Although the goal of ecotourism is to balance tourism with environmental conservation, there are concerns regarding the excessive promotion of tourism. One measure to prevent excessive use is to certify tourism operators. However, certification is inconsistent with regional autonomy, and because it is a voluntary initiative, there may be limitations in its scope. Accordingly, we conducted a case study of a certified eco-tour guide system for Amami-Oshima Island in Japan to determine whether a certification system for eco-tour guides would promote the appropriate use of the environment. This study included interviews with stakeholders and a questionnaire survey administered to certified guides. To obtain certification, guides must complete a training course and possess guide experience. In addition to securing work, the guides also obtained certifications to improve their quality. Guides work at a higher frequency than in other areas. However, concerns arose because of the lack of penalties for self-regulation agreed upon by the Council and the presence of unqualified guides. We conclude that penalties and legal systems should be introduced for malicious cases. In addition, discussions of use regulations should involve both guides and residents in the negotiation process, and a third-party organization should be engaged to provide accreditation, ensuring that the impact of regulations is properly assessed.

Threshold accepting is a s-metaheuristic, local search process that moves from one solution to another within the feasible region of the solution space via a random change to one or more elements of the solution. Threshold accepting can be further characterized as an aspirational combinatorial optimization process that does not guarantee optimality. The quality of outcomes from a threshold accepting search process varies when applied to forest harvest scheduling problems depending on the parameter value assumptions and sub-processes employed. Two relatively small but realistic case study forests are subjected to four management scenarios and outcomes are examined to illustrate how the quality of solutions may differ when the parameter values and processes employed within threshold accepting are adjusted. Statistically significant improvements in solution quality were generally evident with a slowing of the rate of change in the threshold value and the enhancement of the search process by using 2-opt moves and search reversion. While it was rarely observed, the threshold accepting heuristic search process located the optimal solution of most of the problems modeled. In cases where the problems involved maximizing an economic objective, about 47% of the heuristic search solutions had an objective function value that was within 1% of the optimal solutions.

This study developed a way to automate the topographic correction process, specifically the C and SCS+C methods, using the Google Earth Engine (GEE) as a platform. We developed a script that enables users to specify the target path and row, period of acquisition, and the Normalized Difference Vegetation Index (NDVI) threshold required for correction. Then the desired topographic correction is realized based on these inputs. The data to be corrected were obtained from Landsat 8. Users were required to prepare a limited number of parameters to execute code on the GEE correctly; these included the Landsat path, row, image-acquisition period, country name of the largescale international boundary polygons, the NDVI threshold used for the regression analysis, and the center latitude and longitude of the display screen. The NDVI and slope angle obtained from a digital elevation model were used to define the area to be sampled and to obtain the parameters required for the correction model automatically. It was immediately apparent that shadows were removed from the corrected image compared to the uncorrected image. After comparing the correlation coefficients before and after correction for each band, the corrected values were substantially lower than the uncorrected values, indicating that the correction was made appropriately. The code proposed here requires little extra effort to implement, even when both the C and SCS+C correction methods are applied.

Forest monitoring is of great importance for effective forest conservation and management. Changes in forest cover around the Lungga River, Guadalcanal, Solomon Islands, were identified by visual interpretation using the web-mapping service. Four satellite images from Google Earth Pro 7.3 taken in 2014 (April 10), 2016 (September 30), 2018 (July 29), and 2023 (April 22) were used to detect deforestation areas around the river, within a 50 m buffer zone. Deforestation was defined as the conversion of forest land to other land uses. As a result, for the first time, we've been able to show detailed changes in forest cover over 10 years in Guadalcanal, the Solomon Islands, although the area covered is limited. During the period, up to 10% of the forest in the surveyed area had been removed. Although land cover has been gradually recovering with shrubs and grasses within a few years after deforestation, it is a slow process, and the forest will not be able to return to its original pristine state. Increasing population, food security, and economic development are considered the main causes of deforestation on the Solomon Island. Continuous forest monitoring will be necessary in the future for forest conservation and management. Remote sensing and GIS will make a major contribution to this. It would be prudent to prioritize the development of human resources having these skills.