To improve human well-being, there is increasing awareness of elevating aesthetic benefits by landscape design, planning, and management. However, which landscape features and attributes may be associated with aesthetic value of an urban landscape, human aesthetic preference, and landscape practices is still not clear yet. We proposed a comprehensive aesthetic assessment approach to realise the determination of landscape aesthetic indicators, integration of objective indicators and subjective preferences, and validation of estimations. The approach was based on a four-level landscape aesthetic indicator system from the bottom features up to attributes (landscape naturalness, landscape complexity, plant species diversity, water surface, water clarity, and bank naturalness), component qualities, and finally overall quality. Fourteen metrics that could provide objective visual and spatial characters and ecological implications were identified and quantified to indicate landscape aesthetic features. Landscape aesthetic attributes, vegetation and waterbody component qualities, and overall quality were estimated by integrating objective indicators and human subjective preferences. The approach was applied to a case study of four subareas along an artificially restored riparian buffer in Beijing, China. The results showed that the modelled overall aesthetic quality was determined by both vegetation (accounting for 53%) and waterbody. The higher vegetation quality depended on the higher plant abundance, more vegetation patches, and more vegetation patch types; the higher waterbody quality depended on the clearer water and larger water surface. Compared with other features, vertical vegetation configuration, diversity of patch type and patch shape, and shrub species diversity had greater contribution to the attributes of naturalness, complexity, and plant species diversity, respectively. The modelled vegetation aesthetic attributes were directly validated using the surveyed perceptions, and the modelled vegetation and waterbody aesthetic qualities were indirectly validated by correlating with the main recreational activities. The approach is confirmed to be able to address the questions on determination, integration, and validation of landscape aesthetic indicators in some way. Thus, the approach is expected to be used for other landscapes to offer a framework for landscape practices to improve aesthetic value and cultural service.

Grasshopper plagues have seriously disturbed grassland ecosystems in Inner Mongolia, China. The accurate prediction of grasshopper infestations and control of grasshopper plagues have become urgent needs. We sampled 234, 342, 335, and 369 plots in Xianghuangqi County of Xilingol League in 2010, 2011, 2012, and 2013, respectively, and measured the density of the most dominant grasshopper species, Oedaleus decorus asiaticus, and the latitude, longitude, and associated relatively stable habitat factors at each plot. We used Excel–GeogDetector software to explore the effects of individual habitat factors and the two-factor interactions on grasshopper density. We estimated the membership of each grasshopper density rank and determined the weights of each habitat category. These results were used to construct a model system evaluating grasshopper habitat suitability. The results showed that our evaluation system was reliable and the fuzzy evaluation scores of grasshopper habitat suitability were good indicators of potential occurrence of grasshoppers. The effects of the two-factor interactions on grasshopper density were greater than the effects of any individual factors. O. d. asiaticus was most likely to be found at elevations of 1300–1400 m, flat terrain or slopes of 4–6°, typical chestnut soil with 70–80% sand content in the top 5 cm of soil, and medium-coverage grassland. The species preferred temperate bunchgrass steppe dominated by Stipa krylovii and Cleistogenes squarrosa. These findings may be used to improve models to predict grasshopper occurrence and to develop management guidelines to control grasshopper plagues by changing habitats.

Extremely hot environments can trigger serious health problems. To evaluate the effects of microclimate on thermal comfort, we proposed and validated a modified thermal humidity index (MTHI) that combined air temperature and relative humidity with land surface temperature (LST). MTHI was more sensitive to microclimate changes than the general thermal humidity index that includes only T and RH, and thus the thermal comfort could be better indicated. In an urban riparian buffer study, we estimated the temporal dynamics and spatial distribution of MTHI values for 47 vegetation patches and explored how structural characteristics of patches affect the thermal comfort. The results showed that planting could significantly reduce LST and MTHI. Vegetation patches with complex vertical structures had considerably higher thermal comfort than those with simple structures. Decreasing nearest distance to river or increasing plant abundance could reduce the thermal discomfort. There were significant differences in the structure characteristics between the patches with MTHI 70, implying the critical thresholds of variations in thermal comfort with patch structure. Given that people always feel uncomfortable during the daytime in July, optimizing the patch structure is essential to improve the microclimate regulation services of an urban landscape.

Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation analysis, this paper reviews the characteristics of different types of urban flood models and summarizes state-of-the-art technologies for flood model development. Artificial intelligence (AI) technology provides an innovative approach to the construction of data-driven models; nevertheless, developing an AI model coupled with flooding processes represents a worthwhile challenge. Big data (such as remote sensing, crowdsourcing geographic, and Internet of Things data), as well as spatial data management and analysis methods, provide critical data and data processing support for model construction, evaluation, and application. The further development of these models and technologies is expected to improve the accuracy and efficiency of urban flood simulations and provide support for the construction of a multi-scale distributed smart flood simulation system.