TY - JOUR
T1 - Landscape Impacts on Ecosystem Service Values Using the Image Fusion Approach
AU - Wang, Shuangao
AU - Padmanaban, Rajchandar
AU - Shamsudeen, Mohamed
AU - Campos, Felipe S.
AU - Cabral, Pedro
N1 - info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCTA-AMB%2F28438%2F2017/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
Wang, S., Padmanaban, R., Shamsudeen, M., Campos, F. S., & Cabral, P. (2022). Landscape Impacts on Ecosystem Service Values Using the Image Fusion Approach. Land, 11(8), 1-18. [1186]. https://doi.org/10.3390/land11081186 --- FUNDING: This study was supported by the Research on Capitalization of Natural Resources and Corresponding Market Construction in China (grant number 15ZDB162); and partially through the FCT (Fundação para a Ciência e a Tecnologia) under the projects PTDC/CTA-AMB/28438/2017—ASEBIO and UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC). This research was also funded by the Forest Research Centre, a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal (UIDB/00239/2020).
PY - 2022/8/1
Y1 - 2022/8/1
N2 - 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.
AB - 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.
KW - urban sprawl
KW - landscape patterns
KW - urban ecosystems
KW - remote sensing
KW - image fusion
UR - http://www.scopus.com/inward/record.url?scp=85137559993&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000846584000001
U2 - 10.3390/land11081186
DO - 10.3390/land11081186
M3 - Article
SN - 2073-445X
VL - 11
SP - 1
EP - 18
JO - Land
JF - Land
IS - 8
M1 - 1186
ER -