Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single tree-level information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMA^TM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection.

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.

As a result of extensive data collection efforts over the last 20–30 years, there is quite a good understanding of the large‐scale geographic distribution and range limits of African great apes. However, as human activities increasingly fragment great ape spatial distribution, a better understanding of what constitutes suitable great ape habitat is needed to inform conservation and resource extraction management. Chimpanzees (Pan troglodytes troglodytes) and gorillas (Gorilla gorilla gorilla) inhabit the Lobéké National Park and its surrounding forest management units (FMUs) in South‐East Cameroon. Both park and neighboring forestry concessions require reliable evidence on key factors driving great ape distribution for their management plans, yet this information is largely missing and incomplete. This study aimed at mapping great ape habitat suitability in the area and at identifying the most influential predictors among three predictor categories, including landscape predictors (dense forest, swampy forest, distance to water bodies, and topography), human disturbance predictors (hunting, deforestation, distance to roads, and population density), and bioclimatic predictor (annual precipitation). We found that about 63% of highly to moderately suitable chimpanzee habitat occurred within the Lobéké National Park, while only 8.4% of similar habitat conditions occurred within FMUs. For gorillas, highly and moderately suitable habitats occurred within the Lobéké National Park and its surrounding FMUs (82.6% and 65.5%, respectively). Key determinants of suitable chimpanzee habitat were hunting pressure and dense forest, with species occurrence probability optimal at relatively lower hunting rates and at relatively high‐dense forest areas. Key determinants of suitable gorilla habitat were hunting pressure, dense forests, swampy forests, and slope, with species occurrence probability optimal at relatively high‐dense and swampy forest areas and at areas with mild slopes. Our findings show differential response of the two ape species to forestry activities in the study area, thus aligning with previous studies.

Global and regional biodiversity loss is caused by several drivers including urban development, land use intensification, overexploitation of natural resources, environmental pollution, and climate change. The main aim of our study was to adapt the GLOBIO3 model to the conditions of the Czech Republic (CR) to assess loss of naturalness and biodiversity vulnerability at the habitat level on a detailed scale across the entire CR. An additional aim was to assess the main drivers affecting the biodiversity of habitat types. The GLOBIO3 model was adapted to CZ-GLOBIO by adapting global to local scales and using habitat quality and naturalness data instead of species occurrence data. The total mean species abundance (MSA) index of habitat quality, calculated from the spatial overlay of the four MSA indicators by our new equation, reached the value 0.62. The total value of MSA for natural and near-natural habitats was found to be affected mainly by infrastructure development and fragmentation. Simultaneously, intensity of land use change and atmospheric nitrogen deposition contributed primarily to the low total value of MSA for distant natural habitats. The CZ-GLOBIO model can be an important tool in political decision making to reduce the impact of the main drivers on habitat biodiversity in the CR.

The aim of our study is to identify the evolution of land use and the landscape capacity to provide selected ecosystem services (ESs) over the past 28 years. The results obtained should answer whether the recorded land cover development has manifested in the same way as the development of landscape capacity to provide ESs for four different services. Corine Land Cover (CLC) data are used to describe the land cover for five time periods (1990, 2000, 2006, 2012, and 2018) for the area of interest—the whole of the Czech Republic Identification of persistence area. The main trajectories of land cover developments are calculated using overlay spatial operations in GIS. For each analyzed year of landscape development, land cover is evaluated separately, and basic quantification indicators are calculated. At the same time, the filling capacity of selected ESs is evaluated. The results show that the assessed area had the highest capacity to provide ecological integrity in 1990–2006, and then this slightly decreased due to category changes. From a spatial point of view, the worst development trend is seen for provisioning services, where negative development is represented almost all over the country. Ecological integrity and regulating services have similar spatial characteristics of development.

Climate change has greatly altered plant habitats, resulting in greater biodiversity loss at different scales. Therefore, it is important to quantify such changes for better monitoring and conservation. In this study, we adapt the EUROMOVE model and its mean stable area indicator (MSAi) to the conditions in the Czech Republic. Our objective was to predict change in species richness from a representative pool of 687 species from 1990 to 2100 under the RCP 8.5 climate scenario, focusing on the current period (2018). Another objective was to assess the effectiveness of the MSAi as a tool for quantifying landscape vulnerability. Our result shows that species habitat expanded between 1990 and 2018, although about 2 per cent of species were lost. The average MSAi of the most favourable highland habitats may decrease from 0.85 to 0.65 by 2100 as >20% of baseline species may be lost. Indicator species of Alnus (alder) and Festuca (fescue), typical of lowland habitats, are among the most vulnerable, already showing a net loss of their current habitat extent. The MSAi can be applied as a comprehensive tool to quantify the impact of climate change on landscape vulnerability as more survey data becomes available.