Skip to main content
SpringerLink
Log in

Mapping plant communities within quasi‐circular vegetation patches using tasseled cap brightness, greenness, and topsoil grain size index derived from GF-1 imagery

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Quantifying the structure and composition of quasi-circular vegetation patches (QVPs) is key in identifying ecosystem function, which will help create a cost-effective nature-based solution for restoring the degraded wetland ecosystem in the Yellow River Delta (YRD), China. However, research on mapping plant communities of QVPs using remotely sensed data has not been conducted. In this study, we found that the pan-sharpened GF-1 imagery acquired in May was suitable for mapping plant communities of QVPs. Guided by field survey data and finer spatial resolution remotely sensed data, we constructed a simple decision tree classifier using the tasseled cap brightness (TCB), greenness (TCG), and topsoil grain size index (TGSI) of the pan-sharpened GF-1 image acquired in May. The classification results showed that the combination of the TCB and TCG components could efficiently distinguish the vegetation from non-vegetation, and the use of the TGSI was able to capture the variations in plant communities within QVPs in the YRD, China. However, the influence of the acquisition season and mixed pixels of GF-1 imagery (especially small canopy T. chinensis in small QVPs) on classification accuracy still needs further investigation.

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

Similar content being viewed by others

References

  • Aguiar MR, Sala OE (1999) Patch structure, dynamics and implication for the functioning of arid ecosystem. Tree 14:273–277

    Google Scholar 

  • Armas C, Pugnaire FI, Sala OE (2008) Patch structure dynamics and mechanisms of cyclical succession in a Patagonian steppe (Argentina). J Arid Environ 72:1552–1561

    Google Scholar 

  • Bates JD, Miller RF, Svejcar TJ (2000) Understory dynamics in cut and uncut western juniper woodlands. J Range Manag 53:119–126

    Google Scholar 

  • Bordeu I, Clerc MG, Couteron P, Lefever R, Tlidi M (2016) Self-replication of localized vegetation patches in scarce environments. Sci Rep 6:33703

    Google Scholar 

  • Busso CA, Bonvissuto GL (2009) Structure of vegetation patches in northwestern Patagonia, Argentina. Biodivers Conserv 18:3017–3041

    Google Scholar 

  • Cohen-Shacham E, Walters G, Janzen C, Maginnis S (2016) Nature-based Solutions to address global societal challenges. Gland, Switzerland: IUCN. xiii + 97pp. https://doi.org/10.2305/IUCN.CH.2016.13.en

  • Coletti JZ, Vogwill R, Hipsey MR (2017) Water management can reinforce plant competition in salt-affected semi-arid wetlands. J Hydrol 552:121–140

    Google Scholar 

  • Cresda GF-1, Slate. Available online: http://www.cresda.com/EN/satellite/7155.shtml. Accessed 10 Aug 2020

  • Czerwinski CJ, King DJ, Mitchell SW (2014) Mapping forest growth and decline in a temperate mixed forest using temporal trend analysis of Landsat imagery, 1987–2010. Remote Sens Environ 141:188–200

    Google Scholar 

  • Everitt JH, Deloach CJ (1990) Remote sensing of Chinese Tamarisk (Tamarx chinensis) and associated vegetation. Weed Sci 38:273–278

    Google Scholar 

  • Groeneveld DP, Watson RP (2008) Near-infrared discrimination of leafless saltcedar in wintertime Landsat TM. Int J Remote Sens 29:3577–3588

    Google Scholar 

  • Jana A, Maiti S, Biswas A (2016) Seasonal change monitoring and mapping of coastal vegetation types along Midnapur-Balasore Coast, Bay of Bengal using multi-temporal Landsat data. Model Earth Syst Environ 2:7

    Google Scholar 

  • Ji W, Wang L (2016) Phenology-guided satlcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. Remote Sens Environ 173:29–38

    Google Scholar 

  • Kakembo V (2009) Vegetation patchiness and implications for landscape function: the case of Pteronia incana invader species in Ngqushwa Rural Municipality, Eastern Cape, South Africa. Catena 77:180–186

    Google Scholar 

  • Karlson M, Ostwald M, Reese H, Bazie HR, Tankoana B (2016) Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species. Int J Appl Earth Obs 50:80–88

    Google Scholar 

  • Kefi S, Rietkerk M, Alados CL, Pueyo Y, Papanastasis VP, Elaich A, De Ruiter PC (2007) Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449:213–217

    Google Scholar 

  • Kefi S, Guttal V, Brock WA, Carpenter SR, Ellison MA, Livina VN, Seekell DA, Scheffer M, Van Nes EH, Dakos V (2014) Early warning signals of ecological transitions: methods for spatial patterns. PLoS ONE 9:e92097

    Google Scholar 

  • Kim J, Song C, Lee S, Jo HW, Park E, Yu H, Cha S, An J, Song Y, Khamzina A, Lee WK (2020) Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images. Land Degrad Dev 1–14. https://doi.org/10.1002/ldr.3642

  • Lamchin M, Lee JY, Lee WK, Lee EJ, Kim M, Lim CH, Choi HA, Kim SR (2016) Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia. Adv Space Res 57:64–77

    Google Scholar 

  • Lees BG, Ritman L (1991) Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments. Environ Manage 15:823–831

    Google Scholar 

  • Liu Q (2019a) Using the CBERS-04 multispectral data tasseled cap transformation to detect the quasi-circular vegetation patches. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), Yokohama, Japan, July 28-August 2, 2019; IEEE: New York, NY, pp 3708–3711

  • Liu Q (2019b) Using the tasseled cap transformation of the fused GF-1 multispectral image to detect the quasi-circular vegetation patches. In: Proceedings of 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Huaqiao, Suzhou, China, October 19–21, 2019; IEEE: New York

  • Liu Q (2020a) Detection of quasi-circular vegetation patches using GF-2 image with tasseled cap and watershed transformations. IOP Conf Ser: Mater Sci Eng 768:062053

    Google Scholar 

  • Liu Q (2020b) Using GF-2 images to detect tamarix chinensis community within a vegetation patch. J Phys: Conf Ser 1575:012213

    Google Scholar 

  • Liu Q, Liu G, Huang C, Xie C (2014) Using SPOT 5 fusion-ready imagery to detect Chinese tamarisk (saltcedar) with mathematical morphological method. Int J Digit Earth 7:217–228

    Google Scholar 

  • Liu Q, Huang C, Liu G, Yu B (2018a) Comparison of CBERS-04, GF-1, and GF-2 satellite panchromatic images for mapping quasi-circular vegetation patches in the Yellow River Delta, China. Sensors18:2733

  • Liu Q, Liu G, Huang C (2018b) Monitoring desertification processes in Mongolian Plateau using MODIS tasseled cap transformation and TGSI time series. J Arid Land 10:12–26

    Google Scholar 

  • Liu Q. Liu G, Huang C, Li H (2019a) Soil physicochemical properties associated with quasi-circular vegetation patches in the Yellow River Delta, China. Geoderma 337:202–214

    Google Scholar 

  • Liu Q, Song H, Liu G, Huang C, Li H (2019) Evaluating the potential of multi-seasonal CBERS-04 imagery for mapping the quasi-circular vegetation patches in the yellow river delta using random forest. Remote Sens 11:1216

    Google Scholar 

  • Liu Q, Liu G, Huang C, Li H (2019) Remote sensing monitoring of surface characteristics in the Badain Jaran, Tengger, and Ulan Buh Deserts of China. Chin Geogra Sci 29:151–165

    Google Scholar 

  • Liu Q, Liu G, Huang C, Li H (2020) Variation in soil bulk density and hydraulic conductivity within a quasi-circular vegetation patch and bare soil area. J Soils Sediments 20:2019–2030

    Google Scholar 

  • Lozano FJ, Suarez-Seoane S, De Luis E (2007) Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling. Remote Sens Environ 107:533–544

    Google Scholar 

  • Lussem U, Schellberg J. Bareth G (2020) Monitoring forage mass with low-cost UAV data: case study at the Rengen grassland experiment. PFG J Photogramm Remote Sens Geoinf Sci. https://doi.org/10.1007/s41064-020-00117-w

    Article  Google Scholar 

  • Madonsela S, Cho MZ, Mathieu R, Mutanga O, Ramoelo A, Kaszta Z, Kerchove RVD, Wolff E (2017) Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species. Int J Appl Earth Obs 58:65–73

    Google Scholar 

  • Meron E, Yizhaq H, Gilad E (2007) Localized structures in dryland vegetation: forms and functions. CHAOS 17:037109

    Google Scholar 

  • Michishita R, Xu B, Gong P (2008) A decision tree classifier for the monitoring of wetland vegetation using ASTER data in the Poyang Lake region, China. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVII (Part B8):315–321

  • Potsdam AF, Greifswald PS, Bozen SZ, Italien TT, Greifswald KS (2011) Monitoring of the vegetation composition in rewetted peatland with iterative decision tree classification of satellite imagery. PFG J Photogramm Remote Sens Geoinf Sci 3:109–122

    Google Scholar 

  • Powell SL, Cohen WB, Healey SP, Kennedy RE, Moisen GG, Pierce KB, Ohmann JL (2010) Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sens Environ 114:1053–1068

    Google Scholar 

  • Reed DN, Anderson TM, Dempewolf J, Metzger k, Serneels S (2009) The spatial distribution of vegetation types in the Serengeti ecosytems: the influence of rainfall and topographic relief on vegetation patch characteristics. J Biogeogr 36:770–782

    Google Scholar 

  • Rietkerk M, Dekker SC, De Ruiter PC, Van de Koppei J (2004) Self-organized patchiness and catastrophic shifts in ecosystems. Science 305:1926–1929

    Google Scholar 

  • Shi L, Liu Q, Huang C, Li H, Liu G (2020) Comparing pixel-based random forest and the object-based support vector machine approaches to map the quasi-circular vegetation patches using individual seasonal fused GF-1 imagery. IEEE Access 8:228955–228966

    Google Scholar 

  • Silvestri S, Defina A, Marani M (2005) Tidal regime, salinity and salt-marsh plant zonation. Estuar Coast Shelf Sci 62:119–130

    Google Scholar 

  • Soriano A, Sala OE, Perelman SB (1994) Patch structure and dynamics in a Patagonian arid steppe. Vegetatio 111:127–135

    Google Scholar 

  • Townsend PA, Walsh SJ (2001) Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southern USA. Plant Ecol 157:129–149

    Google Scholar 

  • Tsai YH, Stow D, Chen HL, Lewison R, An L, Shi L (2018) Mapping vegetation and land use types in Fanjingshan National Nature Reserve using Google Earth engine. Remote Sens 10:927

    Google Scholar 

  • Wang C, Menenti M, Stoll MP, Belluco E, Marani M (2007) Mapping mixed vegetation communities in salt marshes using airborne spectral data. Remote Sens Environ 107:559–570

    Google Scholar 

  • Wilson MD, Ustin SL, Rocke DM (2004) Classification of contamination in salt marsh plants using hyperspectral reflectance. IEEE T Geosci Remote 42:1088–1095

    Google Scholar 

  • Xiao J, Shen Y, Tateishi R, Bayaer W (2006) Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. Int J Remote Sens 27:2411–2422

    Google Scholar 

  • Xu D, Kang X, Qiu D, Zhuang D, Pan J (2009) Quantitative assessment of desertification using Landsat data on a regional scale-A case study in the Ordos Plateau, China. Sensors 9:1738–1753

    Google Scholar 

  • Zhang Y, Liu Q, Liu G, Tang S (2015) Mapping of circular or elliptical vegetation community patches: a comparative use of SPOT-5, ALOS and ZY-3 imagery. In: Proceedings of the 8th International Congress on Image and Signal Processing, Shenyang, China, 14–16 October 2015; IEEE: New York, pp 666–671

  • Zhang X, Zhang F, Qi Y, Deng L, Wang X, Yang S (2019) New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV). Int J Appl Earth Obs 78:215–226

    Google Scholar 

Download references

Acknowledgements

This research was jointly financially supported by the National Natural Science Foundation of China (Project Nos. 41671422), the National Key Research and Development Program of China (Project No. 2016YFC1402701), the Strategic Priority Research Program of Chinese Academy of Sciences (Project No. XDA20030302), the National Natural Science Foundation of China (Project Nos. 4151144012, 41661144030), the Innovation Project of LREIS (Project Nos. 088RA20CYA, 08R8A010YA), and the National Mountain Flood Disaster Investigation Project (Project No. SHZH-IWHR-57). Thanks to China Center of Resources Satellite Data and Application for providing the GF-1 data products.

Author information

Authors and Affiliations

  1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing, 100101, China

    Qingsheng Liu, Chong Huang & He Li

  2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China

    Qingsheng Liu

Authors
  1. Qingsheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. He Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingsheng Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by: H. Babaie.

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Q., Huang, C. & Li, H. Mapping plant communities within quasi‐circular vegetation patches using tasseled cap brightness, greenness, and topsoil grain size index derived from GF-1 imagery. Earth Sci Inform 14, 975–984 (2021). https://doi.org/10.1007/s12145-021-00608-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-021-00608-3

Keywords

Search

Navigation