Knowledge in the magnitude and historical trends in land use and land cover (LULC) is needed to understand the changing status of the key elements of the landscape and to better target management efforts. However, this information is not easily available before the start of satellite campaign missions. Scanned historical maps are a valuable but underused source of LULC information. As a case study, we used U-Net to automatically extract fields, mires, roads, watercourses, and water bodies from scanned historical maps, dated 1965, 1984 and 1985 for our 900 km $$^2$$2 study area in Southern Finland. We then used these data, along with the topographic databases from 2005 and 2022, to quantify the LULC changes for the past 57 years. For example, the total area of fields decreased by around 27 km $$^2$$2 , and the total length of watercourses increased by around 2250 km in our study area.

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455–2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers—support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.

Human activities are causing rapid biodiversity loss across ecosystems, affecting human well-being and crucial ecosystem services. Traditional biodiversity monitoring tools cannot keep up with the increasing demands of monitoring due to their limited spatial or temporal coverage, high costs, and lack of taxonomic expertise. Thus, implementation of novel molecular monitoring methods such as environmental DNA (eDNA) and DNA metabarcoding, are necessary. Molecular monitoring methods offer significant benefits for biodiversity monitoring and environmental assessment: high sensitivity and accuracy, non-invasive sampling, broad taxonomic range and cost and time efficiency. However, the diverse methodological approaches lead to poor comparability between studies and surveys, highlighting the need for standardised assessments. We used the Technology Readiness Level (TRL) framework to evaluate the maturity of molecular monitoring methods, providing a structured assessment of their readiness for routine use. In a systematic literature review, 420 articles fulfilling the study criteria were assessed and both individual studies and method categories ranked according to the TRL scale. The findings revealed a growing number of studies, particularly in aquatic environments, with most studies validating molecular technologies on a small scale but lacking large-scale system demonstrations. Aquatic eDNA-based methods targeting fish showed overall higher technology readiness compared to other sample types and taxa and applications of molecular monitoring methods ranked into the highest TRL were predominantly freshwater studies. Key barriers to the broader implementation of molecular methods to monitoring include the need for international standards, better quantitative estimates and comprehensive reference libraries. National and international cooperation is crucial for establishing common standards, ensuring reliable and comparable results and expediting the routine use of molecular methods in biodiversity monitoring. Recent efforts towards international standardisation are encouraging, but further coordinated actions are necessary for the global implementation and acceptance of these methods.

Detailed spatial data are an essential part of land use planning and decision-making. Their spatial resolution sets limitations to their use, as coarse datasets are not suitable for detecting small-scale phenomena. In this study, we explored the effects of spatial resolution on the ecological outcome of a conservation prioritization process in Zonation software. Our study area was in Evo, southern Finland, covering a mosaic of managed and conserved forests. We produced the feature layers describing the forest characteristics using high-resolution remote sensing datasets, object-based mapping methods, and forest site type data. We found that increasing the resolution above the 16 m baseline resolution resulted in substantial errors. The conservation errors were the highest for rare features related to European Aspen, whereas the common features related to dominant tree species could benefit from the growth of cell size. We conclude that adequate spatial resolution is a prerequisite for efficient conservation prioritization, and that the size and spatial distribution of the features affect the resolution requirements.