The document discusses high temporal resolution remote sensing. It introduces physical models that can be used in a multi-temporal simulator to realistically simulate the temporal evolution of land cover from sensors like Sentinel-2 and Venus. The simulator uses existing time series from sensors like Formosat-2 to generate simulated multi-temporal data. It also examines using the simulator and radiometric analysis to detect land cover changes, like different states of soil preparation for agriculture.
The Large Interferometer For Exoplanets (LIFE) II: Key Methods and TechnologiesAdvanced-Concepts-Team
The LIFE initiative has the goal to develop the science, the technology and a roadmap for an aspiring space mission that will allow humankind to detect and characterize, via nulling interferometry, the atmospheres of hundreds of nearby extrasolar planets including dozens that may be similar to Earth. This follow-up talk will tackle more of the techniques and technologies that will enable such an ambitious undertaking. I will outline the underlying measuring principle, and provide some overview over essential technologies, their current status and necessary developments.
Seismic QC & Filtering with GeostatisticsGeovariances
The quality of seismic volumes is critical in building reliable reservoir models. Seismic data are often polluted by acquisition or processing artifacts which may have strong impact on subsequent seismic processing or interpretation. Geostatistics allows filtering efficiently seismic noise and artifacts without modifying the signal.
Geovariances provides solutions from seismic data quality control and filtering to reservoir characterization. This technology is based on geostatistics and all algorithms are available in Isatis, leader in geostatistical software solutions.
The Large Interferometer For Exoplanets (LIFE) II: Key Methods and TechnologiesAdvanced-Concepts-Team
The LIFE initiative has the goal to develop the science, the technology and a roadmap for an aspiring space mission that will allow humankind to detect and characterize, via nulling interferometry, the atmospheres of hundreds of nearby extrasolar planets including dozens that may be similar to Earth. This follow-up talk will tackle more of the techniques and technologies that will enable such an ambitious undertaking. I will outline the underlying measuring principle, and provide some overview over essential technologies, their current status and necessary developments.
Seismic QC & Filtering with GeostatisticsGeovariances
The quality of seismic volumes is critical in building reliable reservoir models. Seismic data are often polluted by acquisition or processing artifacts which may have strong impact on subsequent seismic processing or interpretation. Geostatistics allows filtering efficiently seismic noise and artifacts without modifying the signal.
Geovariances provides solutions from seismic data quality control and filtering to reservoir characterization. This technology is based on geostatistics and all algorithms are available in Isatis, leader in geostatistical software solutions.
Presentation from EuroSDR 113th meeting, Cardiff, October 2008. An overview of some of the geospatial research carried out by the different departments, centres and groups at UCL.
Introductory lecture on neuroimaging techniques: intracortical, fMRI, EEG. Tends to explain the ideas of the technologies on a good level of intuition. Presented at AACIMP'14 (http://summerschool.ssa.org.ua/program/42-program/ns-2014/442-machine-learning-on-neuroimaging-data)
Presentation from EuroSDR 113th meeting, Cardiff, October 2008. An overview of some of the geospatial research carried out by the different departments, centres and groups at UCL.
Introductory lecture on neuroimaging techniques: intracortical, fMRI, EEG. Tends to explain the ideas of the technologies on a good level of intuition. Presented at AACIMP'14 (http://summerschool.ssa.org.ua/program/42-program/ns-2014/442-machine-learning-on-neuroimaging-data)
Presentation made at the International Conference on Hydrology and Groundwater Expo, Hilton San Antonio
Airport, Texas, U.S.A, 10th to 12th September, 2012.
3. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
New sensors
Venus
Sentinel (1,2)
LDCM
New applications . . .
. . . which require to closely monitor the temporal
trajectory of the characteristics of land surfaces.
real time classification
evolving nomenclatures
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4. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Challenges
Global coverage every few days
Expectations for land cover change monitoring
Real-time: update the land-cover maps for every
new acquisition
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5. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Approaches
It's not about methods but about
needs/applications
spatio-temporal trajectories of clusters in a
kernelized feature space are cool . . .
but a hard threshold on NDVI can sometimes work
Many scientists have developed models for the
physical processes
Some are easy to use; some are complex
Some can be spatialized; some can't
Many are Open Source (more on this later)
Expert knowledge
i.e. agricultural practices
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7. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Essential Climate Variables
For climate change assessment, mitigation and
adaptation:
River discharge,
Water use,
Groundwater,
Lakes,
Snow cover,
Glaciers and ice caps,
Permafrost,
Albedo,
Land cover (including vegetation type),
Fraction of absorbed photosynthetically active
radiation (FAPAR),
Leaf area index (LAI),
Above-ground biomass,
Fire disturbance
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8. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Models
Areas of interest:
hydrology, agriculture, forestry,
Media:
Aerial, terrestrial, aquatic, mixed
How to find the good balance
complexity,
number of input parameters and variables,
computational cost
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9. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Models
They describe the physical reality
Their assumptions/simplifications are clear
Naturally use/need ancillary data (meteo, ground
measures)
They can be multi-sensor or better . . .
. . . Sensor Agnostic
benefit from the synergy between sensors
increase temporal sampling!
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10. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Open source models - some examples
Prospect: optical model for estimating leaf-level
reflectance and transmittance
Sail: canopy reflectance model
Daisy: mechanistic simulation model of the physical
and biological processes in an agricultural field
6s: a basic RT code used for calculation of
look-up tables in the MODIS atmospheric
correction algorithm
Arts: radiative transfer model for the millimeter
and sub-millimeter spectral range.
etc.
have a look at ecobas.org
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20. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
But we said HTR . . .
How to simulate a multi-t mission?
Venus, Sentinel-2
Realistic temporal evolutions
Use existing image time series
Formosat-2
8 m., 4 bands (B,V,R,NIR), 3 days
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22. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Example of series
March 14, 2006
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23. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Example of series
July 17, 2006
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24. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Example of series
November 2, 2006
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25. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Available data
49 images in 2006
Orthorectification OK
Radiometric corrections OK
TOC and aerosol corrections
Cloud screening
Land-cover map available
Leaf pigments data base for several vegetation
types (LOPEX'93)
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29. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Soil work
Main goal: improve real-time crop classification; soil
work can give hints on the type of crop
Soil map: is also interesting in itself as a product
Inter-crop Stubble disking Deep ploughing
Harrowing Sowing preparation Emergence
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30. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Approach
Radiometry only: only the reflectances and
combinations of them (indexes) are used; no
texture, statistics, nor object-based features.
Index Formula
NDVI NIR−R
NIR+R
Color R−B
R
Brightness √
G2 + R2 + NIR2
Shape 2R−G−B
G−B
Redness R−V
R+V
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31. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Approach
Statistical analysis: the temporal evolution of the
reflectances and the indexes (globally and per
class) are studied.
2 kinds of analysis:
Identification of the soil state: classification
Identification of the transitions between states:
change detection
SVM classification: both used as separability
measure and as classification tool
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32. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Results for change detection
Transition D→H H→SP H→E SP→E
Accuracy (%) 97.0 88.74 87.91 96.76
The number of transitions is very low for some
cases (between 12 and 50 plots; or between 1k
and 10k pixels)
Many transitions between states can't be
detected accurately
However, some changes are well detected (about
90% and more)
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34. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
What we've got
Source code available for many simulators
Ongoing work for
Prospect, Sail & Daisy integration
new hyper/multi- spectral/temporal algorithm
integration
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35. Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
What we need
Engineering - Development
Improve image simulation: MTF, realistic landscapes
Hide physical models under common interfaces
Research
Learn to select the best model set for a given
problem
Incorporate domain expert knowledge
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