Understanding the effects of land cover change on wildlife distribution is very important for resource management and conservation planning. This paper aimed at detecting the effects of land cover change on great apes distribution at the Lobéké National Park and its bounded forest management units (FMUs). Data on great ape nests were collected in the field for the years 2001 and 2014 through distance sampling with line transects. Landsat TM images of South-East Cameroon for the years 2001 and 2014 were acquired from earth explorer and corrected atmospherically for proper visualization. An area of interest comprising the Lobéké National Park and its FMUs was extracted for classification and change detection. A comparison in great apes nest distribution and change per land cover change category was done for both years through point pattern analysis, whereas a time series analysis of the detected land cover change impacts on great apes nest distribution for a period of 13 years was modeled using logistic growth and regression equations in Vensim 7.2. The results could illustrate that, as land cover changes from one cover type in 2001 to another in 2014, increases or decreases in great apes nests were observed within each changed area.

Despite the growing impact of remote sensing technology in forest inventories globally, there is a continuous need for ground measurements on sample plots. Even though the newest volume assessment methodology requires fewer sample plots, the accuracy of ground-recorded data influences the final accuracy of forest stand modeling. Therefore, effective and economically justified tools are in the continuous interest of foresters. In the presented research, a consumer-grade light detection and ranging (LiDAR) sensor mounted on iPad was used for forest inventory sample plot data collection—including tree location and diameter breast height. In contrast to other similar research, feasibility and user-friendliness were also documented and emphasized. The study was conducted in 63 real sample plots used for the inventory of Polish forests. In total, 776 trees were scanned in 3 types of forest stands: pine, birch, and oak. The root mean square error was 0.28 m for tree locations and 0.06 m for diameter breast height. Various additional analyses were performed to describe the usage of an iPad in tree inventories. It was contended that low-cost LiDAR scanners might be successfully used in real forest conditions and can be considered a reliable and easy-to-implement tool in forest inventory measurements.

Landslides and erosion processes cause a high level of morphological diversity which, over the course of time, results in an increased level of forest species and habitat biodiversity. According to the principles of landscape ecology, information concerning points of convergence—understood as transition zones between elementary parts of a slope—could be interpreted as a surrogate for biodiversity. To recognize locations of land surface disruption, points of convergence derived from a high-resolution digital elevation model (DEM) were used in the research. It was assumed that relationships exist between landslide geodiversity and biodiversity and that from the DEM it is possible to select bare-earth surface structures where points of convergence can be identified. In order to verify this hypothesis, indicators of biodiversity such as richness of plant species and species diversity were checked. Based on the spatial distribution of points of convergence, locations of sample plots were planned and these indicators were verified during fieldwork. Samples were taken both in areas with a high concentration of points of convergence and in areas with a low concentration (or absence) of points of convergence. The results show that species diversity in the areas having a high concentration of points of convergence was significantly higher than in the areas having a low concentration of points of convergence. The proposed method allows the selection of parts of forested landslides that have the potential to develop a high level of biological diversity; it thus supports the management of areas of forest ecosystems with high levels of biodiversity within the scope of a cutting system. This may entail avoiding the traversal of areas that potentially possess high biological diversity with skid roads, preserving parts of these areas with thick vegetation for forest game purposes or leaving predominant and dead trees. The proposed method may also be used in scientific research into processes of biodiversity appearing on forested landslides.

Forests grow under dynamic conditions influenced by vegetation structure and environmental factors. However, empirical models to enhance growing stock volume GSV) estimation are commonly established based on structural information from airborne laser scanning (ALS) data, raising important questions regarding the models’ performance across time (temporal transferability). This study presents the integration of ALS and microclimate and site-specific data to assess the temporal transferability of GSV models at the plot level in a mixed forest located in Milicz, Poland, between 2007 (t₁) and 2015 (t₂). We compared random forest (RF), multiple linear regression (MLR), and generalized additive models (GAMs) across three modelling scenarios, ALS + site type + climate (sa), ALS only (sb), and ALS + site type (sc), and also performed internal and external validation to assess temporal transferability. Among the three modelling approaches, GAMs outperformed the MLR and RF models in internal validation, improving the R² by 6%–8% and reducing the rRMSE by 6%–12%. We found that climate was significant in GSV prediction when integrated with ALS and site conditions, with a permutation test (p ≤ 0.023) based on the rRMSE confirming climate significance. The direct contribution of climate to model performance was marginal on a broad scale. However, its influence on GSV and temporal transferability seem stronger in homogenous sites. In general, RF was the most stable in both the forward (t₁→t₂) and backward (t₂→t₁) directions in the sa scenario unlike the GAM, which was more stable in the backward direction. This study provides a framework for assessing the reliability of GSV models and addresses a critical gap in forest monitoring.