http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/70 2026-09-13T19:49:17Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13043 Title: Impact of different land use systems on Biological Properties of Soils Authors: Aashikha, R.; Shayanthan, A.; Kajeevan, K.; Gnanavelrajah, N. Abstract: Soil is a vital natural resource that supports life on Earth and maintains environmental balance. Among the Soil properties, biological properties play major role in ecosystem maintenance and are influenced by different land use systems. This study aimed to assess the impact of different land use systems on soil biological properties in the Vavuniya District, Sri Lanka. Soil samples were collected from six different land use systems: organic, inorganic, integrated nutrient management (INM), forest, permaculture, and bare land at two depths, surface and subsurface. The experimental design was a completely Randomized Design with three replicates. The following parameters were measured: colony counts, respiration, microbial biomass carbon, and organic matter. The data were analysed using ANOVA (SAS 9.4) at the 0.05 significance level, followed by Duncan’s mean separation test. Results showed significant variation in the tested soil biological properties between land use systems. Forest, permaculture and organic land uses were recorded significantly higher colony counts, respiration, microbial biomass and organic matter content compared to bare and inorganic land uses, whereas INM lay in between. Between different depths, biological activity was significantly higher in surface soils than subsurface soils. Overall, this study highlights that organic and permaculture land use systems increased soil biological parameters similar to those of forest. Therefore, findings suggest that soil biological properties can be improved through sustainable soil management. 2026-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12895 Title: Individual Tree Crown Detection of Palmyrah Palm (Borassus flabellifer) using UAV imagery Authors: Jeyavanan, K.; Owari, T.; Hiroshima, T.; Nalina, G.; Sivamathy, S.; Kaneswaran, A.; Venugoban, K. Abstract: 　The Palmyrah palm (Borassus flabellifer) is a multipurpose species highly valued by local communities and often referred to as the “tree of life” due to the extensive use of its various parts. It is predominantly found in the dry zones of Sri Lanka, particularly in the Northern and Eastern regions. However, the current population of Palmyrah palms remains unknown, as manual counting is both labor-intensive and timeconsuming. This study aimed to detect Palmyrah palm in selected areas of the Northern Province of Sri Lanka using unmanned aerial vehicle (UAV) imagery combined with machine learning techniques. The results demonstrated that high-resolution UAV imagery enables accurate detection of Palmyrah palm crowns in the region. 2026-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12894 Title: Improving Individual Tree Crown Detection and Species Classification in a Complex Mixed Conifer–Broadleaf Forest Using Two Machine Learning Models with Different Combinations of Metrics Derived from UAV Imagery Authors: Jeyavanan, K.; Owari, T.; Tsuyuki, S.; Hiroshima, T. Abstract: Individual tree crown detection (ITCD) and tree species classification are critical for forest inventory, species-specific monitoring, and ecological studies. However, accurately detecting tree crowns and identifying species in structurally complex forests with overlapping canopies remains challenging. This study was conducted in a complex mixed conifer– broadleaf forest in northern Japan, aiming to improve ITCD and species classification by employing two machine learning models and different combinations of metrics derived from very high-resolution (2.5 cm) UAV red–green–blue (RGB) and multispectral (MS) imagery. We first enhanced ITCD by integrating different combinations of metrics into multiresolution segmentation (MRS) and DeepForest (DF) models. ITCD accuracy was evaluated across dominant forest types and tree density classes. Next, nine tree species were classified using the ITCD outputs from both MRS and DF approaches, applying Random Forest and DF models, respectively. Incorporating structural, textural, and spectral metrics improved MRS-based ITCD, achieving F-scores of 0.44–0.58. The DF model, which used only structural and spectral metrics, achieved higher F-scores of 0.62–0.79. For species classification, the Random Forest model achieved a Kappa value of 0.81, while the DF model attained a higher Kappa value of 0.91. These findings demonstrate the effectiveness of integrating UAV-derived metrics and advanced modeling approaches for accurate ITCD and species classification in heterogeneous forest environments. The proposed methodology offers a scalable and cost-efficient solution for detailed forest monitoring and species-level assessment. 2025-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12893 Title: Improving the Individual Tree Parameters Estimation of a Complex Mixed Conifer—Broadleaf Forest Using a Combination of Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle RGB and Multispectral Imagery Authors: Jeyavanan, K.; Owari, T.; Tsuyuki, S.; Hiroshima, T. Abstract: Individual tree parameters are essential for forestry decision-making, supporting economic valuation, harvesting, and silvicultural operations. While extensive research exists on uniform and simply structured forests, studies addressing complex, dense, and mixed forests with highly overlapping, clustered, and multiple tree crowns remain limited. This study bridges this gap by combining structural, textural, and spectral metrics derived from unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) and multispectral (MS) imagery to estimate individual tree parameters using a random forest regression model in a complex mixed conifer–broadleaf forest. Data from 255 individual trees (115 conifers, 67 Japanese oak, and 73 other broadleaf species (OBL)) were analyzed. Highresolution UAV orthomosaic enabled effective tree crown delineation and canopy height models. Combining structural, textural, and spectral metrics improved the accuracy of tree height, diameter at breast height, stem volume, basal area, and carbon stock estimates. Conifers showed high accuracy (R2 = 0.70–0.89) for all individual parameters, with a high estimate of tree height (R2 = 0.89, RMSE = 0.85 m). The accuracy of oak (R2 = 0.11–0.49) and OBL (R2 = 0.38–0.57) was improved, with OBL species achieving relatively high accuracy for basal area (R2 = 0.57, RMSE = 0.08m2 tree−1) and volume (R2 = 0.51, RMSE = 0.27 m3 tree−1). These findings highlight the potential of UAV metrics in accurately estimating individual tree parameters in a complex mixed conifer–broadleaf forest. 2025-01-01T00:00:00Z