Global restoration monitoring: the challenge of counting every tree on the planet
Dr Josh Veitch-Michaelis, DS3Lab / Restor Eco
About Restor
https://restor.eco
Restor
Why is transparency and monitoring important?
Many high-profile restoration projects fail.
�In many cases there is no monitoring protocol in place
There is often no transparency and money is wasted
Who should verify pledges?
… fewer than 2 percent of them had survived. The other 98 percent had died or were washed away …. “It was a complete disaster,” agrees Jim Enright, former Asia coordinator of the U.S.-based nonprofit Mangrove Action Project. “But no one that we know of from Guinness or the record-planting proponents have carried out follow-up monitoring.” Guinness has not responded to requests for comment.
The most mangrove trees planted within one hour by a team is 1,009,029 and was achieved by Governor Lray Villafuerte of the El Verde Movement and the people of San Rafael of the municipality of Ragay, Camarines Sur, Philippines, on 8 March 2012.
Guinness World Record Organisation
“Phantom Forests”, Yale Environment 360
Monitoring protocols
Individual Tree Crown Delineation (TCD)
Supports Restor’s mission for science and transparency
Google.org project focuses on AI applications for monitoring
Task: Instance segmentation
Predict masks over all trees in an input RGB image
Why?
Necessary for numerous indicators of restoration progress/health
Transparent monitoring (pledge verification, etc)
Example display showing delineated tree crowns and annotated downstream products
Data modalities for TCD
Green 550±40 nm
Red 660±40 nm
Red Edge 735±10 nm
NIR 790±40 nm
Citrus trees in multi-spectral (Parrot Sequoia)
LIDAR point cloud (DJI)
RGB Images
Also hyper-spectral, thermal, etc
What’s been done?
DeepForest uses LIDAR-derived CHM to initially
Label RGB imagery
Our Tree Mask Dataset
Current dataset using Open Aerial Map
Criteria for inclusion
Over 400k individually labelled trees in our train/test/val split
About $5 per image labelled, takes hours!
A variety of real images from our data. Mixtures of urban, natural and plantation type forests. Some easy to label, some not!
Primary labelling challenge: closed canopy
Open Canopy
Closed Canopy
Tree detection workflow
In: orthomosaic produced from user-submitted images - many ha
TCD Instance Segmentation Model (Mask-RCNN)
Out: 10 cm map showing closed canopy and open canopy/individual tree locations
Canopy coverage
Tree count, stem density, etc.
Crown statistics
Spectral diversity
Derived products
Predict over tiles and merge
Open
canopy
+ Others
Example model results
The model can also identify closed canopy (yellow)
Performance on open canopy in high-contrast
environments is good (as expected)
Example model results on a large image using tiling
Almost all trees are detected, even small ones, but
some issues in tile merging (false positive canopy)
An unseen acquired using the monitoring kit (Mick dos Santos, ERC)
Model performance (test dataset)
mAP is standard, but not a good metric because it doesn’t penalise false positives (overcounting)
Probabilistic Data Quality (Hall et. al, WACV 2020). Best confidence threshold is around 0.6-0.7
Around 75 %
accuracy - comparable to literature
Label QA and lessons learned (ongoing)
Labellers are not experts (so QA is required)
Does this matter?
Instructions must be simple in order to get consistent results
Can ~enforce consistency in post-processing
Trade-off between price/image/time incentives
Tricky, save budget for critical images (test set), active learning, etc.
ML pre-labelling very useful to save time …
… but relies on labellers to understand when the model is correct or not
Our internal web app to QA results from the model versus ground truth
Downstream products
Requires geo-referencing
Requires 3D from LIDAR/Photogrammetry
Doesn’t require geo-referencing
With more spectral information
Scaling to satellite
Zurich, yesterday in 3m resolution - yup, it was a cloudy day (copyright Planet)
What raw data is required (ideally)?
Cheap/Free
High temporal
resolution
High spatial
resolution
6-12 days
10 m
Free
Daily(!)
3 m
$$
0.3-0.5 m, $$$
(Strong) Opinion - satellite imagery is the only practical solution for transparent, high-resolution, high-temporal frequency monitoring at the global scale
High resolution - < 1m/px to see most mature open canopy trees
High time resolution - monthly-yearly
Low cost - could be government funded, or provided as a data layer
What raw data is required (ideally)?
Cheap/Free
High temporal
resolution
High spatial
resolution
6-12 days
10 m
Daily(!)
3 m
0.3-0.5 m
Beckenhof
STF
ESA Sentinel 2A
~10 m Captured 2022-10-18
What raw data is required (ideally)?
Cheap/Free
High temporal
resolution
High spatial
resolution
6-12 days
10 m
Daily(!)
3 m
0.3-0.5 m
Beckenhof
STF
Planetscope
~3 m Captured 2022-10-18
What raw data is required (ideally)?
Cheap/Free
High temporal
resolution
High spatial
resolution
6-12 days
10 m
Daily(!)
3 m
0.3-0.5 m
Beckenhof
STF
ESRI Wayback/Maxar Worldview
0.3 m(?) Captured 2022-10-12
Upcoming Work
Typical full OAM image, 4 gigapixels!
Live tree crown prediction demo
Imagery from Justdiggit
Site: Bumila, Central Tanzania
Input size: 1024 x 1024 px
Hardware: Nvidia GTX3090
Prediction speed: ~5-7 fps
Model: Resnet50-Mask-RCNN
Credit: inTime.de/Justdiggit
https://docs.google.com/presentation/d/18HaHhAPR4Pq3nOlFcnWbcguuMDmtVjLhPdydlPWmO2I/edit
Live tree canopy prediction demo
Imagery from Justdiggit
Site: Chaludewa, Central Tanzania
Input size: 1024 x 1024 px
Hardware: Nvidia GTX3090
Prediction speed: ~7-9 fps
Model: UNet-Resnet34
Credit: Justdiggit
Thank you! Any questions?