Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands

This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders: 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics is also provided (folder: 5_Maps). All 25 LiDAR metrics were calculated using the Laserfarm workflow  (https://laserfarm.readthedocs.io/en/latest/) (building on the user-extendable features from the “Laserchicken” software: https://laserchicken.readthedocs.io/en/latest/#features). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: https://github.com/eEcoLiDAR/laserchicken and Laserfarm: https://github.com/eEcoLiDAR/Laserfarm), as well as on the “Laserchicken” documentation page (https://laserchicken.readthedocs.io/en/latest/). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: 6_Masks).  To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: 7_Auxiliary_data). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: 8_Sensitivity_analysis). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: 9_Use_case). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: 10_Ground_classification).

An overview of all the folders in the repository:

1.     AHN1

2.     AHN2

3.     AHN3

4.     AHN4

5.     Maps

Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset.

6. Masks

• ahn3_10m_mask_building_road_water.tif
• ahn4_10m_mask_building_road_water.tif
• ahn4_10m_mask_powerline.tif
• ahn1_10m_NA_mask.tif
• ahn2_10m_NA_mask.tif
• ahn3_10m_NA_mask.tif
• ahn4_10m_NA_mask.tif

It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0.

7. Auxiliary data

(1) Point_density

• ahn1_10m_point_density.tif
• ahn2_10m_point_density.tif
• ahn3_10m_point_density.tif
• ahn4_10m_point_density.tif

(2) Pulse_density

• ahn3_10m_pulse_density.tif
• ahn4_10m_pulse_density.tif

(3) Flighttime

• ahn3_10m_flighttime.tif
• ahn4_10m_flighttime.tif

(4) DTM_DSM

• ahn2_10m_dtm.tif
• ahn2_10m_dsm.tif
• ahn3_10m_dtm.tif
• ahn3_10m_dsm.tif
• ahn4_10m_dtm.tif
• ahn4_10m_dsm.tif

It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2–AHN4. All raster layers are provided at 10 m resolution.

8. Sensitivity analysis

• Centroids_of_sampling_plots
• Polygons_of_sampling_plots
• Clipped_point_clouds_of_sampling_plots
• Scripts
• Computed_vegetation_metrics
• Scripts_for_plotting
• Plotted_figures

It contains seven subfolders: (1) the centroids of the sampling plots, (2) the polygons of the sampling plots with 10-meter size, (3) the clipped point clouds of the sampling plots and the downsampled point clouds of the five habitat types, (4) the scripts for the sensitivity analysis, (5) the computed vegetation metrics of the five habitat types, (6) the Python scripts for plotting the figure, and (7) the plotted figures.

9. Use case

(1) Multi-temporal_AHN

• Data
• Usecase_multi-temporal_AHN.R

It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R).

(2) Natura2000

• Data
• Natura2000_end2021_HABITATCLASS.csv
• Natura2000_NL_habitat_grouped.csv
• Usecase_Natura2000.R

It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. 

10. Ground classification

• Raw_point_cloud
• Computed_metrics
• Plottings_and_code
• ArcGIS_project

It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project).

Code availability

Jupyter Notebooks for processing AHN datasets:

https://github.com/ShiYifang/AHN

Laserfarm workflow repository:

https://github.com/eEcoLiDAR/Laserfarm

Laserchicken software repository:

https://github.com/eEcoLiDAR/laserchicken

Code for downloading AHN dataset: https://github.com/ShiYifang/AHN/tree/main/AHN_downloading

Code for generating masks for AHN datasets: https://github.com/ShiYifang/AHN/tree/main/AHN_masks

Code for demonstration of ecological use cases: https://github.com/ShiYifang/AHN/tree/main/Use_case