Accurately mapping urban rooftop solar potential is essential for cities like Amsterdam that are pursuing net-zero emissions. This study presents an innovative framework that combines high-resolution geospatial data with an advanced deep learning ensemble to identify existing solar panels and untapped suitable rooftop areas. The predictions from a meticulously trained ensemble of deep learning models were integrated using both simple and performance-weighted majority voting. The weighted ensemble achieved an accuracy of 0.95, an F1 score of 0.91, and a Matthews correlation coefficient of 0.88, outperforming individual models. Rooftop suitability was assessed using a multi-criteria approach, which incorporated a high-resolution digital surface model (DSM) to derive slope, aspect, and solar irradiation. A novel solar irradiation model was developed that enhanced the precision of yield estimates by adjusting atmospheric transmissivity and diffuse fraction based on monthly cloud cover data from Amsterdam. This framework provides district-wise spatiotemporal solar irradiation and photovoltaic yield estimates. Based on our model estimates, current installations have a potential of 140 GWh annually, while there is a significant untapped potential of 1276 GWh on suitable rooftops. These detailed insights can help urban planners optimize solar energy deployment and support the city’s carbon neutrality goal by 2050. Graphical Abstract

Consistent and accurate data on forest biomass and carbon dynamics are essential for optimizing carbon sequestration, advancing sustainable management, and developing natural climate solutions in various forest ecosystems. This study quantifies the forest biomass in designated forests based on GEDI LiDAR datasets with a unique compartment-level monitoring of unexplored hilly areas of Mansehra. The integration of multisource explanatory variables, employing machine learning models, adds further innovation to the study of reliable above ground biomass (AGB) estimation. Integrating Landsat-9 vegetation indices with ancillary datasets improved forest biomass estimation, with the random forest algorithm yielding the best performance (R² = 0.86, RMSE = 28.03 Mg/ha, and MAE = 19.54 Mg/ha). Validation with field data on a point-to-point basis estimated a mean above-ground biomass (AGB) of 224.61 Mg/ha, closely aligning with the mean ground measurement of 208.13 Mg/ha (R² = 0.71). The overall mean AGB model estimated a forest biomass of 189.42 Mg/ha in the designated moist temperate forests of the study area. A critical deficit in the carbon sequestration potential was analysed, with the estimated AGB in 2022, at 19.94 thousand tons, with a deficit of 0.83 thousand tons to nullify CO₂ emissions (20.77 thousand tons). This study proposes improved AGB estimation reliability and offers insights into the CO₂ sequestration potential, suggesting a policy shift for sustainable decision-making and climate change mitigation policies.

Forests are critical ecosystems that regulate climate, preserve biodiversity, and support human livelihoods by providing essential resources. However, they are increasingly vulnerable due to the growing impacts of deforestation and habitat fragmentation, which endanger their value and long-term sustainability. Assessing forest and deforestation fragmentation is vital for promoting sustainable logging, guiding ecosystem restoration, and biodiversity conservation. This study introduces an advanced approach that integrates the Local Connected Fractal Dimension (LCFD) with near real-time (NRT) land use and land cover (LULC) data from the Dynamic World dataset (2017–2024) to enhance deforestation monitoring and landscape analysis. By leveraging high-frequency, high-resolution satellite imagery and advanced imaging techniques, this method employs two fractal indices, namely the Fractal Fragmentation Index (FFI) and the Fractal Fragmentation and Disorder Index (FFDI), to analyze spatiotemporal changes in the forest landscape and enhance deforestation monitoring, providing a dynamic, quantitative method for assessing forest fragmentation and connectivity in real time. LCFD provides a refined assessment of spatial complexity, localized connectivity, and self-similarity in fragmented landscapes, improving the understanding of deforestation dynamics. Applied to Nigeria’s Okomu Forest, the analysis revealed significant landscape transformations, with peak fragmentation observed in 2018 and substantial recovery in 2019. FFI and FFDI metrics indicated heightened disturbances in 2018, with FFDI increasing by 75.2% in non-deforested areas and 61.1% in deforested areas before experiencing rapid declines in 2019 (82.6% and 87%, respectively), suggesting improved landscape connectivity. Despite minor fluctuations, cumulative deforestation trends showed a 160.5% rise in FFDI from 2017 to 2024, reflecting long-term stabilization. LCFD patterns highlighted persistent variability, with non-deforested areas recovering 12% connectivity by 2024 after a 38% reduction in 2019. These findings reveal the complex interplay between deforestation and landscape recovery, emphasizing the need for targeted conservation strategies to enhance ecological resilience and connectivity. Fractal indices offer significant potential to generate valuable insights across multiple spatial scales, thereby informing strategies for biodiversity preservation and adaptive landscape management.