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.
ABSTRACT
Soil total nitrogen (TN) plays a major role in agriculture, geochemical cycles and terrestrial ecosystem functions. Knowledge regarding the TN distribution is crucial for the sustainable use of soil resources. This paper therefore aims to characterize the spatiotemporal distribution of soil TN and improve the current understanding of how various factors influence changes in TN. Natural characteristics and remote sensing (RS) variables were used in conjunction with the random forest (RF) model to map the TN distribution in a low hilly region of southeastern China in 1979, 2004 and 2014. The means and changes of TN in different geographic regions and farmland protection regions were also analyzed. The results showed that: (1) the TN showed an increasing trend in the early periods and exhibited a decreasing trend from 2004 to 2014; (2) the geographic and RS variables played more important roles in predicting TN distribution than did the other variables; and (3) changes in the fertilization and crop planting structure caused by soil testing and formulated fertilization techniques (STFFT-Soil Testing and Formulated Fertilization Techniques) as well as farmland protection policies influenced the spatiotemporal variability of TN. Evidently, more attention should be focused on improving the quality and soil fertility in the surrounding low mountainous areas.
