In this paper, the contribution of visual and acoustic preference to multi-sensory landscape evaluation was quantitatively compared. The real landscapes were treated as dual-sensory ambiance and separated into visual landscape and soundscape. Both were evaluated by 63 respondents in laboratory conditions. The analysis of the relationship between respondent's visual and acoustic preference as well as their respective contribution to landscape preference showed that (1) some common attributes are universally identified in assessing visual, aural and audio-visual preference, such as naturalness or degree of human disturbance; (2) with acoustic and visual preferences as variables, a multi-variate linear regression model can satisfactorily predict landscape preference (R2 = 0.740), while the coefficients of determination for a unitary linear regression model were 0.345 and 0.720 for visual and acoustic preference as predicting factors, respectively; (3) acoustic preference played a much more important role in landscape evaluation than visual preference in this study (the former is about 4.5 times of the latter), which strongly suggests a rethinking of the role of soundscape in environment perception research and landscape planning practice.

Higher urbanization rates cause new urban environmental problems and changing trends. New technologies also provide novel techniques for environmental management. This paper establishes a framework for an Environmental Internet of Things (EIoT) and describes key technologies, including Wireless Sensor Network (WSN), network techniques, Geographic Information System (GIS), WebGIS, and distributed database techniques. The framework of the EIoT from bottom to top includes environmental sensors, real-time network monitoring system, environmental databases, environmental information platform, and environmental management system. We discuss the construction of our EIoT and show how it provides real-time monitoring at the residential level of environmental factors such as water, soil, atmosphere, noise, and wind. The EIoT can also realize online environmental simulation and management and is currently being extended to the city, regional, and national levels. The EIoT can improve understanding of the urban environment and help to provide advanced technological solutions for increasingly serious environmental problems.

Ecological and environmental protection of the Xianghe Segment of China’s Grand Canal (XSCGC) has gained increasing attention. The Xianghe Segment is located in a highly urbanized area, and it is imperative to promote the environmental quality of this region. Application of the Environmental Internet of Things (EIoT) can greatly improve the performance and effectiveness of ecological and environmental management. In this study, we propose and describe an ecological and environmental management system based on EIoT to monitor canal construction. This system includes a digital management platform and an environmental early warning platform, which monitor and manage water resources, soil–plant biodiversity, waste production, and energy use in real time. Based on a regional analysis of the construction plan and environmental and anthropogenic factors, the key monitoring areas were determined and the spatial distribution of the EIoT sensing layer was arranged. The construction of EIoT should be carried out in three stages to effectively monitor the ecology and environment of the study region. The application of landsenses ecology to the EIoT system will help improve ecological planning.

The study of landscape fragmentation offers promising approaches for better understanding how the interaction between natural conditions and socioeconomic factors generates environmental changes. In this paper, the state of landscape fragmentation due to urban-transportation networks in Fujian Province, China, was calculated on different spatial scales, using effective mesh size as an index for fragmentation degree. The relationship between fragmentation degree and regional socioeconomic development was explored to detect how socioeconomic processes, such as urbanization or industrialization, affect landscape change. The results demonstrated that even more than population increase, economic growth has a more significant impact on landscape. Growth from all three sectors (agriculture, industry, and service) can significantly accelerate the process of landscape fragmentation, and those from industry and service are the recent actual drivers leading to a fragmented landscape in China. The results als...

This research aims to identify the key indicators of land-use change that affect ecosystem service in the coastal city of Xiamen. The methods of transfer matrix and land-use dynamic degree are used to analyze land-use change, and the spatial distribution of ecosystem service values (ESV) is mapped from 1989 to 2018 using cluster analysis. During this 30-year period, the built-up land expanded rapidly through occupation of farmland and landfilling of the watershed. The biggest contribution to the reduction of ESV in this stage is the loss of farmland followed by the loss of watershed. By 2018, the spatial distribution of ESV had become very unbalanced and polarized. The high-value areas are mainly distributed in the northern mountainous areas, with the low-value areas concentrated in the flat areas near the coastline, and only a few medium-value areas of ESV remained. Generally, from 1989 to 2018, the ESV in Xiamen decreased by about CNY 200 million in total, with the largest proportion of ESV reduction (CNY 120 million) occurring in the 2000–2010 period. Considering ESV categories, the significant reduction of Regulating Service (53.5–57.8%) was mainly due to the loss of water areas (CNY −70 million) to low ESV areas (built-up land) in urbanization, followed by the loss of farmland (CNY −50 million). This means that Xiamen should strengthen the protection of ecological lands in future urban planning to alleviate and reverse the current ecological imbalance.

This study focuses on the coastal city of Xiamen, examining the factors and driving mechanisms influencing land use changes and spatial patterns. Spatial logistic regression and Statistical Package for the Social Sciences (SPSS) software were employed using grid data with a resolution of 100m to analyze the spatial relationships between six driving factors (such as elevation and slope) and five land use types within the study area. Regression models were established for each factor, and all Relative Operating Characteristic (ROC) tests were passed. Based on the results of the logistic regression analysis, land use changes and spatial distribution were simulated using the updated Conversion of Land Use and its Effects (CLUE) model so as to validate the driving mechanisms. The findings indicate that the six driving factors effectively explain the spatial patterns of land use in the study area. The distance to the coastline is the primary influencing factor in the evolution of spatial patterns, particularly impacting built-up land and farmland, while for forest land, slope is the main factor affecting the spatial distribution. The simulation and accuracy analysis revealed an overall simulation accuracy ranging from 73% to 90.1%, demonstrating that the selected driving factors have effective explanatory power for the spatial distribution of land use. Thus, this study’s results provide valuable insights into the complexity of land use changes and serve as a reference for relevant departments in land use management and planning.