission (TRMM) is a research satellitedesigned to increase our understanding of the water cycle. Althoughrainfall has been measured for more than 2,000 years, it is still notknown how much rain falls in many remote areas of the globe, inparticular over the oceans. With the TRMM it is now possible to directlymeasure such rainfall rates. The TRMM satellite carries a passivemicrowave detector and an active spaceborne weather radar called thePrecipitation Radar (PR).

TRMM data enhance the understanding of interactions betweenthe sea, air and land. These interactions produce changes in globalrainfall and climate. TRMM observations also help to improve themodelling of tropical rainfall processes and their influence on globalcirculation. This leads to better predictions of rainfall and its variabilityat various time scales.

Source: TRMM.


Forest height

Many data serve as building blocks for more highly processed data sets.These “derived” data sets tend to require a substantial period of time todevelop at a satisfactory level of quality.

Scientists have used a combination of satellite data sets to

produce a map that details the height of the world’s forests. Datacollected by multiple satellites are also being used to build an inventoryof how much carbon the world’s forests store and how fast carbon cyclesthrough ecosystems and back into the atmosphere.


Sea surface temperature

This sea surface temperature (SST) map was created from datacollected by the Advanced Along Track Scanning Radiometer. The imageis an average of all data available for one year. The colours representthe sea surface temperature, from dark blue (cold) to dark red (warm).

SST measures are used to monitor and predict the El Niño and La Niña phenomena. They are extensively used in hurricane and cycloneprediction and numerical weather and ocean forecasts.

Source: AASTR/ESA.

typhoon lupit - our planet€™s probable path and whether it would ... 500 1,000 km 250 500 miles...

Documents