DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis
Abstract
:1. Introduction
2. Materials
2.1. Project Area
2.2. SAR Images
- Orbit file correction: Updates orbit metadata with a restituted orbit file.
- GRD border noise removal: Removes low-intensity noise and invalid data on scene edges.
- Thermal noise removal: Removes additive noise in sub-swaths to help reduce discontinuities between sub-swaths for scenes in multi-swath acquisition modes.
- Radiometric calibration: Computes backscatter intensity using sensor calibration parameters in the GRD metadata.
- Terrain correction (orthorectification): Converts data from ground range geometry, which does not take terrain into account, to normalized backscatter coefficient using the Shuttle Radar Topography Mission (SRTM) 30 m Digital Elevation Models.
- During the first half of September 2021, issues with the image processing pipeline substantially reduced the availability of S1 images on the GEE platform. The images acquired during this period became available later in the same month.
- On 23 December 2021, a power supply-related issue prevented switching on the Sentinel-1B SAR acquisition subsystem. Further investigation did not succeed on fixing the compromised systems. Although the satellite is still orbiting normally, its operation is still stopped as of July 2022, and the future of the mission is uncertain. As a consequence of this incident, the mean number of images been made available over the DETER-R interest area dropped from 15.4 to 11.4 images/day.
2.3. Forest/Non-Forest Masks
- Deforestation map produced by the Program for Deforestation Monitoring in the Brazilian Legal Amazon (PRODES) [22]. This map is updated manually on DETER-R system every time the INPE/PRODES team issues an update. Normally this happens twice a year. The used map includes the residual (smaller than 6.25 ha) polygons which are not publicly available.
- INPE’s Forest/Non-forest map, built by visual interpretation. This medium resolution map outlines regional non-forest compounds, such as Roraima’s lavrados. This map is available at INPE’s terrabrasilis website (http://terrabrasilis.dpi.inpe.br/ (accessed on 18 January 2022)).
- The German Aerospace Center (Deutsches Zentrum für Luft-und Raumfahrt - DLR) Forest/Non-Forest map, built automatically using mainly TerraSAR-X data [23]. This map contributes to capture small features such as isolated outcrops and savanna patches among rainforest regions.
- Flooded and beach areas mapped by the Brazilian Institute of Geography and Statistics (IBGE) [26]. This ancillary layer will avoid false positives arising from seasonally flooding and coastline tidal variations.
2.4. Validation Imagery
- The Planet Basemaps Imagery, made available by the Norway’s International Climate & Forests Initiative (NICFI) program. Monthly and semiannual mosaics with 3 m spatial resolution are used, both in the visual and normalized analytic modes.
- Sentinel-2 images, freely distributed by ESA with 10 m spatial resolution and hosted on the Amazon Web Services (AWS). The two most recent images for each location at the time of analysis are used.
- The Landsat images selected and pre-processed by the PRODES team in the previous year. Pre-processing includes the application of contrast and color composition Short-wave infrared (R)/Near-infrared (G)/Red (B). Images are used with original 30 m spatial resolution.
3. Methodology
3.1. Image Selection
3.2. Disturbance Detection
3.2.1. Preprocessing
3.2.2. Computation of Warning Rasters
3.2.3. Warning Vectorization and Filtering
- Number of warnings: the mean value, across the polygon, of the total number of values below the threshold on the detection collection, computed for every pixel on the polygon.
- Day of change: The mode of the Julian day corresponding to the first perturbation on the detection collection.
- Intensity of change: median value of the difference between the threshold and the minimum value of the detection collection.
- Number of warnings: Polygon mean must be higher than 1. A value of 1 means that this polygon pixels were flagged only once, and the polygon should be discarded. This procedure mainly aims to remove the anomalies related to convection clouds, which provoke sudden and dramatic drops on backscattering [9].
- Size of the warning polygon: Polygons smaller than the system MMU will be discarded. Although theoretically this threshold can be fixed to values as low as one single pixel area, such a reduced MMU will raise the number of warnings caused by small-scale or spurious events associated with moisture variations [32] or speckle. Already existing S1-based detection systems [14,17] fix their MMU to values around 0.1–0.2 ha, to reduce the amount of false positives while allowing the detection of small deforested patches. In our case, after discussion with the system main users and stakeholders we decided to use a MMU of 1 ha, in order to encompass the main objectives of the environmental teams’ field campaigns and their budgetary limitations.
3.2.4. Merging
3.3. Warnings Validation
- Recent Deforestation: complete and recent removal of the forest cover due to clear-cut or as the result of successive disturbance events. A deforestation process is considered recent if it occurred within the year of the PRODES project (August to July).
- Recent Degradation: partial loss of forest canopy and consequent exposure of soil and/or understory vegetation.
- Burnt areas: forested areas impacted by fire. It may or may not contain arboreal vegetation.
- Residue: old deforestation process, i.e., complete removal of the forest cover that can be detected in the images used by PRODES in the previous year.
- Water-flooded areas: previously forested areas that have been flooded or engulfed by river dynamics. This class was only considered from mid-June 2021. Early validated warnings of this class were labeled as Non-forest formations.
- Non-forest formations: recent alterations occurring in areas not originally covered by forests.
- False positive: forested areas with no detectable forest disturbances.
- Cloud: warnings that could not be assessed due to clouds in the optical images used for validation.
- No reference data: areas that could not be evaluated due to the absence of recent optical images at the validation time.
- Agreement: warnings of forest disturbances correctly detected as High/Low Impact.
- Minor disagreement: warnings of forest disturbances incorrectly detected as High/Low Impact.
- Major disagreement: warnings that do not correspond to forest disturbances.
- Not Evaluated: warnings that could not be evaluated.
3.4. Warning Delivery
4. Results
4.1. Preliminary Field Validation
4.2. Forest Disturbance Warnings
4.3. Operational Validation
4.4. Delivered Warnings
5. Discussion
5.1. Differences among DETER-R and Other Operational NRT Systems
5.2. System Caveats
5.3. DETER-R Data as a Field Enforcement Tool
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Validation Class | Detection Class | |
---|---|---|
High Intensity | Low Intensity | |
Automatic (deforestation) | Agreement | Minor disagreement |
Recent Deforestation | Agreement | Minor disagreement |
Recent Degradation | Minor disagreement | Agreement |
Burnt areas | Minor disagreement | Agreement |
Residue | Minor disagreement | Minor disagreement |
Water-flooded areas | Major disagreement | Major disagreement |
Non-forest formations | Major disagreement | Major disagreement |
False positive | Major disagreement | Major disagreement |
Cloud | Not evaluated | Not evaluated |
No reference data | Not evaluated | Not evaluated |
Validation Class | Detected Polygons | Detected Area (ha) | ||
---|---|---|---|---|
High Intensity | Low Intensity | High Intensity | Low Intensity | |
Automatic (deforestation) | 33,933 (39.7) | 12,980 (15.2) | 288,798.8 (63.9) | 32,482.9 (7.2) |
Recent Deforestation | 23,060 (26.9) | 6443 (7.5) | 77,899.3 (17.2) | 12,815.4 (2.8) |
Recent Degradation | 460 (0.5) | 670 (0.8) | 1214.0 (0.3) | 1349.3 (0.3) |
Burnt areas | 1322 (1.5) | 1859 (2.2) | 17,550.6 (3.9) | 5771.3 (1.3) |
Residue | 1580 (1.8) | 462 (0.5) | 2619.8 (0.6) | 697.6 (0.2) |
Water-flooded areas | 126 (0.1) | 11 (<0.1) | 170.5 (<0.1) | 20.1 (<0.1) |
Non-forest formations | 449 (0.5) | 194 (0.2) | 1535.0 (0.3) | 393.2 (0.1) |
False positive | 224 (0.3) | 119 (0.1) | 434.7 (0.1) | 233.1 (0.1) |
Cloud | 467 (0.5) | 165 (0.2) | 3527.4 (0.8) | 360.0 (0.1) |
No reference data | 737 (0.9) | 319 (0.4) | 2997.1 (0.7) | 898.6 (0.2) |
Agreement | 59,522 (69.6) | 378,818.6 (82.7) | ||
Minor disagreement | 23,237 (27.2) | 67,380.2 (14.9) | ||
Major disagreement | 1123 (1.3) | 2786.5 (0.6) | ||
Not evaluated | 1688 (2.0) | 7783.0 (1.7) |
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Share and Cite
Doblas, J.; Reis, M.S.; Belluzzo, A.P.; Quadros, C.B.; Moraes, D.R.V.; Almeida, C.A.; Maurano, L.E.P.; Carvalho, A.F.A.; Sant’Anna, S.J.S.; Shimabukuro, Y.E. DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis. Remote Sens. 2022, 14, 3658. https://doi.org/10.3390/rs14153658
Doblas J, Reis MS, Belluzzo AP, Quadros CB, Moraes DRV, Almeida CA, Maurano LEP, Carvalho AFA, Sant’Anna SJS, Shimabukuro YE. DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis. Remote Sensing. 2022; 14(15):3658. https://doi.org/10.3390/rs14153658
Chicago/Turabian StyleDoblas, Juan, Mariane S. Reis, Amanda P. Belluzzo, Camila B. Quadros, Douglas R. V. Moraes, Claudio A. Almeida, Luis E. P. Maurano, André F. A. Carvalho, Sidnei J. S. Sant’Anna, and Yosio E. Shimabukuro. 2022. "DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis" Remote Sensing 14, no. 15: 3658. https://doi.org/10.3390/rs14153658