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.
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.
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).
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
.
The plot_diary
can 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
Questions and mistakes but also suggestions and solutions are welcome.
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