C O P E R N I C U S F O R G I P R O F E S S I O N A L S Downstream Applications MALTA 2017-06-26 Pascal Lory, EUROGI EU EU EU www.copernicus.eu
S c o p e Identifying urban housing density: Stella Ofori-Ampofo, CERSGIS University of Ghana, Spreading of fertilizer on farm fields: Rita Hørfarter, SEGES, Denmark, Dynamic forest monitoring: SPACEBEL, EUGENIUS, Land cover monitoring: University of Maribor, Slovenia. -> A foretaste of what GI community will benefit from free & open COPERNICUS data
I n t r o d u c t i o n PIRI REIS map 1526. SATELLITE -> From PIRI REIS map to satellite imagery, around 5 centuries.
S E N T I N E L - 2:D e d i c a t e d t o C o p e r n i c u s Key Features: High revisit frequency: 10 days / one satellite, 5 days / two satellites Multispectral optical sensor 13 spectral bands 4 bands at 10 m, 6 at 20 m and 3 at 60 m spatial resolution swath of 290 km Products at different levels: 1C and 2A 786 km altitude, polar orbiting Around the planet in 90 minutes Launched on 23/6/2015 and 7/03/2017
R e v i s i t t i m e -> https://scihub.copernicus.eu/dhus : 24 imageries on Malta from 2017-01-18 to 2017-06-07 -> Provision of free data & services: a significant EU gesture -> Global coverage, same free data policy applies outside EU
URBAN HOUSING DENSITY MAPPING: ACCRA USE CASE S t e l l a O f o r i - A m p o l o U n i v e r s i t y o f G h a n a C O P E R N I C U S F O R G I P R O F E S S I O N A L S EU EU EU www.copernicus.eu
U r b a n h o u s i n g d e n s i t y a n a l y s i s : t h e p r o b l e m Overall population growing rapidly, urbanization taking place at increasing rate. To ensure well-being and productivity: a need for adequate infrastructure and services. -> Regular updated mapping regarding new urban settlement plays a vital role in services planning
U r b a n h o u s i n g d e n s i t y a n a l y s i s : t h e p r o b l e m Density of urban development has important economic implications over time: -> Each new infrastructure has upfront capital cost and ongoing operational cost. Imperative to understand and monitor changes in urban density over time. -> Regular mapping helps policy makers make informed decisions.
O u t l i n e o f m e t h o d o l o g y Download Level-1C SENTINEL-2 MSI from the Sentinel Scientific Data Hub (https://scihub.copernicus.eu/dhus) Data preparation(stacking 10m bands i.e. bands 2,3,4 and 8, and image sub-setting/clipping, band combination) Unsupervised image classification (based on ISODATA algorithm) Reclassifying/Recoding spectral classes into 5 information classes; high, medium, low density, vegetation, water.
H a r d w a r e a n d s o f t w a r e Hardware : i7 Laptop with 2.4GHz speed 1 Terabyte storage space 12GB Ram Software : ESRI ArcGIS Data : Level-1C SENTINEL-2 satellite imagery obtained from Sentinel Scientific Data Hub in.jp2 format Project area extent in.shp (vector format)
T e c h n i c a l i s s u e s a n d r e s o l u t i o n s Limited internet bandwidth for downloading & Huge amount of storage space required: -> Download only an extent of imagery rather than the entire scene, or only specific bands Difficulty in interpreting image metadata: -> Provision of guidelines https://sentinel.esa.int/web/sentinel/userguides/sentinel-2-msi Cloud cover over areas with high level of precipitation. Cloud mask unable to identify some cloud areas: -> High revisit frequency, a clear asset
V i d e o d e m o n s t r a t i o n Link to demonstration: CERSGIS Urban Density Mapping.wmv
C o n c l u d i n g c o m m e n t s Accessing, processing data requires an understanding of the data and tools available, Video shows only the first step in what is quite a long process, the decision for a new infrastructure require more survey, Free and open data provided by represents a significant enabler.
FERTILIZER APPLICATION TO MONITOR NITROGEN: DANISH USE CASE R i t a H ø r f a r t e r, S E G E S, D e n m a r k. C O P E R N I C U S F O R G I P R O F E S S I O N A L S EU EU EU www.copernicus.eu
F e r t i l i z e r a p p l i c a t i o n b a s e d o n d a t a f r o m S e n t i n e l - 2 A Main basic stages: Sentinel-2 Vegetation map Fertilizer variable rate application ESA -> In agriculture 10 meter resolution of Sentinel-2 fits quite well -> revisiting any point in Europe at least every 5 days : more opportunity to obtain cloud free imagery
F e r t i l i z e r a p p l i c a t i o n : t h e p r o b l e m The Problem is: Growing world population -> more need for food Reducing loss of Nitrogen and Phosphorous to meet the EU Water Frame Directive Requirements for agricultural production in the future are: To increase yield per hectare because no more land is available in EU To increase quality of agricultural products To reduce losses -> CropSat application can make a main contribution to achieve these goals.
C r o p S A T. d k - What is CropSAT? O v e r v i e w Free internet based application Farmers can prepare a crop protection map or a fertilizer map on behalf of a Normalized Distribution Vegetation Index (NDVI) calculated from satellite Sentinel 2A data Target Audience Farmers who want to follow the crops through the growing season Farmers who have tractors which are GPS linked and can be programmed to distribute different amounts of fertilizer in different parts of a field User Skills Required For basic use no skills required For farmers with GPS programmable tractors. only an ability to transfer the CropSAT output into their tractors
C r o p S A T. d k T h e B a c k e n d Hardware Computer with internet access Software Web application developed by Datavaxt, Sweden ESA Data ESA cloud mask QI_DATA/S2A_OPER_MSK_CLOUDS.gml ESA atmospheric corrections (Sen2Cor), http://step.esa.int/main/third-party-plugins-2/sen2cor/ Calculate the Vegetation Index NDVI, automatic on-the-fly process performed by Sentinel Hub Sentinel hub developed by SINERGISE, http://sentinel-hub.com/
C r o p S A T S o m e T e c h n i c a l I s s u e s ( 1 ) CLOUD COVER Sentinel-2 Level-1C products embed a vector mask including an indicator specifying the cloud type: dense, cirrus or cloud-free If the whole field or parts of the field is covered with clouds the field(s) is not valid in CropSAT BUT ESA s cloud mask do not find all the clouds. Result: some areas are registered as valid, but the Vegetation Index in these areas is (falsely) very low.
C r o p S A T S o m e T e c h n i c a l I s s u e s ( 2 ) Pixels Falling Across Field Boundaries Pixels falling across the boundaries gives false NDVI values (too high/low) In CropSAT pixels 10 meter inside the field border are deleted And pixels from the neighbor pixels extrapolates out to the field boundary Pixels influenced by trees Pixels 10 m inside border deleted
C r o p S A T S o m e T e c h n i c a l I s s u e s ( 3 ) Size of Sentinel2 tile imageries are squares with sides of 100 km with overlap: -> Means that the same field can be in two pictures Picture 1 Picture 2 Overlap Picture 1 + 2
C r o p S A T S o m e T e c h n i c a l I s s u e s ( 4 ) Automatic Upload Manually uploading of satellite data to CropSAT is too slow and time consuming. so Automatically upload via FTP server Big amount of data Requires a good data structure and a lot of storage space
C r o p S A T T h e P r o c e s s f o r t h e F a r m e r Step 1 Log onto the CropSAT web site. Type in Cropsat.dk Step 2 Identify own field(s) Step 3 Decide on the amount of fertilizer on a 1 5 scale Step 4 Decide on the level of crop protection on a 1 5 scale Step 5 Run the application Step 6 Review the map output showing variable amounts of fertilizer to be applied across the field(s) Step 7 Download the variable rate application file into tractor computer Step 8 Spread the fertilizer across the field(s) according the CropSAT map
V i d e o D e m o n s t r a t i o n Link to demonstration: CropSAT.dk-SD.mp4
DYNAMIC FOREST MONITORING: BELGIAN USE CASE S p a c e b e l, E U G E N I U S. C O P E R N I C U S F O R G I P R O F E S S I O N A L S EU EU EU www.copernicus.eu
T r e e S p e c i e s I d e n t i f i c a t i o n Purpose Service consists in a classification of tree species: Coniferous/deciduous, Dominant tree species (spruce, pine, oak, ash). -> Support improved forest management. Processing Forest mask is generated with Sentinel-1 time series and Sentinel-2 cloud-free image. Tree species classification is generated with Sentinel-2 time series and previously generated forest mask. Typical end users Forest cooperatives and Public forestry authorities.
S i g n i f i c a n t v e g e t a t i o n c h a n g e d e t e c t i o n a n d m a p p i n g Purpose Service detects potential clear-cuts, illegal logging and burnt zones in forested areas. It provides extent of change and time (interval) of occurrence. -> Helps prioritize field inspection. Processing Anomaly detection based Sentinel-1 and Sentinel-2 time series. Confirmation of detection on subsequent Sentinel-1/Sentinel-2 acquisitions. Typical end users Forest cooperatives and Public forestry authorities.
Demonstration Forest Classification Species Change Forest Mask Confidence Classification Detection Clear-cuts occurred between 10/06/2016 and 07/07/2016 -> One finding: Sentinel time series are more relevant to identify tree imagery Google Sentinel-2 Earth 27/08/2016 resolution 07/08/2015 26/11/2016 species than fine spatial
LAND COVER MONITORING: SLOVENIAN USE CASE U n i v e r s i t y o f M a r i b o r, S l o v e n i a. C O P E R N I C U S F O R G I P R O F E S S I O N A L S EU EU EU www.copernicus.eu
V i d e o d e m o n s t r a t i o n Link to demonstration : LC_Classification.m4v
F i n a l c o m m e n t s f r o m G I p r o f e s s i o n a l p o i n t o f v i e w -> Because of their high revisit frequency, Sentinel time series are relevant in many GI use cases where evolution is crucial -> 10m resolution can be either sufficient in some GI use cases -> or complementary (first step) with other aerial GI sensors with finer spatial resolution imagery in some others Do you have any questions?