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tegory":"Deforestation and Reforestation","monthly_downloads":773,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"  whisp\r\n  =====\r\n  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/openforis/sepal/blob/master/license.txt)\r\n  [![Data Protection Policy](https://img.shields.io/badge/Data_Protection_and_Privacy-FAO-lightblue.svg)](https://www.fao.org/contact-us/data-protection-and-privacy/en/)\r\n  [![DOI](https://img.shields.io/badge/DOI-10.4060%2Fcd0957en-brightgreen.svg)](https://doi.org/10.4060/cd0957en)\r\n\r\n\r\n  ![Whisp_OpenForis_Banner_Github](https://github.com/user-attachments/assets/84f002fe-1848-46a1-814d-3949c22728cb)\r\n\r\n  ## Convergence of Evidence \u003ca name=\"whisp_coe\"\u003e\u003c/a\u003e\r\n  ***Whisp*** stands for \"***Wh****at* ***is*** *in that* ***p****lot*\"?\r\n\r\n  Numerous publicly available Earth Observation maps provide data on tree cover, land use, and forest disturbances. However, these maps often differ from one another because they use various definitions and classification systems. As a result, no single map can provide a complete picture of any specific area. To address this issue, the [Forest Data Partnership (FDaP)](https://www.forestdatapartnership.org) and the [AIM4Forests Programme](https://www.fao.org/in-action/aim4forests/en/) advocate for the **Convergence of Evidence** approach.\r\n\r\n  The Forest Data Partnership promotes this approach for forest and commodities monitoring, assuming that\r\n  - no single source of geospatial data can tell the whole story around any given plot of land;\r\n  - all the existing, published and available datasets contribute to telling that story.\r\n  \u003cbr\u003e\u003cbr\u003e\r\n\r\n\r\n  ## Contents\r\n  - [Whisp pathways](#whisp_pathways)\r\n  - [Whisp datasets](#whisp_datasets)\r\n  - [Whisp notebooks](#whisp_notebooks)\r\n  - [Add data layers](#whisp_add_data)\r\n  - [Contribute to the code](#whisp_contribute)\r\n  - [Code of conduct](#whisp_conduct)\r\n  - [Feedback](#whisp_feedback)\r\n\r\n  \u003cbr\u003e\r\n\r\n  ## Whisp pathways \u003ca name=\"whisp_pathways\"\u003e\u003c/a\u003e\r\n  ***Whisp*** can currently be used directly or implemented in your own code through three different pathways:\r\n\r\n\r\n  1. The Whisp App with its simple interface can be accessed [here](https://whisp.openforis.org/) or called from other software by [API](https://whisp.openforis.org/documentation/api-guide). The Whisp App currently supports the processing of up to 5,000 geometries per job. The original JS \u0026 Python code behind the Whisp App and API can be found [here](https://github.com/forestdatapartnership/whisp-app).\r\n\r\n  2. [Whisp in Earthmap](https://whisp.earthmap.org/?aoi=WHISP\u0026boundary=plot1\u0026layers=%7B%22CocoaETH%22%3A%7B%22opacity%22%3A1%7D%2C%22JRCForestMask%22%3A%7B%22opacity%22%3A1%7D%2C%22planet_rgb%22%3A%7B%22opacity%22%3A1%2C%22date%22%3A%222020-12%22%7D%7D\u0026map=%7B%22center%22%3A%7B%22lat%22%3A7%2C%22lng%22%3A4%7D%2C%22zoom%22%3A3%2C%22mapType%22%3A%22satellite%22%7D\u0026statisticsOpen=true) supports the visualization of geometries on actual maps with the possibility to toggle different relevant map products around tree cover, commodities and deforestation. It is practical for demonstration purposes and spot checks of single geometries but not recommended for larger datasets.\r\n\r\n  3. Datasets of any size, especially when holding more than 5,000 geometries, can be analyzed with Whisp through the [python package on pip](https://pypi.org/project/openforis-whisp/). See example [Colab Notebook](https://github.com/forestdatapartnership/whisp/blob/main/notebooks/Colab_whisp_geojson_to_csv.ipynb) for implementation with a geojson input. For further notebooks processing options see [Whisp notebooks](#whisp_notebooks).\r\n\r\n\r\n  ## Whisp datasets \u003ca name=\"whisp_datasets\"\u003e\u003c/a\u003e\r\n  ***Whisp***  implements the convergence of evidence approach by providing a transparent and public processing flow using datasets covering the following categories:\r\n\r\n  1) Tree and forest cover (at the end of 2020);\r\n  2) Commodities (i.e., crop plantations and other agricultural uses at the end of 2020);\r\n  3) Disturbances **before 2020** (i.e., degradation or deforestation until 2020-12-31);\r\n  4) Disturbances **after 2020** (i.e., degradation or deforestation from 2021-01-01 onward).\r\n\r\nAdditional categories are specific for the timber commodity, considering a harvesting date in 2024:\r\n\r\n  5) Primary forests in 2020;\r\n  6) Naturally regenerating forests in 2020;\r\n  7) Planted and plantation forests in 2020;\r\n  8) Planted and plantation forests in 2024;\r\n  9) Treecover in 2024;\r\n  10) Commodities or croplands in 2024.\r\n  11) Logging concessions;\r\n\r\n  There are multiple datasets for each category. Find the full current [list of datasets used in Whisp here](https://github.com/forestdatapartnership/whisp/blob/main/layers_description.md).\r\n\r\n  ### Whisp risk assessment \u003ca name=\"whisp_risk\"\u003e\u003c/a\u003e\r\n\r\nWhisp checks the plots provided by the user by running zonal statistics on them to answer the following questions:\r\n\r\n  1) Was there tree cover in 2020?\r\n  2) Were there commodity plantations or other agricultural uses in 2020?\r\n  3) Were there disturbances until 2020-12-31?\r\n  4) Were there disturbances after 2020-12-31 / starting 2021-01-01?\r\n\r\nAnd specifically for the timber commodity, considering a harvesting date in 2024:\r\n\r\n  5) Were there primary forests in 2020?\r\n  6) Were there naturally regenerating forests in 2020?\r\n  7) Were there planted and plantation forests in 2020?\r\n  8) Were there planted and plantation forests in 2024?\r\n  9) Was there treecover in 2024?\r\n  10) Were there commodity plantations or other agricultural uses in 2024?\r\n  11) Is it part of a logging concession?\r\n\r\n  The Whisp algorithm outputs multiple statistical columns with disaggregated data from the input datasets, followed by aggregated indicator columns, and the final risk assessment columns.\r\n    All output columns from Whisp are described in [this excel file](https://github.com/forestdatapartnership/whisp/blob/main/whisp_columns.xlsx)\r\n\r\nThe **relevant risk assessment column depends on the commodity** in question:\r\n\r\n\u003ctable\u003e\r\n  \u003ctr\u003e\r\n    \u003cth\u003eCommodity\u003c/th\u003e\r\n    \u003cth\u003eRisk Assessment Column\u003c/th\u003e\r\n    \u003cth\u003eDescription\u003c/th\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003eCoffee\u003c/td\u003e\r\n    \u003ctd rowspan=\"4\"\u003eRisk_PCrop\u003c/td\u003e\r\n    \u003ctd rowspan=\"4\"\u003ePerennial crop deforestation risk\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\u003ctd\u003eCocoa\u003c/td\u003e\u003c/tr\u003e\r\n  \u003ctr\u003e\u003ctd\u003eRubber\u003c/td\u003e\u003c/tr\u003e\r\n  \u003ctr\u003e\u003ctd\u003eOil palm\u003c/td\u003e\u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003eSoy\u003c/td\u003e\r\n    \u003ctd\u003eRisk_ACrop\u003c/td\u003e\r\n    \u003ctd\u003eAnnual crop deforestation risk\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003eLivestock\u003c/td\u003e\r\n    \u003ctd\u003eRisk_Livestock\u003c/td\u003e\r\n    \u003ctd\u003eLivestock driven deforestation risk (coming soon)\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003eTimber\u003c/td\u003e\r\n    \u003ctd\u003eRisk_Timber\u003c/td\u003e\r\n    \u003ctd\u003eTimber extraction deforestation risk\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n\u003c/table\u003e\r\n\r\n  **Note:** the specific datasets named in the diagrams below are examples only and may not all be reflected in the current codebase. For the datasets currently used, see the [layers description](https://github.com/forestdatapartnership/whisp/blob/main/layers_description.md).\r\n\r\n  *The Whisp algorithm for **Perennial Crops** visualized:*\r\n  ![CoE_Graphic 5](https://github.com/user-attachments/assets/007b5f50-3939-4707-95fa-98be4d56745f)\r\n\r\n  For perennial crops, Whisp assesses a deforestation risk for a plot of land based on the datasets from categories 1–4 on the presence of tree cover, agricultural use, and disturbances before and after 2020. The end-of-2020 date is used as the cutoff for regulatory frameworks such as the EU Deforestation Regulation (EUDR).\r\n\r\n  If no treecover dataset indicates any tree cover for a plot by the end of 2020, **Whisp will categorize the deforestation risk as low.**\r\n\r\n  If one or more treecover datasets indicate tree cover on a plot by the end of 2020, but a commodity dataset indicates agricultural use by the end of 2020, **Whisp will categorize the deforestation risk as low.**\r\n\r\n  If one or more treecover datasets indicate tree cover on a plot by the end of 2020, no commodity datasets indicate agricultural use, but a disturbance dataset indicates disturbances before the end of 2020, **Whisp will categorize the deforestation risk as \u003cu\u003elow\u003c/u\u003e.** This approach accounts for the characteristics of some perennial crops, which can be established under significant canopy cover (e.g. coffee, cocoa); disturbances prior to 2020 are interpreted as potential evidence of crop establishment before the end of 2020, and thus not considered high risk.\r\n\r\n  Now, if the datasets under categories 1–3 indicate that there was tree cover, but no agriculture and no disturbances before or by the end of 2020, the Whisp algorithm checks whether degradation or deforestation have been reported in a disturbance dataset after 2020-12-31. If they have, **Whisp will categorize the deforestation risk as \u003cu\u003ehigh\u003c/u\u003e.** \u003cbr\u003e\r\n  However, under the same circumstances but with \u003cu\u003eno\u003c/u\u003e disturbances reported after 2020-12-31 there is insufficient evidence and the **Whisp output will be \"More info needed\".** Such can be the case for, e.g., cocoa or coffee grown under the shade of treecover or agroforestry.\r\n\r\n\r\n  *The Whisp algorithm for **Annual Crops** visualized:*\r\n  ![annual_decision_tree](https://github.com/user-attachments/assets/0148736f-d9a1-4511-a8f8-8b32287483cb)\r\n\r\n  For annual crops, Whisp applies the same general framework based on tree cover, commodity, and disturbance datasets. However, disturbances before 2020-12-31 are not considered relevant, since annual crops are not typically established under significant canopy cover.\r\n\r\n  If no tree cover is indicated for a plot by the end of 2020, **Whisp will categorize the deforestation risk as low.**\r\n\r\n  If one or more tree cover datasets indicate tree cover on a plot by the end of 2020, and commodity datasets indicate agricultural use by the same date, **Whisp will categorize the deforestation risk as low.**\r\n\r\n  If tree cover is present and no agricultural use is recorded by the end of 2020, Whisp evaluates disturbance datasets after 2020-12-31:\r\n  - If deforestation or degradation is detected, **Whisp will categorize the deforestation risk as high.**\r\n  - If no such disturbances are detected, the **Whisp output will be \"More info needed\"**, as the available evidence is insufficient for a definitive classification.\r\n\r\n\r\n  *The Whisp algorithm for **Timber** visualized:*\r\n  ![CoE_Graphic_timber 7](https://github.com/user-attachments/assets/44a0d21e-8d41-4950-975d-7ea8e12f1fe1)\r\n\r\n  For timber, Whisp applies a different decision logic compared to perennial and annual crops. In addition to the **deforestation risk**, Whisp also evaluates **degradation risk**, defined as a change in forest structure. In addition, different decision rules apply depending on whether the forest at the end of 2020 is identified as a **planted/plantation forest** or a **natural forest** (primary or naturally regenerating). The analysis is based on datasets from categories 2 and 5–11.\r\n\r\n  If commodity datasets indicate agricultural use by the end of 2020, **Whisp will categorize the deforestation risk as low.**\r\n\r\n  If datasets indicate planted or plantation forest at the end of 2020 and no agricultural use is detected after 2020, **Whisp will categorize the deforestation risk as low.** If any dataset indicates an agricultural use after 2020, **Whisp will categorize the deforestation risk as high.**\r\n\r\n  If datasets indicate a natural forest at the end of 2020, and either agricultural use or conversion to a planted/plantation forest is detected after 2020, **Whisp will categorize the deforestation risk as high.** If neither of those is detected, Whisp checks for evidence of tree cover, regrowth after 2020, or the presence of the plot in a known logging concession. If any of these are found, **Whisp will categorize the deforestation risk as low.** If no additional information is available, **the Whisp output will be \"More info needed\".**\r\n\r\n  If datasets indicate no agricultural use, no planted or plantation forest, and no natural forest by the end of 2020, **Whisp will categorize the deforestation risk as low.**\r\n\r\n\r\n  ## Run Whisp python package from a notebook \u003ca name=\"whisp_notebooks\"\u003e\u003c/a\u003e\r\n\r\n  For most users we suggest using the Whisp App to process their plot data. But for some, using the python package directly will fit their workflow.\r\n\r\n  An example of the package functionality can be seen in this [Colab Notebook](https://github.com/forestdatapartnership/whisp/blob/main/notebooks/Colab_whisp_geojson_to_csv.ipynb)\r\n\r\n  For running locally on your machine (or in Sepal), see: [whisp_geojson_to_csv.ipynb](https://github.com/forestdatapartnership/whisp/blob/main/notebooks/whisp_geojson_to_csv.ipynb) or if datasets are very large (e.g., \u003e100,000 features), you could also try [whisp_ee_asset_to_drive.ipynb](https://github.com/forestdatapartnership/whisp/blob/main/notebooks/whisp_ee_asset_to_drive.ipynb).\r\n\r\n  ### Requirements for running the package\r\n\r\n  - A Google Earth Engine (GEE) account.\r\n  - A registered cloud GEE project.\r\n  - Some experience in Python or a similar language.\r\n\r\n\r\n  ### Python package installation\r\n\r\n  The Whisp package is available on pip\r\n  https://pypi.org/project/openforis-whisp/ and changelog [here](https://github.com/forestdatapartnership/whisp/releases)\r\n\r\n\r\n  It can be installed with one line of code:\r\n\r\n  ```\r\n  pip install --pre openforis-whisp\r\n  ```\r\n\r\n\r\n  The package relies upon the google earth engine api being setup correctly using a registered cloud project.\r\n\r\n  More info on Whisp can be found [here](https://openknowledge.fao.org/items/e9284dc7-4b19-4f9c-b3e1-e6c142585865)\r\n\r\n\r\n\r\n## How to add data layers to Whisp \u003ca name=\"whisp_add_data\"\u003e\u003c/a\u003e\r\n\r\n\r\n\r\nThere are two main approaches:\r\n\r\n1) Request that a layer be incorporated into the core Whisp inputs, or\r\n\r\n2) Add your own data directly to complement the core datasets.\r\n\r\n\r\n\r\n---\r\n\r\n\r\n\r\n### Requesting a layer addition\r\n\r\n\r\n\r\nIf you think a particular dataset has wide applicability for Whisp users, you can request it be added to the main Whisp repository by logging it as an issue in GitHub [here](https://github.com/forestdatapartnership/whisp/issues/).\r\n\r\n\r\n\r\nBefore submitting a request, consider the following:\r\n\r\n\r\n\r\n- Is the resolution high enough for plot-level analysis? (e.g., 30m or 10m resolution)\r\n\r\n- Is there an indication of data quality? (e.g., accuracy assessment detailed in a scientific publication)\r\n\r\n- Is there relevant metadata available?\r\n\r\n\r\n\r\n---\r\n\r\n\r\n### Adding your own data directly\r\n\r\nThe python notebooks allow the user to add custom data layers. You can edit the Prepare layers section to do this in the [Colab Notebook](https://github.com/forestdatapartnership/whisp/blob/main/notebooks/Colab_whisp_geojson_to_csv.ipynb)\r\nTo add your own data directly you will need some coding experience as well as familiarity with Google Earth Engine.\r\n\r\n\r\n## Contributing \u003ca name=\"whisp_contribute\"\u003e\u003c/a\u003e\r\n\r\nContributions are welcome!\r\n- Fork the repo, make changes, and open a pull request.\r\n- For adding new datasets to the codebase and for project-specific coding standards see [.github/copilot-instructions.md](.github/copilot-instructions.md)\r\n\r\n## Code of Conduct \u003ca name=\"whisp_conduct\"\u003e\u003c/a\u003e\r\n\r\n**Purpose**\r\nWe are dedicated to maintaining a safe and respectful environment for all users. Harassment or abusive behavior will not be tolerated. \u003cbr\u003e\r\n\r\n**Scope**\r\nThis Code applies to all interactions on the repository and on the app.\r\n\r\n**Expectations** \u003cbr\u003e\r\n*- Respect others:* Treat all contributors and users with courtesy and kindness. \u003cbr\u003e\r\n*- Constructive communication:* Engage respectfully, even in disagreements. \u003cbr\u003e\r\n*- Protect privacy:* Do not share personal information without consent.\r\n\r\n**Prohibited Conduct** \u003cbr\u003e\r\n*- Harassment:* Unwanted or abusive communication, stalking, threats, or bullying.\u003cbr\u003e\r\n*- Discrimination:* Any form of hate speech or exclusion based on race, gender, orientation, or other identities.\u003cbr\u003e\r\n*- Inappropriate Content:* Posting offensive, harmful, or explicit material.\r\n\r\n**Reporting** \u003cbr\u003e\r\nUsers can report violations of this Code of Conduct confidentially by contacting the Open Foris team at\r\n[open-foris@fao.org](mailto:open-foris@fao.org).\r\n\r\n\r\n## Feedback \u003ca name=\"whisp_feedback\"\u003e\u003c/a\u003e\r\n- For issues or feature requests [open a GitHub issue](https://github.com/forestdatapartnership/whisp/issues).\r\n- For general questions, feedback or support, email [open-foris@fao.org](mailto:open-foris@fao.org).\r\n\r\nWe welcome all feedback and contributions!\r\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.4060/cd0957en"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/300004","html_url":"https://ost.ecosyste.ms/projects/300004"}