This Jupyter notebook provides a specialized solution for generating trap and antiattractant placement strategies using 30-meter resolution satellite data. It's an independent adaptation of modules originally developed for the TANABBO project (Version 1.27.1), where I contributed. Unlike the original GRASS Python files used in TANABBO, this notebook is tailored for a more focused application in forest pest management.
- Basic knowledge of Jupyter notebooks.
- Understanding of GIS and remote sensing data.
- Yearly Time Series of Forest Losses: TIFF files named as
bb_spot_[year].tif, e.g.,bb_spot_2018.tif. - Spruce Forest Mask: A mask file (e.g.,
s50mask.tif) showing spruce forest distribution. A good start could be forest units older than 50 years with >50% spruce.
- Format: TIFF
- Resolution: 30 meters
- Consistent file size across datasets.
- Clone or download this notebook.
- Install Python and necessary libraries.
- Launch the notebook in a Jupyter environment.
- Loading Data: Import forest loss and spruce forest mask files.
- Trap Placement Algorithm:
- Determines optimal trap placement.
- Determines optimal antiattractant placement.
- Adjustable trap spacing between each other, with a fixed distance of 15 meters from the forest edge.
- Customizable Trap Spacing: Modify the distance between traps.
- Edge Detection: For strategic trap placement.
- Visualization: Maps to display proposed placements.
- Optimized for 30m resolution. Not tested with other resolutions or sizes.
- Assumes uniformity in input data.
Feel free to contribute to this project. Please follow the guidelines outlined in the tanabbo repository for contributions.
This notebook was developed as an independent extension of my work on TANABBO Version 1.27.1. Special thanks to the TANABBO team and community.
The tool is provided "as is", with no warranties. Users are responsible for their application of the tool and interpretations of the results.