Closed bw4sz closed 4 years ago
I took a first swipe at this.
https://github.com/weecology/NEON_crown_maps/blob/master/analysis.py
Things to go
I picked a very hard tile to start with, but initial results suggest we have more work to do on the predictions before we can get this level of detail. Hand checks on the LiDAR vertical accuracy are encouraging.
I also think it’s important to add that between your alignment continues to look quite good. Mostly because they enforce this through the lidar, this is a positive news for @Dylan, and it’s an argument for using multitemporal work to try to help areas of image artifact. I saw many areas where the artifacts were present in one year, but not the next.
On Mon, Mar 9, 2020 at 3:10 PM Ben Weinstein notifications@github.com wrote:
I took a first swipe at this.
https://github.com/weecology/NEON_crown_maps/blob/master/analysis.py
Things to go
- Generalize to multiple years. If there is 2017, 2018, 2019, what order to match polygons? Use all years simultaneously. What if a polygon from 2017 matches 2019, but not 2018. It will happen.
- Better matching of polygon centroids
- reasonable thresholds for matching and height limits.
I picked a very hard tile to start with, but initial results suggest we have more work to do on the predictions before we can get this level of detail. Hand checks on the LiDAR vertical accuracy are encouraging.
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We need a "same tree" criteria. If the polygon centroid has only moved X meters, and the height hasn't changed by more than X meters, call it the same tree.
Plot changes in height across forest levels.
@marconiS and @ethanwhite
This issue is grouped with #16 #17 as a general outline for the crown map paper to bring to the larger set of authors.