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Age information retrieval of Larix gmelinii forest using Sentinel-2 data

  • role： First author第一作者
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

  • Email：tangsf1120@163.com
  • Introduction：唐少飞，1994年生,男,硕士研究生, 研究方向为区域森林信息遥感定量反演。E-mail: tangsf1120@163.com
TANG Shaofei12,  
  • role： Corresponding author通信作者
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

  • Email：tianqj@nju.edu.cn
  • Introduction：田庆久，1964年生，男，教授，研究方向为高光谱遥感与遥感信息定量化。E-mail: tianqj@nju.edu.cn
TIAN Qingjiu12*,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

XU Kaijian12,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

XU Nianxu12,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

YUE Jibo12

resumen

The information of forest age structure can effectively reflect the carbon sequestration capacity of regional forest communities at different growth stages. This way is important for assessing the health status of forest ecosystems. In this study, the typical dominant tree species Larix gmelinii forest in temperate zone of China is selected as the object, and Sentinel-2 images of its bud germination period, elongating period of leaf, and defoliation period are selected. The retrieval model of Larix gmelinii stand age is constructed using Multiple Linear Regression (MLR), Random Forest (RF), support vector regression, feedforward back propagation neural network, and multiple adaptive regression spline. The optimal phenophase of remote sensing retrieval is first determined through correlation analysis. On this basis, five optimal characteristic variables, namely, Canopy Water Content (CWC), normalized difference water index, leaf area index, fraction of absorbed photosynthetically active radiatio, and fractional vegetation cover, are selected for model retrieval according to the difference in correlation. Results show that the elongating period of leaf is the optimal remote sensing retrieval phenophase. Except for the plant senescence reflectance index and NDVI and RVI in defoliation period, a negative correlation exists between the stand age of Larix gmelinii and each index, among which the correlation between the stand age and (CWC is the closest, and the correlation coefficient of Pearson reaches -0.74 (p<0.01). The results of different model retrievals indicate that RF model is the best model for estimating the age of Larix gmelinii, and its average coefficient of determination (R2) and mean Root Mean Square Error (RMSE) are 0.89 and 2.91 a, respectively. MLR is the worst for estimating Larix gmelinii forest age, and its average R2 and RMSE are 0.57 and 5.69 a, respectively. Nonlinear models can better explain the relationship between stand age and modeling variables.

palabra clave

remote sensing;Sentinel-2;Larix gmelinii;stand age retrieval;biophysical parameters;Random Forest

References

  1. 1.
    Birth G S and McVey G R. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6): 640-643
  2. 2.
    Champion I, Dubois-Fernandez P, Guyon D and Cottrel M. 2008. Radar image texture as a function of forest stand age. International Journal of Remote Sensing, 29(6): 1795-1800
  3. 3.
    Chen B Q, Cao J H, Wang J K, Wu Z X, Tao Z L, Chen J M, Yang C and Xie G S. 2012. Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data: a case study in Hainan Island, China. Forest Ecology and Management, 274: 222-230
  4. 4.
    Chen L Y. 2015. Adaptive Regression Model and Application of Multivariate Smoothing Splines. Tangshan: North China University of Science and Technology
  5. 5.
    Cutler A, Cutler D R and Stevens J R. 2011. Random forests. Machine Learning, 45(1): 157-176
  6. 6.
    Dai L M, Jia J, Yu D P, Lewis B J, Zhou L, Zhou W M, Zhao W and Jiang L H. 2013. Effects of climate change on biomass carbon sequestration in old-growth forest ecosystems on Changbai Mountain in Northeast China. Forest Ecology and Management, 300: 106-116
  7. 7.
    Dai M, Zhou T, Yang L L and Jia G S. 2011. Spatial pattern of forest ages in China retrieved from national-level inventory and remote sensing imageries. Geographical Research, 30(1): 172-184
  8. 8.
    Eggers J J, Bauml R, Tzschoppe R and Girod B. 2003. Scalar costa scheme for information embedding. IEEE Transactions on Signal Processing, 51(4): 1003-1019
  9. 9.
    Filippi A M, Güneralp İ and Randall J. 2014. Hyperspectral remote sensing of aboveground biomass on a river meander Bend using multivariate adaptive regression splines and stochastic gradient boosting. Remote Sensing Letters, 5(5): 432-441
  10. 10.
    Fiorella M and Ripple W J. 1993. Analysis of conifer forest regeneration using Landsat Thematic Mapper data. Photogrammetric Engineering and Remote Sensing, 59(9): 1383-1388
  11. 11.
    Fraser R H and Li Z. 2002. Estimating fire-related parameters in boreal forest using SPOT VEGETATION. Remote Sensing of Environment, 82(1): 95-110
  12. 12.
    Friedman J H. 1991. Multivariate adaptive regression splines. The Annals of Statistics, 19(1): 1-67
  13. 13.
    Gao B C. 1996. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266
  14. 14.
    Gitelson A A, Gritz Y and Merzlyak M N. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3): 271-282
  15. 15.
    Grant L. 1987. Diffuse and specular characteristics of leaf reflectance. Remote Sensing of Environment, 22(2): 309-322
  16. 16.
    Güneralp İ, Filippi A M and Randall J. 2014. Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling. International Journal of Applied Earth Observation and Geoinformation, 33: 119-126
  17. 17.
    Gunn S R. 1998. Support Vector Machines for Classification and Regression. University of Southampton: 1-28
  18. 18.
    Haywood A and Stone C. 2017. Estimating large area forest carbon stocks- a pragmatic design based strategy. Forests, 8(4): 99
  19. 19.
    He L M, Chen J M, Pan Y D, Birdsey R and Kattge J. 2012. Relationships between net primary productivity and forest stand age in U.S. forests. Global Biogeochemical Cycles, 26(3):
  20. 20.
    Jensen J R, Qiu F and Ji M H. 1999. Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. International Journal of Remote Sensing, 20(14): 2805-2822(18)
  21. 21.
    Jiang W Y and Chen F L. 2005. Dictionary of Resources and Environment Law. Beijing: China Legal Publishing House
  22. 22.
    Jiao L C. 1990. Neural Network System Theory. Xi’an: Xidian University Press
  23. 23.
    Ju W Z, Wang X J and Sun Y J. 2011. Age structure effects on stand biomass and carbon storage distribution of Larix olgensis plantation. Acta Ecologica Sinica, 31(4): 1139-1148
  24. 24.
    Kimes D S, Nelson R F, Salas W A and Skole D L. 1999. Mapping secondary tropical forest and forest age from SPOT HRV data. International Journal of Remote Sensing, 20(18): 3625-3640
  25. 25.
    Knipling E B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1(3): 155-159
  26. 26.
    Lal R. 2008. Carbon sequestration. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1492): 815-830
  27. 27.
    Li D Q, Ju W M, Fan W Y and Gu Z J. 2014. Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons. Journal of Applied Remote Sensing, 8(1): 083670
  28. 28.
    Li F, Li M Z, Shi Z L, Jiang H Y and An J P. 2018. Estimates stand age distribution based on forest survey and remote sensing data. Forest Engineering, 34(2): 30-34
  29. 29.
    Li Q, Zhu J H, Feng Y and Xiao W F. 2018. Carbon storage and carbon sequestration potential of the forest in China. Climate Change Research, 14(3): 287-294
  30. 30.
    Li R, Li C J, Xu X G, Wang J H, Yang X D, Huang W J and Pan Y C. 2009. Winter wheat yield estimation based on support vector machine regression and multi-temporal remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 25(7): 114-117
  31. 31.
    Li X H. 2013. Using “random forest” for classification and regression. Chinese Bulletin of Entomology, 50(4): 1190-1197
  32. 32.
    Litvak M, Miller S, Wofsy S C and Goulden M. 2003. Effect of stand age on whole ecosystem CO2 exchange in the Canadian boreal forest. Journal of Geophysical Research: Atmospheres, 108(D3): 8225
  33. 33.
    Liu L. 2005. Support Vector Machine and Its Application in Remote Sensing Image Processing. Hefei: University of Science and Technology of China
  34. 34.
    Liu R, Miao Q G, Huang B M, Song J F and Debayle J. 2016. Improved road centerlines extraction in high-resolution remote sensing images using shear transform, directional morphological filtering and enhanced broken lines connection. Journal of Visual Communication and Image Representation, 40: 300-311
  35. 35.
    Liu Z Y, Zhang J and Chen L. 2017. The latest change in the Qinghai-Tibetan Plateau vegetation index and its relationship with climate factors. Climatic and Environmental Research, 22(3): 289-300
  36. 36.
    Louis J. 2016. Sentinel 2 MSI-level 2A Product Definition. Paris: European Space Agency: 4
  37. 37.
    Main-Knorn M, Pflug B, Louis J, Debaecker V, Müller-Wilm U and Gascon F. 2017. Sen2Cor for Sentinel-2//Proceedings of the SPIE 10427 Image and Signal Processing for Remote Sensing XXIII. Warsaw: SPIE: 3
  38. 38.
    Meng X Y. 2006. Holzmesslehre. Beijing: China Forestry Publishing House, 2006 (孟宪宇. 2006. 测树学. 北京: 中国林业出版社)
  39. 39.
    Merzlyak M N, Gitelson A A, Chivkunova O B and Rakitin V Y U. 1999. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106(1): 135-141
  40. 40.
    Miao Z L, Shi W Z, Zhang H and Wang X X. 2013. Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines. IEEE Geoscience and Remote Sensing Letters, 10(3): 583-587
  41. 41.
    Muller-Wilm U, Devignot O and Pessiot L. 2016. Sen2Cor Configuration and User Manual. S2-PDGS-MPC-L2A-SUM-V2.3 Issue: 01. European Space Agency
  42. 42.
    Shen W J and Li M S. 2017. Mapping disturbance and recovery of plantation forests in southern China using yearly Landsat time series observations. Acta Ecologica Sinica, 37(5): 1438-1449
  43. 43.
    Sivanpillai R, Smith C T, Srinivasan R, Messina M G and Wu X B. 2006. Estimation of managed loblolly pine stand age and density with Landsat ETM+ data. Forest Ecology and Management, 223(1/3): 247-254
  44. 44.
    Suratman M N, Bull G Q, Leckie D G, Lemay V M, Marshall P L and Mispan M R. 2004. Prediction models for estimating the area, volume, and age of rubber (Hevea brasiliensis) plantations in Malaysia using Landsat TM data. International Forestry Review, 6(1): 1-12
  45. 45.
    Tauxe G M, MacWilliam D, Boyle S M, Guda T and Ray A. 2013. Targeting a dual detector of skin and CO2 to modify mosquito host seeking. Cell, 155(6): 1365-1379
  46. 46.
    Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150
  47. 47.
    Van der Linden S, Rabe A, Held M, Jakimow B, Leitão P J, Okujeni A, Schwieder M, Suess S and Hostert P. 2015. The EnMAP-Box—a toolbox and application programming interface for EnMAP data processing. Remote Sensing, 7(9): 11249-11266
  48. 48.
    Wang X C, Wang C K and Yu G R. 2008. Spatio-temporal patterns of forest carbon dioxide exchange based on global eddy covariance measurements. Science in China Series D: Earth Sciences, 51(8): 1129-1143
  49. 49.
    Watson R T, Noble I R, Bolin B, Ravindranath N H, Verardo D J and Dokken D J. 2000. Land Use, Land-Use Change, and Forestry. Cambridge University Press: 375
  50. 50.
    Weiss M and Baret F. 2016. S2 Toolbox Level 2 Products: LAI, FAPAR, FCOVER. INRA []
  51. 51.
    Wu W N and Wan T. 2013. Progress of dating methods of tree age. Journal of Green Science and Technology, (7): 152-155
  52. 52.
    Wulder M A, Skakun R S, Kurz W A and White J C. 2004. Estimating time since forest harvest using segmented Landsat ETM+ imagery. Remote Sensing of Environment, 93(1/2): 179-187
  53. 53.
    Xu K J, Tian Q J, Yue J B and Tang S F. 2018. Forest tree species identification and its response to spatial scale based on multispectral and multi-resolution remotely sensed data. Chinese Journal of Applied Ecology, 29(12): 3986-3994
  54. 54.
    Yang B, Li D, Wang L and Chen C. 2017. Retrieval of surface vegetation biomass information and analysis of vegetation feature based on Sentinel-2A in the upper of Minjiang River. Science and Technology Review, 35(21): 74-80
  55. 55.
    Yu H N, Lee W K, Son Y, Kwak D, Nam K, Kim M, Byun J, Lee S and Kwon T. 2013. Estimating carbon stocks in Korean forests between 2010 and 2110: a prediction based on forest volume-age relationships. Forest Science and Technology, 9(2): 105-110
  56. 56.
    Zhang Q F, Pavlic G, Chen W J, Latifovic R, Fraser R and Cihlar J. 2004. Deriving stand age distribution in boreal forests using SPOT VEGETATION and NOAA AVHRR imagery. Remote Sensing of Environment, 91(3/4): 405-418
  57. 57.
    Zhang Z W. 2013. Investigation and observation of growth and annual cycle phenology of four Larix species. Anhui Agricultural Science Bulletin, 19(15): 106-107
  58. 58.
    Zhou F Y. 2017. Relationships among DBH, crown width and stand age of Pinus sylvestris var. mongolica plantation in sand land. Protection Forest Science and Technology, (2): 19-21 (周凤艳. 2017. 沙地樟子松人工林林木胸径、冠幅等生长指标与林龄相关性研究. 防护林科技, (2): 19-21) [DOI: 10.13601/j.issn.1005-5215.2017.02.007]

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