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envima / ForestModellingRLP

Forest species and habitat types for Rheinland Pfalz
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Lidar data #2

Open Friessn opened 4 years ago

Friessn commented 4 years ago

We need lidar indices for forest regions of Rheinland-Pfalz using the same raster as the sentinel data. Excluding vegetation < 10m was not straightforward in the RSDB, @swoellauer will help us extracting the indices.

Friessn commented 4 years ago

We calculate the following indices: BE_ELEV_MEAN BE_ELEV_SLOPE surface ratio (of DTM,,DSM,CHM) TCH vergetation coverage (1,2,5,10m) BE_H_MEDIAN BE_H_KURTOSIS BE_H_MAX BE_H_SD BE_H_SKEW BE_H_P30, BE_H_P70 LAI BE_PR_CAN BE_PR_UND BE_PR_REG BE_RD_CAN BE_RD_UND BE_RD_REG BE_FHD surface intensity mean ABG VDR chm_height_max chm_height_mean chm_height_sd chm_survace_ratio dtm_elevation_max dtm_elevation_sd

swoellauer commented 4 years ago

Task is running with indices:

BE_ELEV_MEAN, BE_ELEV_SLOPE, dtm_surface_ratio, dsm_surface_ratio, chm_surface_ratio, TCH, vegetation_coverage_01m_CHM, vegetation_coverage_02m_CHM, vegetation_coverage_05m_CHM, vegetation_coverage_10m_CHM, BE_H_MEDIAN, BE_H_KURTOSIS, BE_H_MAX, BE_H_SD, BE_H_SKEW, BE_H_P30, BE_H_P70, LAI, BE_PR_CAN, BE_PR_UND, BE_PR_REG, BE_RD_CAN, BE_RD_UND, BE_RD_REG, BE_FHD, surface_intensity_mean, AGB, VDR, chm_height_max, chm_height_mean, chm_height_sd, dtm_elevation_max, dtm_elevation_sd

Task:

{ "task_pointcloud": "index_raster", "pointcloud": "rheinland_pfalz", "rasterdb": "RLP_forest_mask_20m_i4", "indices": [ "BE_ELEV_MEAN", "BE_ELEV_SLOPE", "dtm_surface_ratio", "dsm_surface_ratio", "chm_surface_ratio", "TCH", "vegetation_coverage_01m_CHM", "vegetation_coverage_02m_CHM", "vegetation_coverage_05m_CHM", "vegetation_coverage_10m_CHM", "BE_H_MEDIAN", "BE_H_KURTOSIS", "BE_H_MAX", "BE_H_SD", "BE_H_SKEW", "BE_H_P30", "BE_H_P70", "LAI", "BE_PR_CAN", "BE_PR_UND", "BE_PR_REG", "BE_RD_CAN", "BE_RD_UND", "BE_RD_REG", "BE_FHD", "surface_intensity_mean", "AGB", "VDR", "chm_height_max", "chm_height_mean", "chm_height_sd", "dtm_elevation_max", "dtm_elevation_sd" ], "mask_band": 1 }
aliceziegler commented 4 years ago

additionally: pulse_return_mean BE_H_10

@swoellauer: something like openness/skyview factor/gapfraction would be nice. I used gap_fraction for the Kili project. Did I just miss it in the database indices? Perhaps I calculated it seperately from the points directly.

swoellauer commented 4 years ago

Processing Done. 121448.323s -> 34 hours Raster layer: RLP_forest_mask_20m_i4

More indices can be added with a new processing task. Currently indices suggested from @aliceziegler are not included in the raster.

Friessn commented 4 years ago

Raw Lidar Data from the flight in 2013 is missing although DEM data at the respective locations is available. The area that is missing coincides with the border from the UTM Zones 31 and 32, maybe this is a clue. @JannisGottwald may check with the data provider whether something went missing on their side @swoellauer continues having his eyes open.

The missing area is quite important as it includes the Eifel. Nevertheless, we will proceed with the current plan.

Friessn commented 4 years ago

additionally: pulse_return_mean BE_H_10

@swoellauer: something like openness/skyview factor/gapfraction would be nice. I used gap_fraction for the Kili project. Did I just miss it in the database indices? Perhaps I calculated it seperately from the points directly.

I would also add the topographic wetness index (TWI) to the list. @swoellauer the TWI is currently not implemented in the rsdb, is it possible to add it?

swoellauer commented 4 years ago

TWI depends on flow pathways from DTM. As the flow accumulates over possibly large areas it does not fit well in the approach of calculating indices from small local areas on-demand.