Anterior|Siguiente

Information extraction of temporal and spatial distribution of short-rotation plantations in Guangxi Zhuang Autonomous Region

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：duanwensheng18@mails.ucas.ac.cn
  • Introduction：段文胜，研究方向为资源环境遥感应用。E-mail： duanwensheng18@mails.ucas.ac.cn
DUAN Wensheng12,  
  • Affiliation：

    Land Consolidation and Rehabilitation Center,Ministry of Natural Resources, Beijing 100035, China

CHEN Yuanpeng3,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：wangli@radi.ac.cn
  • Introduction：王力，研究方向为全球、区域生态环境和碳源汇变化。E-mail： wangli@radi.ac.cn
WANG Li1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

HUANG Ni1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

HE Yuanhuizi14,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHANG Changsai14,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

ZHANG Yangjian12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHOU Quan14,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

NIU Zheng14

resumen

Short-Rotation Plantations (SRPs) as the main economic forests have an important impact on ecological environmental protection and social economic development, but detailed information on the temporal and spatial distribution of SRPs is lacking. SRPs have nearly half a century of plantation history and extensive distribution in South China. This study aims to extract the long-term temporal and spatial distribution information of SRPs and analyze its changing trends and driving factors.The Guangxi Zhuang Autonomous Region, where SRPs are most widely grown in China, was used as the research area in this study. Based on the Google Earth Engine cloud platform and Landsat image data from 1986 to 2019, the 34-year Normalized Burn Ratio (NBR) long-term series data were first reconstructed by per pixel composite method. Then, the LandTrendr time series trajectory segmentation algorithm was used to segment and fit the NBR time series data for extracting the spatiotemporal distribution information of SRPs. Finally, Google Earth high-resolution images were used to select samples for verifying the accuracy of classification and extraction and analyzing the spatiotemporal characteristics and related factors of planting area changes in SRPs.(1) The accuracy of the SRP information extraction results was evaluated by the confusion matrix: the overall accuracy of the binary classifications reached 80.52%, the mapping accuracy of SRPs was 79.6%, the user accuracy of SRPs was 81.2%, and the kappa coefficient was over 0.6, which indicate that the classification model has a good classification effect. (2) The planting area of SRPs in Guangxi has been increasing steadily and rapidly year by year in the past 30 years. The planting area was only 1.93×105 ha in 1990, and it reached 4.04×106 ha by 2019, with an average annual growth rate of 1.33×105 ha. (3) In terms of spatial distribution, SRPs are concentrated in eastern and southern Guangxi. They are mainly distributed in low-altitude areas below 500 m and slopes with a surface slope of about 20°. Among them, Hechi City is the prefecture-level city with the largest planting and distribution area of SRPs in Guangxi. (4) A strong correlation exists between the change trend of SRP planting area and forestry output value (r=0.830894, p<0.001), which suggests that SRPs are an important affecting factor of forestry economy.The method proposed in this study based on the LandTrendr time series trajectory segmentation algorithm of SRP spatiotemporal information extraction is proven to be very effective. The mapping and analysis of the spatiotemporal distribution of SRPs can provide decision support for forestry management and provide basic data for the research on forest carbon cycle.

palabra clave

remote sening;Short-Rotation Plantations;LandTrendr;Landsat;Spatiotemporal distribution mapping;long time series;Time series trajectory segmentation

References

  1. 1.
    Brockerhoff E G, Jactel H, Parrotta J A, Quine C P and Sayer J. 2008. Plantation forests and biodiversity: oxymoron or opportunity? Biodiversity and Conservation, 17(5): 925-951
  2. 2.
    Bu Q L, Tan Z H and Zhang Y P. 2020. Carbon dynamic of a tropical forest succession series in Southwest China. Acta Ecologica Sinica, 40(15): 5258-5265
  3. 3.
    Claverie M, Vermote E F, Franch B and Masek J G. 2015. Evaluation of the landsat-5 TM and landsat-7 ETM+surface reflectance products. Remote Sensing of Environment, 169: 390-403
  4. 4.
    Dong J W, Xiao X M, Menarguez M A, Zhang G L, Qin Y W, Thau D, Biradar C and Moore B. 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and google earth engine. Remote Sensing of Environment, 185: 142-154
  5. 5.
    Flood N. 2013. Seasonal composite Landsat TM/ETM+ images using the Medoid (a multi-dimensional median). Remote Sensing, 5(12): 6481-6500
  6. 6.
    Ge Q S, Dai J H, He F N, Pan Y and Wang M M. 2008. Land use changes and their relations with carbon cycles over the past 300 a in China. Science in China Series D: Earth Sciences, 51(6): 871-884
  7. 7.
    Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27
  8. 8.
    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. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850-853
  9. 9.
    Huang G Q and Zhao Q G. 2014. The history, status quo, ecological problems and countermeasures of Eucalyptus plantations in Guangxi. Acta Ecologica Sinica, 34(18): 5142-5152
  10. 10.
    Kennedy R E, Yang Z Q and Cohen W B. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment, 114(12): 2897-2910
  11. 11.
    Kennedy R E, Yang Z Q, Gorelick N, Braaten J, Cavalcante L, Cohen W B and Healey S. 2018. Implementation of the LandTrendr algorithm on google earth engine. Remote Sensing, 10(5): 691
  12. 12.
    Lan X, Du H, Song T Q, Zeng F P, Peng W X, Liu Y X, Fan Z L and Zhang J Y. 2019. Vegetation carbon storage in the main forest types in Guangxi and the related influencing factors. Acta Ecologica Sinica, 39(6): 2043-2053
  13. 13.
    Le Maire G, Dupuy S, Nouvellon Y, Loos R A and Hakamada R. 2014. Mapping short-rotation plantations at regional scale using MODIS time series: case of eucalypt plantations in Brazil. Remote Sensing of Environment, 152: 136-149
  14. 14.
    Meng L L, Zhong S Q, Mo J F, Chen Y L and Liang W H. 2019. Research on eucalyptus information extraction based on GF-1 object-oriented. Geospatial Information, 17(8): 24-29
  15. 15.
    National Forestry and Grassland Administration. 2019. China Forestry and Grassland Statistical Yearbook 2018. Beijing: China Forestry Publishing House
  16. 16.
    Padarian J, Minasny B and McBratney A B. 2015. Using Google's cloud-based platform for digital soil mapping. Computers and Geosciences, 83: 80-88
  17. 17.
    Qiao H L, Wu M Q, Shakir M, Wang L, Kang J and Niu Z. 2016. Classification of small-scale eucalyptus plantations based on NDVI time series obtained from multiple high-resolution datasets. Remote Sensing, 8(2): 117
  18. 18.
    Qiu B B. 2017. Study on the difference of spectral and urban green land biomass estimation based on Landsat OLI and ETM+ image. Hangzhou: Zhejiang Agriculture and Forestry University
  19. 19.
    Roy D P, Kovalskyy V, Zhang H K, Vermote E F, Yan L, Kumar S S and Egorov A. 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185: 57-70
  20. 20.
    Shen W J and Li M S. 2014. Method for Landsat dense time series data format unification and surface reflectance conversion. Remote Sensing for Land and Resources, 26(4): 78-84
  21. 21.
    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
  22. 22.
    Shen W J, Li M S and Wei A S. 2017. Spatio-temporal variations in plantation forests’ disturbance and recovery of northern Guangdong Province using yearly Landsat time series observations (1986-2015). Chinese Geographical Science, 27(4): 600-613
  23. 23.
    Thomas V A, Wynne R H, Kauffman J, McCurdy W, Brooks E B, Thomas R Q and Rakestraw J. 2021. Mapping thins to identify active forest management in southern pine plantations using Landsat time series stacks. Remote Sensing of Environment, 252: 112127
  24. 24.
    Williams R A. 2015. Mitigating biodiversity concerns in eucalyptus plantations located in South China. Journal of Biosciences and Medicines, 3(6): 1-8
  25. 25.
    Woodcock C E, Allen R, Anderson M, Belward A, Bindschadler R, Cohen W, Gao F, Goward S N, Helder D, Helmer E, Nemani R, Oreopoulos L, Schott J, Thenkabail P S, Vermote E F, Vogelmann J, Wulder M A and Wynne R. 2008. Free access to Landsat imagery. Science, 320(5879): 1011-1011
  26. 26.
    Wu X Q, Qin X L, Zhou R L, Lu Y, Chen E X and Li Z Y. 2010. Progress of study on forest cover change detection by using remote sensing technique. Forest Resources Management, (4): 82-87
  27. 27.
    Zhang S S, Cui Y P, Fu S L, Fu Y M, Liu X Y, Tan X Y, Chen L Y and Li N. 2020. Modeling greenhouse gas sequestration potential of forest change in China. Acta Ecologica Sinica, 40(4): 1140-1149
  28. 28.
    Zhang Z J, Zhang H, Chang Y G and Chen Z C. 2015. Review of radiometric calibration methods of Landsat series optical remote sensors. Journal of Remote Sensing, 19(5): 719-732
  29. 29.
    Zhu Z, Wang S X and Woodcock C E. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159: 269-277

Leer el texto completo

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