Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Different electroencephalogram (EEG) signals were generated through different inner experiences and feelings felt by people watching video stimuli of the different landscape types. The electroencephalogram (EEG) features were extracted to obtain the mean amplitude spectrum (MAS), power spectrum density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU) in the five frequency bands of delta, theta, alpha, beta, and gamma. According to electroencephalogram (EEG) features, four classifiers including the back propagation (BP) neural network, k-nearest neighbor classification (KNN), random forest (RF), and support vector machine (SVM) were used to classify the landscape types. The results showed that the support vector machine (SVM) classifier and the random forest (RF) classifier had the highest accuracy of landscape recognition, which reached 98.24% and 96.72%, respectively. Among the six classification features selected, the classification accuracy of MAS, PSD, and DE with frequency domain features were higher than those of the spatial domain features of DASM, RASM and DCAU. In different wave bands, the average classification accuracy of all subjects was 98.24% in the gamma band, 94.62% in the beta band, and 97.29% in the total band. This study identifies and classifies landscape perception based on multi-channel EEG signals, which provides a new idea and method for the quantification of human perception.

Perception of the Values of the Biocultural Landscape Types of Slovakia by the Population

The perception of the landscape by society is becoming an integral part of many studies in terms of the quality of the living environment, sport and recreation and building and developing social relationships. To evaluate the perception and appreciation of individual landscape types by society, we used an online questionnaire as a form of sociological survey. We used the statistical method of non-metric multidimensional scaling NMDS in R package to determine the variability of responses in relation to respondents. The relationship between demographic factors and landscape perception and landscape type preferences was evaluated. The results of multidimensional scaling show a strong relationship between young men and a preference for recreation over agro-tourism. The middle generation with university education looks more frequently for cultural monuments. University-educated middle-aged men perceive the natural landscape as degraded and endangered, and middle-aged men with secondary education understand the need for the protection of traditional agricultural landscapes. It is important to integrate people’s preferences and needs into the landscape planning and decision-making processes, so that they can contribute to the creation of development plans and other strategic documents.

A Study of Forest Swamp Mapping in Hani Wetland Integrating Sentinel-1 and Sentinel-2 Satellite Images

As a unique wetland type, forest swamps play an important role in regional carbon cycling and biodiversity conservation. Taking Hani wetland in Jilin province as the research object, we integrated the application of Sentinel-1 radar and Sentinel-2 multispectral images, fully exploited the potential of Sentinel-1 multi-polarization band features and Sentinel-2 red edge index for forest swamp remote sensing identification, and applied the random forest method to realize the extraction of forest swamp distribution information of Hani wetland. The results show that when the optimal number of decision trees for forest swamp information extraction is 1200, the fusion of Sentinel-1VV and VH backscattering coefficient radar band features and Sentinel-2 red-edge band features can significantly improve the extraction accuracy of forest swamp distribution information, and the overall accuracy and Kappa coefficient of forest swamp information extraction in protected areas are as high as 89% and 0.85, respectively. The overall accuracy and Kappa coefficient of forest swamp information extraction in the protected area were 89% and 0.85, respectively. The landscape types of Hani Wetlands of International Importance are diversified, with natural wetlands, artificial wetlands and non-wetland landscape types co-existing. Among the natural wetland types, the forest swamp has the largest area of 27.1 km2, accounting for 11.2% of the total area of the reserve; the river has the smallest area of 0.7 km2, accounting for 0.3% of the total area of the reserve. The forest swamp extraction method provides data support for the sustainable management of Hani wetlands and case guidance for forest swamp mapping in other regions.

Mosquito Communities Vary across Landscape and Vertical Strata in Indian River County, Florida

Mosquito and arbovirus surveillance is essential to the protection of public health. A majority of surveys are undertaken at ground level. However, mosquitoes shelter, breed, and quest for hosts across vertical strata, thus limiting our ability to fully describe mosquito and arboviral communities. To elucidate patterns of mosquito vertical stratification, canopy traps were constructed to sample mosquitoes at heights of 1.5, 5.0, and 8.7 m across three different landscape types in a Florida coastal conservation area. We assessed trapping efforts using individual-based rarefaction and extrapolation. The effects of height, landscape, site location, and sampling date on mosquito community composition were parsed out using permutational ANOVA on a Hellinger-transformed Bray–Curtis dissimilarity abundance matrix. Lastly, a generalized linear mixed effects model (GLMM) was used to explore species-specific vertical patterns. We observed differences in sampling effort and community composition structure across various heights and landscapes. Our GLMM revealed significant effects of trap height for Aedes taeniorhynchus, Anopheles crucians, Anopheles quadrimaculatus, and Culex coronator, but not for Culex nigripalpus, the ultra-dominant species present in this area. Together these data provide evidence that height and landscape significantly affect mosquito community structures and highlight a need to develop sampling regimes to target specific vector and nuisance species at their preferred height and across different landscape types.

Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar

Accurate and up-to-date forest monitoring plays a significant role in the country’s society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their specific classification systems, methodologies, and remote sensing data sources, which makes end-users difficult to select an appropriate dataset in different regions. This study aims to explore the accuracy, consistency, and discrepancies of eight widely-used forest datasets in Myanmar, including Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020. Firstly, accuracy assessment is conducted by using 934 forest and non-forest samples with four different years. Then, spatial consistency of these eight datasets is compared in area and spatial distribution. Finally, the factors influencing the spatial consistency are analyzed from the aspects of terrain and climate. The results indicate that in Myanmar the forest area derived from GlobeLand30 has the best accuracy, followed by FROM-GLC10 and FROM-GLC2017. The eight datasets differ in spatial detail, with the mountains of northern Myanmar having the highest consistency and the seaward areas of southwestern Myanmar having the highest inconsistency, such as Rakhine and the Ayeyarwady. In addition, it is found that the spatial consistency of the eight datasets is closely related to the terrain and climate. The highest consistency among the eight datasets is found in the range of 1000–3500 m above sea level and 26°–35° slope. In the subtropical highland climate (Cwb) zone, the percentage of complete consistency among the eight datasets is as high as 60.62%, which is the highest consistency among the six climatic zones in Myanmar. Therefore, forest mapping in Myanmar should devote more effort to low topography, seaward areas such as border states like Rakhine, Irrawaddy, Yangon, and Mon. This is because these areas have complex and diverse landscape types and are prone to confusion between forest types (e.g., grassland, shrub, and cropland). The approach can also be applied to other countries, which will help scholars to select the most suitable forest datasets in different regions for analysis, thus providing recommendations for relevant forest policies and planning in different countries.

Electroencephalogram Application for the Analysis of Stress Relief in the Seasonal Landscape

This study proposes an integrated approach to assess the psychological and physiological responses of people in natural seasonal landscapes. The questionnaire of restoration outcomes scale (ROS), willingness to visit (WTV), cultural ecosystem services (CES) cognitive classification, and the neuroscientific technique based on electroencephalogram (EEG) measurements were applied. The effects of different landscapes on human perception were studied by comparing the EEG data of different landscape types and different seasons. The coupling relationship between EEG data and stress recovery was also examined. The results showed the following: First, there was a significant difference between the winter landscape and the summer natural landscape. Second, only the winter landscape showed significant gender differences. Third, the values of ROS and WTV in the summer landscape were greater than those in the winter landscape. Fourth, the number of CES in the summer landscape was significantly higher than that in the winter landscape, and the number of CES in water was higher than that in the forest and grassland. Thus, brain wave data and quantified values from questionnaires including ROS, WTV, and CES showed significant seasonality. Therefore, an EEG can be used as a new, more objective tool and method for landscape evaluation and planning in the future.

Landscape Preferences of Visitors to the Danube Floodplains National Park, Vienna

Successfully managing heavily visited protected riverscapes requires information about visitor preferences for the social, biophysical and infrastructural attributes of river landscapes. This study analyzed the landscape preferences of 520 on-site visitors to the peri-urban Danube Floodplains National Park using an image-based discrete choice experiment. The study explored the effects of various landscape types (water bodies, terrestrial landscapes), recreational infrastructures (trail types, facilities) and trail use conditions (trail user numbers, activities) on respondents’ preferences. The results indicated that natural features, such as floodplain forests in combination with meadows or xeric alluvial biotopes, were preferred, while dense forests and, particularly, open agrarian structures were less preferred. Water bodies with 50% reed cover, few people on the trail, alleys of trees and gravel trails were favored. The outcomes serve as the basis for design recommendations for planned recreational areas surrounding the national park with the aim of absorbing visitors and reducing use pressure on the protected area.