期刊
LAND DEGRADATION & DEVELOPMENT
卷 32, 期 13, 页码 3731-3742出版社
WILEY
DOI: 10.1002/ldr.3837
关键词
Dyna‐ CLUE; ecosystem services; ecosystem services values; Kathmandu Valley; land‐ use– land‐ cover change
This study assessed the impacts of historical and projected land-use and land-cover changes on ecosystem services values in the Kathmandu Valley. Results showed a decline in ecosystem services values due to urbanization, with a significant loss dominated by the decline in agricultural land and deforestation.
Ecosystems provide vital services to humans, which are regulated by the underlying ecological processes. Because of rapid population growth and urbanization, land-use and land-cover (LULC) changes ultimately impact the services an ecosystem can offer. The LULC of the Kathmandu Valley, Nepal have been altered due to urbanization, which is further expected to change in the future. However, a quantitative assessment of LULC impact on ecosystem services has not been undertaken. The purpose of this study is to assess the impacts affecting the ecosystem services values (ESV) in the Kathmandu Valley due to historical and projected LULC changes. We used satellite-based datasets to assess LULC changes in the past, and Dyna-CLUE to model future LULC changes. The benefit transfer approach was used to estimate ESV and their changes across time. Results confirmed the increase in urban area of 3.4% per year. This led to declining ESV from 1990 (US$ 231 million) to 2010 (US$ 205 million), which is predicted to decline further (US$ 157 million in 2050); on average US$ 12.3-13.2 million every decade u 1990 2050. This loss in ESV was dominated by the decline in agricultural land (40%, US$ 46.9 million), followed by deforestation (11%, US$ 11.1 million ). Development (like urbanization) often overlooks the values from ecosystem services (ES), but ES quantification serves as a tool to make appropriate decisions during those changes. As shown in this study, ESV can be effectively quantified via application of a parsimonious approach by utilizing globally available datasets. This is especially more important for the data scarce regions of developing countries that are undergoing rapid changes.
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