Abinash Devkota

Monitoring forest regeneration is vital for sustainable management, yet traditional ground surveys and early aerial imagery methods face cost, labor, and resolution limitations. Unmanned Aerial Vehicles (UAVs) offer cost-effective, flexible platforms for acquiring ultra-high-resolution data to address these gaps. This paper reviews recent research on UAV applications in forest regeneration surveys (FRS), tracing the evolution from field-based surveys and conventional aerial approaches to current UAV practices, and synthesizing developments in data acquisition, processing workflows, and analysis. It contrasts established Canopy Height Model (CHM) and point-cloud approaches with the growing use of deep learning, particularly Convolutional Neural Networks (CNNs) for seedling detection, crown delineation, density, height estimation, and species classification. Particular attention is given to accuracy assessment, examining sampling design, reference data, prediction-to-reference matching, and evaluation metrics, and highlighting the disconnect between traditional map-validation principles and standard deep-learning metrics that often neglect background classes. The case study applying Mask R-CNN to red pine seedlings in an Adirondack Park plantation achieved stand-level recall of 70.3% and precision of 98.7%, while plot-level DL detections represented only 36.8% of the field-observed seedling count. These results demonstrate the potential of DL for reliably identifying visible red pine seedlings while highlighting its limitations for complete regeneration inventories, particularly when seedlings have small crown sizes.

The wildlife population is an important part of the forest ecosystem and plays a crucial role in maintaining ecosystem health and integrity. In many grassland ecosystems, wild herbivores face substantial competition for space and resource use from livestock over grazing resources. Livestock and ungulates have shared a large portion of the Terai forest resources for decades, but little information has been explored about the influence of livestock on the grassland ecosystems of the protected areas of the Terai region of Nepal. We assessed the impact of livestock grazing on wild ungulate habitat in the Khata Corridor, Bardiya, Nepal. We used direct field observation, key informant interview (n=10), focus group discussion (n=5), and a questionnaire survey with local households to study the seasonal occurrence of wild ungulates in multiple habitats, livestock rearing practices, stocking density, and resource use by domestic livestock in and around the forest land of villages located in the Khata Corridor, Bardiya, Nepal. We followed livestock herds from the early morning to their return for consecutive days in each sample village to estimate the grazing circuits. We individually recorded a total number of grazing cattle in five different habitats while following the cattle grazing path by direct and indirect observation. Morisitas index was used to evaluate the habitat overlap between domestic livestock and wild ungulates. The domestic cattle had a higher habitat overlap and effective stocking density in winter than wild ungulates in summer in the corridor forest. Lower availability of forage around the village led to the concerted effort of grazing in the forest, increasing the higher effective stocking density in winter, leading to the competition with wild ungulates. However, the carrying capacity is highly unevenly observed across the region, and overgrazing is found in many areas of the forest corridor. We recommend the development of comprehensive wildlife livestock grazing strategies for planning sustainable livestock farming and for important wildlife areas to maintain long-term landscape connectivity to protect migrating endangered wild species.