Last modified by Wai-Tim Ng on 2018/10/16 08:44
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edited by Wai-Tim Ng
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edited by Wai-Tim Ng
on 2018/10/16 08:44
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5 5  geo-information retrieval, in particular using Earth Observation (EO) sensors and Sentinel-2 time series.
6 6  
7 7  The Institute of Surveying, Remote Sensing and Land Information (IVFL) is internationally recognized as one of the leading institutes specialized in the processing and analysis of
8 -Sentinel-2 data and other EO time series. The institute developed a unique web-based processing chain for pre-processing of Sentinel-2 time series, including the extraction of relevant
9 -vegetation traits such as leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fractional coverage, broadband albedo and canopy chlorophyll content (CCC)
10 -(https:~/~/s2.boku.eodc.eu/). Key expertise of the team includes machine learning and neural nets for image analysis, forward and inverse modeling of canopy spectral signatures for the
11 -retrieval of vegetation traits using physically-based radiative transfer models, time series analysis, extraction of land surface phenology and drought indicators. Care is taken that the derived
12 -information products that can be readily uptaken by non-EO experts, for example in the field of precision farming (e.g. [[https:~~/~~/eo4water.com/>>url:https://eo4water.com/]]) or for large scale drought monitoring and
13 -disbursement of disaster contingency funds (DCFs) (e.g. https:~/~/ivfl-arc.boku.ac.at/kenya/map/).
8 +Sentinel-2 data and other EO time series. The institute developed a unique web-based processing chain for pre-processing of [[Sentinel-2 time series>>https://s2.boku.eodc.eu/]], including the extraction of relevant
9 +vegetation traits such as leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fractional coverage, broadband albedo and canopy chlorophyll content (CCC).
10 +Key expertise of the team includes machine learning and neural nets for image analysis, forward and inverse modeling of canopy spectral signatures for the retrieval of vegetation
11 +traits using physically-based radiative transfer models, time series analysis, extraction of land surface phenology and drought indicators. Care is taken that the derived information
12 +products that can be readily uptaken by non-EO experts, for example in the field of precision farming (e.g. [[EO4WATER>>https://eo4water.com/]]) or for large scale drought monitoring and disbursement
13 +of disaster contingency funds (DCFs) (e.g. [[Kenya>>https://ivfl-arc.boku.ac.at/kenya/map/]]).
14 14  )))|[[image:boku.jpg||style="float:right"]]
15 15  
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