earth engine for cropland mapping - usgs · earth engine for cropland mapping tyler erickson,...
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Earth Engine for cropland mappingTyler Erickson, [email protected]
Noel Gorelick
Google Earth Engine Team
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Background
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"To organize the world's information and make it universally accessible and useful."
https://www.google.com/about/company/
Google Mission Statement
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-Jim Gray (1944-2007)
“Often it turns out to be more efficient to move the questions than to move the data.”
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Reshaping Big Data Analysis Workflows
Create Hypothesis
Download Data
Code Algorithm
Process Data
Test Hypothesis
Setup Hardware
Download Data
Setup Hardware
Code Algorithm
Process Data
Test Hypothesis
Create Hypothesis
iterateiterate
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Before Earth Engine
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Three Major Vertical Efforts
● Forests● Water● Emerging Infectious Diseases
June 2015 - Vertical strategy meetings were conducted at Google HQ.
Supported projects in many other areas:Disaster risk management, biodiversity, urban growth monitoring, supply chain monitoring, agriculture and food security, air quality and pollution, climate change adaptation, etc....
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Forests
Goal: Conserve the world’s forests.
Needs: Plot-scale global monitoring at <= monthly cadence, comparing new data to long-term trends.
Key metric: Fraction of world’s forests actively monitored with EE.
Progress: • Currently: 62% of tropical rainforest actively monitored.• Global map of forest change (Science 2013).• 2015: EE-generated alerts for 100% of tropical rainforest.
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Water
Goal: Save lives by monitoring water quality and quantity.
Needs: Monthly tracking of location, quantity and quality of surface water & plot-scale water use (evapotranspiration).
Key metric: Fraction of high-risk area actively monitored with EE.
Progress: • Global map of permanent and transient water with EU/JRC.• Evapotranspiration algorithms close to deployment.• US drought monitoring system in development.
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Emerging Infectious Diseases
Goal: Save lives by eliminating malaria, dengue, and other EIDs.
Needs: Bringing together a diverse range of data sources at near-real-time to support disease risk mapping and operations.
Key metric: Fraction of high-risk area actively monitored with EE.
Progress: • Malaria risk mapping tools in development with UCSF, Oxford.• Strong relationships formed with Gates Foundation, Wellcome
Trust, and others.• Invited to present at White House expert panel on public
health.
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Data Catalog
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> 200 public datasets
MODISDaily, NBAR, LST, ...
TerrainSRTM, GTOPO, NED, ...
AtmosphericNOAA NCEP, OMI, ...
Land CoverGlobCover, NLCD, ...
The Earth Engine Public Data Catalog
> 4000 new images every day
> 5 million images > 5 petabytes of data
Landsat 4, 5, 7, 8Raw, TOA, SR, ...
... and many more, updating daily!
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Sentinel
Sentinel-1
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API
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Data Types and Geospatial Processing Functions
● Image - band math, clip, convolution, neighborhood, selection ...● Image Collection - map, aggregate, filter, mosaic, sort ...● Feature - buffer, centroid, intersection, union, transform ...● Feature Collection - aggregate, filter, flatten, merge, sort …● Filter - by bounds, within distance, date, day-of-year, metadata ...● Reducer - mean, linearRegression, percentile, histogram ….● Join - simple, inner, outer, inverted ...● Kernel - square, circle, gaussian, sobel, kirsch …● Machine Learning - CART, random forests, bayes, SVM, kmeans, cobweb …● Projection - transform, translate, scale …
over 1000 data types and operators, and growing!
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GeospatialDatasets
AlgorithmicPrimitives
add
focal_min
filter
reduce
join
distancemosaic
convolve
Results
Storage and Compute
Requests
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Relevant Datasets and Applications
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Training Data
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Surface water occurrence - Pekel et al., JRC
南昌
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Simplified surface energy balance, reference evapotranspiration
Modesto
Allen, Kilic, Huntington (UIdaho, UNL, DRI)
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Prasad's Requests
(a) Earth Engine set up(b) coding(c) Landsat time-series EVI, NDVI etc. mosaics of the world(d) k-means algorithm(e) supervised vs. unsupervised classification
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Request Access at earthengine.google.com
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Python API Installation
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Code Editor Example - Computing NDVI Mosaic
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Classification
Supervised
Unsupervised
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Classification
Supervised
Unsupervised
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Classification
Supervised
Unsupervised
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Supervised Classification using a Sampled Training Raster
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Regression - Fitting models to time series
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Cloud/Shadow Masking
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Radar Data - Sentinel 1 Temporal Mosaic
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Other Topics
• Datasets• Sentinel 1 / 2• Hyperion• Proba-V ?• VIIRS ?
• Tools• Export to Google Cloud Storage• Render computed products as Earth Engine assets• Exploration of Ommision/Commision maps
• Distribution of Products• Data viewers• Earth Engine public data layers
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GFSAD v1.0
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Summary
• Earth Engine has datasets and algorithms that are relevant for global cropland mapping.
• Cropland mapping is of interest to Google Earth Engine, because of alignment with the Water Vertical.
• We are interested in feedback on datasets, algorithms, and tools for interacting with the data.
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Other Topics
• Uploading data (docs)• Render• IP / open data
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How should Google engage with the GFSDA30 project?
● Access to reference data / image products○ Distribution using mobile app (trendy lights)
● Classification Analysis○ Adding/improving EE primitives
● Reference Data○ Helping to share generated data○ Improving tools for calculating/characterizing/exploring errors
■ Tools for characterizing the sampling balance■ https://code.earthengine.google.com/0600d7c2b140cda25f644cad627f4529
○ Generating additional reference data ■ Streetview Service■ FAO Collect Earth■ Crowd Compute on VHR images