Near real time deforestation monitoring in French Guiana using Sentinel-1 data

Transcription

1 Near real time deforestation monitoring in French Guiana using Sentinel-1 data OTB User Day 19 oct 2018 Cédric Lardeux Mathieu Rahm

2 Context Fund by ONF to help Gold Mining deforestation monitoring Difficult to monitor deforestation in French Guiana Low number of ground people for big size of the area Optical data limited by important cloud cover : Sentinel-1A and 1B launch (C band radar) Free data, stable radiometry without cloud 10-20m resolution every 6 days from 2016 September Capability to develop a free and semi-automatic tools to monitor deforestation

3 Outline Needs Tools and Methodology Results Conclusion and Perspective

4 Outline Needs Tools and Methodology Results Conclusion and Perspective

5 Needs Near real time deforestation monitoring Free tools and data «easy to use» for GIS expert Production tools vs R&D Capacity building over non radar expert

6 Outline Needs Tools and Methodology Results Conclusion and Perspective

7 Tools and Methodolodgy Optic vs Radar Optic (Sentinel-2 10m) RADAR (Sentinel-1)

8 Tools and Methodolodgy Processing Chain Pre- Processing (OTB + Python) Clip Calibration Orthorectification Temporal adaptative speckle filtering Post- Processing (Python) Adjust time series level over stable forest areas Temporal smoothing Deforestation detection (Python) Detection Mask no forest areas Remove small detection

9 Tools and Methodolodgy Processing Chain

10 Tools and Methodolodgy Post-processing VH polarisation db Original temporal filtered data Adjust level to fit forest radiometry (using polygon) Temporal smoothing Adjusted and smoothed time series

11 Tools and Methodolodgy Deforestation detection Get mean and standard deviation on some dates before current analysis date Compare the current value to the past label as anomaly or not Jean-Paul van Brakel Search anomaly in the time series Get anomaly if the change between current value and past mean higher than a fixed threshold

12 Outline Needs Tools and Methodology Results Conclusion and Perspective

13 Results Benchmarking sites

14 Results Benchmarking sites Site precision recall 1 92,0 68,0 2 95,1 83,3 4 92,0 75,9 Site 2 and 4 - Quite good accuracy over gold mining areas (fast deforestation from forest to bare soil) Site 1 Higher omission due to deforestation over fallow land (deforestation take more time and vegetation still on the ground

15 Results Examples Ortho IGN 2015 Landsat 2017

16 Results Examples Ortho IGN radar deforestation Landsat radar deforestation

17 Results Examples Color composition of three VH dates Sentinel-1 Landsat radar deforestation

18 Results Examples Color composition of three VH dates Sentinel-1 Landsat radar deforestation

19 Results Examples Composition colorée série temporelle Sentinel-1 Landsat radar deforestation Deforestation 08/2017 Deforestation 02/2017

20 Results Examples Ortho IGN 2015 Sentinel-2 08/2017

21 Results Examples Ortho IGN 2015 Sentinel-2 08/2018

22 Results Examples Ortho IGN radar deforestation Landsat radar deforestation

23 Results Limitations Deforestation/degradation < 0,5-1ha High dynamics areas like rural Small and linear deforestation (10-30m pistes, orpaillage clandestins)

24 Outline Needs Tools and Methodology Results Conclusion and Perspective

25 Conclusion Tools developed using OTB, Python and QGIS GIS expert can use it Works on small laptop like 4 GO ram (thanks to OTB lib and RIOS python tile processing library!!!) Processing chain actually in 3 steps (download, pre/post processing, detection) Update deforestation map over all French Guiana every 15 days for ONF First version of the tool available here (outdated, version 2 used in this presentation will be available at the end of this year)

26 Perspectives Would like to automatize all the chain Apply it on other areas

27 Cédric Lardeux Mathieu Rahm Thanks for your attention