As a part of the research project Sentinel4Nature “Monitoring and mapping of environmental gradients using Sentinel 2 data in combination with supplemental data from Sentinel-1” (Blumen-trath et al., 2016), tree pixels were segmented at a spatial resolution of 10 x 10 m (figure 33).
Sentinel-116 carry a C-band Synthetic Aperture Radar (SAR) instrument and has a spatial reso-lution ranging from 5 to 40 m. Sentinel-217 has a Multi Spectral Instrument (MSI) covering 13 spectral bands ranging from 443 nm to 2190 nm (including 3 bands for atmospheric corrections), a spectral resolution ranging from 1 nm– 180 nm and a spatial resolution of 10, 20 and 60 m.
16 https://sentinel.esa.int/documents/247904/1653440/Sentinel-1_Data_Access_and_Products
17 http://www.d-copernicus.de/fileadmin/Content/pdf/Sentinels_update_170510_final_printed.pdf In brief
The TGI-corrected segmentation method used in this report under-estimates tree canopy area rela-tive to a vegetation classification of ALS raw data by 95.2 % (2011).
To compare the spatial coverage of the segmented ALS tree crown data with the S4N-tree cov-erage (Sentinel 1 and 2), we rasterized and reclassified the segmented tree data from Oslo 2017 into the same spatial resolution, position and orientation as the Sentinel-2 tree pixels (figure 33 and 34).
Figure 33: Comparison of Sentinel-2 and segmented LIDAR tree pixels for the Oslo built-up zone medio 2017.
Figure 34: Spatial distribution of tree cover detection between Sentinel-2 (2017) and LIDAR (2017) for the URBAN EEA sample block 150 in the Ullern city region.
Comparing the Sentinel-2 and the aggregated ALS tree pixels reveal a relative similar spatial distribution pattern. The aggregated ALS tree pixels give a more detailed tree canopy map throughout the entire urban landscape. This was expected due to the original segmentation units of the ALS tree canopy (0.5 x 0.5-meter pixels) compared to the Sentinel2 tree canopy segmen-tation units (10 x 10 m).
Sentinel 2 tree pixels ALS tree pixels
Table 10: Comparison of the 2D tree canopy area (given in daa) within the Oslo built-up zone as detected from LIDAR and Sentinel2.
City region (Oslo built-up area Lower resolution data
overesti-mates canopy area (y/n) ALS
(daa) Sentinel-2 (daa)
Alna yes 3379 4506
Bjerke yes 1491 1584
Frogner yes 2508 2934
Gamle Oslo yes 1770 2116
Grorud yes 2425 2676
Grünerløkka yes 850 948
Nordre Aker yes 3719 4680
Nordstrand yes 6320 8238
Sagene no 551 516
Sentrum no 260 243
St.Hanshaugen yes 846 891
Stovner yes 2102 2813
Søndre Nordstrand yes 7482 9324
Ullern yes 2695 3957
Vestre Aker yes 6829 7233
Østensjø yes 2669 4981
Sentinel-2 contain less details and seems to overestimate tree canopy area in the urban land-scape with many spatially segregated individual trees. Two exceptions are found in the inner city and may be in part be explained by misclassification of building sites in ALS data (Sentrum).
This needs further work as the explanation is not clear-cut for the Sagene district. At an aggre-gated city region level, the relationship between the two variables are highly significant (r = 0.98) (table 10 above and figure 35 & 36).
Figure 35: Statistical distribution of the 2D tree canopy area (given in daa) within the Oslo built-up zone as detected from ALS and Sentinel 2.
Figure 36: Correlation between the ALS tree canopy 2D area and the Sentinel2 tree cover area within the Oslo built up zone.
As mentioned above, the Sentinel4Nature (S4N) project used Sentinel 1 & 2 to estimate relative tree canopy cover for a 10 x 10km study area in Oslo (blue outline), at a pixel resolution of 10 x 10 m (figure 37, left map). The relative ALS tree canopy area was aggregated to the same pixel resolution and compared to the S4N relative tree canopy cover (figure 37, right map).
Figure 37: Relative tree canopy cover for a 10x10km study area in Oslo as estimated in the S4N-project (left map) and based on the aggregated ALS tree canopy cover (right map).
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
0 1000 2000 3000 4000 5000 6000 7000 8000
Se nt in el -2 (d aa)
ALS (daa)
Sentinel 1 and 2 tree pixels ALS tree pixels
The comparison reveals a very similar spatial configuration of relative tree canopy cover, and a tendency that S4N heavily overestimates the relative tree canopy coverage. A statistical pixel-by-pixel comparison between S4N and the aggregated ALS relative tree canopy coverage esti-mates reveal a slightly positive, but not significant, relationship between the two variables (r = 0.63) (figure 38).
Figure 38: Comparing the relative tree canopy coverage estimates of S4N (Sentinel 1 and 2) and ALS.
In Table 11 differences between Sentinel 1&2 and ALS are analysed at the level of SSBs 500 x 500 m reporting units.
Table 11: Aggregated mathematical 2D tree canopy area difference (SSB500 reporting units) between Sentinel-2 vs. ALS and Sentinel-1-Sentinel-2 vs. ALS.
Area categories Difference = S1 & 2- ALS
Number of SSB500 units Difference = S2 – ALS Number of SSB500 units
As table 11 and figure 39 below show, the differences between Sentinel-2 and ALS are largest from -25 to 75 daa, whereas the differences between Sentinel-1 & 2 and ALS are largest from 125 to 225 daa relative canopy.
Figure 39: aggregated mathematical 2D tree canopy area difference between 2 vs. ALS and Sentinel-1-2 vs. ALS. The Y-axis represent number of SSB-units and the X-axis represents area classes in daa,
Comparing the aggregated 2D tree canopy area (SSB500 reporting units) between Sentinel-2 vs. ALS reveals a highly significant relationship (r = 0.93) between the two variables (figure 40).
The relationship between Sentinel-1 & 2 vs. ALS is weakly positive, but not significant (r=0.45) (table 11, figure 41).
In summary, use of Sentinel-2 alone (S2) overestimates tree canopy cover in the SSB500 grids with smallest tree cover (the most built-up areas). For larger tree can-opy areas (> 100 daa) Sentinel-2 and ALS produce sim-ilar tree canopy extent. This bias in the inner urban area can be corrected by combining optical (S2) and radar (S1) remote sensing, but increases differences for ar-eas with greater forest cover in the outer city. The con-clusion is that the ALS based approach is best for urban areas with canopy densities of ca. <20%, while for can-opy densities > 20% satellite optical remote sensing
data is enough and more cost-efficient for canopy extent accounting.
Figure 40: The relationship between 2D tree canopy area (aggregated to SSB500 reporting units) detected by Sentinel-2 and ALS.
0 50 100 150 200 250
0 50 100 150 200
Se nt in el -2 ( daa)
ALS (daa)
In brief
The ALS based approach is best for urban areas with canopy densi-ties of ca. < 20%, while for canopy densities > 20%, satellite optical remote sensing data is enough and more cost-efficient for canopy extent accounting.
Figure 41: The relationship between 2D tree canopy area (aggregated to SSB500 reporting units) detected by Sentinel-1 & 2 and ALS.
0 50 100 150 200 250
0 50 100 150 200