Accuracy of smartphone based dendrometric measurements in periurban forests in Nepal

Accurate dendrometric measurements are fundamental for forest inventory, biomass and carbon stock estimation, and sustainable forest management. Smartphone-based applications are increasingly promoted as low-cost alternatives to conventional measurement tools; however, their performance under Nepalese forest conditions remains largely untested. This study compared different smartphone applications including Light Detection and Ranging (LiDAR)-assisted iPhone Operating System (iOS), non-LiDAR iOS, and Android to evaluate the accuracy of diameter at breast height (DBH), height, and canopy measurements in a peri-urban forest of Hetauda sub-metropolitan municipality, Makwanpur, Central Nepal. A total of 668 trees from 75 forest plots were sampled using a purposive design to capture a wide range of DBH and height across Sal-dominated and mixed-species plots, and 100 isolated trees in and around residential areas were evaluated individually. DBH measurements were highly accurate (R 2 > 0.95) for both Android and LiDAR-assisted iOS applications, while height measurements showed moderate accuracy (R 2 = 0.80–0.82) due to inherent challenges in estimating tree height. Canopy structure metrics were weakly to moderately correlated with reference data (r = 0.34–0.44), with absolute errors generally within an acceptable range (< 10%). We found similar accuracy for DBH and height measurements in both forest plots and isolated trees. Canopy structure and height measurements were more variable than DBH, reflecting the inherent challenges in estimating these parameters with smartphone applications. These findings indicate that smartphone applications can provide reliable DBH measurements and reasonably accurate height estimates under peri-urban broadleaf forest conditions in Nepal. Given their portability, low cost, and ease of use, such tools offer practical potential for rapid forest assessments and community-based forest management, although further validation in more structurally complex natural forests is recommended to support broader operational adoption.