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Soil Salinity Mapping of Urban Greenery Using Remote Sensing and Proximal Sensing Techniques; The Case of Veale Gardens within the Adelaide Parklands

Author

Listed:
  • Hamideh Nouri

    (Department of Water Engineering and Management, University of Twente, 7500 AE Enschede, The Netherlands)

  • Sattar Chavoshi Borujeni

    (Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Centre, AREEO, Isfahan 8174835117, Iran)

  • Sina Alaghmand

    (Department of Civil Engineering, Monash University, 23 College Walk, Clayton, VIC 3800, Australia)

  • Sharolyn J. Anderson

    (School of Natural and Built Environments, University of South Australia, Adelaide, SA 5095, Australia)

  • Paul C. Sutton

    (School of Natural and Built Environments, University of South Australia, Adelaide, SA 5095, Australia
    Department of Geography and the Environment, University of Denver, Denver, CO 80208, USA)

  • Somayeh Parvazian

    (National Centre for Vocational Education Research (NCVER), Adelaide, SA 5000, Australia)

  • Simon Beecham

    (Information Technology, Engineering and the Environment, University of South Australia, Mawson Lakes, SA 5095, Australia)

Abstract

More well-maintained green spaces leading toward sustainable, smart green cities mean that alternative water resources (e.g., wastewater) are needed to fulfill the water demand of urban greenery. These alternative resources may introduce some environmental hazards, such as salt leaching through wastewater irrigation. Despite the necessity of salinity monitoring and management in urban green spaces, most attention has been on agricultural fields. This study was defined to investigate the capability and feasibility of monitoring and predicting soil salinity using proximal sensing and remote sensing approaches. The innovation of the study lies in the fact that it is one of the first research studies to investigate soil salinity in heterogeneous urban vegetation with two approaches: proximal sensing salinity mapping using Electromagnetic-induction Meter (EM38) surveys and remote sensing using the high-resolution multispectral image of WorldView3. The possible spectral band combinations that form spectral indices were calculated using remote sensing techniques. The results from the EM38 survey were validated by testing soil samples in the laboratory. These findings were compared to remote sensing-based soil salinity indicators to examine their competence on mapping and predicting spatial variation of soil salinity in urban greenery. Several regression models were fitted; the mixed effect modeling was selected as the most appropriate to analyze data, as it takes into account the systematic observation-specific unobserved heterogeneity. Our results showed that Soil Adjusted Vegetation Index (SAVI) was the only salinity index that could be considered for predicting soil salinity in urban greenery using high-resolution images, yet further investigation is recommended.

Suggested Citation

  • Hamideh Nouri & Sattar Chavoshi Borujeni & Sina Alaghmand & Sharolyn J. Anderson & Paul C. Sutton & Somayeh Parvazian & Simon Beecham, 2018. "Soil Salinity Mapping of Urban Greenery Using Remote Sensing and Proximal Sensing Techniques; The Case of Veale Gardens within the Adelaide Parklands," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2826-:d:162864
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    References listed on IDEAS

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    1. Khan, Nasir M. & Rastoskuev, Victor V. & Sato, Y. & Shiozawa, S., 2005. "Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 96-109, August.
    2. Yao, Rongjiang & Yang, Jingsong, 2010. "Quantitative evaluation of soil salinity and its spatial distribution using electromagnetic induction method," Agricultural Water Management, Elsevier, vol. 97(12), pages 1961-1970, November.
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    Cited by:

    1. Zhanqiang Zhu & Wei Lang & Xiaofang Tao & Jiali Feng & Kai Liu, 2019. "Exploring the Quality of Urban Green Spaces Based on Urban Neighborhood Green Index—A Case Study of Guangzhou City," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    2. Wu Xiao & Wenqi Chen & Tingting He & Linlin Ruan & Jiwang Guo, 2020. "Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    3. Qianqian Liu & Gulimire Hanati & Sulitan Danierhan & Guangming Liu & Yin Zhang & Zhiping Zhang, 2020. "Identifying Seasonal Accumulation of Soil Salinity with Three-Dimensional Mapping—A Case Study in Cold and Semiarid Irrigated Fields," Sustainability, MDPI, vol. 12(16), pages 1-14, August.
    4. Hesham M. Aboelsoud & Mohamed A. E. AbdelRahman & Ahmed M. S. Kheir & Mona S. M. Eid & Khalil A. Ammar & Tamer H. Khalifa & Antonio Scopa, 2022. "Quantitative Estimation of Saline-Soil Amelioration Using Remote-Sensing Indices in Arid Land for Better Management," Land, MDPI, vol. 11(7), pages 1-19, July.
    5. Tharani Gopalakrishnan & Lalit Kumar, 2021. "Linking Long-Term Changes in Soil Salinity to Paddy Land Abandonment in Jaffna Peninsula, Sri Lanka," Agriculture, MDPI, vol. 11(3), pages 1-12, March.
    6. Romeu Gerardo & Isabel P. de Lima, 2022. "Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal," Agriculture, MDPI, vol. 12(9), pages 1-20, September.
    7. Tharani Gopalakrishnan & Lalit Kumar, 2020. "Modeling and Mapping of Soil Salinity and its Impact on Paddy Lands in Jaffna Peninsula, Sri Lanka," Sustainability, MDPI, vol. 12(20), pages 1-15, October.

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