Southern Africa Fire Network overview
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1 Southern Africa Fire Network overview
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5 Estimation of live fuel moisture content Implementation of Dr Marta Yebra s FMC algorithm Currently running om MODIS MCD 43 c6 Applied on Sentinel 2 and Landsat 8 in 2018 for South Africa Field campaign currently underway in the western and southern Cape
6 FMC: MODIS
7 Field validation: Cape Fynbos
8 Implementation of an automated burned area mapping system using historical Landsat TM and ETM+ time series data in South Africa Karen Steenkmap,, Derick Swanepoel, Riaan Stegmann, Konrad Wessels, Linda Kleyn, Lufuno Vhengani, Frans van den Bergh, Prince Sibanda, Philip Frost, Melissa Hankel CSIR Meraka Institute and Lisa Collett, Dan Tindall, Neil Flood, Nicholas Goodwin Remote Sensing Centre, Queensland Government, Australia
9 Objectives Methodology Goodwin, N.R., Collett L.J. (2014). Development of an automated method for mapping fire history captured in Landsat TM and ETM + time series across Queensland, Australia, Remote Sensing of Environment, 148, Local implementation of Remote Sensing Centre (RSC) code for automated burned area mapping, Testing and validation in fire prone biomes in South Africa, Development of days since last burn / veld age product for SA National Fire Danger Rating System.
10 Automated Burned area mapping by the Remote Sensing Centre, Queensland Government, Australia System Automated burned area mapping system utilizing temporal, spectral and contextual information in hierarchical framework processes entire time series of Landsat 30m resolution data acquired since the mid 1980 s Accommodates TM/ETM sensors on board Landsat 4,5,7,8 platforms System produces atmospherically, BRDF and terrain corrected surface reflectance data Cloud + cloud shadow, topographic shadow - masking Implemented Repeatable burned area maps for entire archive of Landsat for Queensland, Australia (> images) Over 80% of burned area were detected with less than 30% commission error in savanna dominated area
11 Remote Sensing Centre s system System specifically developed for savannas Time series of Landsat TM band 4 (B4) and sum of reflectance band 4 + band 5 (B45) 3 key processing stages Detection of time series outliers Region growing of change clusters Attribution of change objects Data acquisition Apply median filters (seasonal/ nonseasonal) Landsat TM/ETM Multi-date change detection Detect time series outliers Watershed region growing Attribution of change objects Goodwin, N.R., Collett L.J. (2014). Development of an automated method for mapping fire history captured in Landsat TM and ETM + time series across Queensland, Australia, Remote Sensing of Environment, 148, Optimisation Validation
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13 Implementation of automated system in South Africa 6 Path/ Row selections based on high fire risk area, biomes, available ground truth data and high income land use
14 Burned area mapped: Savanna Landsat 8 27 June 2014 Burned area Change unrelated to fire Unclassified change
15 Validation Validation on 10 randomly selected images per Path/Row, 1000 stratified random samples per scene, 500 within burned area and 500 in unburned, 3 operators identifying burned vs unburned points
16 Validation: Savanna & Grassland User s accuracy for Savanna and Grassland more than 80% with less than 18% commission error Biome Path/Row Commission (%) Omission (%) User's accuracy (%) Savanna Grassland Grassland Fynbos
17 Validation: Fynbos User s accuracy for Fynbos is low at 58% and commission error high at 42% Biome Path/Row Commission (%) Omission (%) User's accuracy (%) Savanna Grassland Grassland Fynbos
18 AFIS FireFlies Nanosatellite constellation
19 FireFlies Constellation AFIS FireFly NanoSat is a custom designed nanosatellite optimised sensor making use of novel sensing technology to detect wildfires through potassium emission signatures (K-line) Sensor Concept FireSat sensor, operating at two very narrow spectral bands, designed to detect and discriminate emissions which originate from large vegetation fires Aim: 60km Swath, with 60-90m ground resolution for fire detection Mission Concept Constellation of nanosatellites flying at km Concept mission in space April st FireFly in Space March nd and 3 rd FireFly in Space March 2020
20 Sensor Concept Investigation of the feasibility and optimisation of a vegetation fire sensor operating in the near-infrared that discriminates flame on the basis of spectral emissions from vegetation for implementation as a satellite payload on a cube satellite in low earth orbit
21 Imager Payload Ground sampling distance of 63.6 m from altitude of 600 km Satellite ground track velocity of km/s Orbit period of 96.7 minutes Captures ±75 Mbyte of uncompressed images per pass over South Africa Data downlink takes ±2 minutes with highspeed S-band radio system (CPUT) Payload consumes less than 2 W average power Embedded Linux operating system
22 K-Line detection Range : ~8 km Exposure Time : 20 ms Gain : 0 Total Period : 2 hours Speedup : 60x
23 Meet our Team Business and Technical skills that drive the AFIS development CAM EO Head of AFIS Lee Annamalai Competence Area Manager Earth Observation Systems and ICT Philip Frost Product Manager Innovation and R & D 14 years of experience in technology & business management. Internationally recognised in the Space and Geospatial domain. Senior Developer Cheewai Lai Sys Admin Satellite Product Production Front End and User Admin System Security 14 years of involvement in Fire information system. Internationally recognised Deeply embedded in the fire management and response domain System Analysist Linda Klein System and Requirements analysis Remote Sensing, Innovation and R & D 18 years of experience in hosted ICT System Administration and Geo-spatial and Satellite Product production systems 20 years of experience in Remote Sensing, Geo-spatial and Satellite Product production systems
24 Meet our Team Passionate team of Developers and R&D Remote Sensing Scientists UI Developer Werner Raath Frontend and Android app developer User Support Report Engine Developer Ndumiso Booi Report and Stats Engine developer User Pilot Projects Senior Developer Riaan vd Dool AFIS Backend Db and Web Front End User Support R&D Analytics Developer Melissa Henkel Remote Sensing modelling, Algorithm development Senior Developer Derrick Swanepoel High performance Back End software, cube server. ios app R&D Analytics Developer Lufuno Vhengani Remote Sensing modelling, Burn area algorithm development
25 Contact details Philip Frost CSIR Meraka
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