African mountains are characterized by high levels of biodiversity and provide ecosystem services to millions of people. Due to steep environmental gradients, growing human populations and geographical isolation, these coupled socio-ecological systems are highly vulnerable to climate change impacts. The capacity of local stakeholders to anticipate future changes and to assess their potential impacts is paramount for enhancing adaptation and resilience. Here we apply a participatory scenario development framework in two parts of the Eastern Afromontane Biodiversity Hotspot: Taita Hills in Kenya and Jimma rural area in Ethiopia. In each area, we facilitated local stakeholders in envisioning adaptation scenarios under projected climate changes by mid-21st century, and assessed the potential impacts of these pathways on land use and land cover. In the Taita Hills, under a business-as-usual scenario, human population and activities concentrate at high elevation, triggering cascade effects on remnant forest cover, biodiversity and ecosystem services. Alternative adaptation scenarios envisage reforestation associated with either improved agricultural practices or ecosystem restoration. In the Jimma area, rising temperatures are expected to disrupt traditional coffee production under a business-as-usual scenario, resulting in the loss of coffee-forest canopies and reduction of forest-dependent biodiversity. Alternative adaptation scenarios envisage either expansion of commercial coffee plantations or expansion of agroforestry, including traditional coffee farming. In the both Taita and Jimma, adaptation pathways present trade-offs between provisioning, supporting and regulating services, and between livelihoods and biodiversity conservation. Our findings encourage the use of multidisciplinary, bottom-up approaches for developing locally tailored, climate-smart and sustainable adaptation pathways.

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.