Volume 45 Issue 2
Apr.  2025
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Article Navigation >  Chinese Journal of Space Science  > 2025  >  45(2): 397-412 Open Access
YAN Yan, WU Ling, LI Junji, ZHAO Yuxin, YE Xin. Forest Disturbance Attribution under Small Sample Conditions Based on Confidence Learning Mutual Guidance Framework (in Chinese). Chinese Journal of Space Science, 2025, 45(2): 397-412 doi: 10.11728/cjss2025.02.2024-0146
Citation: YAN Yan, WU Ling, LI Junji, ZHAO Yuxin, YE Xin. Forest Disturbance Attribution under Small Sample Conditions Based on Confidence Learning Mutual Guidance Framework (in Chinese). Chinese Journal of Space Science, 2025, 45(2): 397-412 doi: 10.11728/cjss2025.02.2024-0146
shu

Forest Disturbance Attribution under Small Sample Conditions Based on Confidence Learning Mutual Guidance Framework

doi: 10.11728/cjss2025.02.2024-0146 cstr: 32142.14.cjss.2024-0146
  • 1.

    School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083

  • 2.

    Guangxi Forestry Research Institute, Nanning 530002

  • 3.

    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083

  • Received Date: 2024-10-30
  • Rev Recd Date: 2025-02-15
    • Available Online: 2025-03-21
    Abstract
  • As the core technical means for human beings to carry out scientific research on the Earth system and the application of spatial information in multiple fields, remote sensing technology has comprehensively improved human beings’ understanding of the complex process of changes in the Earth system, and the use of remote sensing technology with spatial and temporal continuity to carry out disturbance monitoring and classification research on forest ecosystems, which are important components of the Earth’s land surface, can be more accurate and efficient. The use of remote sensing technology with spatial and temporal continuity to monitor and classify disturbances in forest ecosystems, an important component of the Earth’s land surface, can be more accurate and efficient. However, Forest disturbance attribution requires a large number of disturbance type samples, and the laborious manual labeling of high-quality samples and the sparseness of the change region itself limit the number of labelable disturbance samples. In this study, we propose a forest disturbance attribution method under small sample conditions based on the confidence learning mutual guidance framework. In this study, we first used Landsat long time-series remote sensing data to detect forest disturbances between 2000 and 2021 based on the Continuous Change Detection and Classification (CCDC) algorithm to obtain a large amount of unlabeled disturbance data, and then used a small number of manually labeled samples and the mutual guidance framework constructed with Random Forest (RF) and Categorical Boosting (CatBoost) classifiers to iteratively filter high-confidence data from the unlabeled disturbance data through confidence learning, expanding the labeled sample size of each other’s classifiers, and then guided each other's classifications to improve the classification accuracy of forest disturbance attribution. The results show that the overall accuracy of forest disturbance attribution based on the confidence learning mutual guidance framework is 91.4%. Compared with results using only a single classifier, the accuracy is improved by 5%. The method demonstrates excellent performance under small sample conditions and provides an efficient and reliable solution for forest disturbance type classification research.

     

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    • References(47)
  • [1]
    施建成, 赵天杰, 杨晓峰. 空间地球科学视角下的全球水循环研究[J]. 遥感学报, 2021, 25(4): 847-855 doi: 10.11834/jrs.20219467

    SHI Jiancheng, ZHAO Tianjie, YANG Xiaofeng. Global water cycle studies from the perspective of space earth science[J]. National Remote Sensing Bulletin, 2021, 25(4): 847-855 doi: 10.11834/jrs.20219467
    [2]
    田镇朋, 周维, 袁敬毅, 等. 基于多源遥感数据的植被冠层高度估算[J]. 空间科学学报, 2023, 43(6): 1176-1193 doi: 10.11728/cjss2023.06.2023-0074

    TIAN Zhenpeng, ZHOU Wei, YUAN Jingyi, et al. Forest canopy height mapping based on multi-source remote sensing data[J]. Chinese Journal of Space Science, 2023, 43(6): 1176-1193 doi: 10.11728/cjss2023.06.2023-0074
    [3]
    XU R, LI Y, TEULING A J, et al. Contrasting impacts of forests on cloud cover based on satellite observations[J]. Nature Communications, 2022, 13(1): 670 doi: 10.1038/s41467-022-28161-7
    [4]
    FEDERICI S, TUBIELLO F N, SALVATORE M, et al. New estimates of CO2 forest emissions and removals: 1990-2015[J]. Forest Ecology and Management, 2015, 352: 89-98 doi: 10.1016/j.foreco.2015.04.022
    [5]
    MACDICKEN K G. Global forest resources assessment 2015: What, why and how?[J]. Forest Ecology and Management, 2015, 352: 3-8 doi: 10.1016/j.foreco.2015.02.006
    [6]
    STAHL A T, ANDRUS R, HICKE J A, et al. Automated attribution of forest disturbance types from remote sensing data: a synthesis[J]. Remote Sensing of Environment, 2023, 285: 113416 doi: 10.1016/j.rse.2022.113416
    [7]
    SENF C, SEIDL R. Mapping the forest disturbance regimes of Europe[J]. Nature Sustainability, 2021, 4(1): 63-70
    [8]
    MCDOWELL N G, ALLEN C D, ANDERSON-TEIXEIRA K, et al. Pervasive shifts in forest dynamics in a changing world[J]. Science, 2020, 368(6494): 964
    [9]
    OESER J, PFLUGMACHER D, SENF C, et al. Using intra-annual Landsat time series for attributing forest disturbance agents in Central Europe[J]. Forests, 2017, 8(7): 251 doi: 10.3390/f8070251
    [10]
    SEBALD J, SENF C, SEIDL R. Human or Natural? Landscape context improves the attribution of forest disturbances mapped from Landsat in central Europe[J]. Remote Sensing of Environment, 2021, 262: 112502 doi: 10.1016/j.rse.2021.112502
    [11]
    PICKETT S T A, WHITE P S. The Ecology of Natural Disturbance and Patch Dynamics[M]. New York: Academic Press, 1985
    [12]
    BONAN G B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests[J]. Science, 2008, 320(5882): 1444-1449 doi: 10.1126/science.1155121
    [13]
    GOLDSTEIN G, SANTIAGO L S. Tropical Tree Physiology: Adaptations and Responses in A Changing Environment[M]. Cham: Springer, 2016: 337-355
    [14]
    MASCORRO V S, COOPS N C, KURZ W A, et al. Choice of satellite imagery and attribution of changes to disturbance type strongly affects forest carbon balance estimates[J]. Carbon Balance and Management, 2015, 10(1): 30 doi: 10.1186/s13021-015-0041-6
    [15]
    LEHNERT L W, BäSSLER C, BRANDL R, et al. Conservation value of forests attacked by bark beetles: highest number of indicator species is found in early successional stages[J]. Journal for Nature Conservation, 2013, 21(2): 97-104 doi: 10.1016/j.jnc.2012.11.003
    [16]
    SEIDL R, RAMMER W, JÄGER D, et al. Impact of bark beetle (Ips typographus L. ) disturbance on timber production and carbon sequestration in different management strategies under climate change[J]. Forest Ecology and Management, 2008, 256(3): 209-220 doi: 10.1016/j.foreco.2008.04.002
    [17]
    DE SY V, HEROLD M, ACHARD F, et al. Land use patterns and related carbon losses following deforestation in South America[J]. Environmental Research Letters, 2015, 10(12): 124004 doi: 10.1088/1748-9326/10/12/124004
    [18]
    CURTIS P G, SLAY C M, HARRIS N L, et al. Classifying drivers of global forest loss[J]. Science, 2018, 361(6407): 1108-1111 doi: 10.1126/science.aau3445
    [19]
    CHEN X, ZHAO W Z, CHEN J G, et al. Mapping large-scale forest disturbance types with multi-temporal CNN framework[J]. Remote Sensing, 2021, 13(24): 5177 doi: 10.3390/rs13245177
    [20]
    LI Y T, XU X, WU Z Z, et al. A forest type-specific threshold method for improving forest disturbance and agent attribution mapping[J]. GIScience :Times New Roman;">& Remote Sensing, 2022, 59(1): 1624-1642
    [21]
    DE MARZO T, GASPARRI N I, LAMBIN E F, et al. Agents of forest disturbance in the argentine dry Chaco[J]. Remote Sensing, 2022, 14(7): 1758 doi: 10.3390/rs14071758
    [22]
    ZHANG Y T, WOODCOCK C E, CHEN S J, et al. Mapping causal agents of disturbance in boreal and arctic ecosystems of North America using time series of Landsat data[J]. Remote Sensing of Environment, 2022, 272: 112935 doi: 10.1016/j.rse.2022.112935
    [23]
    SHIMIZU K, OTA T, MIZOUE N, et al. A comprehensive evaluation of disturbance agent classification approaches: strengths of ensemble classification, multiple indices, Spatio-temporal variables, and direct prediction[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158: 99-112 doi: 10.1016/j.isprsjprs.2019.10.004
    [24]
    吴伶, 刘湘南, 刘美玲, 等. 融合遥感时间序列时空谱信息的森林扰动检测与归因研究进展[J]. 遥感学报, 2024, 28(3): 558-575

    WU Ling, LIU Xiangnan, LIU Meiling, et al. Review of the detection and attribution of multi-type forest disturbances using an ensemble of Spatio-temporal-spectral information from remote sensing images[J]. National Remote Sensing Bulletin, 2024, 28(3): 558-575
    [25]
    XIAO P F, SHENG G W, ZHANG X L, et al. Direction-dominated change vector analysis for forest change detection[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 103: 102492 doi: 10.1016/j.jag.2021.102492
    [26]
    FRANKLIN S, ROBITAILLE S. Forest insect defoliation and mortality classification using annual Landsat time series composites: a case study in Northwestern Ontario, Canada[J]. Remote Sensing Letters, 2020, 11(12): 1175-1180 doi: 10.1080/2150704X.2020.1828659
    [27]
    LI Y T, WU Z Z, XU X, et al. Forest disturbances and the attribution derived from yearly Landsat time series over 1990-2020 in the Hengduan mountains region of Southwest China[J]. Forest Ecosystems, 2021, 8(1): 73 doi: 10.1186/s40663-021-00352-6
    [28]
    VOGELER J C, SLESAK R A, FEKETY P A, et al. Characterizing over four decades of forest disturbance in Minnesota, USA[J]. Forests, 2020, 11(3): 362 doi: 10.3390/f11030362
    [29]
    MURILLO-SANDOVAL P J, HILKER T, KRAWCHUK M A, et al. Detecting and attributing drivers of forest disturbance in the Colombian Andes using Landsat Time-Series[J]. Forests, 2018, 9(5): 269 doi: 10.3390/f9050269
    [30]
    DAS P, MUDI S, BEHERA M D, et al. Automated mapping for long-term analysis of shifting cultivation in Northeast India[J]. Remote Sensing, 2021, 13(6): 1066 doi: 10.3390/rs13061066
    [31]
    GAO B C. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from Space[J]. Remote Sensing of Environment, 1996, 58(3): 257-266 doi: 10.1016/S0034-4257(96)00067-3
    [32]
    CHEN S J, OLOFSSON P, SAPHANGTHONG T, et al. Monitoring shifting cultivation in Laos with Landsat Time series[J]. Remote Sensing of Environment, 2023, 288: 113507 doi: 10.1016/j.rse.2023.113507
    [33]
    CARDILLE J A, PEREZ E, CROWLEY M A, et al. Multi-sensor change detection for within-year capture and labelling of forest disturbance[J]. Remote Sensing of Environment, 2022, 268: 112741 doi: 10.1016/j.rse.2021.112741
    [34]
    FRAZIER R J, COOPS N C, WULDER M A. Boreal shield forest disturbance and recovery trends using Landsat time series[J]. Remote Sensing of Environment, 2015, 170: 317-327 doi: 10.1016/j.rse.2015.09.015
    [35]
    SENF C, PFLUGMACHER D, HOSTERT P, et al. Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130: 453-463 doi: 10.1016/j.isprsjprs.2017.07.004
    [36]
    高珺烨. 基于多源遥感数据的森林类型动态变化研究[D]. 长沙: 中南林业科技大学, 2021

    GAO Junye. Dynamic Change of Forest Types Based on Multi-Source Remote Sensing Data[D]. Changsha: Central South University of Forestry & Technology, 2021
    [37]
    ZHANG T W, WU L, LIU X N, et al. Detection of forest disturbances with different intensities using Landsat time series based on adaptive exponentially weighted moving average charts[J]. Forests, 2023, 15(1): 19 doi: 10.3390/f15010019
    [38]
    王开新, 夏昕. 基于GIS技术的通道县森林资源特征分析[J]. 南方农业, 2024, 18(13): 258-261

    WANG Kaixin, XIA Xin. Analysis on forest resource characteristics in Tongdao County based on GIS technology[J]. South China Agriculture, 2024, 18(13): 258-261
    [39]
    吴子斌, 龙丁秀, 陆志旺, 等. 湖南省通道县侗族聚居村寨环境生态系统调查[J]. 绿色科技, 2016(6): 70-73,75 doi: 10.3969/j.issn.1674-9944.2016.06.032

    WU Zibin, LONG Dingxiu, LU Zhiwang, et al. Investigation on habitation village ecosytems dominated by dong people in Tongdao County, Hu'nan province[J]. Journal of Green Science and Technology, 2016(6): 70-73,75 doi: 10.3969/j.issn.1674-9944.2016.06.032
    [40]
    ZHU Z, WOODCOCK C E. Continuous change detection and classification of land cover using all available Landsat Data[J]. Remote Sensing of Environment, 2014, 144: 152-171 doi: 10.1016/j.rse.2014.01.011
    [41]
    MASEK J G, VERMOTE E F, SALEOUS N E, et al. A Landsat surface reflectance data set for North America, 1990-2000[J]. Geoscience and Remote Sensing Letters, 2006, 3(1): 68-72 doi: 10.1109/LGRS.2005.857030
    [42]
    OLOFSSON P, FOODY G M, HEROLD M, et al. Good practices for estimating area and assessing accuracy of land change[J]. Remote Sensing of Environment, 2014, 148: 42-57 doi: 10.1016/j.rse.2014.02.015
    [43]
    FCPF. Guidelines on the application of the Methodological framework Number 2-On Technical Corrections to GHG emissions and removals reported in the reference period[EB/OL]. (2020-11-01)[2024-10-29]. https://www.forestcarbonpartnership.org/sites/fcp/files/FCPF%20Guidelines%20on%20the%20Application%20of%20the%20Methodological%20Framework%20Number%202_2020_0.pdf
    [44]
    CAVALHEIRO L P, BERNARD S, BARDDAL J P, et al. Random forest kernel for high-dimension low sample size classification[J]. Statistics and Computing, 2024, 34(1): 9 doi: 10.1007/s11222-023-10309-0
    [45]
    DONG J X, YU Z S, ZHANG X K, et al. Data-driven predictive prognostic model for power batteries based on machine learning[J]. Process Safety and Environmental Protection, 2023, 172: 894-907 doi: 10.1016/j.psep.2023.02.081
    [46]
    NORTHCUTT C G, JIANG L, CHUANG I. Confident learning: estimating uncertainty in dataset labels[J]. Journal of Artificial Intelligence Research, 2021, 70: 1373-1411 doi: 10.1613/jair.1.12125
    [47]
    TAI X X, LI M J, XIANG M, et al. A mutual guide framework for training hyperspectral image classifiers with small data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5510417
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