Description MOFgeoDB

General Setting

The MOFgeoDB is a SQlite database using the spatial extension spatialite and serves our data of vegetation (mainly trees) and study plots for ecosystem research (forest structure, experiments) over time. Additional there are some auxiliary datasets (geopackage) of infrastructure for visual reasons. QGIS is used as back-end for analysis and visualization. All data is available at GitHub Repo MOFgeoDB.

Relational Structure

First overview of the relational structure gives figure 1.

ER diagram MOFgeoDB complete

Figure 1: Complete ER diagram of MOFgeoDB without lut_creator table

The database can be divided into following sections:

  • Trees,
  • Plots,
  • Lookup tables.

When necessary the creator (referenced via creator_id, see fig. 2) and the date (day) of each single measurement is noted. Creator are stored in the look-up table lut_creator which can be a single person or (study) group and is designed rather simple.

Table structure lut_creator

Figure 2: Table lut_creator

Trees

Trees are measured as single individiums from a diameter of 7 cm. All trees have a unique identifier (tree_id) which is a combination of a lowercase letter (r=red, b=blue) and a five digit number. These identifiers are nailed to the real trees as in figure 3 shown to identify them in the field. Beside the geolocation of the center point at surface (easting,northing) and the method of this measurement (geomeasurement_id, see chapter geomeasurements), the species is mandatory via the species list (species_id from german_sl, GermanSL).

Marker on tree

Figure 3: Marker on tree as tree_id, here b00930

Geolocation is possible via a direct measurement of coordinates (usually using some sort of GNSS or terrestrial survey) or the determination via a topopoint using angle and distance as polar coordinates. Triggers calculate the coordinates on insert and update of relevant items.

Following items can be additionally stored via the corresponding tables using the referencing tree_id and are dependent on time:

  • general parameters
    • diameter: default breast height 1.3 m, triggers calculation of perimeter or diameter on circle
    • height: method is free text
    • state: looking up tree state via state_id as shown in table lut_tree_state.
    • social position: after Eichhorn et al. (2020) p.11
    • vitality: after Roloff (2001)
    • habitat: multiple micro-habitats per tree after Larrieu et al. (2018)
  • special investigations
    • sapflow: some trees installed with sapflow measurement (no values available by now)
    • dendroband: manual dendrometer, see figure foto
    • yearring: investigation of yearrings using cores (conducted by Burkhard Neuwirth, DELAWI)
    • roost: determined roosts for bats
  • dead wood
    • lying deadwood: size and position of lying deadwood >20 cm diameter
    • decomposition: decomposition rate for deadwood after Schwill et al. (2016) p.10

Plots

There are different type of survey plots (see also lut_plottype):

  • forest structure fs
  • exclosure plot exc
  • dendro ecology de
  • dendrometer band field dm
  • pasture structure ps
  • polygon unspecified pg
  • subplot sub

Most common is the fs-plot for forest structure survey. Most of the plots get their geolocation from a fixed benchmark in the field (see figure 4) documented in topopoint.

Benchmark

Figure 4: Benchmark

The plot_diarycan be used to document surveys on a plot linking specific actions (lut_action) to the desired plot and time. Automatic generation of subplots are possible and is implemented using triggers for the exclosure plot design. Triggers also manage the insertion, update and deletion of topopoints and plots.

All plottypes are documented in the post Plots.

Views

Views are created in the database to collect data from different tables for various reasons. It is a very convenient way to work with just one table for further analysis or visualizations. It insures the use of the desired data and has the capability to generate new fields like statistical parameters. All generated views are discussed in an special post.

Lookup Tables

Lookup tables just serve the idea to connect recurring items using a primary key. All lookup tables are documented in a separate post.

Auxiliary Data

Vector data

Other geographic datasets are stored as geopackage. In the supplementary geopackage following layers are available:

  • streets: including footpaths, etc. based on OSM, arial images and Lidar
  • water:
  • forest divisions: as used by HessenForst
  • infrastructure: point data on scietifc instruments and other POIs
  • area plots: non scientific plots or fences

The elevation_simple geopackage shows simplified contours derived from the DEM.

QGIS

A QGIS projectfile is offered for a sophisticated cartographic representation of the data.

Raster Data

All raster data is stored in seperated files as geoTiff.

UAV

Orthofotos and pointclouds are generated on behalf of NATUR4.0 from UAV raw data. More derivates are calculateted like tree segmentation, canopy heights, etc. (see somewhere? cite?)

LIDAR

Lidar Data from the state authorities are used to calculate DEM, DGM and canopy height.

Arial / Satellite Imagery

Arial RGB orthofotos from the state authoroties and several satellite imagery (Sentinel, planetscope) are available.

Resources

Eichhorn, J., Roskams, P., Potočić, N., Timmermann, V., Ferretti, M., Mues, V., Szepesi, A., Durrant, D., Seletković, I., Schröck, H.-W., Nevalainen, S., Bussotti, F., Garcia, P., & Wulff, S. (2020). Part IV: Visual Assessment of Crown Condition and Damaging Agents. In UNECE ICP Forests Programme Co-ordinating Centre (Ed.), Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests (Version 03/2020, p. 55). Thünen Institute of Forest Ecosystems. http://www.icp-forests.org/manual.htm
Larrieu, L., Paillet, Y., Winter, S., Bütler, R., Kraus, D., Krumm, F., Lachat, T., Michel, A. K., Regnery, B., & Vandekerkhove, K. (2018). Tree related microhabitats in temperate and Mediterranean European forests: A hierarchical typology for inventory standardization. Ecological Indicators, 84, 194–207. https://doi.org/10.1016/j.ecolind.2017.08.051
Roloff, A. (2001). Baumkronen: Verständnis und praktische Bedeutung eines komplexen Naturphänomens. Ulmer. https://www.ulmer.de/usd-1556341/baumkronen-.html
Schwill, S., Schleyer, E., & Plane, J. (2016). Handbuch Waldmonitoring auf Flächen des Nationalen Naturerbes (Naturstiftung David, Ed.). https://www.naturstiftung-david.de/fileadmin/Medien/Downloads/NNE_Infoportal/Monitoring/Handbuch_Waldmonitoring.pdf

Questions and mistakes but also suggestions and solutions are welcome.

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