Mapping fire: Can spatially explicit criteria and indicators be developed?
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The document discusses the development of spatially explicit criteria and indicators for mapping wildfires in Indonesia, emphasizing the challenges posed by extreme droughts and the impact of peat fires on greenhouse gas emissions. Various fire mapping methods and satellite sensors are reviewed, highlighting their advantages and limitations in accurately assessing burn areas and peat fire depths. Recommendations are made for improving fire mapping through validation, selection of appropriate methods, and consideration of available resources.
Mapping fire: Can spatially explicit criteria and indicators be developed?
- 1. Solichin Manuri Subarno Mapping fire: Can spatially explicit criteria and indicators be developed (Session #2)
- 2. Background • In the past four decades, severe wildfires have become common and repetitive events in the tropical ecosystem of Indonesia (Goldammer and Seibert, 1990). • These fires were the result of unsustainable practices of forest and land management, which coincide with extreme drought attributed to the El-Niño Southern Oscillation and the Indian Ocean Dipole Mode (Goldammer etal, 1990; Cochrane etal, 2003). • Drained peatland during dry season become susceptible to fires. During prolonged dry season, peat fires contribute huge amount of GHG emissions (Siegert etal, 2001; Lohbeger etal, 2017). • The impact of peat fires in Indonesia is limitedly explored, or with relatively high uncertainty, in particular regarding the size of the burn areas.
- 3. Challenges in fire mapping in the tropics Mapping of burn areas in tropical region is challenging, not only because of cloud persistence but also due to smoke or haze occurrence during the fires and short windows of opportunity. NASA, MODIS
- 4. 1982/83 1997/98 2015 20192000 Lennertz and Panzer, 1984 Siegert and Hoffm an, 2000 MODIS Burn Area MCD 45 MODIS Burn Area MCD 64 2006 MoEF GFED4.0 Lohberger, etal, 2017 LAPAN 2016 Copernicus Sub NationalGlobal National Fire mapping in the tropics and Indonesia
- 5. Fire mapping methods in tropics and Indonesia Burned Year and location Scope, resolution, continuity Method Lennertz and Panzer, 1984 1982/83; East Kalimantan, discontinued Visual classification of Landsat MSS images Siegert and Hoffman, 2000 1997/98; East Kalimantan; 25 m; discontinued Object based image analysis of ERS2 SAR data; PCA method GFED4.0 1995 – present; Global; 27.8 m Digital classification using MODIS MCD64A1, VIIRS, ATRS, TRMM MODIS Burn Area MCD 45 MODIS Burn Area MCD 64 2000 – present; Global; 500 m; continued Thresholds of burn index was applied to define burn areas and filtered with active fire data Copernicus Burn Area 2014-present Global; 500 m The annual BA derived from SPOT Vegetation using supervised classification Lohberger, etal, 2017 2015; Indonesia; 10 m Object based image analysis of Sentinel 1 backscatters layers and temporal change metrics, training data from ground and drone survey MoEF, 2016 2006 - present Indonesia; ?; continued Visual classification of Landsat ETM, OLI images, with aid of hotspot clusters and verified with ground data or high resolution images LAPAN 2015, 2016, 2019, Indonesia;30-m; most likely continue during extreme fire years; Change detection using dNBR of Landsat ETM/OLI images and VH polarization of Sentinel 1, hotspot>30% confidence level, filtering water body
- 6. Comparison of 2015 Fires Size 1,8 2,5 4,6 2,6 2,385 0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 GFED4.0 MODIS MCD 64 Lohberger etal, 2017 MoEF, 2016 LAPAN, 2016 2015 Burn Area Comparison (in mill ha) Peat Mineral soil All
- 7. Summary of Used Methods Component Sensor types Active (optical) and passive (radar) sensors; or hybrid Spatial resolution 10 m (Sentinel), 30 m (Landsat), 500m (MODIS), 1km (SPOT Vegetation Temporal repetition Monthly to annually Number of image data or sensors Single to multiple data sources, including additional GIS layers, such as water body, active fires/hotspots. Classification Visual classification: Visually inspect and delineate burn areas; require prior knowledge or ground thruthing data on burn area Pixel based classification: apply threshold for change index, which defined by analysing training samples Object based image analysis: based on probabilities of objects fall into burn category, derived from fuzzy logic threshold value and training samples
- 8. Sensors + - Passive • Easy to process • Long history of data acquisition • Hindered by cloud, smoke and haze Active • Ability to penetrate cloud • Relief displacement effects Resolution + - Medium • Better accuracy • Sufficient for national wall to wall mapping • Lower temporal resolution Low • High temporal resolution, thus able to get cloud and haze free image in shorter period • Lower spatial resolution and accuracy Classification + - Manual • Full control of operators • Time consuming • Required skilled operators Automatic • Fast and consistent results • Rely on good training samples Pros and Cons
- 9. Satellite sensors used for burned area mapping Satellite (Sensor) Operator Operational Dates Temporal Resolution Spatial Resolution Launch Date End Operation Landsat 1-3 (MSS) NASA/USGS July 23, 1972 September 7, 1983 18 days 375-750 m Landsat 4-5 (TM) NASA/USGS July 16, 1982 June 5, 2013 16 days 30-120 m Landsat 7 (ETM+) NASA/USGS October 5, 1993 Planned lifespan for 5-21 years or 2022 16 days 15/30-60 m Landsat 8 (OLI/TIRS)NASA/USGS February 11, 2013 Planned lifespan for 5 years, but still operating 16 days OLI: 15/30 m TIRS: 100 m SPOT 1–7 (HRV) CNES February 22, 1986 2024* 26 days 2.5 to 20 m SPOT 4-5 (VGT) CNES March 24, 1998 July 1, 2013 1-2 days 1000 m NOAA-7-20 (AVHRR) NOAA October 19, 1978 Still operating 1-2 days 1100 m JPSS (VIIRS) NOAA October 28, 2011 2031 1-2 days 375-750 m Aqua (MODIS) NASA May 4, 2002 Still operating 1-2 days 250-1000 m Terra (MODIS) NASA December 18, 1999 Still operating 1-2 days 250-1000 m ENVISAT (MERIS) ESA March 1, 2002 May 9, 2012 2-3 days 300-1200 m ERS-2 ESA 1995 2011 35 days 25 m PROBA V ESA May 7, 2013 Missions duration 2-5 years, but will be end in 2020 1-2 days 300 m Sentinel 1A ESA April 3, 2014 7-12 years or until 2021-2026 6 days 5-20 m Sentinel 2A ESA June 23, 2015 7.25 years or until 2022 5 days 10-20-60 m Sentinel 1B ESA April 25, 2016 7-12 years or until 2023-2028 6 days 5-20 m Sentinel 3A ESA February 16, 2016 7.5 years or until 2023 1-2 days 500 SLSTR m Sentinel 2B ESA March 7, 2017 7.25 years or until 2024 5 days 10-20-60 m Sentinel 3B ESA April 25, 2018 7 years or until 2025 1-2 days 300 OLCI mChuevieco, et al (2019)
- 10. 2018 7 years or until 2025 2024* Satellite Lifespan Sentinel 1B 2016 Landsat 1-3 (MSS) 1972 1983 Landsat 4-5 (TM) 1982 2013 SPOT 4-5 (VGT) 1998 2013 EVISAT (MERIS) 2002 2012 ERS-2 1995 2011 1978 NOAA-7-19 (AVHRR) Still Operating SPOT 1-7 (HRV) 1986 Landsat 7 (ETM+) 1993 Planned lifespan for 5-21 years or 2022 Terra (MODIS) 1999 Still Operating JPPSS (VIIRS) 2011 2031 Landsat 8 (OLI/TIRS) 2013 Planned lifespan for 5 year, but still operating Aqua (MODIS) 2002 Still Operating PROBA V 2013 Missions duration 2-5 years, but will be end in 2020 Sentinel 1A 2014 7-12 years or until 2021-2026 Sentinel 2A 2015 7.25 years or until 2022 7-12 years or until 2023-2028 Sentinel 3A 2016 7.5 years or until 2023 Sentinel 2B 2017 7.25 years or until 2024 Sentinel 3A
- 11. Giglio etal, 2018 Siegert and Hoffman (2000) conducted an accuracy assessment using aerial survey for the 25-m resolution ERS SAR BA. While Giglio et al (2018) carried out uncertainty analysis for the 500 m MODIS BA using higher resolution images, i.e. 30-m Landsat Accuracy Assessment Siegert and Hoffman, 2000
- 12. Burn Peat Depth Mapping • Accurate information on burn peat depth is crucial for estimating emissions from peat fires. • Yet limited studies related to burn peat depth mapping in Indonesia, involving manual measurement and lidar application • Manual measurement poses potential biases due to the difficult accessibility of remote areas and time consuming • On the other hand, airborne lidar has been used for various height-related studies with high precision (sub meter). Balhorn etal, 2009 Study using Lidar Data Burnt Peat Depth (m) SE (m) Konecny et al, 2016 0.13 0.16 Simpson et al, 2016 0.23 0.19 Ballhorn et al, 2009 0.33 0.18
- 13. • Several initiatives on fire mapping are available globally and nationally using different satellite images and different classification method. • Spatially explicit criteria of fire mapping should be developed specifically depending on the method and data used, combined with knowledge from ground or aerial surveys. • Validation of the burn area using ground thruthing data or high- resolution images is required to understand the uncertainty of the data • Selection of method will rely on the need of fire mapping, i.e. mapping coverage, reporting interval, technical capacity and funding availability. • It is important to select the remote sensing data based on the spatial resolution, technical and funding capacity as well as the lifespan and the continuity of the satellite program. • Apart from mapping the size of fires, it is also important to estimate the depth of peat fires Main messages
- 14. Thank you