udef-arp

UDef-ARP was developed by Clark Labs, in collaboration with TerraCarbon, to facilitate implementation of the Verra tool, VT0007 Unplanned Deforestation Allocation (UDef-A).

https://github.com/clarkcga/udef-arp

Science Score: 36.0%

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Keywords

deforestation geospatial geospatial-processing remote-sensing
Last synced: 6 months ago · JSON representation

Repository

UDef-ARP was developed by Clark Labs, in collaboration with TerraCarbon, to facilitate implementation of the Verra tool, VT0007 Unplanned Deforestation Allocation (UDef-A).

Basic Info
  • Host: GitHub
  • Owner: ClarkCGA
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 11.5 MB
Statistics
  • Stars: 33
  • Watchers: 5
  • Forks: 16
  • Open Issues: 1
  • Releases: 6
Topics
deforestation geospatial geospatial-processing remote-sensing
Created over 2 years ago · Last pushed 8 months ago
Metadata Files
Readme License

README.md

Unplanned Deforestation Allocated Risk Modeling and Mapping Procedure (UDef-ARP)

UDef-ARP was developed by Clark Labs, in collaboration with TerraCarbon, to facilitate implementation of the Verra tool, VT0007 Unplanned Deforestation Allocation (UDef-A). It is used in conjunction with a raster-capable GIS for input data preparation and output display. Tools are provided for the development of models using the Calibration Period (CAL) and subsequent testing during the Confirmation Period (CNF). Based on these evaluations, the selected procedure uses the full Historical Reference Period (HRP) to build a model and prediction for the Validity Period (VP). The final output is a map expressed in hectares/pixel/year of expected forest loss.

Fitting and Prediction Phases and Chronology of the Testing and Application Stages, sourced from the VT0007 report Fitting and Prediction Phases and Chronology of the Testing and Application Stages (VT0007)

UDef-ARP provides the basis for developing a benchmark model as well as tools for comparative testing against alternative empirical models. The benchmark is intentionally simple – it requires only two inputs, distance from the forest edge (non-forest) and a map of administrative divisions that are fully nested within the jurisdiction. Based on these, it uses a relative frequency approach to determine the density of expected deforestation. In testing, this was found to provide a strong benchmark. However, it is intended that users incorporate more sophisticated empirical models, which may be used in UDef-A if they can be shown to be superior to the benchmark for both the fitted model in the Calibration Period and the prediction model in the Confirmation Period. Note that the manner in which alternative models are incorporated and tested is very specifically defined by the UDef-A protocol. UDef-ARP facilitates this testing process.

Some important points:

  1. At present, UDef-ARP only supports Windows platforms.
  2. A Windows installer is available as an alternative to working with the Python code.
  3. At present, only limited bulletproofing has been done. Please read the UDef-A document carefully regarding required inputs.
  4. UDef-ARP is still under development. Frequent updates are expected.

Requirements

Operating System

The UDef-ARP is currently operational exclusively on Windows systems.

Dependencies

Hardward Requirements

UDef-ARP was created with open source tools. In the current version, all raster inputs are stored in RAM during processing. Therefore, large jurisdictions will require substantial RAM allocations (e.g., 64Gb). The interface was developed in Qt 5. A minimum screen resolution of 1920 x 1080 (HD) is required. A 4K resolution is recommended.

Conda Environment Setup

Step 1: Download Anaconda

Download and install the latest version of Anaconda from https://www.anaconda.com/download

Step 2: Create a Virtual Environment

Open the Anaconda Prompt. Use the YAML file with the following command to create your virtual environment:

conda env create -f UDef-ARP_conda_env.yml Activate the environment you just created: conda activate udefarp

Before You Start

Step 1: Clone or Download the UDef-ARP Folder

Clone the repository or download the folder to your local directory.

Step 2: Open the GUI

1. Use your Python IDE to Open

Open the UDef-ARP.py file using any Python IDE.

2. Use Anaconda Prompt to Open

After activating your environment, change the directory to the folder directory: cd your_folder_directory Then, open the UDef-ARP.py file: Python UDef-ARP.py

Step 3: Prepare Your Data

UDef-ARP accepts raster map data is either a Geotiff “.tif” or TerrSet “.rst” (binary flat raster ) format. Similarly, outputs can be in either format. All map data are required to be on an Equal Area Projection. All map inputs must be co-registered and have the same resolution and the same number of rows and columns.

GUI Image

COPYRIGHT AND LICENSE

©2023-2024 Clark Labs. This software is free to use and distribute under the terms of the GNU-GLP license.

Owner

  • Name: Center for Geospatial Analytics
  • Login: ClarkCGA
  • Kind: organization
  • Location: United States of America

Center for Geospatial Analytics at Clark University

GitHub Events

Total
  • Create event: 2
  • Release event: 2
  • Issues event: 17
  • Watch event: 8
  • Issue comment event: 32
  • Push event: 61
  • Pull request event: 26
  • Fork event: 3
Last Year
  • Create event: 2
  • Release event: 2
  • Issues event: 17
  • Watch event: 8
  • Issue comment event: 32
  • Push event: 61
  • Pull request event: 26
  • Fork event: 3

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 162
  • Total Committers: 5
  • Avg Commits per committer: 32.4
  • Development Distribution Score (DDS): 0.049
Past Year
  • Commits: 52
  • Committers: 2
  • Avg Commits per committer: 26.0
  • Development Distribution Score (DDS): 0.038
Top Committers
Name Email Commits
Yao-Ting 9****o 154
Andrew Copenhaver a****r@v****g 4
tmorganbrown 1****n 2
Tammy Woodard t****d@c****u 1
Eli Simonson e****n@c****u 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 26
  • Total pull requests: 45
  • Average time to close issues: 9 days
  • Average time to close pull requests: 2 days
  • Total issue authors: 14
  • Total pull request authors: 4
  • Average comments per issue: 2.73
  • Average comments per pull request: 0.56
  • Merged pull requests: 40
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 13
  • Pull requests: 27
  • Average time to close issues: 13 days
  • Average time to close pull requests: 2 days
  • Issue authors: 10
  • Pull request authors: 1
  • Average comments per issue: 2.54
  • Average comments per pull request: 0.04
  • Merged pull requests: 24
  • Bot issues: 0
  • Bot pull requests: 0
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Issue Authors
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Pull Request Authors
  • YaoTingYao (55)
  • tmorganbrown (6)
  • agcopenhaver (4)
  • ESimonson95 (1)
Top Labels
Issue Labels
bug (2) enhancement (1)
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