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Alpine landscapes are projected to be degraded under climate change, which would threaten their benefits to society. Previous studies, however, have been limited to aesthetic change, and it remains unclear how much the aesthetic change would affect human welfare. To address this issue and gain insights into climate change adaptation policies, we conducted a choice experiment survey using digitally manipulated images based on climate change scenarios and natural scientific knowledge in a mountainous national park in Japan. We uncovered that park visitors appreciate the alpine landscapes that include snow patches on mountains and some types of alpine flowers by analyzing the data from 445 respondents. Conversely, both the invasion of alpine vegetation by dwarf bamboo and the disappearance of snow patches due to climate change substantially deteriorated the perceived aesthetic benefits from alpine landscapes. The economic loss caused by climate-induced landscape degradation was estimated at more than 100 USD per visitor, at maximum; the disappearance of snow patches and invasion by dwarf bamboo reduced the benefits by approximately 13 USD and 101 USD, respectively. Our findings suggest that sustaining the aesthetic value of alpine landscapes in national parks via climate change adaptation has potentially significant economic benefits. By supposing that the mountain national park attracts 70,000 visitors in summer, climate change would cause as a minimum of eight million USD economic loss at the park without appropriate measures annually. Our findings highlight the importance of climate change measures by considering climate change impacts on social benefits associated with alpine landscapes.

Wildlife damage to agriculture is serious in Japan; therefore, it is important to understand changes in wildlife population sizes. Although several studies have been conducted to detect wildlife from drone images, behavioral changes (such as wildlife escaping when a drone approaches) have been confirmed. To date, the use of visible and near-infrared images has been limited to the daytime because many large mammals, such as sika deer (Cervus nippon), are crepuscular. However, it is difficult to detect wildlife in the thermal images of urban areas that are not open and contain various heat spots. To address this issue, a method was developed in a previous study to detect moving wildlife using pairs of time-difference thermal images. However, the user’s accuracy was low. In the current study, two methods are proposed for extracting moving wildlife using pairs of airborne thermal images and deep learning models. The first method was to judge grid areas with wildlife using a deep learning classification model. The second method detected each wildlife species using a deep learning object detection model. The proposed methods were then applied to pairs of airborne thermal images. The classification test accuracies of “with deer” and “without deer” were >85% and >95%, respectively. The average precision of detection, precision, and recall were >85%. This indicates that the proposed methods are practically accurate for monitoring changes in wildlife populations and can reduce the person-hours required to monitor a large number of thermal remote-sensing images. Therefore, efforts should be made to put these materials to practical use.

SecSel, a protected-area prioritization tool, has been developed to help design areas that efficiently protect multiple features, including conservation of biodiversity and use of ecosystem services. The prioritization by SecSel is based on evaluation of the local units of each feature. The evaluation metrics should be quantitative but need not be ratio scale. The minimum requirement of the input data is that they are ordinal. The conservation target is the number of local units with high values of each feature to be protected in the area. SecSel can handle conflicts among features, including conflicts between conservation and utilization of land or specific ecosystem functions. Before the selection procedure, one of a conflicting pair of features in a site is discarded. That decision is based on the dispensability of the local unit to fulfilling the conservation target of each feature. SecSel also considers the cost of including each site in the protected area and the compactness of the area in terms of total boundary length or the distance to the nearest site. To demonstrate the functionality of Secsel, we used it to design land use in an alpine region of northern Japan where conservation of alpine vegetation and its recreational use are important considerations.

Autumn leaf coloring is an essential cultural ecosystem service, but mechanisms of color brightness have scarcely been focused on, even though the autumn color crisis (leaf fall without coloring) has been reported in Japanese alpine shrubs. To approach the mechanisms, we analyzed the relationship between brightness and environmental conditions and projected possible future changes. Autumn color brightness was measured by repeated camera observations at three Japanese alpine sites. Environmental factors were analyzed using linear mixed model comparisons. Results showed earlier green-up timing was related to a duller autumn leaf color, possibly because a prolonged leaf period creates older leaves and reduces physiological vitality in autumn, affecting anthocyanin biosynthesis. Green-up timing based on snowmelt day and degree-day-based prediction of snowmelt day predicted earlier snowmelt and green-up in the future, with decreases in autumn color brightness. The amount of color reduction varied among climate models and scenarios, from a decline of several percent under MRI-CGCM3 RCP 2.6 to around 15% under MIROC5 RCP8.5 by the end of this century. Projected reductions in autumn color brightness could have an economic impact on tourism, and it could also be linked to modifications in material cycles and the ecosystem’s productivity.

Permafrost is known to occur in high mountainous areas such as the Daisetsu Mountains in Japan, which are located at the southernmost limit of the permafrost distribution in the world. In this study, areas with climatic conditions suitable for sustaining permafrost in the Daisetsu Mountains are projected using bias-corrected and downscaled climate model outputs and statistical relationships between surface air temperatures and permafrost areas. Using freezing and thawing indices, the size of the area in the Daisetsu Mountains where climatic conditions were suitable for permafrost were estimated to be approximately 150 km 2 in 2010. Under the RCP8.5 scenario, this area is projected to decrease to about 30 km 2 by 2050 and it is projected to disappear by around 2070. Under the RCP2.6 scenario, the area is projected to decrease to approximately 20 km 2 by 2100. The degradation of mountain permafrost could potentially affect the stability of trekking trails due to slope displacement, and it may also have deleterious effects on current alpine ecosystems. It is therefore important to accurately monitor changes in the mountain ecosystem environment and to implement measures to adapt to an environment that is projected to change significantly in the future.

Accurate evaluation of forest biomass distribution and its long-term change over wide areas is required for effective forest carbon management and prediction of landscape-scale forest dynamics. We evaluated a landscape-scale (225 km 2 ) decadal forest carbon budget at a 1 ha spatial resolution in a cool-temperate forest, by repeating airborne laser observations 10 years apart and partitioning net forest biomass change (FBC) into growth and mortality. Using >10 000 samples, we revealed that naturally regenerated forests have large spatial heterogeneity in net biomass change, and 3/4 of the photosynthetically acquired carbon stock moved to necromass even without anthropogenic disturbances. Actual carbon residence time as living tree biomass was estimated by dividing biomass by growth or mortality rates. The residence time was 107 and 106 years, respectively with large spatial variation among stands (48 and 42 years, respectively, as the difference between 25 and 75 percentile), although studied forest stands have small variation in the forest functional type in a landscape-scale. The best predictors of subsequent decadal biomass changes were two forest structural factors, mean canopy height and canopy height variation in addition to one environmental factor, elevation. Considering the long lifetime of trees, these structural factors may be an indicator of forest soundness rather than a cause of forest growth or mortality. However, in any cases, these structural factors can be powerful predictors of subsequent FBC.

We evaluated the use of the Green-Red Vegetation Index (GRVI) as a phenological indicator based on multiyear stand-level observations of spectral reflectance and phenology at several representative ecosystems in Japan. The results showed the relationships between GRVI values and the seasonal change of vegetation and ground surface with high temporal resolution. We found that GRVI has the following advantages as a phenological indicator: (1) “GRVI = 0” can be a site-independent single threshold fordetection of the early phase of leaf green-up and the middle phase of autumn coloring, and (2) GRVI can show a distinct response to subtle disturbance and the difference of ecosystem types.

Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems. This study aimed to develop a novel method to draw georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Using time‐lapse imagery and a recurrent neural network, we reliably classified the vegetation in our study area in the Japanese Alps. We also showed that our novel approach to georectification outperformed conventional methods.

Information about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it is difficult to apply such methods to large areas. To address this problem, we have developed several support systems for the automated detection of wild animals in remote sensing images. In this study, we applied one of the developed algorithms, the computer-aided detection of moving wild animals (DWA) algorithm, to thermal remote sensing images. We also performed several analyses to confirm that the DWA algorithm is useful for thermal images and to clarify the optimal conditions for obtaining thermal images (during predawn hours and on overcast days). We developed a method based on the algorithm to extract moving wild animals from thermal remote sensing images. Then, accuracy was evaluated by applying the method to airborne thermal images in a wide area. We found that the producer’s accuracy of the method was approximately 77.3% and the user’s accuracy of the method was approximately 29.3%. This means that the proposed method can reduce the person-hours required to survey moving wild animals from large numbers of thermal remote sensing images. Furthermore, we confirmed the extracted sika deer candidates in a pair of images and found 24 moving objects that were not identified by visual inspection by an expert. Therefore, the proposed method can also reduce oversight when identifying moving wild animals. The detection accuracy is expected to increase by setting suitable observation conditions for surveying moving wild animals. Accordingly, we also discuss the required observation conditions. The discussions about the required observation conditions would be extremely useful for people monitoring animal population changes using thermal remote sensing images.

Although camera trapping has been effectively used for wildlife monitoring, its application to multihabitat insects ( i.e. , insects requiring terrestrial and aquatic ecosystems) is limited. Among such insects, perching dragonflies of the genus Sympetrum (darter dragonflies) are agroenvironmental indicators that substantially contribute to agricultural biodiversity. To examine whether custom-developed camera traps for perching dragonflies can be used to assess the relative population density of darter dragonflies, camera trapping, a line-transect survey of mature adult dragonflies, and a line-transect survey of exuviae were conducted for three years in rice paddy fields in Japan. The detection frequency of camera traps in autumn was significantly correlated with the density index of mature adults recorded during the transect surveys in the same season for both Sympetrum infuscatum and other darter species. In analyses of camera-detection frequency in autumn and exuviae in early summer, a significant correlation was observed between the camera-detection frequency of mature adults and the exuviae-density index in the following year for S. infuscatum ; however, a similar correlation was not observed for other darter species. These results suggest that terrestrial camera trapping has the potential to be effective for monitoring the relative density of multihabitat users such as S. infuscatum , which shows frequent perching behavior and relatively short-distance dispersal.