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Landscape Impacts on Ecosystem Service Values Using the Image Fusion Approach

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  • Shuangao Wang

    (School of Economic Management, Beijing City University, No. 269, North 4th Ring Middle Road, Haidian District, Beijing 100083, China
    Beijing Academy of Science and Technology, No. 27, Beike Building, Haidian District, Beijing 100089, China
    NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal)

  • Rajchandar Padmanaban

    (Centre of Geographic Studies, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal)

  • Mohamed Shamsudeen

    (NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal
    Institute for Geoinformatics (IFGI), University of Münster, Heisenbergstraße 2, 48149 Münster, Germany)

  • Felipe S. Campos

    (NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal)

  • Pedro Cabral

    (NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal)

Abstract

The landscape is a complex mosaic of physical and biological patches with infrastructures, cultivable lands, protected ecosystems, water bodies, and many other landforms. Varying land-use changes are vulnerable to the world and need the mitigation and management of landforms to achieve sustainable development, which without proper oversight, may lead to habitat destruction, degradation, and fragmentation. In this study, we quantify the land-use and land-cover (LULC) changes using downscaled satellite imagery and assess their effects on ecosystem services (ES) and economic values in Ningxia Province, China. Various landscape metrics are derived to study the pattern and spatial configuration over 15 years (2005–2020), in which the landscapes are evolving. The impact of LULC change in various ES is analyzed using ecosystem service values (ESV) and validated with a sensitivity index. Finally, the level of urban sprawl (US) due to overpopulation is established using Renyi’s entropy. Using Landsat 8′s Operational Land Imager (OLI) datasets, we downscaled the MODIS data of 2005, 2010, 2015, and 2020 to prepare the LULC map through a rotation forest algorithm. Results demonstrate that water bodies, woodlands, and built-up landscapes increased in their spatial distribution over time and that there was a decrease in farmlands. Results further suggest that the connectivity and uniformity of the landscape pattern improved in the later period due to several plans formulated by the government with a slight improvement in landscape diversity. Overall ESV get improved, while LULC classes such as farmland and water bodies have decreased and increased ESV, respectively, and a sensitivity analysis is used to test the reliability of ESV on LULC classes. The level of US is 0.91 in terms of Renyi’s entropy, which reveals the presence of a dispersion of settlements in urban fringes. The simulated US for 2025 shows urbanization is more severe over a prolonged time and finally the impacts of the US in ESV are analyzed. Using an interdisciplinary approach, several recommendations are formulated to maintain the ESV despite rapid LULC changes and to achieve sustainable development globally.

Suggested Citation

  • Shuangao Wang & Rajchandar Padmanaban & Mohamed Shamsudeen & Felipe S. Campos & Pedro Cabral, 2022. "Landscape Impacts on Ecosystem Service Values Using the Image Fusion Approach," Land, MDPI, vol. 11(8), pages 1-18, July.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:8:p:1186-:d:874944
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    References listed on IDEAS

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    1. Andrea Cristina Ramirez Herrera & Sonja Bauer & Victor Peña Guillen, 2022. "Water-Sensitive Urban Plan for Lima Metropolitan Area (Peru) Based on Changes in the Urban Landscape from 1990 to 2021," Land, MDPI, vol. 11(12), pages 1-20, December.

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