Real Forest Research Articles

Regenerative Food Forest: A Case Study of Vanya Organic Farm

Food Forests involve planned agro-forestry. A food forest like a real forest needs no pesticides, herbicides, weeding, crop rotation, mowing or digging. It will ensure the sustainability of flora and fauna, birds, animals and most importantly bees. This is a case study of one such food forest, Vanya Organic which is a dream turned into reality by one of its owners, Mr. Patanjali Jha. He grew native trees, creepers and shrubs in combination with symbiotic vegetation to ensure maximum carbon sequestration, provide forest cover and usable by-products A very important part of his food forest is the inclusion of Vetiver Grass. Mr. Jha suggests that Food Forests and Vetiver Plantations should be looked at together and set up together and called a Regenerative Food Forest. Compressed Bio Gas (CBG) or Renewable Natural Gas (RNG), a Green Fuel, is derived from agriculture waste or other forms of organic waste called biomass which is produced in abundance by the regenerative food forests. A CBG plant produces Sulphur stripped Methane of automotive grade and also produces a very high-quality organic fertilizer as a by-product which has been approved for regular use by the Ministry of Agriculture. Therefore, there is a need to link these CBG Plants with regenerative food forests. It will provide jobs, increase farm incomes, and over a period of time, this will induce positive sustainable changes in air, water and soil quality. The case study has been prepared with his consent and approval.

Simulation of silvicultural treatments based on real 3D forest data from mobile laser scanning point clouds

Forest management has a direct influence on the structure and stability of forests. In this study, we used the 3D data from mobile laser scanning in real forest stands dominated by European beech (Fagus sylvatica L.) to simulate different silvicultural treatments and assess their impact on the structural complexity and short-term economic return. For the structural assessment, we used the box-dimension (Db), a holistic measure of structural complexity in forest. The expected net revenues of the silvicultural treatments were used as a proxy for short-term economic gain. We simulated six different treatments in 19 different real-world forest stands. The results showed that each treatment had a negative impact on the structural complexity of the stands but with varying severity. The treatments with the smallest effect on stand structural complexity showed the highest net revenue, indicating no trade-offs if a forest owner strives for small stand structural changes and high economic return. The approach used here allows quantifying the structural and economic consequences of different treatments in forest stands prior to the actual application in the real world. This holds large potential for decision making according to the forest owner's objective.

A review of forest visualization and forest fire simulation technology research

A review of forest visualization and forest fire simulation technology research

A Design and Development of the Smart Forest Alert Monitoring System Using IoT

Forest is one of the main sources of living organisms. Its needs start from the human breath to usage of the wood. But due to many reasons, area occupied by the forest is reducing every year. The reasons behind these environmental impacts are natural disasters (forest fires), deforestation activities, and unlawful actions. Forest fire could be creating most serious threat to wild animals and resources of human welfares. The primary phenomenon of the wild fire occurrence is circumstance hotness of the forest. The dry and hot atmosphere caused the fire in the forest. The deforestation and smuggling activities are also tridents to the available forest. The main consideration of this article is to detect wildfires in advance and protect forest resources from social crimes through advanced sensor integration in the IoT (Internet of Things) environment. A smart forest alert monitoring system has been proposed in this article to avoid forest mishap over by automated self‐decision‐making protective actions such as parameter measures and alert and implementing the harm mitigation actions related to the hot temperature, humidity, smoke, and smuggling of trees. All the sensors work as per the algorithm designed by the specific application of IoT (Internet of Things). The accurate predictions of the forest fire events and ensuring the forest safety have been tested and verified by a conducted case study on the real forest zone environment.

Forest digital twin as a relaxation environment: A pilot study

Forest environments have been proven beneficial for physiological well-being, supporting relaxation and meditative processes. Unfortunately, some groups, predominantly those with reduced mobility, are prevented from forest visitation. Presenting such environments in virtual reality could provide a viable substitute. However, as forest structure and composition are important aspects of its restorative power, to accurately compare the efficacy of virtual forests to that of real natural spaces, the virtual environment should match the real location as closely as possible. Furthermore, if participants achieve similar benefits in both settings, virtual copies (digital twins) of forests could be a viable option for studying forest bathing in a controlled environment. We collected LiDAR scans of a forest location near Prague, took spatial audio recordings of the forest ambiance, and built the forest’s digital twin in Unreal Engine. To compare the therapeutic efficacy of the virtual forest with its real counterpart, groups of volunteers spent half an hour in either the real forest, the virtual forest, or both. We collected participants’ demographic and psychometric data, assessing their relaxation, emotional state, and cybersickness before and after the session. Our data show an increase in relaxation with no significant differences between the environments, although participants’ emotional states did not improve in either condition. We found that participants’ experiences were comparable between the environments, but cybersickness limited the potential efficacy of virtual forest bathing. The limitations of the virtual forests as a platform for research into forest bathing are discussed.

Semi-supervised wildfire smoke detection based on smoke-aware consistency.

The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smoke-aware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.

No matter how much space and light are available, radial growth distribution in Fagus sylvatica L. trees is under strong biomechanical control

Key messageThis study presents the first attempt to quantify how the thigmomorphogenetic syndrome is involved in Fagus sylvatica L. tree growth responses to thinning. An experimental design preventing mechanosensing in half of the trees demonstrated that radial growth distribution in roots and along the tree stem is under strong biomechanical control.ContextStudies on the mechanosensitive control of growth under real forest conditions are rare and those existing to date all deal with conifer species. In the current context of global changes, it is important to disentangle how different biotic and abiotic factors affect tree growth.AimsWhereas growth changes after thinning are usually interpreted as responses to decreased competition for resources, this study investigates the importance of how mechanosensing controls growth distribution inside the tree.MethodsIn an even-aged beech stand, 40 pole-sized trees (size class at first thinning) were selected, half of the plot was thinned and, within each sub-plot (thinned and unthinned), half of the tree were guy-wired in order to remove mechanical stimulations to the lower part of the stem. Four years later, all trees were felled and volume increment, ring width distribution along the tree height, and the largest ring width of the structural roots were measured. The effect of mechanical stimulation in the two treatments (thinned and unthinned) was assessed.ResultsRemoval of mechanical stimulation decreased the volume increment in the lower part of the stem as well as radial root growth but did not affect axial growth. When mechanical strain was removed, the ring width distribution along the stem height changed drastically to an ice-cream cone-like distribution, indicating a strong mechanosensitive control of tree shape.ConclusionIn a forest stand, the growth allocation inside the tree is under strong mechanical control. Mechanical stimulations explain more than 50% of the increment stimulated by thinning, whatever the growth indicator. A further challenge is to better understand how cambial cells perceive strains during growth in order to integrate mechanosensing into process-based tree-growth modeling.

A new framework for prognosing forest resources under intensified disturbance impacts: Case of the Czech Republic

Forest management decisions increasingly rely on modelling tools, which help identify future risks, optimize management decisions, and provide a suite of indicators beyond timber production. Here we developed and tested a novel simulation and upscaling framework (SUF) and used it for prognosing forest resources of the Czech Republic (Central Europe), which is currently one of Europe's hotspots of disturbance intensification.The SUF is based on an empirical forest model that simulates the development of 8240 forest stands representing forest conditions in 206 administrative districts of the Czech Republic. The effect of natural disturbances is considered via empirical species- and age-specific mortality probability (MP) functions parameterized based on the national forest damage reports and remote sensing data. An upscaling procedure was developed to obtain district- and country-wide estimates. We tested this framework for its ability to (i) reproduce the initial forest conditions from the year 2003, (ii) reproduce forest dynamics in 2003–2016 (i.e., before the recent disturbance wave), (iii) reproduce the recent mortality pulse in 2017–2020, and (iv) generate plausible and consistent outputs under several disturbance and management settings in 2003–2050.The SUF reliably reproduced forest dynamics in both testing periods. The country-wide growing stock (GS) simulated for 2004–2050 oscillated around the initial value of 661 mill. m3 if the reference MP and management were considered. Using the elevated MP (corresponding with the recent disturbance period) increased the mean annual mortality rate from 0.78 % to 1.19 % and caused GS to decrease by 21 % in 2050. The wave of elevated mortality lasted 16 years, ceasing in 2033 due to the depletion of vulnerable stands. Reducing the rotation length by 40 % increased the harvests temporarily and caused GS to decrease by 29 and 33 % in 2050 under reference and elevated MP, respectively. At the same time, mortality was reduced by up to 18 % due to the removal of potentially vulnerable stands.The presented SUF is able to accommodate diverse forestry data, reproduce real forest dynamics, and generate outputs that correspond with the national forestry statistics. Flexible adoption of different mortality and management regimes makes it a versatile tool for supporting management decisions and policies. The presented simulations highlighted the negative prospects of the regional forests and the need for a profound transformation of management practices and the regional forest-based sector.

Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data

Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios.

A Forest Point Cloud Real-Time Reconstruction Method with Single-Line Lidar Based on Visual–IMU Fusion

In order to accurately obtain tree growth information from a forest at low cost, this paper proposes a forest point cloud real-time reconstruction method with a single-line lidar based on visual–IMU fusion. We build a collection device based on a monocular camera, inertial measurement unit (IMU), and single-line lidar. Firstly, pose information is obtained using the nonlinear optimization real-time location method. Then, lidar data are projected to the world coordinates and interpolated to form a dense spatial point cloud. Finally, an incremental iterative point cloud loopback detection algorithm based on visual key frames is utilized to optimize the global point cloud and further improve precision. Experiments are conducted in a real forest. Compared with a reconstruction based on the Kalman filter, the root mean square error of the point cloud map decreases by 4.65%, and the time of each frame is 903 μs; therefore, the proposed method can realize real-time scene reconstruction in large-scale forests.

Mapping snow cover in forests using optical remote sensing, machine learning and time-lapse photography

The accurate spatial information of snow cover is useful for understanding the impact of global warming, and it is of high significance for hydrological disaster prediction, water resources management, and climate change research. The Normalized Difference Snow Index (NDSI) based approach has been used extensively around the world for mapping snow, and they displayed high accuracy in open areas. However, capturing snow cover in forests remains problematic due to the obstruction effects of the forest canopy, which causes the snow cover area to be seriously underestimated. In this paper, we present a new algorithm based on machine learning (ML) technology to improve the accuracy of binary snow cover (BSC) mapping in forests, using the remotely sensed surface reflectance and ground truth data. A time-lapse photography network with a two-hour resolution was established in the eastern Qilian Mountains in northwestern China to obtain the ground truth data both in forests and open areas. We trained Random Forests (RF) with the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data from bands 1–7 to generate BSC results (RF-BSC). Then we evaluated RF-BSC and the NDSI-derived BSC maps with three different NDSI thresholds (i.e., 0.10, 0.29, and 0.40) against ground-truth data. The results indicate that the proposed algorithm has a high performance in forest BSC mapping in this area, compared to the NDSI-threshold approach. The RF-BSC can retrieve 67% of all real forest snow pixels, while the NDSI-based BSC can only detect 8–14%. We also find that the performance of the algorithm seems to be sensitive to changes in solar illumination conditions and forest coverage. This study suggests that machine learning with the fusion of optical remote sensing and ground-based observations is an effective approach for improving the accuracy of forest snow cover mapping at regional scales.

The Restorative Effects of Virtual Reality Forests on Elderly Individuals During the COVID-19 Lockdown

This study focuses on the restorative effects of immersive virtual reality (VR) forest experiences on elderly people during the COVID-19 lockdown. A field experiment with 63 elderly participants was conducted in an elderly care institution in China. The results showed that a five-minute VR forest experience with three minutes of subsequent reliving can bring immediate psychological improvements (i.e., increased positive affect, decreased negative affect, and enhanced stress recovery) to elderly individuals. The negative affect decrease and stress recovery enhancement were more obvious among introverted individuals. Furthermore, participating in three VR forest experiences over 3 consecutive days can bring continuous psychological improvements. Moreover, short VR forest experiences were unable to significantly decrease the blood pressure of participants. The effects of three VR experiences over 3 days on blood pressure improvement were also nonsignificant. Additionally, VR forest experiences can increase elderly participants’ intentions to undertake real forest therapy.

Retrieving the Infected Area of Pine Wilt Disease-Disturbed Pine Forests from Medium-Resolution Satellite Images Using the Stochastic Radiative Transfer Theory

Pine wilt disease (PWD) is a global destructive threat to forests which has been widely spread and has caused severe tree mortality all over the world. It is important to establish an effective method for forest managers to detect the infected area in a large region. Remote sensing is a feasible tool to detect PWD, but the traditional empirical methods lack the ability to explain the signals and can hardly be extended to large scales. The studies using physically-based models either ignore the within-canopy heterogeneity or rely too much on prior knowledge. In this study, we propose an approach to retrieve PWD infected areas from medium-resolution satellite images of two phases based on the simulations of an extended stochastic radiative transfer model for forests infected by pests (SRTP). A small amount of prior knowledge was used, and a change of background soil was considered in this approach. The performance was evaluated in different study sites. The inversion method performs best in the three-dimensional model LESS simulation sample plots (R2 = 0.88, RMSE = 0.059), and the inversion accuracy decreases in the real forest sample plots. For Jiangxi masson pine stand with large coverage and serious damage, R2 = 0.57, RMSE = 0.074; and for Shandong black pine stand with sparse and a small number of single plant damage, R2 = 0.48, RMSE = 0.063. This study indicates that the SRTP model is more feasible for pest damage inversion over different regions compared with empirical methods. The stochastic radiative transfer theory provides a potential approach for future monitoring of terrestrial vegetation parameters.

Numerical Investigation on the Effects of Forest Heterogeneity on Wind-Turbine Wake

This work aims at investigating the effects of forest heterogeneity on a wind-turbine wake under a neutrally stratified condition. Three types of forests, homogeneous (idealized), a real forest having natural heterogeneity, and an idealized forest having a strong heterogeneity, are considered in this study. For each type, three forest densities with Leaf Area Index (LAI) values of 0.42,1.7, and 4.25 are investigated. The data of the homogeneous forest are estimated from a dense forest site located in Ryningsnäs, Sweden, while the real forest data are obtained using an aerial LiDAR scan over a site located in Pihtipudas, about 140 km north of Jyväskylä, Finland. The idealized forest is made up of small forest patches to represent a strong heterogeneous forest. The turbine definition used to model the wake is the NREL 5 MW reference wind turbine, which is modeled in the numerical simulations by the Actuator Line Model (ALM) approach. The numerical simulations are implemented with OpenFOAM based on the Unsteady Reynolds Averaged Navier–Stokes (U-RANS) approach. The results highlight the effects of forest heterogeneity levels with different densities on the wake formation and recovery of a stand-alone wind-turbine wake. It is observed that the homogeneous forests have higher turbulent kinetic energy (TKE) compared to the real forests for an LAI value less than approximately 2, while forests with an LAI value above 2 show a higher TKE in the real forest than in the homogeneous and the strong heterogeneous (patched) forest. Technically, the deficits in the wake region are more pronounced in the strong heterogeneous forests than in other forest cases.

Fumigation of Insect-Infested Wooden Logs by EDN Using Two Scenarios of Plastic Tent-Tarpaulin Sealing: Wooden Logs Stacks Placed on Bottom Plastic Sheets or Directly on Underlying Soil

Recently, the largest bark beetle calamity (Ips typographus) of the last 100 years has erupted in Central Europe, including the Czech Republic. This calamity may threaten the sustainability of growing large areas of monoculture Norway spruce (Picea abies) in this area. Limited economically feasible physical and chemical pest control options are available to prevent the bark beetle spread. However, from these options, only mechanical bark removal or gas-insecticide fumigation result in instant deactivation of all pest stages throughout the entire stack of stored logs. Due to the ban on the use of methyl bromide (MeBr) for the fumigation of log piles under tarpaulin, the adaptation of the phyto-quarantine method EDN to the destruction of I. typographus bark beetles in harvested wood directly in forests was newly proposed in the Czech Republic. The methodical experience with fumigation of the harvested logs, however, was only available for wood fumigation in chambers or in tent-tarps fumigation placed on paved (concrete/asphalt) surfaces mainly in ship harbors. Prior to the introduction of the new EDN fumigation method of bark beetle infested wood stored directly in the forests, it was initially unclear whether it was necessary to use or neglect bottom sheets to optimize exposure and maximize Ct products for the permitted fixed initial dose (50 g·m−3). Therefore, the initial validation pilot trials were aimed at the estimation and comparison of EDN temporal dynamics and Ct products under real forest conditions and two fumigation scenarios: Wooden log pile placed on bottom plastic sheets or directly on soil without the bottom sheets. Field trials were performed under both identical as well as different environmental conditions. In addition, they revealed that the concentration decrease was significantly faster and Ct products were significantly lower in the case of trials without the bottom sheets. The experiments indicated high EDN sorption by the uncovered soil under the tent. Quantitatively, the average Ct product was 4.8 (identical conditions) and 3.7 (different conditions) times lower in EDN trials without the bottom sheets when compared to the use of bottom sheets. The initial field-forest fumigation validation trials indicated a necessity to carry out EDN fumigation under tent-tarps also using bottom sheets, although this fumigation procedure increases the labor-demands to some extent. Based on the presented results and additional data, the Czech authority issued for the EDN plant protection product an authorization for the limited and controlled use of wooden logs. Therefore, this work became the basis of the historically and first officially authorized use of any fumigant for pest control under commercial forest conditions.

Management‐mediated predation rate in the caribou–moose–wolf system: spatial configuration of logging activities matters

AbstractLandscape complexity can determine the population dynamics of interacting predators and prey. Yet, management plans are commonly developed from aspatial predictive models. This oversight may result in unexpected outcomes or the loss of opportunities to make spatial interventions that would increase a plan's effectiveness. The management of the threatened woodland caribou (Rangifer tarandus caribou), boreal population, provides an example of such shortcomings when using an aspatial approach. Currently, the most influential management recommendation is to maintain at least 65% of undisturbed forests in areas occupied by caribou populations, regardless of the spatial configuration of the forest cover. Using a spatially explicit individual‐based model (IBM), we evaluated the effects of the spatial configuration of cuts and roads on the mortality of boreal caribou living in sympatry with wolves (Canis lupus) and moose (Alces alces), an apparent competitor. Starting with a real forest landscape, we created forest management scenarios of the specific spatial distribution of cuts (mosaic, small, or large agglomeration) with increasing disturbance levels. We then ran the IBM with simulated agents, representing individuals of the three species, moving according to movement rules determined from radio‐collared individuals. We found that movement responses to land cover types and roads differed among species. For example, caribou and moose generally avoided areas close to roads, contrary to wolves. Those differences influenced the mortality of caribou agents, which not only depended on the levels of disturbance but also depended on the spatial distribution of cuts and roads. After controlling for disturbance level, wolves were more successful when forest management required an extensive road network resulting in relatively high habitat fragmentation. Caribou agents experienced lower mortality in landscapes with low densities of road and disturbance‐related edges. The effect remained much stronger, however, for the level than the spatial configuration of human disturbances. Still, our IBM demonstrated how landscape management could be used to manipulate species interactions, with the intent of either increasing or decreasing predation rates on specific populations, depending on management goals.

Development of an acoustic method for wood disease assessment

Over the past years, the Lecanosticta acicola fungus or brown band is seriously damaging the pine forests of northern Spain. This fungus happens to dry the leaves and branches of trees. The present article describes the equipment and methodology for assessing the severity and spread of diseases in trees quickly, easily and cheaply. The concept is based on a basic assumption: a sick tree does not sound the same when it is hit as a healthy one does. The breakthrough character of the present work comes from a basic mechanical principle: if a tree loses water its density is reduced, so the sound that it makes when it is hit, also known as its acoustic response, is higher. If the tree is healthy it will grow, so the sound will be the same or deeper. The human ear cannot distinguish these variations or quantify them, but a microphone with an acquisition system can. After initial proofs of concept on real trunks, the function that relates the frequency increase to the degree of dehydration of the tree, and therefore to the severity of the disease, has been identified. Then, the method has been tested in a real forest with sick pines and compared to visual inspection of an expert. After several months of tests, it has been concluded that the trend of the acoustic method coincides with the visual assessment, with the difference that it is objective, while visual inspection is not. It has even demonstrated to be able to foresee the healing of trees before new leaves start to sprout.

Automatic machine learning Framework for Forest fire forecasting

Based on the automatic machine learning framework, combined with the characteristics of forest fire meteorological data and the adaptive requirements of forest fire prediction, this paper optimizes the data preprocessing, parameter learning, loss function and other links of auto-sklearn, builds a forest fire risk prediction framework with regional adaptive characteristics. Based on the forest meteorological fire risk data, a forest fire risk prediction model with regional characteristics and self-learning characteristics is constructed to solve the problems of low compatibility of the existing machine learning methods with binary unbalanced forest fire data, improve the accuracy of forest fire prediction and provide decision-making basis for forestry risk management. The comparative analysis results show that the prediction accuracy of the improved framework in different test sets is improved by 13% on average. Compared with the existing machine learning model for forest fire prediction, the prediction accuracy of the framework proposed in this paper is comprehensively better than the existing methods in terms of real forest fire data.

Comparative Study of the Restorative Effects of Forest and Urban Videos during COVID-19 Lockdown: Intrinsic and Benchmark Values.

The prolonged lockdown imposed to contain the COrona VIrus Disease 19 COVID-19 pandemic prevented many people from direct contact with nature and greenspaces, raising alarms for a possible worsening of mental health. This study investigated the effectiveness of a simple and affordable remedy for improving psychological well-being, based on audio-visual stimuli brought by a short computer video showing forest environments, with an urban video as a control. Randomly selected participants were assigned the forest or urban video, to look at and listen to early in the morning, and questionnaires to fill out. In particular, the State-Trait Anxiety Inventory (STAI) Form Y collected in baseline condition and at the end of the study and the Part II of the Sheehan Patient Rated Anxiety Scale (SPRAS) collected every day immediately before and after watching the video. The virtual exposure to forest environments showed effective to reduce perceived anxiety levels in people forced by lockdown in limited spaces and environmental deprivation. Although significant, the effects were observed only in the short term, highlighting the limitation of the virtual experiences. The reported effects might also represent a benchmark to disentangle the determinants of health effects due to real forest experiences, for example, the inhalation of biogenic volatile organic compounds (BVOC).

Effect of Viewing Real Forest Landscapes on Brain Activity

Benefits related to health promotion by getting closer to forests have received increasing attention in modern stressful society; however, evidence-based research about how our brain activity changes when we stay in a forest environment is limited. Thus, the goal of this study was to evaluate the influence of viewing real forest landscapes on the activity in the prefrontal cortex area of young women’s brains. The experiment included 29 women (age: 21.0 ± 1.4 years) and was executed in five forest and five urban areas. The participants stayed in the forest and urban areas and viewed each view for 15 min. While viewing scenery, they had the oxyhemoglobin (oxy-Hb) concentrations in the left and right areas of their prefrontal cortexes measured continuously. Viewing real forest landscapes was associated with a significantly lower oxy-Hb concentration in the right area of the prefrontal cortex than when seeing urban areas. In conclusion, viewing real forest landscapes substantially diminished oxy-Hb concentrations in the right area of the prefrontal cortex, which is linked to physiological relaxation.