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スペースボーンフォトンカウンティングLiDARを使用したスイスアールゴー州の森林蓄積量推定のための冠高3D構造特性の改善

  • role： First author第一作者
  • Affiliation：

    Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

  • Email：ak_4798@126.com
  • Introduction：孔丹，研究方向为星载、机载激光雷达信号处理及林业应用。E-mail： ak_4798@126.com
KONG Dan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

  • Email：pangy@ifrit.ac.cn
  • Introduction：庞勇，研究方向为林业遥感机理模型、激光雷达信号处理及林业应用、森林碳汇计量、森林变化监测。E-mail： pangy@ifrit.ac.cn
PANG Yong12*,  
  • Affiliation：

    Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf 8903, Switzerland

WANG Zuyuan3,  
  • Affiliation：

    Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

LI Zengyuan12

サマリー

宇宙搭載光子計数LiDARは、迅速に大範囲の植生情報を取得する能力を持ち、森林のパラメータ推定に広く使用されているが、普遍性が低いなどの問題があります。 この研究は、ICEStat-2 ATLASデータを使用して、冠高と冠高の異質性、垂直構造特性を利用して森林の蓄積量推定モデルの精度を向上させる可能性に焦点を当てています。 まず、除去されたICEStat-2データを100mの推定単位に分割し、品質管理を行い、異常な単位を識別および削除してデータの品質を確保します。 これをベースに、特徴パラメータのグループ事前選択および規則的な部分集合選択法を使用して、ポイントクラウドの高さ分布特性、冠高および冠高の異質性特性、垂直構造特性を総合的に利用して森林の蓄積量を推定し、最適な特徴組合せ形式を選択します。 研究結果は、スイスのアールゴー州の針広葉樹混交林に対して、ATLAS蓄積量の推定最適モデルが冠層の上部平均高さ、65％密度分位数、葉面積重み付き冠層積および枝葉プロフィールの平均値で構成されていることを示しています。 モデルの10倍交差検証の平均精度は、R2=0.78、RMSE=92.48 m3・hm-2、rRMSE=0.24です。。 月のR2が約10%向上し、rRMSEは0.28から0.24に低下し、冠層の異質性の高い森林区での蓄積量推定モデルの能力が向上しました。 この研究は、ICEStat-2データを使用した森林蓄積量推定の精度を向上させ、広範囲の森林蓄積量および炭素量のモニタリングアプリケーションに使用する方法を提供します。

キーワード

森林蓄積量; ICEStat-2 ATLAS; 森林3D構造特性; 冠高異質性; 垂直構造; 品質管理; グループ事前選択; 規則的な部分集合選択

References

  1. 1.
    Almeida D R A, Stark S C, Chazdon R, Nelson B W, Cesar R G, Meli P, Gorgens E B, Duarte M M, Valbuena R, Moreno V S, Mendes A F, Amazonas N, Gonçalves N B, Silva C A, Schietti J and Brancalion P H S. 2019. The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration. Forest Ecology and Management, 438: 34-43
  2. 2.
    Asner G P and Mascaro J. 2014. Mapping tropical forest carbon: calibrating plot estimates to simple LiDAR metric. Remote Sensing of Environment, 140: 614-624
  3. 3.
    Bouvier M, Durrieu S, Fournier R A and Renaud J P. 2015. Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment, 156: 322-334
  4. 4.
    Chen L, Ren C Y, Zhang B, Wang Z M, Liu M Y, Man W D and Liu J F. 2021. Improved estimation of forest stand volume by the integration of GEDI LiDAR data and multi-sensor imagery in the Changbai Mountains Mixed forests Ecoregion (CMMFE), Northeast China. International Journal of Applied Earth Observation and Geoinformation, 100: 102326
  5. 5.
    Coops N C, Tompalski P, Goodbody T R H, Queinnec M, Luther J E, Bolton D K, White J C, Wulder M A, van Lier O R and Hermosilla T. 2021. Modelling lidar-derived estimates of forest attributes over space and time: a review of approaches and future trends. Remote Sensing of Environment, 260: 112477
  6. 6.
    Du L M, Pang Y, Wang Q, Huang C Q, Bai Y, Chen D S, Lu W and Kong D. 2023. A LiDAR biomass index-based approach for tree- and plot-level biomass mapping over forest farms using 3D point clouds. Remote Sensing of Environment, 290: 113543
  7. 7.
    Goldbergs G, Levick S R, Lawes M and Edwards A. 2018. Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR. Remote Sensing of Environment, 205: 141-150
  8. 8.
    Hall S A, Burke I C, Box D O, Kaufmann M R and Stoker J M. 2005. Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208(1/3): 189-209
  9. 9.
    Harding D J, Lefsky M A, Parker G G and Blair J B. 2001. Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests. Remote Sensing of Environment, 76(3): 283-297
  10. 10.
    Jiang Y, Ruetschi M, Garnot V S F, Marty M, Schindler K, Ginzler C and Wegner J D. 2023. Accuracy and consistency of space-based vegetation height maps for forest dynamics in alpine terrain. Science of Remote Sensing, 8: 100099
  11. 11.
    Knapp N, Fischer R, Cazcarra-Bes V and Huth A. 2020. Structure metrics to generalize biomass estimation from lidar across forest types from different continents. Remote Sensing of Environment, 237: 111597
  12. 12.
    Lefsky M A. 2010. A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophysical Research Letters, 37(15): L15401
  13. 13.
    Lefsky M A, Keller M, Pang Y, De Camargo P B and Hunter M O. 2007. Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms. Journal of Applied Remote Sensing, 1(1): 013537
  14. 14.
    Liu M and Popescu S. 2022. Estimation of biomass burning emissions by integrating ICESat-2, Landsat 8, and Sentinel-1 data. Remote Sensing of Environment, 280: 113172
  15. 15.
    Meng G, Zhao D, Xu C, Chen J H, Li X W, Zheng Z J and Zheng Y. 2024. Forest aboveground biomass estimation in Zhejiang province combining ICESat-2 and GEDI spaceborne lidar data. National Remote Sensing Bulletin, 28(6): 1632-1647
  16. 16.
    Næsset E. 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80(1): 88-99
  17. 17.
    Næsset E. 2009. Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data. Remote Sensing of Environment, 113(1): 148-159
  18. 18.
    Nandy S, Srinet R and Padalia H. 2021. Mapping forest height and aboveground biomass by integrating ICESat-2, Sentinel-1 and Sentinel-2 data using random forest algorithm in Northwest Himalayan Foothills of India. Geophysical Research Letters, 48(14): e2021GL093799
  19. 19.
    Narine L L, Popescu S C and Malambo L. 2019. Synergy of ICESat-2 and landsat for mapping forest aboveground biomass with deep learning. Remote Sensing, 11(12): 1503
  20. 20.
    Narine L L, Popescu S C and Malambo L. 2020. Using ICESat-2 to estimate and map forest aboveground biomass: a first example. Remote Sensing, 12(11): 1824
  21. 21.
    Neuenschwander A, Duncanson L, Montesano P, Minor D, Guenther E, Hancock S, Wulder M A, White J C, Purslow M, Thomas N, Mandel A, Feng T, Armston J, Kellner J R, Andersen H E, Boschetti L, Fekety P, Hudak A, Pisek J, Sánchez-López N and Stereńczak K. 2024. Towards global spaceborne lidar biomass: developing and applying boreal forest biomass models for ICESat-2 laser altimetry data. Science of Remote Sensing, 10: 100150
  22. 22.
    Neuenschwander A L and Magruder L A. 2016. The potential impact of vertical sampling uncertainty on ICESat-2/ATLAS terrain and canopy height retrievals for multiple ecosystems. Remote Sensing, 8(12): 1039
  23. 23.
    Neumann T A, Martino A J, Markus T, Bae S, Bock M R, Brenner A C, Brunt K M, Cavanaugh J, Fernandes S T, Hancock D W, Harbeck K, Lee J, Kurtz N T, Luers P J, Luthcke S B, Magruder L, Pennington T A, Ramos-Izquierdo L, Rebold T, Skoog J and Thomas T C. 2019. The ice, cloud, and land elevation satellite-2 mission: a global geolocated photon product derived from the advanced topographic laser altimeter system. Remote Sensing of Environment, 233: 111325
  24. 24.
    Pang Y, Lefsky M, Sun G Q, Miller M E and Li Z Y. 2008. Temperate forest height estimation performance using ICESat GLAS data from different observation periods. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(Part B7): 777-782
  25. 25.
    Queinnec M, White J C and Coops N C. 2021. Comparing airborne and spaceborne photon-counting LiDAR canopy structural estimates across different boreal forest types. Remote Sensing of Environment, 262: 112510
  26. 26.
    Roussel J R, Caspersen J, Béland M, Thomas S and Achim A. 2017. Removing bias from LiDAR-based estimates of canopy height: accounting for the effects of pulse density and footprint size. Remote Sensing of Environment, 198: 1-16
  27. 27.
    Schneider F D, Ferraz A, Hancock S, Duncanson L I, Dubayah R O, Pavlick R P and Schimel D S. 2020. Towards mapping the diversity of canopy structure from space with GEDI. Environmental Research Letters, 15(11): 115006
  28. 28.
    Silva C A, Duncanson L, Hancock S, Neuenschwander A, Thomas N, Hofton M, Fatoyinbo L, Simard M, Marshak C Z, Armston J, Lutchke S and Dubayah R. 2021. Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping. Remote Sensing of Environment, 253: 112234
  29. 29.
    Song H Y, Xi L, Shu Q T, Wei Z Y and Qiu S. 2023. Estimate forest aboveground biomass of mountain by ICESat-2/Atlas data interacting cokriging. Forests, 14(1): 13
  30. 30.
    Tompalski P, White J C, Coops N C and Wulder M A. 2019. Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data. Remote Sensing of Environment, 227: 110-124
  31. 31.
    van Ewijk K, Tompalski P, Treitz P, Coops N C, Woods M and Pitt D. 2020. Transferability of ALS-derived forest resource inventory attributes between an eastern and western Canadian boreal forest mixedwood site. Canadian Journal of Remote Sensing, 46(2): 214-236
  32. 32.
    Varvia P, Korhonen L, Bruguière A, Toivonen J, Packalen P, Maltamo M, Saarela S and Popescu S C. 2022. How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass? Remote Sensing of Environment, 280: 113174
  33. 33.
    Varvia P, Saarela S, Maltamo M, Packalen P, Gobakken T, Næsset E, Ståhl G and Korhonen L. 2024. Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference. Remote Sensing of Environment, 311: 114249
  34. 34.
    Wu Z C and Shi F L. 2023. Mapping forest canopy height at large scales using ICESat-2 and Landsat: an ecological zoning random forest approach. IEEE Transactions on Geoscience and Remote Sensing, 61: 4400116
  35. 35.
    Yu G R, Zhu J X, Xu L and He N P. 2022. Technological approaches to enhance ecosystem carbon sink in China: nature-based solutions. Bulletin of Chinese Academy of Sciences, 37(4): 490-501

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