Skip to main content
SpringerLink
Log in

Identification of shelterbelt width from high-resolution remote sensing imagery

  • Published:
Agroforestry Systems Aims and scope Submit manuscript

Abstract

Shelterbelt width not only influences protection effectiveness, but also represents a variable in a porosity model. Following extensive planting of shelterbelts globally, it is important to accurately identify shelterbelt width to evaluate its effectiveness on the regional scale. However, obtaining such an inventory is challenging given the narrow linear characteristic and extensive distribution. The development of high-resolution remote sensing monitoring offers the possibility of accurate identification of shelterbelt width. This study developed a method for identification of shelterbelt width using 3-m spatial resolution JL1–02A remote sensing imagery. The primary principles of the method are as follows: (1) generate two-dimensional waveforms of shelterbelts using remote sensing images; (2) recognize intact parts of shelterbelts from wave features in the direction of belt orientation; (3) determine the belt edge and pixels related to belt width through neighboring pixel analysis in the direction of belt width; and (4) establish an algorithm for shelterbelt width identification through analysis of its relationship with edge and inner pixels. Using the proposed method, shelterbelt width was identified in the study area in Jilin Province (Northeast China), and the accuracy of the measurement was analyzed using a 0.75-m resolution fusion image (sample number: 140). Results showed that the coefficient of determination (R2) was 0.93, root mean square error was 2.18 m, mean absolute error was 1.76 m, maximum error was 5.11 m, and minimum error was 0.01 m, which verified the accuracy and reliability of the method for shelterbelt width identification. The result could be used to determine shelterbelt structure (e.g., number of rows), thereby providing the possibility for remote simulation of shelterbelt porosity, which was previously only measured from the ground-level approach. The proposed method could help promote accurate monitoring and management of shelterbelt structure and function.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Amichev BY, Bentham MJ, Cerkowniak D, Kort J, Kulshreshtha S, Laroque CP, Piwowar JM, Van Rees KCJ (2015) Mapping and quantification of planted tree and shrub shelterbelts in Saskatchewan. Canada Agrofor Syst 89:49–65

    Article  Google Scholar 

  • Brandle JR, Hodges L, Zhou XH (2004) Windbreaks in North American agricultural systems. Agrofor Syst 61:65–78

    Google Scholar 

  • Cai XL, Henderson M, Wang LG, Su YH, Liu BH (2021) Shelterbelt structure and crop protection from increased typhoon activity in Northeast China. Agriculture 11:995

    Article  Google Scholar 

  • Chen YY, Ming DP, Zhao L, Lv BR, Zhou KQ, Qing YZ (2018) Review on high spatial resolution remote sensing image segmentation evaluation. Photogramm Eng Remote Sens 84(10):629–646

    Article  Google Scholar 

  • Deng RX, Li Y, Wang WJ, Zhang SW (2013) Recognition of shelterbelt continuity using remote sensing and waveform recognition. Agrofor Syst 87:827–834

    Article  Google Scholar 

  • Deng RX, Li Y, Xu XL, Wang WJ, Wei YC (2017) Remote estimation of shelterbelt width from SPOT5 imagery. Agrofor Syst 91(1):1–12

    Article  Google Scholar 

  • Deng RX, Wang WJ, Li Y, Shi XL, Wei YC, Hao LJ, Li CJ, Liu WY (2019) Analysis of changes in shelterbelt landscape in Northeast China. Appl Ecol Environ Res 17(5):11655–11668

    Article  Google Scholar 

  • Ghimire K, Dulin MW, Atchison RL, Goodin DG, Hutchinson JMS (2014) Identification of windbreaks in Kansas using object-based image analysis, GIS techniques and field survey. Agrofor Syst 88:865–875

    Article  Google Scholar 

  • Gutman G, Ignatov G (1998) The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int J Remote Sens 19(8):1533–1543

    Article  Google Scholar 

  • Ha TV, Amichev BY, Belcher KW, Bentham MJ, Kulshreshtha SN, Laroque CP, Van Rees KCJ (2019) Shelterbelt agroforestry systems inventory and removal analyzed by object-based classification of satellite data in Saskatchewan, Canada. Can J Remote Sens 45(2):246–263

    Article  Google Scholar 

  • Jia JX, Wang YM, Chen JS, Guo R, Shu R, Wang JY (2020) Status and application of advanced airborne hyperspectral imaging technology: a Review. Infrared Phys Technol 104:103115

    Article  CAS  Google Scholar 

  • Jia JX, Sun HB, Jiang CH, Karila K, Karjalainen M, Ahokas E, Khoramshahi E, Hu PL, Chen C, Xue TR, Wang TH, Chen YW, Hyyppa J (2021) Review on active and passive remote sensing techniques for road extraction. Remote Sens 13:4235

    Article  Google Scholar 

  • Jiang FQ, Zhou XH, Fu MH, Zhu JJ, Lin HM (1994) Shelterbelt porosity model and its application. Chin J Appl Ecol 5(3):251–255 ((in Chinese with English abstract))

    Google Scholar 

  • Kristensen SP, Caspersen OH (2002) Analysis of changes in a shelterbelt network landscape in central jutland, denmark. J Environ Manage 66(2):171–183

    Article  PubMed  Google Scholar 

  • Lei SJ, Sun H, Liu H, Jiang FG, Chen S, Wu T, Xie Y (2020) Study on extraction method of farmland shelterbelt supported by Landsat 8 time series image. J Cent South Univ for Technol 40(4):57–63 ((in Chinese with English abstract))

    Google Scholar 

  • Lunden J, Koivunen V (2007) Automatic radar waveform recognition. IEEE J Sel Top Signal Process 1(1):124–136

    Article  Google Scholar 

  • Santiago JL, Martin F, Cuerva A, Bezdenejnykh N, Sanz-Andres A (2007) Experimental and numerical study of wind flow behind windbreaks. Atmos Environ 41:6406–6420

    Article  CAS  Google Scholar 

  • Shi XL, Li Y, Deng RX (2011) A method for spatial heterogeneity evaluation on landscape pattern of farmland shelterbelt networks: a case study in Midwest of Jilin Province. China Chin Geogr Sci 21(1):48–56

    Article  Google Scholar 

  • Su N, Yan YM, Qiu MJ, Zhao CH, Wang LG (2018) Object-based dense matching method for maintaining structure characteristics of linear buildings. Sensors 18(4):1035

    Article  PubMed Central  Google Scholar 

  • Thuyet DV, Do TV, Sato T, Hung TT (2014) Effects of species and shelterbelt structure on wind speed reduction in shelter. Agrofor Syst 88(2):237–244

    Article  Google Scholar 

  • Torita H, Satou H (2007) Relationship between shelterbelt structure and mean wind reduction. Agric for Meteorol 145:186–194

    Article  Google Scholar 

  • Wiseman G, Kort J, Walker D (2009) Quantification of shelterbelt characteristics using high-resolution imagery. Agric Ecosyst Environ 131:111–117

    Article  Google Scholar 

  • Wu TG, Yu MK, Wang G, Wang ZX, Duan X, Dong Y, Cheng XR (2013) Effects of stand structure on wind speed reduction in a Metasequoia glyptostroboides shelterbelt. Agrofor Syst 87:251–257

    Article  Google Scholar 

  • Xing ZF, Li Y, Deng RX, Zhu HL, Fu BL (2016) Extracting Farmland Shelterbelt Automatically Based on ZY-3 Remote Sensing Images. Sci Silv Sin 52:11–20 ((in Chinese with English abstract))

    Google Scholar 

  • Yang XG, Yu Y, Fan WY (2017) A method to estimate the structural parameters of windbreaks using remote sensing. Agrofor Syst 91(1):37–49

    Article  Google Scholar 

  • Yang XG, Li FR, Fan WY, Liu GW, Yu Y (2021) Evaluating the efficiency of wind protection by windbreaks based on remote sensing and geographic information systems. Agrofor Syst 95(2):353–365

    Article  Google Scholar 

  • Yi F, Te RG, Zhao YH, Xu GC (2022) EUNetMTL: multitask joint learning for road extraction from high-resolution remote sensing images. Remote Sens Lett 13(3):258–268

    Article  Google Scholar 

  • Zhang M, Liu LT, Diao M (2016) LPI radar waveform recognition based on time-frequency distribution. Sensors 16(10):1682

    Article  PubMed Central  Google Scholar 

  • Zheng X, Zhu JJ, Yan Y (2013) Estimation of farmland shelterbelt area in the three-north shelter/protective forest program regions of China based on multi-scale remote sensing data. Chin J Appl Ecol 32(5):1355–1363 ((in Chinese with English abstract))

    Google Scholar 

  • Zheng X, Zhu JJ, Xing ZF (2016) Assessment of the effects of shelterbelts on crop yields at the regional scale in northeast China. Agric Syst 143:49–60

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (31971723), the Distinguished Young Talents in Higher Education of Henan Province (2020GGJS101) and the Key Technologies Research and Development Program of Henan Province (192102110122).

Author information

Authors and Affiliations

  1. College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China

    Rongxin Deng, Gao Yang, Zhengran Xu, Lu Zhang & Chunjing Li

  2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China

    Ying Li & Xing Zhang

Authors
  1. Rongxin Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhengran Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chunjing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: RD and YL; Methodology: GY and XZ; Formal analysis and investigation: ZX and LZ; Writing—original draft preparation: RD; Writing—review and editing: GY and CL; Funding acquisition: RD.

Corresponding author

Correspondence to Rongxin Deng.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, R., Yang, G., Li, Y. et al. Identification of shelterbelt width from high-resolution remote sensing imagery. Agroforest Syst 96, 1091–1101 (2022). https://doi.org/10.1007/s10457-022-00768-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10457-022-00768-1

Keywords

Search

Navigation