Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics

Transcription

1 Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics Caitlin Descartes Labs

2 Overview What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

3 Overview What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

4 Who we are Who are we? o New Mexico-based startup spun out of Los Alamos National Lab in December 2014 o Machine learning, computer vision, satellite imagery o Team of 30+ physicists, philosophers, mathematicians, software engineers, and geographers o Acquire, process, and store imagery (NASA, ESA, Planet) o Building a living atlas of the world: persistent, real-time, multi-modal o First application: global, real-time forecasts of commodity agriculture

5 Who we are MODIS daily 250m/pixel Landsat weekly 30m/pixel Planet RapidEye monthly 5m/pixel Sentinel-2 weekly 10m/pixel Sentinel-1 weekly 20m/pixel

6 Who we are MODIS daily 250m/pixel Landsat weekly 30m/pixel Planet RapidEye monthly 5m/pixel Sentinel-2 weekly 10m/pixel Sentinel-1 weekly 20m/pixel

7 Overview What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

8 Who am I? Who am I? o PhD in Geography from University of Wisconsin at Madison in 2016 o Used Landsat to study land cover and land use changes in southern Vietnam & CERES-Rice to investigate possible impacts of climate change to rice o Spent every February/March in the Mekong River Delta during graduate school collecting ground-truth data and conducting farmer interviews o Joined the engineering team at Descartes Labs in November of 2015 o Big fan of SAR data!

9 Overview What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

10 SAR overview What is synthetic aperture radar (SAR) image: Sentinel 1 composite from maps.descarteslabs.com

11 SAR overview Sentinel 1 satellite o Launched by European Space Agency in 2014 o C band ( cm) o 20-meter spatial resolution o VV, VH, HH, HV capabilities that vary by region and temporal cycle o Free!

12 SAR overview Mosaic of Landsat 8 imagery over Borneo: December 2016 Mosaic of Sentinel 1A imagery over Borneo: December 2016

13 Overview What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

14 Case study: Vietnamese Mekong River Delta image: Landsat 8 composite at maps.descarteslabs.com

15 Vietnam is one of world s largest exporters of rice

16 Vietnam is one of world s largest exporters of rice and nearly all of it is grown in the densely populated Mekong River Delta.

17 Why should we care about rice? o o o o Over 20% of the global calorie supply (Dawe et al. 2010) Staple grain for over 900 million people who live on less than $1.25 per day (Dawe et al. 2010) Declining yields are correlated with rising nighttime temperatures (Peng et al. 2004) Volatile! 80% of trade is controlled by 5 countries

18 Rice phenology

19 Proof of concept: image thresholding Minimum VV backscatter: 2015 growing season threshold the lowest 20% of values since rice paddies are flooded prior to planting

20 Proof of concept: image thresholding Minimum VV backscatter: 2015 growing season Mean VV backscatter: 2015 growing season threshold the lowest 20% of values since rice paddies are flooded prior to planting threshold the highest 45% of values since as rice grows the backscatter will increase

21 Proof of concept: image thresholding Minimum VV backscatter: 2015 growing season Mean VV backscatter: 2015 growing season Estimated extent of rice paddy for Can Tho Province threshold the lowest 20% of values since rice paddies are flooded prior to planting threshold the highest 45% of values since as rice grows the backscatter will increase 93.3% overall accuracy when compared to 150 random points

22 Next steps: classification with machine-learning

23 Generate temporal statistics for the 2015 growing season for VV and VH backscatter

24 Generate temporal statistics for the 2015 growing season for VV and VH backscatter

25 Create and label a random sample of points o o Label with high resolution Google Earth imagery 129 non-rice points & 133 rice points

26 Create and label a random sample of points o o Label with high resolution Google Earth imagery 129 non-rice points & 133 rice points Split into testing & training data o 70% to training; 30% for testing

27 Extract feature data from image statistics for each point to build and train a random forest classifier o o o Tune the parameters Apply to test data Apply to full image set

28 Extract feature data from image statistics for each point to build and train a random forest classifier o o o Tune the parameters Apply to test data Apply to full image set Classification Rice Not rice Total Producer s accuracy Rice % Not rice % Truth Total User s accuracy 98.5% 99.2% 98.9%

29 Classifying the number of rice harvests per growing season

30 Season winter - spring summer - autumn autumn - winter Planting date mid-october mid-march mid-july

31 Ideal signature (over a single year) Single-cropped rice Double-cropped rice Triple-cropped rice

32 Ideal signature (over a single year) Single-cropped rice Actual Landsat EVI signature (over a three year period) Double-cropped rice Triple-cropped rice

33 2015 winter-spring rice paddy extent 2015 summer-autumn rice paddy extent 2015 autumn-winter rice paddy extent 414,000+ hectares 453,600+ hectares 188,000+ hectares

34 Future work 1. Validate annual number of harvests estimates 2. Incorporate Sentinel-1B data to move toward real-time monitoring of rice management 3. Field-level analysis Use the Descartes Labs edge detection algorithm, which uses dense time stacks of SAR data to identify boundaries, to classify land cover/use at the field scale

35 Thanks! Come join us in New Mexico. We re hiring! or join our developer newsletter!