Articles | Volume 18, issue 2
https://doi.org/10.5194/essd-18-1601-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-1601-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
02 Mar 2026
Data description article |
| 02 Mar 2026
| 02 Mar 2026
Global high-resolution forest disturbance type dataset
Li Wang
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Wanjuan Song
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Shengping Ding
Faculty of Science, University of Copenhagen, Copenhagen 1350, Denmark
Jie Zhang
School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
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Cited articles
Acil, N., Sadler, J. P., Senf, C., Suvanto, S., and Pugh, T. A. M.: Landscape patterns in stand-replacing disturbances across the world's forests, Nature Sustainability, 8, https://doi.org/10.1038/s41893-024-01450-3, 2025.
Aquino, C., Mitchard, E. T. A., McNicol, I. M., Carstairs, H., Burt, A., Vilca, B. L. P., Ebanega, M. O., Dikongo, A. M., Dassi, C., Mayta, S., Tamayo, M., Grijalba, P., Miranda, F., and Disney, M.: Reliably mapping low-intensity forest disturbance using satellite radar data, Frontiers in Forests and Global Change, 5, https://doi.org/10.3389/ffgc.2022.1018762, 2022.
Betts, M. G., Wolf, C., Ripple, W. J., Phalan, B., Millers, K. A., Duarte, A., Butchart, S. H. M., and Levi, T.: Global forest loss disproportionately erodes biodiversity in intact landscapes, Nature, 547, 441–444, https://doi.org/10.1038/nature23285, 2017.
Blaschke, P. M., Trustrum, N. A., and Derose, R. C.: Ecosystem processes and sustainable land-use in New-Zealand steeplands, Agr. Ecosyst. Environ., 41, 153–178, https://doi.org/10.1016/0167-8809(92)90107-m, 1992.
Burrus, C. S., Barreto, J. A., and Selesnick, I. W.: Iterative reweighted least-squares design of fir filters, IEEE T. Signal Proces., 42, 2926–2936, https://doi.org/10.1109/78.330353, 1994.
Chen, S. J., Olofsson, P., Saphangthong, T., and Woodcock, C. E.: Monitoring shifting cultivation in Laos with Landsat time series, Remote Sens. Environ., 288, https://doi.org/10.1016/j.rse.2023.113507, 2023a.
Chen, X. L., Taylor, A. R., Reich, P. B., Hisano, M., Chen, H. Y. H., and Chang, S. X.: Tree diversity increases decadal forest soil carbon and nitrogen accrual, Nature, 618, 94–101, https://doi.org/10.1038/s41586-023-05941-9, 2023b.
Chowdhury, S., Chao, D. K., Shipman, T. C., and Wulder, M. A.: Utilization of Landsat data to quantify land-use and land-cover changes related to oil and gas activities in West-Central Alberta from 2005 to 2013, Gisci. Remote Sens., 54, 700–720, https://doi.org/10.1080/15481603.2017.1317453, 2017.
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., and Hansen, M. C.: Classifying drivers of global forest loss, Science, 361, 1108–1111, https://doi.org/10.1126/science.aau3445, 2018.
Feng, Y., Ziegler, A. D., Elsen, P. R., Liu, Y., He, X. Y., Spracklen, D. V., Holden, J., Jiang, X., Zheng, C. M., and Zeng, Z. Z.: Upward expansion and acceleration of forest clearance in the mountains of Southeast Asia, Nature Sustainability, 4, 892–899, https://doi.org/10.1038/s41893-021-00738-y, 2021.
Finger, D. J. I., McPherson, M. L., Houskeeper, H. F., and Kudela, R. M.: Mapping bull kelp canopy in northern California using Landsat to enable long-term monitoring, Remote Sens. Environ., 254, https://doi.org/10.1016/j.rse.2020.112243, 2021.
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., and Townshend, J. R. G.: High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
He, Y., Wang, L., Pei, J., Liu, S., Yang, H., Cao, J., Li, W., Niu, Z., Huang, N., Xu, X., Duan, J., Nath, B., Ding, S., and Chen, F.: Differential vegetation drought adaptability in global karst areas, npj Climate and Atmospheric Science, 8, 343, https://doi.org/10.1038/s41612-025-01223-5, 2025.
Hwang, Y., Ryu, Y., and Qu, S.: Expanding vegetated areas by human activities and strengthening vegetation growth concurrently explain the greening of Seoul, Landscape Urban Plan., 227, https://doi.org/10.1016/j.landurbplan.2022.104518, 2022.
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An introduction to statistical learning: with applications in R, Springer, 59–128, https://doi.org/10.1007/978-1-0716-1418-1, 2013.
Jiang, S., Meng, J. J., Zhu, L. K., and Cheng, H. R.: Spatial-temporal pattern of land use conflict in China and its multilevel driving mechanisms, Sci. Total Environ., 801, https://doi.org/10.1016/j.scitotenv.2021.149697, 2021.
Kittel, T. G. F., Steffen, W. L., and Chapin, F. S.: Global and regional modelling of Arctic-boreal vegetation distribution and its sensitivity to altered forcing, Glob. Change Biol., 6, 1–18, https://doi.org/10.1046/j.1365-2486.2000.06011.x, 2000.
Leverkus, A. B., Lindenmayer, D. B., Thorn, S., and Gustafsson, L.: Salvage logging in the world's forests: Interactions between natural disturbance and logging need recognition, Global Ecol. Biogeogr., 27, 1140–1154, https://doi.org/10.1111/geb.12772, 2018.
Liu, S., Wang, L., and Zhang, J.: The dataset of main grain land changes in China over 1985–2020, Scientific Data, 11, 1430, https://doi.org/10.1038/s41597-024-04292-y, 2024.
Liu, S., Wang, L., and Song, W.: Global forest main disturbance types between 2000 and 2020, figshare [data set], https://doi.org/10.6084/m9.figshare.28465178, 2025a.
Liu, S., Wang, L., Zhang, J., and Ding, S.: Opposite effect on soil organic carbon between grain and non-grain crops: Evidence from Main Grain Land, China, Agr. Ecosyst. Environ., 379, 109364, https://doi.org/10.1016/j.agee.2024.109364, 2025b.
Liu, S. D., Zhang, J., Wang, L., Ciais, P., Zhang, J. J., Penuelas, J., Nath, B., Jacquet, I., Wu, X., Ding, S. P., Li, W., Huang, N., Song, W. J., Ni, W. J., and Niu, Z.: Mapping previously undetected trees reveals overlooked changes in pan-tropical tree cover, Nat. Commun., 16, https://doi.org/10.1038/s41467-025-60662-z, 2025c.
Mason, K. E., Oakley, S., Street, L. E., Arróniz-Crespo, M., Jones, D. L., DeLuca, T. H., and Ostle, N. J.: Boreal Forest Floor Greenhouse Gas Emissions Across a Pleurozium schreberi-Dominated, Wildfire-Disturbed Chronosequence, Ecosystems, 22, 1381–1392, https://doi.org/10.1007/s10021-019-00344-2, 2019.
Mayer, M., Baltensweiler, A., James, J., Rigling, A., and Hagedorn, F.: A global synthesis and conceptualization of the magnitude and duration of soil carbon losses in response to forest disturbances, Global Ecol. Biogeogr., 33, 141–150, https://doi.org/10.1111/geb.13779, 2024.
Miatto, A., Dawson, D., Nguyen, P. D., Kanaoka, K. S., and Tanikawa, H.: The urbanisation-environment conflict: Insights from material stock and productivity of transport infrastructure in Hanoi, Vietnam, J. Environ. Manage., 294, https://doi.org/10.1016/j.jenvman.2021.113007, 2021.
Oeser, J., Heurich, M., Senf, C., Pflugmacher, D., and Kuemmerle, T.: Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances, Ecol. Appl., 31, https://doi.org/10.1002/eap.2269, 2021.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A.: Good practices for estimating area and assessing accuracy of land change, Remote Sens. Environ., 148, 42–57, https://doi.org/10.1016/j.rse.2014.02.015, 2014.
Peng, L. Q., Searchinger, T. D., Zionts, J., and Waite, R.: The carbon costs of global wood harvests, Nature, 620, 110–115, https://doi.org/10.1038/s41586-023-06187-1, 2023.
Piao, S. L., Wang, X. H., Park, T., Chen, C., Lian, X., He, Y., Bjerke, J. W., Chen, A. P., Ciais, P., Tommervik, H., Nemani, R. R., and Myneni, R. B.: Characteristics, drivers and feedbacks of global greening, Nature Reviews Earth & Environment, 1, 14–27, https://doi.org/10.1038/s43017-019-0001-x, 2020.
Reza, M. I. H. and Abdullah, S. A.: Regional Index of Ecological Integrity: A need for sustainable management of natural resources, Ecol. Indic., 11, 220–229, https://doi.org/10.1016/j.ecolind.2010.08.010, 2011.
Rivera, J. D., de los Monteros, A. E., Saldaña-Vázquez, R. A., and Favila, M. E.: Beyond species loss: How anthropogenic disturbances drive functional and phylogenetic homogenization of Neotropical dung beetles, Sci. Total Environ., 869, https://doi.org/10.1016/j.scitotenv.2023.161663, 2023.
Roffe, T. G., Couturier, S., and García-Romero, A.: Suitability of the global forest cover change map to assess climatic megadisturbance impacts on remote tropical forests, Sci. Rep., 12, https://doi.org/10.1038/s41598-022-13558-7, 2022.
Ross, M. R. V., Nippgen, F., McGlynn, B. L., Thomas, C. J., Brooks, A. C., Shriver, R. K., Moore, E. M., and Bernhardt, E. S.: Mountaintop mining legacies constrain ecological, hydrological and biogeochemical recovery trajectories, Environ. Res. Lett., 16, https://doi.org/10.1088/1748-9326/ac09ac, 2021.
Scheeres, J., de Jong, J., Brede, B., Brancalion, P. H. S., Broadbent, E. N., Zambrano, A. M. A., Gorgens, E. B., Silva, C. A., Valbuena, R., Molin, P., Stark, S., Rodrigues, R. R., Rodrigues, R., Santoro, G. B., de Almeida, C. T., and de Almeida, D. R. A.: Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR, Remote Sens. Environ., 290, https://doi.org/10.1016/j.rse.2023.113533, 2023.
Scholten, R. C., Jandt, R., Miller, E. A., Rogers, B. M., and Veraverbeke, S.: Overwintering fires in boreal forests, Nature, 593, 399–404, https://doi.org/10.1038/s41586-021-03437-y, 2021.
Skakun, S., Vermote, E. F., Artigas, A. E. S., Rountree, W. H., and Roger, J. C.: An experimental sky-image-derived cloud validation dataset for Sentinel-2 and Landsat 8 satellites over NASA GSFC, Int. J. Appl. Earth Obs., 95, https://doi.org/10.1016/j.jag.2020.102253, 2021.
Skidmore, A. K., Coops, N. C., Neinavaz, E., Ali, A., Schaepman, M. E., Paganini, M., Kissling, W. D., Vihervaara, P., Darvishzadeh, R., Feilhauer, H., Fernandez, M., Fernández, N., Gorelick, N., Geizendorffer, I., Heiden, U., Heurich, M., Hobern, D., Holzwarth, S., Muller-Karger, F. E., Van De Kerchove, R., Lausch, A., Leitau, P. J., Lock, M. C., Mücher, C. A., O'Connor, B., Rocchini, D., Turner, W., Vis, J. K., Wang, T. J., Wegmann, M., and Wingate, V.: Priority list of biodiversity metrics to observe from space, Nature Ecology & Evolution, 5, 896–906, https://doi.org/10.1038/s41559-021-01451-x, 2021.
Stehman, S. V.: Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes, Int. J. Remote Sens., 35, 4923–4939, https://doi.org/10.1080/01431161.2014.930207, 2014.
Tollerud, H. J., Zhu, Z., Smith, K., Wellington, D. F., Hussain, R. A., and Viola, D.: Toward consistent change detection across irregular remote sensing time series observations, Remote Sens. Environ., 285, https://doi.org/10.1016/j.rse.2022.113372, 2023.
Tong, X. W., Brandt, M., Yue, Y. M., Ciais, P., Jepsen, M. R., Penuelas, J., Wigneron, J. P., Xiao, X. M., Song, X. P., Horion, S., Rasmussen, K., Saatchi, S., Fan, L., Wang, K. L., Zhang, B., Chen, Z. C., Wang, Y. H., Li, X. J., and Fensholt, R.: Forest management in southern China generates short term extensive carbon sequestration, Nat. Commun., 11, https://doi.org/10.1038/s41467-019-13798-8, 2020.
Wang, D. C., Chen, X. N., Jiang, M. Y., Du, S. H., Xu, B. J., and Wang, J. D.: ADS-Net:An Attention-Based deeply supervised network for remote sensing image change detection, Int. J. Appl. Earth Obs., 101, https://doi.org/10.1016/j.jag.2021.102348, 2021.
Xu, R., Li, Y., Teuling, A. J., Zhao, L., Spracklen, D., Garcia-Carreras, L., Meier, R., Chen, L., Zheng, Y. T., Lin, H. Q., and Fu, B. J.: Contrasting impacts of forests on cloud cover based on satellite observations, Nat. Commun., 13, https://doi.org/10.1038/s41467-022-28161-7, 2022.
Yan, X. R., Wang, J. L., Liu, X. T., Zhao, H. Y., and Wu, Y. X.: Mining the drivers of forest cover change in the upper Indus Valley, high Asia region from 1990 to 2020, Ecol. Indic., 144, https://doi.org/10.1016/j.ecolind.2022.109566, 2022.
Yang, Y., Anderson, M., Gao, F., Hain, C., Noormets, A., Sun, G., Wynne, R., Thomas, V., and Sun, L.: Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data, Remote Sens. Environ., 238, https://doi.org/10.1016/j.rse.2018.12.017, 2020.
Ygorra, B., Frappart, F., Wigneron, J. P., Moisy, C., Catry, T., Baup, F., Hamunyela, E., and Riazanoff, S.: Monitoring loss of tropical forest cover from Sentinel-1 time-series: A CuSum-based approach, Int. J. Appl. Earth Obs., 103, https://doi.org/10.1016/j.jag.2021.102532, 2021.
Zhao, Y. L., Diao, C. Y., Augspurger, C. K., and Yang, Z. J.: Monitoring spring leaf phenology of individual trees in a temperate forest fragment with multi-scale satellite time series, Remote Sens. Environ., 297, https://doi.org/10.1016/j.rse.2023.113790, 2023.
Zhu, Z. and Woodcock, C. E.: Continuous change detection and classification of land cover using all available Landsat data, Remote Sens. Environ., 144, 152–171, https://doi.org/10.1016/j.rse.2014.01.011, 2014.
Short summary
The study introduces the high-resolution global forest disturbance dataset for 2000–2020. Key drivers of forest cover changes are forestry activities (44 %), shifting cultivation (24 %), and forest fires (11 %). Both human activities and natural events widely impact forest ecosystems, with regional differences across tropical, temperate, and boreal zones. Forest fires concentrated in Siberia and North America; and shifting cultivation dominant in tropical areas.
The study introduces the high-resolution global forest disturbance dataset for 2000–2020. Key...
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