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MEEO S.r.l.
http://www.meeo.it
Meeo S.r.l. - Meteorological Environmental Earth ObservationMeeo is an italian
privately-held company focused mainly on the implementation and development of
products and services based on satellite observation of the Earth-atmosphere
system.
Meeo provides a
wide range of commercial products and services “off the shelf”, based on
satellite data analysis for agriculture, soil management, environmental
monitoring and cloud and precipitation estimation based on single sensor,
multisensor, multispectral and multitemporal data analyses.
Meeo currently
provide specific Earth Observation products and services:soil mapper®, original spectral classification system for
automatic detection of up to 85 soil categories,PM MAPPeR ®,
innovative particulate matter monitoring system, andSOMAFID a new
system for active fire detection.
Moreover,Meeo develops
customised and dedicated services based on remote sensing applications, wave
propagation, data mining and data fusion.
soil mapper®is a fully automated, multi-sensor, spectral
rule-based preliminary classifier of mid (1000m) to very high resolution (up to
0.61m) Earth Observation imagery.
soil mapper® is fully automatic (unsupervised)
and requires neither user supervision nor ground truth data sample.
Spectral categories detected bysoil mapper® have a semantic
meaning belonging to the following categories:
·
Vegetation
·
Bare soil
/ Built-up
·
Snow / Ice
·
Clouds /
Smoke plumes
·
Water /
Shadows
·
outliers
Depending on the sensor spectral characteristics, a different number of
classes belonging to each semantic category is generated (i.e. up to 26
different vegetation classes are provided for Landsat and MODIS data).
In its current version,soil mapper® automatically generates three output classification maps with different
levels of informational granularity: “Large classification set”, “Intermediate
classification set”, “Small classification set”.
Besides classification maps,soil mapper® generates a series of Value Added Products (VAPs) providing continuous
spectral indexes potentially useful for further application-dependent image
analysis, like:
·
Greenness
index
·
Canopy
chlorophyll content index
·
Canopy
water content index
·
Water
index.
Moreover a series of masks for vegetation, clouds, urban seed pixels, bare
soil and build up areas, water, shadow, red roof (only for very high resolution
data) can also be generated.
PM MAPPeR®
The content
of fine and ultra-fine particulate matter in the air is becoming more and more
important as a field of study in the health sciences.PM MAPPeR®allows monitoring fine particulate
matter as PM2.5 from space. Using specific satellite-borne sensors, daily Earth
coverage is possible with spatial resolution to a thousand meters.
The computational process involves three main phases:
·
Multispectral
satellite data loading, cloud masking and soil coverage parameters computation;
·
Particulate
Matter calculation and map generation;
·
Particulate
Matter map classification and health risk map generation; during this phase the
US EPA 2006 health quality criteria are used to simply and effectively identify
the impact of air quality on different categories of people (Air Quality Index
– AQI concept).
SOMAFID
SOMAFID (SOIL MAPPeR Fire Detection) is the new system developed by MEEO to detect active
fires in a MODIS and MSG-2 SEVIRI scene.
TheSOMAFID algorithm improves the MODIS Fire
Detection (MOFID) algorithm, developed by the MODIS Science Team as a Level 2
product (MOD14).
The
following improvements have been introduced:
1. Reduction of the number of false
alarms due to clouds and water bodies by means ofSOIL MAPPeR® classification-based masking.
2. Definition of 3 different background
conditions of active fires:
·
High
biomass vegetation (for example, forests)
·
Low
biomass vegetation (for example, low humidity biomass like tree bark)
·
No-vegetation
(for example, bare soil and buildings)
3. Fire pixel recognition of 3 distinct
fire stages, characterized by different fire intensity, temperature, combustion
efficiency and emission ratios:
·
flaming fire
·
smoldering fire
·
mixed stage
TheSOMAFID system identifies active fires and
generates nine different output classes, three for each background type.
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