International Partnership Space Programme Earth Observation for the Preservation of Ecological Bacalar Corridor Platform and Products Terri Freemantle, Raffaella Guida, Paula Marti, Pasquale Iervolino and Steve Spittle ERIS workshop Chetumal, 1 December 2015
An end-user driven Process Collection of end-users requirements Definition of platform functionalities Definition of products
The Platform
Climate and Environmental Monitoring from Space (CEMS) Cloud Computing Environment 3 Main Functions: Community Cloud High Performance Computing Data Sharing To provide access to a wide range of climate, Earth Observation (EO) and non- EO data alongside various processing and analytical tools
CEMS Architecture Scalable and secure infrastructure Configurable virtual machines The within total privately combined managed resource: virtual 340+ CPU data cores centres (>1400 Ghz) Hosting applications, 6TB of RAM public facing 1.3PB web of attached portals and storage development environments. High Performance Computing for CPU intensive processing jobs, such as archiving satellite mission data.
CEMS Satellite Data and Resources Access to Data Catalogue of free spatial and non-spatial data EO database 10Gb connection
CEMS Sentinel Data Access Service UK Collaborative Ground Segment holds rolling archive of Sentinel data
IPSP Bacalar Monitoring Platform Overview 1 Provide ease of use within the browser Maximise visual aspects of the data whilst maintaining data integrity and rendering time Include authentication system to limit access to specified users Provide free and open source Spatial Data Infrastructure
IPSP Bacalar Monitoring Platform Overview 2 Map based interface On the fly image processing using CEMS CPU GIS functionality including data querying, creation of shapefiles etc. Direct access to database, allowing upload and download of datasets
Potential further developments for the IPSP Bacalar Monitoring Platform 1. Future Cities
Working With Local Authorities: Urban Development 18/01/2016
Not yet implemented in the beta version of the Bacalar Platform Platform will be able to clearly visualise change between two satellite images 18/01/2016
The Products
Priorities Human activities - Land cover change between 2005 and 2015 - Urban growth map between 2005 and 2015 Water Quality -Water quality maps of Chetumal Bay -Bathymetry map of Chetumal Bay Mangrove Ecosystem -Mangrove Distribution Map -Flooding extent in coastal mangroves -Mangrove degradation map post Hurricane Dean
Satellite Derived Bathymetry RapidEye (5m) - 2010 RapidEye (5m) - 2015 Relative Water Depth created using bottom albedo-independent Bathymetry algorithm
Satellite Derived Bathymetry Catapult Overview Catapult Overview Earth Catapult Observation Overview Products for IPSP Bacalar Project Earth Observation Products for IPSP Bacalar Project Earth Bathymetry Observation Products for IPSP Bacalar Project Bathymetry Mapping Bathymetry mangrove distribution and Mapping mangrove distribution and degradation Mapping mangrove distribution and degradation Water degradation Quality Water Quality Platforms Water Quality Platforms Platforms CEMS Infrastructure CEMS Infrastructure Bacalar CEMS Monitoring Infrastructure Platform Bacalar Monitoring Platform Example Bacalar of Monitoring Urban change Platform detection platform Example of Urban change detection platform Example of Urban change detection platform Landsat 8 30m
Mapping mangrove distribution and degradation Catapult Overview Catapult Overview Earth Catapult Observation Overview Products for IPSP Bacalar Project Earth Observation Products for IPSP Bacalar Project Earth Bathymetry Observation Products for IPSP Bacalar Project Bathymetry Mapping Bathymetry mangrove distribution and Mapping mangrove distribution and degradation Mapping mangrove distribution and degradation Water degradation Quality Water Quality Platforms Water Quality Platforms Platforms CEMS Infrastructure CEMS Infrastructure Bacalar CEMS Monitoring Infrastructure Platform Bacalar Monitoring Platform Example Bacalar of Monitoring Urban change Platform detection platform Example of Urban change detection platform Example of Urban change detection platform Terri, are you going to add something here? - We do not currently have any products, I can add an example or leave it blank? Cornforth et al (2013) Advanced Land Observing Satellite Phased Array Type L-Band SAR (ALOS PALSAR) to Inform the Conservation of Mangroves: Sundarbans as a Case Study
Water Quality in Laguna Bacalar and Chetumal Bay RapidEye (5m) 2010 Normalised Turbidity Index
Evapotranspiration Weekly water demand for fruit trees o o Local and global water demand for Arica (Chile) calculated from evapotranspiration over several weeks The maps show the water demand dropped from week 1 to week 9 and this was due to a drop in the temperatures week1 week4 week7 week9
Land Use CropID A map of UK vegetable crops Start with satellite imagery and field boundaries Apply software algorithms using a machine learning approach The result is the most probable crop in each field
Land Cover Corredor Transversal Costero, Chetumal Start with satellite imagery Rough classification Further processing reveals the differences on the land cover RapidEye 2010 True colour image Unsupervised classification Ground data will help analysis
Change Detection Deforestation areas in Brazil - Detection of recent deforestation areas in Rondonia (Brazil) - Comparison of DEIMOS- 1 2010 data with Google Earth database.
Forestry Analysis of impact after a fire Step 1: Multi-temporal analysis (false color) Step 2: Perimeter identification Step 3: Analysis Unburned Burned Perimeter: 9.7 km Area: 138,873 acres Perimeter: 71.4 km Area: 1,960,912 acres
SAR in change detection Sentinel-1 datasets 13 Dec 2014 06 May 2015 Parameter Sensor Mode Polarization Product Spatial Resolution Value Sentinel-1 IWS VV GRD 10 x 10 m
SAR in change detection Sentinel-1 datasets 13 Dec 2014 06 May 2015
SAR in change detection TerraSAR-X datasets 11 Feb 2011 Parameter Sensor Mode Polarization Product Spatial Resolution Value TerraSAR-X Spotlight HH SLC 0.9 x 0.5 m
Surface Water Quality with SAR Oil10 HH S-band X-band Crude oil Diesel oil Look-alikes Fused image (Db1_1max)
Land cover mapping with Data Fusion Panchromatic Temporal gap among acquisitions (can we use datasets acquired at different times?) Spatial gap among datasets (can we use datasets presenting different resolution and coverage?) Information diversity among datasets (how to put together information from Radar Cross Section and Reflectivity curves?) SAR Multispectral LiDAR
Land cover mapping with Data Fusion Lake Sea Pools Bare soil Sand Sea green Trees Shrubs Grass SAR+Multispectral+Panchromatic Buildings Roads Shadows or not classified Overall accuracy: 85.53% (78.74% Multispectral only, 83.20% Multi+Pan) Sea Pools Bare soil Sand Trees Shrubs Grass SAR+LiDAR Buildings Roads Shadows or not classified Overall accuracy: 85.16% (71.09% LiDAR only)
Gracias!