| Literature DB >> 31539983 |
Shan He1, Yue Su1, Amir Reza Shahtahmassebi1, Lingyan Huang1, Mengmeng Zhou1, Muye Gan1, Jinsong Deng1, Gen Zhao2, Ke Wang3.
Abstract
Despite research on monitoring and mapping cultural ecosystem services (CESs) increasing exponentially in recent years, our knowledge of the CESs of farmlands is still inadequate, particularly in megacities. Analyzing the CESs of farmlands is a daunting challenge partly due to the lack of appropriate frameworks, and partly because of paucity of information on farmland. In this paper, the maximum entropy (Maxent) model along with agricultural big data were combined to measure and map CESs supply with respect to aesthetics and recreation, within farmlands in the Hangzhou metropolitan area of China between 2010 and 2016. We also quantified the characteristics of CESs based on demand and flow to enhance the understanding of the CESs of farmlands. The results indicated that the Maxent model was robust in mapping CESs supply. Moreover, the farmlands with high aesthetic supply were closely related to natural attributes and mainly distributed in rural areas. In contrast, the farmlands with high recreational supply were highly dependent on human factors which led to the rapid growth of such farmlands near the city center compared to aesthetic farmlands. Farmlands located along water bodies provided high integrated supply, while high demand was characteristic of strongly urbanized areas, and flow patterns of farmlands were greatly affected by popular scenic spots. The growth of CESs supply, demand and flow within farmlands might be closely related to the increase of agritourism, improved infrastructures, increasing income and leisure time of beneficiaries. This study highlighted the importance of the CESs of farmlands in the Hangzhou metropolitan area, although more research is needed to demonstrate the utility of the CESs of farmlands in urban environments.Entities:
Keywords: Aesthetics; Agritourism; Big data; Maxent; Recreation
Year: 2019 PMID: 31539983 DOI: 10.1016/j.scitotenv.2019.07.160
Source DB: PubMed Journal: Sci Total Environ ISSN: 0048-9697 Impact factor: 7.963