سابِق|تال

Performance of ICESat-2 ATL08 product on the estimation of forest height by referencing to small footprint LiDAR data

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Beijing 100094, China

    School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：dongjc@radi.ac.cn
  • Introduction：董佳臣，1995年生，男，硕士研究生， 研究方向为森林遥感。E-mail： dongjc@radi.ac.cn
DONG Jiachen12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Beijing 100094, China

  • Email：niwj@radi.ac.cn
  • Introduction：倪文俭，1982年生，男，研究员， 研究方向为森林遥感。E-mail：niwj@radi.ac.cn
NI Wenjian1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Beijing 100094, China

ZHANG Zhiyu1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Beijing 100094, China

    Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA, 20742

SUN Guoqing13

ملخص

The ICESat-2 satellite, which operates through the technology of multi-beam single-photon-counting, provides a new opportunity for the mapping of global forest structures. Although previous studies based on the airborne simulator of ICESat-2 have shown that it has great potential to estimate forest structure parameters using single-photon-counting data, the performance of ICESat-2 needs to be examined due to the major differences between airborne system and ICESat-2. The National Aeronautics and Space Administration has released nine types of ICESat-2 data products, one of which is the vegetation canopy height and surface elevation data (ATL08). Therefore, the purpose of this paper is to evaluate the performance of ATL08 on the estimation of forest height by referencing to small-footprint LiDAR data.The basis for the accurate estimation of forest height using photon-counting LiDAR data is the correct classification of photons, that is, identification of noise photons, ground photons, and vegetation photons. The ATL08 only records photon classification and forest structure parameters, and the geographical coordinates of each photon are recorded in the Terrain Elevation (ATL03) product. Therefore, ATL03 and ATL08 need to be connected first according to the organization of two products. Taking the data of small-footprint LiDAR as reference, the ATL08 products were evaluated from two aspects. First, the classification results of photons are evaluated by referencing to the profiles from the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived using the small-footprint LiDAR data. Then, percentile height metrics of each 100-m-long segment in ATL08 product were evaluated by referencing to corresponding metrics calculated using the point cloud data of small-footprint LiDAR.This study was carried out in two test sites: Snyder County in Pennsylvania, United States and Ketapang in West Kalimandin, Indonesia. These sites represented temperate forest and tropical rainforest, respectively. In the temperate forest, the classification accuracy of noise photons, ground photons, and canopy photons in ATL08 products were acceptable. The estimation accuracy of maximum canopy height was slightly better than that of mean canopy height with R²=0.61 against 0.54 and RMSE=10.71% against 16.78%. In the tropical rainforest with relatively low canopy cover, the ground photons in ATL08 could be correctly identified. However, in the forest with high canopy cover, the number of photons near the ground is inadequate. In addition, the identified ground photons in ATL08 were not enough to fit the terrain under the forest, which caused obvious errors in the estimation of forest height. The results of the quantitative analysis of the error of mean canopy height between canopy cover and terrain show that with the increase of canopy cover, the error of canopy height calculated by ATL08 will increase. In the tropical rainforest, the error of the mean canopy height will increase as the slope increases. When the slope is 0°—10°, 10°—20°, and 20°—30°, the error is 5.7 m, 6.6 m, and 9.3 m, respectively.Ie can be concluded based on the results of this study that ATL08 product could be used to estimate the height of temperate forests. However, in dense tropical rainforests, due to the limited penetration capabilities of LiDAR and difficulties in the correct identification of ground photons, the existing ATL08 data will be difficult to use for forest height estimation.

مفهوم

ICESat-2;ATLAS;ATL08;forest structure parameters

References

  1. 1.
    Abdalati W, Zwally H J, Bindschadler R, Csatho B, Farrell S L, Fricker H A, Harding D, Kwok R, Lefsky M, Markus T, Marshak A, Neumann T, Palm S, Schutz B, Smith B, Spinhirne J and Webb C. 2010. The ICESat-2 laser altimetry mission. Proceedings of the IEEE, 98(5): 735-751
  2. 2.
    Brown M E, Arias S D, Neumann T, Jasinski M F, Posey P, Babonis G, Glenn N F, Birkett C M, Escobar V M and Markus T. 2016. Applications for ICESat-2 data: from NASA’s early adopter program. IEEE Geoscience and Remote Sensing Magazine, 4(4): 24-37
  3. 3.
    Brunt K M, Neumann T A, Amundson J M, Kavanaugh J L, Moussavi M S, Walsh K M, Cook W B and Markus T. 2016. MABEL photon-counting laser altimetry data in Alaska for ICESat-2 simulations and development. The Cryosphere, 10(4): 1707-1719
  4. 4.
    Chen B W, Pang Y, Li Z Y, North P, Rosette J, Sun G Q, Suárez J, Bye I and Lu H. 2019. Potential of forest parameter estimation using metrics from photon counting LiDAR data in Howland research forest. Remote Sensing, 11(7): 856
  5. 5.
    Cook B D, Corp L A, Nelson R F, Middleton E M, Morton D C, McCorkel J T, Masek J G, Ranson K J, Ly V and Montesano P M. 2013. NASA Goddard's LiDAR, hyperspectral and thermal (G-LiHT) airborne imager. Remote Sensing, 5(8): 4045-4066
  6. 6.
    Farrell S L, Laxon S W, McAdoo D C, Yi D H and Zwally H J. 2009. Five years of Arctic sea ice freeboard measurements from the Ice, Cloud and land Elevation Satellite. Journal of Geophysical Research: Oceans, 114(C4): C04008
  7. 7.
    Gaumont-Guay D, Black T A, Barr A G, Griffis T J, Jassal R S, Krishnan P, Grant N and Nesic Z. 2014. Eight years of forest-floor CO2 exchange in a boreal black spruce forest: spatial integration and long-term temporal trends. Agricultural and Forest Meteorology, 184: 25-35
  8. 8.
    Huang W L, Sun G Q, Dubayah R, Cook B, Montesano P, Ni W J and Zhang Z Y. 2013. Mapping biomass change after forest disturbance: applying LiDAR footprint-derived models at key map scales. Remote Sensing of Environment, 134: 319-332
  9. 9.
    Khan S A, Kjær K H, Bevis M, Bamber J L, Wahr J, Kjeldsen K K, Bjørk A A, Korsgaard N J, Stearns L A, van den Broeke M R, Liu L, Larsen N K and Muresan I S. 2014. Sustained mass loss of the northeast Greenland ice sheet triggered by regional warming. Nature Climate Change, 4(4): 292-299
  10. 10.
    Li M, Guo Y, Yang G and Shu R. 2017. A noise filter method for the push-broom photon counting LiDAR and airborne cloud data verification. Science Technology and Engineering, 17(9): 53-58
  11. 11.
    Li Z Y, Liu Q W and Pang Y. 2016. Review on forest parameters inversion using LiDAR. Journal of Remote Sensing, 18(6): 1138-1150
  12. 12.
    Lu D S, Chen Q, Wang G X, Liu L J, Li G Y and Moran E. 2016. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1): 63-105
  13. 13.
    Ma L X, Zheng G, Eitel J U H, Magney T S and Moskal L M. 2017. Retrieving forest canopy extinction coefficient from terrestrial and airborne LiDAR. Agricultural and Forest Meteorology, 236: 1-21
  14. 14.
    Magruder L A and Brunt K M. 2018. Performance analysis of airborne photon-counting LiDAR data in preparation for the ICESat-2 mission. IEEE Transactions on Geoscience and Remote Sensing, 56(5): 2911-2918
  15. 15.
    Manuri S, Andersen H E, McGaughey R J and Brack C. 2017. Assessing the influence of return density on estimation of LiDAR-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia. International Journal of Applied Earth Observation and Geoinformation, 56: 24-35
  16. 16.
    Markus T, Neumann T, Martino A, Abdalati W, Brunt K, Csatho B, Farrell S, Fricker H, Gardner A, Harding D, Jasinski M, Kwok R, Magruder L, Lubin D, Luthcke S, Morison J, Nelson R, Neuenschwander A, Palm S, Popescu S, Shum C K, Schutz B E, Smith B, Yang Y K and Zwally J. 2017. The ice, cloud, and land elevation satellite-2 (ICESat-2): science requirements, concept, and implementation. Remote Sensing of Environment, 190: 260-273
  17. 17.
    Melendy L, Hagen S C, Sullivan F B, Pearson T R H, Walker S M, Ellis P, Kustiyo, Sambodo A K, Roswintiarti O, Hanson M A, Klassen A W, Palace M W, Braswell B H and Delgado G M. 2018. Automated method for measuring the extent of selective logging damage with airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 228-240
  18. 18.
    Narine L L, Popescu S, Neuenschwander A, Zhou T, Srinivasan S and Harbeck K. 2019. Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data. Remote Sensing of Environment, 224: 1-11
  19. 19.
    Neuenschwander A and Pitts K. 2019. The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sensing of Environment, 221: 247-259
  20. 20.
    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
  21. 21.
    Nie S, Wang C, Xi X H, Luo S Z, Li G Y, Tian J Y and Wang H T. 2018. Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data. Optics Express, 26(10): A520-A540
  22. 22.
    Popescu S C, Zhou T, Nelson R, Neuenschwande A, Sheridan R, Narine L and Walsh K M. 2018. Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data. Remote Sensing of Environment, 208: 154-170
  23. 23.
    Xia S B, Wang C, Xi X H, Luo S Z and Zeng H C. 2014. Point cloud filtering and tree height estimation using airborne experiment data of ICESat-2. Journal of Remote Sensing, 18(6): 1199-1207

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website