Recreation and tourism are important ways that people interact with and derive benefits from natural environments. Understanding how and where nature provides recreational opportunities and benefits is necessary for management decisions that impact the environment. This study develops and tests an approach for mapping tourism patterns, and assessing people’s preferences for cultural and natural landscapes, using user-generated geographic content. The volume of geotagged images and tweets shared publicly on Flickr and Twitter and proprietary mobile phone traffic provided by a telecommunications company, are used to map visitation rates to potential tourist destinations across Jeju Island, South Korea. We find that densities of social media posts and mobile phone traffic are all correlated with ticket sales and counts of gate entries at tourist sites. Using multivariate linear regression, we measure the degree to which attributes of the natural and built environment explain variation in visitation rates, and find that tourists to Jeju Island prefer to recreate near beaches, sea cliffs, golf courses and hiking trails. We conclude that high-resolution and spatially-explicit visitation data provided by user-generated content open the door for statistical models that can quantify recreation demand. Managers and practitioners could combine these flexible and relatively inexpensive user-generated data with more traditional survey data to inform sustainable tourism development plans and policy decisions. These methods are especially useful in the context of landscape or regional-scale ecosystem service assessments, where there is a need to map the multiple ecological, economic, and cultural benefits of the environment.

Biodiversity loss is progressing despite biodiversity being essential for human survival, prosperity, and well-being. Conservation, restoration, and sustainable use of the habitat, given that its change is the most prominent factor causing the deterioration of biodiversity, represents a highly effective way of securing biodiversity. Therefore, we assessed and monitored habitat quality as a proxy for biodiversity with habitat quantity in Jeju Island, South Korea. We used an InVEST model with data on the habitat type, suitability, sensitivity, accessibility, and threat factors. Natural habitats throughout Jeju had rapidly decreased in area by 24.9% from 1989 to 2019, and this change contributed to the degradation of habitat quality by 15.8%. We provided significant evidence on the critical degradation of habitat for a long period of over 30 years and highlighted the urgent need for policies and behaviors that enhance biodiversity. We proposed appropriate strategies to prompt people to conserve better, restore effectively, and use biodiversity sustainably. We expect that our findings will provide scientific and evidence-based guidance for policy-making on biodiversity enhancement and will further support achievement of the Sustainable Development Goals and Aichi Biodiversity Targets, in addition to compliance with the New Deal for Nature and People.

Oil spills cause socioeconomic and ecological damage to the marine environment and local communities. Implementing policies to effectively cope with such incidents is a challenging task due to the negative public perceptions about governmental responses. Using social big data, this study analyzed such negative perceptions in South Korea and the factors influencing them. The findings indicate that the public pays relatively little attention to oil spills but expresses serious concerns about the economic and ecological damage and the health and safety of volunteers and local residents. To improve public perception of oil spills, response strategies should aim to (1) analyze it using social big data to reduce the gap between governmental and public spheres, (2) release timely and accurate information to resolve civil distrust and dissatisfaction, (3) minimize direct damage to local communities and ecosystems affected by oil spills, and (4) reduce the impact on volunteers’ and local residents’ health and safety. Furthermore, through a multidisciplinary approach that combines social big data analysis methods with marine scientific research, it can contribute to creating a disaster response policy tailored to policy consumers.

There is an increasing need for spatial planning to manage coastal tourism, and applying social media data has emerged as an effective strategy to support coastal tourism spatial planning. Researchers and decision-makers require spatially explicit information that effectively reveals the current visitation state of the region. The purpose of this study is to identify coastal tourism hotspots considering appropriate spatial units in the regional scale using social media data to examine the advantages and limitations of applying spatial hotspots to spatial planning. Data from Flickr and Twitter with 30″ spatial resolution were obtained from four South Korean regions. Coastal tourism hotspots were then derived using Getis-Ord Gi. Comparing the derived hotspot maps with the visitation rate distribution maps, the derived hotspot maps sufficiently identified the spatial influences of visitors and tourist attractions applicable for spatial planning. As the spatial autocorrelation of social media data differs based on the spatial unit, coastal tourism hotspots according to spatial unit are inevitably different. Spatial hotspots derived from the appropriate spatial unit using social media data are useful for coastal tourism spatial planning.

In countries and regions where development projects are frequently implemented, there is a significant change in the value of carbon sequestration services according to land use and land cover (LULC) changes. In this study, we analyzed the changes in the carbon sequestration services which occurred due to the LULC changes over a 20 years period (1989–2009) in Korea where local development projects have been active, since 1990s. As a result, the total carbon stocks decreased by about 0.07 billion t C. Significant changes in the carbon stocks mostly occurred in areas where development projects were frequently implemented. The loss of economic value due to the changes in carbon stocks over 20 years was 4.7 trillion won (4159 million USD) when market price of carbon is applied. Therefore, in countries and regions where there is an active development, it is necessary to monitor the land-use changes with high carbon stocks, to reconsider the value of the carbon when making policy decisions which cause LULC changes, and to internalize social costs into the market prices. Especially at a local level, it is necessary to promote management policy based on carbon sequestration services in accordance with local conditions such as size and types of the changes in carbon stocks.

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked with spatial-scale data on tourist numbers estimated from social media data. To classify the activities, which included recreation, appreciation, education, and other activities, an image-supervised classification model was trained using 12,229 images, and the test accuracy was found to be 0.7244. On the Flickr platform, 43% of the image data located in the coastal land of South Korea are other activities, 39% are appreciation activities, and 18% are recreation and education activities. Other activities are mainly located in urban areas with a high population density and are spatially concentrated, while appreciation activities are mainly located in the natural environment and tend to be spatially spread out. Data on tourist activity categorization through content classification, combined with traditional tourist volume estimates, can help us understand previously overlooked information and context about a space.