Integer programming has been extensively utilized for solving forest management planning or spatially constrained harvest scheduling problems in the past decades. In addition to determining the timing and location of harvest activities over the forest landscape, there are other environmental requirements that call for the setting aside of forest units for conservation purposes. The creation of contiguous forest stands for the protection of wildlife habitat protection can be one of those requirements. A review of existing literature on environmental management shows that a great deal of attention has been paid to nature reserve design in the selection of corridor connection among fragmented habitats. In this paper, we present a new exact optimization model which uses mixed integer programming framework to seek optimal corridor connection and the selection of suitable forage reserves from fragmented habitats, in a spatially constrained harvest scheduling problem under maximum opening size requirements, over space and time. We rely on the concept of the maximum flow problem to deal with spatial aggregation for forest units as well as corridor connection and forage reserve network. The proposed model does not need a priori enumeration and allows for multiple harvests over time. In addition to corridor connection, our novel approach takes into account forage reserves within an exact solution framework of an area restriction model.

Laos has experienced enormous deforestation and forest degradation since 1975. Despite years of diligent effort by the government of Laos to decrease deforestation, the trend has not reversed. Rather, there have been further increases in certain parts of the country at various degrees. This is evident by the growing concern about forestland encroachment within National Protected Areas (NPA), which have been designated as reserves for biodiversity conservation. To reduce forestland encroachment within NPAs and halt the decline of forest area in Laos, it is important to analyze the factors that contribute to forestland encroachment. This paper analyzes influencing factors of forestland encroachment by the local people within NPA, in the central part of Laos. Our study utilized both primary data from field surveys and secondary data from government official reports, as well as used previous research papers to explore the factors of forestland encroachment. Logistic regression model was then applied to analyze the factors of forestland encroachment in Phou Hin Poun NPA. The results of the study indicate that the forestland encroachment inside this NPA is strongly associated with villagers’ high demand for cassava growing, power balance between villages, and the number of household members. On the other hand, other factors such as educational level, legal familiarity, economic status, and the proximity of the owner’s land to forest did not significantly influence forestland encroachment on the study site. This study provides basic information to the relevant authorities who are responsible for taking effective measures against forestland encroachment inside the NPA, in Laos. The findings and knowledge gained from this study can be used by policy makers to solve the current issue of forestland encroachment as well as forest management in other countries with similar conditions.

As a consequence of climate change, forest fires are increasingly threatening Europe’s forest ecosystems, including regions where such events have historically been rare, such as Central and Northern Europe. Remote sensing technologies now offer innovative methods for analyzing and collecting data on fire activity, providing valuable insights for both researchers and practitioners. This study analyzes novel data from the Sentinel5-P TROPOMI satellite to monitor chemical emission during the largest fire in modern Czech, which occurred in the Czech Switzerland National Park. We evaluated the responses of individual data products using the changepoint package in R and a space-time cube with change-point analysis in ArcGIS Pro. The carbon monoxide (CO) data product provided significant differences in the emission plume during the fire compared to the unaffected areas. Our analysis identified an affected area of approximately 27,000 hectares, and the infestation lasted 6 days from the fire outbreak. Satellite observations indicated a 37.6% increase in CO emissions during the first three days of the fire, and an 18.5% increase in CO during the fourth to sixth days of the fire compared to background data. This mapping approach offers a valuable tool for post-disturbance assessment and strategic planning.

The environmental contribution of South Korea’s domestic timber supply chains is gaining increasing attention amid efforts toward sustainable forest management and a low-carbon economy. Although undisturbed forests store more carbon in the short term, the long-term climate mitigation potential of timber utilization, especially through product substitution and carbon storage, can surpass that of preservation of forests. This study assessed how different timber utilization scenarios and by-product strategies affect environmental benefits, focusing on carbon storage and substitution effects. Using sawing simulation models and empirical stand data for larch (Larix kaempferi), the study compared environmental outcomes across scenarios involving the production of temporary construction lumber, general lumber, and structural lumber. The analysis revealed that producing high-value products such as structural lumber resulted in environmental benefits approximately 3 to 7 times greater than those of temporary construction lumber. This enhancement was primarily driven by significantly higher substitution effect of structural-grade products. Furthermore, utilizing by-products for bioplastic production enhanced environmental benefits by up to 17 to 20 times compared to conventional applications. These findings underscore the importance of integrating both primary timber and byproduct utilization strategies to maximize the environmental benefits of domestic timber resources. The results will contribute to designing sustainable timber utilization strategies that support South Korea’s transition to a renewable and low-carbon bioeconomy.

Due to its diverse diet and rooting behavior, the non-native wild boar (Sus scrofa) poses significant conservation challenges worldwide. On Tokashiki Island, Okinawa, Japan, introduced wild boars cause various environmental damages, including predation on rare species and increased red soil runoff. In recent years, concerns have grown over their predation on sea turtle eggs, a key tourism resource. To mitigate this threat, effective and efficient capture methods are essential. Camera trap monitoring provides crucial insights into wild boar predation behavior, helping improve control efforts. However, manually identifying and analyzing wild boars in the vast number of images and videos - most of which are false trigger events where no target wildlife species are captured - is highly labor-intensive and time-consuming. Recently, deep learning-based object detection techniques have gained attention as promising tools for wildlife monitoring. This study evaluates the performance of YOLO-based “one-stage object detection” models (GELAN, YOLOv9, and YOLOv10) using image datasets from motion-sensor camera traps set up on sea turtle nesting beaches in Tokashiki Island. The survey recorded a total of 226.6 hours of video, of which 95% (214.4 hours) consisted of empty background footage (without animals). The videos also captured goats (6.5 hours) in addition to wild boars (2.1 hours). Among the seven models tested, GELAN-C showed the highest overall performance (Precision: 0.96, Recall: 0.89, AP@0.5: 0.93). For wild boar videos (74 clips), the model correctly identified 92% (68 clips). For empty background footage (without animals) (29 clips), it correctly identified 72% (21 clips). With this empty background detection accuracy, approximately 70% of the empty footage can be pre-filtered, reducing the required video review time by about 155 hours.

Evaluating the progress towards global and national net-zero emissions goals requires a thorough assessment of historical emission levels and future targets. However, little attention has been paid to the actual reporting by the parties themselves. In this analysis, we examine parties reporting historical emissions and removals for Agriculture, Forestry, and Other Land Use (AFOLU) sector, as well as their commitments outlined in the Nationally Determined Contributions (NDCs) and the Long-term Low Emission Development Strategies (LT-LEDS). Our analysis reveals a worldwide decrease in historical net AFOLU emissions, spanning from 1990 to 2020. This decline primarily relates to increased removals in the LULUCF sector in non-Annex I countries. In 1990, global AFOLU emissions were recorded at 4,400 MtCO₂eq, but by 2020, they had been reduced to approximately 2,200 MtCO₂eq. Looking ahead, countries have committed to further reduce global net AFOLU emissions by 600–1,700 MtCO₂eq by 2030 compared to 2020 levels. Moreover, fulfilment of the LT-LEDS commitment can provide an additional reduction of 2,300–3,400 MtCO₂eq. By integrating these datasets, the study provides insights into the progress towards achieving climate goals, highlighting the importance of land-based mitigation strategies. The findings reveal disparities between Annex I countries and Non-Annex I countries, particularly in the ambition of the commitments and objectives. As countries begin to submit their biennial transparency reports to the United Nations Framework Convention on Climate Change (UNFCCC), our recommendation is for countries to enhance transparency in reporting and communicating their progress of implementation.