layers_description.md
June 1, 2026 · View on GitHub
Source list of the layers used in whisp
To view the layers in action, go to https://whisp.earthmap.org/.
| Dataset name | Dataset description | Source and GEE asset that the dataset is based on |
|---|---|---|
| Tree cover datasets: | ||
| EUFO_2020 | Binary values, where 1 is forest. | Bourgoin, C.; Verhegghen, A.; Carboni, S.; Ameztoy, I.; Degreve, L.; Fritz, S.; Herold, M.; Tsendbazar, N.; Lesiv, M.; Achard, F.; Colditz, R. (2025) Global map of forest cover 2020 version 3. European Commission, Joint Research Centre (JRC) PID: https://data.jrc.ec.europa.eu/dataset/8c561543-31df-4e1b-9994-e529afecaf54 |
| ee.Image(“JRC/GFC2020/V3”) | ||
| GLAD_Primary | Binary input layer representing primary forest in 2001. Loss pixels 2001-2020 removed with ancillary dataset. | Turubanova, S., Potapov, P. V., Tyukavina, A., & Hansen, M. C. (2018). Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environmental Research Letters, 13(7), 074028. https://doi.org/10.1088/1748-9326/aacd1c |
| ee.ImageCollection (‘UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1’) Ancillary: ee.Image("UMD/hansen/global_forest_change_2025_v1_13") | ||
| TMF_undist | Mosaic for Dec 2020, representing undisturbed cover (class 1) . | Vancutsem, C., Achard, F., Pekel, J.-F., Vieilledent, G., Carboni, S., Simonetti, D., Gallego, J., Aragão, L. E. O. C., & Nasi, R. (2021). Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances, 7(10). https://doi.org/10.1126/sciadv.abe1603 |
| ee.ImageCollection(‘projects/JRC/TMF/v1_2025/AnnualChanges’) | ||
| GFC_TC_2020 | Areas of tree cover over 10 percent in 2020 (loss pixels removed). | Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693. Data available online from: https://glad.earthengine.app/view/global-forest-change. |
| ee.Image("UMD/hansen/global_forest_change_2025_v1_13") | ||
| ESA_TC_2020 | Tree and mangrove classes (i.e., 10 and 95) for 2020. | Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, L., Tsendbazar, N.-E., Ramoino, F., Arino, O. (2021). ESA WorldCover 10 m 2020 v100 (v100) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.5571936 |
| ee.ImageCollection("ESA/WorldCover/v100") | ||
| Forest_FDaP | Forest persistence for 2020 based on combining multiple forest/ tree cover datasets. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/community_forests/ForestPersistence_2020") | ||
| Commodity datasets: | ||
| TMF_plant | Classes representing any type of plantation from transition map (classes 81-85). Deforestation data after 2020 removed so remaining areas represent plantations at end of 2020. | Vancutsem, C., Achard, F., Pekel, J.-F., Vieilledent, G., Carboni, S., Simonetti, D., Gallego, J., Aragão, L. E. O. C., & Nasi, R. (2021). Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances, 7(10). https://doi.org/10.1126/sciadv.abe1603 |
| ee.ImageCollection('projects/JRC/TMF/v1_2025/TransitionMap_Subtypes') ee.ImageCollection('projects/JRC/TMF/v1_2025/DeforestationYear') | ||
| Oil_palm_Descals | Classes from the “classification band” representing oil palm plantations (i.e., 0 & 1). | Descals, A., Wich, S., Meijaard, E., Gaveau, D. L. A., Peedell, S., & Szantoi, Z. (2021). High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth System Science Data, 13(3), 1211–1231. https://doi.org/10.5194/essd-13-1211-2021 |
| ee.ImageCollection(‘BIOPAMA/GlobalOilPalm/v1’) | ||
| Oil_palm_FDaP | Palm probability model. Filtered collection to 2020 data. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/palm/model_2025b") | ||
| Coffee_FDaP | Coffee probability model. Filtered collection to 2020 data. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/coffee/model_2025b") | ||
| Cocoa_FDaP | Cocoa probability model. Filtered collection to 2020 data. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/cocoa/model_2025b") | ||
| Cocoa_ETH | Binary product where 1 represents cocoa. Product derived from a cocoa probability map where the recommended threshold of >65%, had already been applied. | Kalischek, N., Lang, N., Renier, C., Daudt, R. C., Addoah, T., Thompson, W., Blaser-Hart, W. J., Garrett, R., Schindler, K., & Wegner, J. D. (2022). Satellite-based high-resolution maps of cocoa planted area for Côte d’Ivoire and Ghana ( 5). arXiv. https://doi.org/10.48550/ARXIV.2206.06119 |
| ee.Image(‘projects/ee-nk-cocoa/assets/cocoa_map_threshold_065’) | ||
| Cocoa_bnetd | Commodity class for cocoa (i.e., class 9). For Côte d'Ivoire only. | BNETD (2024). Occupation des sols de la Côte d'Ivoire en 2020 (Version 2). Centre d'Information Géographique et du Numérique / Bureau National d'Études Techniques et de Developpement. Côte d'Ivoire, 2024. Data available online from: https://arcg.is/0uHOi90 |
| ee.Image("projects/ee-bnetdcign2/assets/OCS_CI_2020vf") | ||
| Rubber_RBGE | Binary layer for South East Asia. | Wang et al., (2024) Wang, Y., Hollingsworth, P.M., Zhai, D. et al. High-resolution maps show that rubber causes substantial deforestation. Nature 623, 340–346 (2023). https://doi.org/10.1038/s41586-023-06642-z |
| ee.Image("users/wangyxtina/MapRubberPaper/rRubber10m202122_perc1585DifESAdist5pxPF") | ||
| Rubber_FDaP | Rubber probability model. Filtered collection to 2020 data. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/rubber/model_2025b") | ||
| Annual crops: | ||
| Soy_Song_2020 | Soya expansion in South America 2000-2023, binary map for 2020 where 1 is soya. | Song, X.-P., Hansen, M.C., Potapov, P., Adusei, B., Pickering, J., Adami, M., Lima, A., Zalles, V., Stehman, S.V., Di Bella, C.M., Cecilia, C.M., Copati, E.J., Fernandes, L.B., Hernandez-Serna, A., Jantz, S.M., Pickens, A.H., Turubanova, S., Tyukavina A. (2021). Massive soybean expansion in South America since 2000 and implications for conservation. Nature Sustainability, 4, 784–792 https://doi.org/10.1038/s41893-021-00729-z |
| ee.ImageCollection("projects/glad/soy_annual_SA/2020") | ||
| Datasets of disturbances before 2020-12-31: | ||
| TMF_deg_before_2020 TMF_def_before_2020 | Binary masks of aggregate degradation & deforestation between 2000 and 2020. | Vancutsem, C., Achard, F., Pekel, J.-F., Vieilledent, G., Carboni, S., Simonetti, D., Gallego, J., Aragão, L. E. O. C., & Nasi, R. (2021). Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances, 7(10). https://doi.org/10.1126/sciadv.abe1603 |
| ee.ImageCollection('projects/JRC/TMF/v1_2025/DegradationYear') ee.ImageCollection('projects/JRC/TMF/v1_2025/DeforestationYear') | ||
| GFC_loss_before_2020 | Binary mask of aggregate tree cover losses between 2000 and 2020. | Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., , S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693. Data available online from: https://glad.earthengine.app/view/global-forest-change. |
| ee.Image("UMD/hansen/global_forest_change_2025_v1_13") | ||
| RADD_before_2020 | Binary mask of aggregate confirmed (i.e., class 3) alerts in 2019 & 2020. | Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.-E., Odongo-Braun, C., Vollrath, A., Weisse, M. J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., & Herold, M. (2021). Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters, 16(2), 024005. https://doi.org/10.1088/1748-9326/abd0a8 |
| ee.ImageCollection('projects/radar-wur/raddalert/v1') | ||
| MODIS_fire_before_2020 | Binary mask of aggregate burnt areas between 2000 and 2020. | Giglio, L., Justice, C., Boschetti, L., & Roy, D. (2021). MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061 [Dataset]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD64A1.061 |
| ee.ImageCollection("MODIS/061/MCD64A1") | ||
| ESA_fire_before_2020 | Binary mask of aggregate burnt areas between 2001 and 2020. | Lizundia-Loiola, J., Otón, G., Ramo, R., & Chuvieco, E. (2020). A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sensing of Environment, 236, 111493. https://doi.org/10.1016/j.rse.2019.111493 |
| ee.ImageCollection("ESA/CCI/FireCCI/5_1") | ||
| Datasets of disturbances after 2020-12-31: | ||
| TMF_deg_after_2020 TMF_def_after_2020 | Binary masks of aggregate degradation & deforestation from 2021 onward. | Vancutsem, C., Achard, F., Pekel, J.-F., Vieilledent, G., Carboni, S., Simonetti, D., Gallego, J., Aragão, L. E. O. C., & Nasi, R. (2021). Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances, 7(10). https://doi.org/10.1126/sciadv.abe1603 |
| ee.ImageCollection('projects/JRC/TMF/v1_2025/DegradationYear') ee.ImageCollection('projects/JRC/TMF/v1_2025/DeforestationYear') | ||
| GFC_loss_after_2020 | Binary mask of aggregate tree cover losses from 2021 onward. | Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., , S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693. Data available online from: https://glad.earthengine.app/view/global-forest-change. |
| ee.Image("UMD/hansen/global_forest_change_2025_v1_13") | ||
| RADD_after_2020 | Binary mask of aggregate confirmed (i.e., class 3) alerts from 2021 onward. | Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.-E., Odongo-Braun, C., Vollrath, A., Weisse, M. J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., & Herold, M. (2021). Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters, 16(2), 024005. https://doi.org/10.1088/1748-9326/abd0a8 |
| ee.ImageCollection('projects/radar-wur/raddalert/v1') | ||
| GLAD-L_before_2020 | Binary mask of aggregate confirmed GLAD Landsat forest alerts (confidence >= 2) from 2017 to 2020 inclusive. Note: although some areas have data for 2015 and 2016 the assets do not exist in GEE. Coverage: Tropics (30°N to 30°S). | Hansen, M.C., Krylov, A., Tyukavina, A., Potapov, P.V., Turubanova, S., Zutta, B., Ifo, S., Margono, B., Stolle, F., & Moore, R. (2016). Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11(3), 034008. https://doi.org/10.1088/1748-9326/11/3/034008 |
| ee.ImageCollection('projects/glad/alert/2017final') ee.ImageCollection('projects/glad/alert/2018final') ee.ImageCollection('projects/glad/alert/2019final') ee.ImageCollection('projects/glad/alert/2020final') | ||
| GLAD-L_after_2020 | Binary mask of aggregate confirmed GLAD Landsat forest alerts (confidence >= 2) from 2021 onwards. Combines yearly data from 2021-2023 and 2025-2026. Note: 2024 data is not available currently in Google Earth Engine. Note: 2015 and 2016 assets do not exist in GEE. | Hansen, M.C., Krylov, A., Tyukavina, A., Potapov, P.V., Turubanova, S., Zutta, B., Ifo, S., Margono, B., Stolle, F., & Moore, R. (2016). Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11(3), 034008. https://doi.org/10.1088/1748-9326/11/3/034008 |
| ee.ImageCollection('projects/glad/alert/2017final') ee.ImageCollection('projects/glad/alert/2018final') ee.ImageCollection('projects/glad/alert/2019final') ee.ImageCollection('projects/glad/alert/2020final') ee.ImageCollection('projects/glad/alert/2021final') ee.ImageCollection('projects/glad/alert/2022final') ee.ImageCollection('projects/glad/alert/2023final') ee.ImageCollection('projects/glad/alert/UpdResult') | ||
| GLAD-S2_before_2020 | Binary mask of aggregate confirmed GLAD Sentinel-2 forest alerts (confidence >= 2) from 2019-01-01 (i.e., first date available) to 2020-12-31 inclusive. Coverage: Primary humid tropical forest within the Amazon basin region. Note: Data starts from 2019. | Pickens, A.H., Hansen, M.C., Adusei, B., & Potapov, P. (2020). Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. |
| ee.Image('projects/glad/S2alert/alert') ee.Image('projects/glad/S2alert/alertDate') | ||
| GLAD-S2_after_2020 | Binary mask of aggregate confirmed GLAD Sentinel-2 forest alerts (confidence >= 2) from 2021 onwards. Coverage: Primary humid tropical forest within the Amazon basin region. | Pickens, A.H., Hansen, M.C., Adusei, B., & Potapov, P. (2020). Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. |
| ee.Image('projects/glad/S2alert/alert') ee.Image('projects/glad/S2alert/alertDate') | ||
| MODIS_fire_after_2020 | Binary mask of aggregate burnt areas from 2021 onward. | Giglio, L., Justice, C., Boschetti, L., & Roy, D. (2021). MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061 [Dataset]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD64A1.061 |
| ee.ImageCollection("MODIS/061/MCD64A1") | ||
| Primary forests: | ||
| GFT_primary | Primary forest class (10) from the Global Forest Types map V1 (forest extent of GFC2020 V3). | European Commission: Joint Research Centre, BOURGOIN, C., VERHEGGHEN, A., CARBONI, S., DEGREVE, L., AMEZTOY ARAMENDI, I., CECCHERINI, G., COLDITZ, R. and ACHARD, F., Global Forest Maps for the Year 2020 to Support the EU Regulation on Deforestation-free Supply Chains, Publications Office of the European Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/1975879, JRC141702. |
| ee.Image("JRC/GFC2020_subtypes/V1") | ||
| IFL_2020 | Intact Forest Landscape binary map (1 is IFL). | Potapov, P., Hansen, M.C., Laestadius, L., Turubanova, S., Yaroshenko, A., Thies, C., Smith, W., Zhuravleva, I., Komarova, A., Minnemeyer, S., others, 2017. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Science Advances 3, e1600821. |
| ee.ImageCollection(“users/potapovpeter/IFL_2020”) | ||
| European_Primary_Forest | Harmonized geodatabase of 48 datasets of primary forests spread across 33 European countries. | Sabatini, F.M., Bluhm, H., Kun, Z., Aksenov, D., Atauri, J.A., Buchwald, E., Burrascano, S., Cateau, E., Diku, A., Duarte, I.M., Fernández López, Á.B., Garbarino, M., Grigoriadis, N., Horváth, F., Keren, S., Kitenberga, M., Kiš, A., Kraut, A., Ibisch, P.L., Larrieu, L., Lombardi, F., Matovic, B., Melu, R.N., Meyer, P., Midteng, R., Mikac, S., Mikoláš, M., Mozgeris, G., Panayotov, M., Pisek, R., Nunes, L., Ruete, A., Schickhofer, M., Simovski, B., Stillhard, J., Stojanovic, D., Szwagrzyk, J., Tikkanen, O.-P., Toromani, E., Volosyanchuk, R., Vrška, T., Waldherr, M., Yermokhin, M., Zlatanov, T., Zagidullina, A., Kuemmerle, T., 2021. European primary forest database v2.0. Sci Data 8, 220. https://doi.org/10.1038/s41597-021-00988-7 |
| ee.FeatureCollection(“HU_BERLIN/EPFD/V2/polygons”) | ||
| Naturally regenerating forests: | ||
| GFT_naturally_regenerating | Naturally regenerating forest class (1) from the Global Forest Types map V1 (forest extent of GFC2020 V3). | European Commission: Joint Research Centre, BOURGOIN, C., VERHEGGHEN, A., CARBONI, S., DEGREVE, L., AMEZTOY ARAMENDI, I., CECCHERINI, G., COLDITZ, R. and ACHARD, F., Global Forest Maps for the Year 2020 to Support the EU Regulation on Deforestation-free Supply Chains, Publications Office of the European Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/1975879, JRC141702. |
| ee.Image("JRC/GFC2020_subtypes/V1") | ||
| Planted/plantation forests: | ||
| GFT_planted_plantation | Planted and plantation forests class (20) from the Global Forest Types map V1 (forest extent of GFC2020 V3). | European Commission: Joint Research Centre, BOURGOIN, C., VERHEGGHEN, A., CARBONI, S., DEGREVE, L., AMEZTOY ARAMENDI, I., CECCHERINI, G., COLDITZ, R. and ACHARD, F., Global Forest Maps for the Year 2020 to Support the EU Regulation on Deforestation-free Supply Chains, Publications Office of the European Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/1975879, JRC141702. |
| ee.Image("JRC/GFC2020_subtypes/V1") | ||
| IIASA_planted_plantation | Planted or plantation forests classes (31,32) of the IIASA Global Forest Management map. | Lesiv, M., Schepaschenko, D., Buchhorn, M., See, L., Dürauer, M., Georgieva, I., Jung, M., Hofhansl, F., Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Brenes, C.L.M., Krivobokov, L., Ntie, S., Tsogt, K., Pietsch, S.A., Tikhonova, E., Kim, M., Di Fulvio, F., Su, Y.-F., Zadorozhniuk, R., Sirbu, F.S., Panging, K., Bilous, S., Kovalevskii, S.B., Kraxner, F., Rabia, A.H., Vasylyshyn, R., Ahmed, R., Diachuk, P., Kovalevskyi, S.S., Bungnamei, K., Bordoloi, K., Churilov, A., Vasylyshyn, O., Sahariah, D., Tertyshnyi, A.P., Saikia, A., Malek, Ž., Singha, K., Feshchenko, R., Prestele, R., Akhtar, I. ul H., Sharma, K., Domashovets, G., Spawn-Lee, S.A., Blyshchyk, O., Slyva, O., Ilkiv, M., Melnyk, O., Sliusarchuk, V., Karpuk, A., Terentiev, A., Bilous, V., Blyshchyk, K., Bilous, M., Bogovyk, N., Blyshchyk, I., Bartalev, S., Yatskov, M., Smets, B., Visconti, P., Mccallum, I., Obersteiner, M., Fritz, S., 2022. Global forest management data for 2015 at a 100 m resolution. Sci Data 9, 199. https://doi.org/10.1038/s41597-022-01332-3 |
| ee.ImageCollection(“projects/sat-io/open-datasets/GFM/FML_v3-2”) | ||
| Tree cover post 2020: | ||
| TMF_regrowth_2024 | Binary map of Regrowth class (4) for the TMF Annual change year 2024 | Vancutsem, C., Achard, F., Pekel, J.-F., Vieilledent, G., Carboni, S., Simonetti, D., Gallego, J., Aragão, L. E. O. C., & Nasi, R. (2021). Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances, 7(10). https://doi.org/10.1126/sciadv.abe1603 |
| ee.ImageCollection(“projects/JRC/TMF/v1_2025/AnnualChanges”) | ||
| ESRI_2024_TC | Tree cover class (2) of the 2024 ESRI LC map | Karra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.”IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. |
| ee.ImageCollection(“"projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS”) | ||
| Agricultural land post 2020: | ||
| ESRI_crop_gain_2024 | Crop gain in 2024 compared to year 2020 of the ESRI LC map (class 5) | Karra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.”IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. |
| ee.ImageCollection(“"projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS”) | ||
| Oil_palm_2024_FDaP | Palm probability model. Filtered collection to 2024 data. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/palm/model_2025b") | ||
| Rubber_2024_FDaP | Cocoa probability model. Filtered collection to 2024 data. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/rubber/model_2025b") | ||
| Coffee_FDaP_2024 | Coffee probability model. Filtered collection to 2024 data. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/coffee/model_2025b") | ||
| Cocoa_2024_FDaP | Rubber probability model. Filtered collection to 2024 data. Threshold set for Whisp based on the intersection of recall and precision in charts for accuracy. | FDaP (2025). Forest Data Partnership https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership |
| ee.ImageCollection("projects/forestdatapartnership/assets/cocoa/model_2025b") | ||
| logging concessions: | ||
| GFW_logging | Logging concessions from GFW (polygon data) | http://data.globalforestwatch.org/datasets?q=logging |
| ee.FeatureCollection('projects/ee-whisp/assets/logging/') | ||
| Plot location: | ||
| Country | ISO3 country code for the unit covering most of each plot, derived from the GAUL 2024 feature collection's gaul1_code and converted to iso3_code via a lookup table. Boundaries may contain errors and do not imply official endorsement or acceptance. | Franceschini, G., Khan, A., Moretti, L., Nyabuti, K., Asif, M., Bezuidenhoudt, E. and Morteo, K. 2025. The Global Administrative Unit Layers (GAUL) 2024. Technical guidelines. Rome, FAO. https://doi.org/10.4060/cd4262en |
| ee.Image("projects/ee-whisp/assets/admin_bounds/gaul_2024_level_1_code_500m") | ||
| ProducerCountry | ISO2 country code for the country covering most of each plot, derived from the feature collection's gaul1_code and converted using a lookup table with country-converter mappings. Boundaries may contain errors and do not imply official endorsement or acceptance. | Franceschini, G., Khan, A., Moretti, L., Nyabuti, K., Asif, M., Bezuidenhoudt, E. and Morteo, K. 2025. The Global Administrative Unit Layers (GAUL) 2024. Technical guidelines. Rome, FAO. https://doi.org/10.4060/cd4262en |
| ee.Image("projects/ee-whisp/assets/admin_bounds/gaul_2024_level_1_code_500m") | ||
| Admin_Level_1 | Level-1 administrative name for the unit covering most of each plot, derived from the GAUL 2024 feature collection’s gaul1_code and converted to gaul1_name using a lookup table. Boundaries may contain errors and do not imply official endorsement or acceptance. | Franceschini, G., Khan, A., Moretti, L., Nyabuti, K., Asif, M., Bezuidenhoudt, E. and Morteo, K. 2025. The Global Administrative Unit Layers (GAUL) 2024. Technical guidelines. Rome, FAO. https://doi.org/10.4060/cd4262en |
| ee.Image("projects/ee-whisp/assets/admin_bounds/gaul_2024_level_1_code_500m") | ||
| In_waterbody | Binary mask for permanent water. Used to detect potential plot location errors based on pixel value for the plot centroid. JRC's Global Surface Water data for inland water bodies (classes 1, 2, or 7 from the transitions layer); areas outside USGS Global Shoreline Vector (GSV) boundaries for marine. | Pekel, JF., Cottam, A., Gorelick, N., Belward, A.S. (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540, 418-422. (doi:10.1038/nature20584) |
| ee.Image("JRC/GSW1_4/GlobalSurfaceWater") | ||
| Sayre, R., S. Noble, S. Hamann, R. Smith, D. Wright et al., (2019). A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. Journal of Operational Oceanography , 12: sup 2, S47-S56, DOI: 10.1080/1755876X.2018.1529714ee. | ||
| ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/mainlands'); ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/big_islands'); ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/small_islands'); | ||
The sources listed in the table above are analyzed and disaggregated into ~200 different layers by the Whisp algorithms, some of which are run directly on Google Earth Engine through Forest Data Partnership's account and some of which are in the Python codes of this repository. When a geometry (e.g., a polygon) is scanned with Whisp, the zonal statistics of each of these ~200 layers are calculated for that geometry, producing a dataframe that holds the ~200 different values for that specific geometry. The ~200 layers are listed in lookup_datasets.csv. Whisping with Whisp API produces a CSV holding all those values in ~200 columns, as well as some metadata and crucially the results of the Whisp risk analysis, which is explained in the ReadMe. This risk analysis is based on only a subset of all the layers, some of which are, however, aggregate layers that summarize the results from the other layers. These layers used for the risk analysis are marked in lookup_datasets.csv using the columns use_for_risk_pcrop (perennial crops), use_for_risk_acrop (annual crops), and use_for_risk_timber (timber). A value of '1' means the layer feeds that crop type's risk decision tree. All layers marked by no value or value '0' do not contribute directly to the risk analysis, but only indirectly by being part of the aggregate layers.
Overall, the output CSV from running Whisp zonal statistics processing for a geometry therefore holds:
- 124 values from disaggregated layers;
- 26 values from aggregate or stand-alone layers crucial to the risk analysis;
- 29 (optional) national layers described in the whisp_columns.xlsx in the whisp_outputs_national tab.
- the yes & no answers to the risk categories in the decision tree and the final risk category ("low", "high", or "more info needed") for the perennial crops, annual crops, timber and livestock;
- some additional metadata and geographic information, e.g. ID, geometry type, hectares, country, etc...
(Disclaimer: The number of layers is subject to changes. The number of ~200 layers mentioned might deviate slightly, but not fundamentally.)