Understanding how ecological dominance aligns with culturally and economically valued plant use is critical for participatory forest management. This study integrated vegetation structure and ethnobotanical valuation to assess angiosperm importance across three forest strata (Mixed Deciduous Forest (MDF), Dry Dipterocarp Forest site 1 (DDF1), and Dry Dipterocarp Forest site 2 (DDF2)) within the Ngao Model Forest, Northern Thailand. Fifteen 10 × 10 m vegetation plots (five per forest stratum) were surveyed to calculate the Importance Value Index (IVI), and 198 semi-structured interviews were conducted to derive the Use Value Index (UVI) and a standardized Socio-Economic Value Index (SEVI). A total of 112 angiosperm species were recorded across forest types, with strong structural dominance by dipterocarps in DDF sites and greater compositional heterogeneity in MDF. Spearman rank correlation analysis supported the working hypothesis that ecological dominance is only weakly associated with cultural and socio-economic importance. IVI showed weak but significant positive correlations with UVI (ρ = 0.288, p < 0.05) and SEVI (ρ = 0.300, p < 0.05), indicating partial but limited alignment between structural abundance and livelihood value. Several species with moderate or low IVI exhibited disproportionately high UVI and SEVI scores, reflecting their importance in food, medicinal, and commercial use categories. Conversely, certain canopy dominants showed limited ethnobotanical significance. These findings demonstrate that ecological abundance alone is an insufficient proxy for community-defined species value. Integrating structural, cultural, and socio-economic indices provides a more comprehensive framework for identifying priority species in community-managed forest systems. The IVI–UVI–SEVI comparative approach offers practical insights for model forest governance by distinguishing ecological dominants, multipurpose livelihood species, and culturally significant taxa occurring outside forest interiors. This multidimensional valuation framework strengthens participatory forest management and biodiversity prioritization in heterogeneous tropical landscapes.

Importance of the work: There is a recognized need to enhance productivity in agroforestry systems through the utilization of Melientha suavis Pierre, a native Thai tree valued for its significant economic and conservation benefits. Objectives: To analyze the impact of environmental factors and management practices on the yield of M. suavis leaves across different seasons and to create yield prediction models to support sustainable management strategies for agroforestry systems. Materials & Methods: The study was conducted using four alley farming plots in Saraburi province, Thailand based on measurements of plant growth and environmental variables: canopy gap, leaf area index (LAI), soil moisture, soil temperature and electrical conductivity of pore water. Data were collected seasonally and analyzed using Pearson’s correlation coefficient and generalized linear mixed models, with Akaike information criterion (AIC) values utilized for model comparisons. Results: Management practices and environmental conditions significantly influenced leaf yield, with distinct responses between dry and wet seasons. Canopy gap, LAI and soil moisture were particularly influential, with the lowest AIC value of 1509 indicating the best-fitting yield prediction model. Main finding: LAI and soil moisture were critical in determining the seasonal yield of M. suavis leaves. Species selection and spatial arrangements in tree alley farming significantly affect productivity. The diameter over bark at ground level of M. suavis was the most influential factor regarding leaf yield, suggesting that management should focus on promoting radial growth to enhance yield efficiency and sustainability.

Rapid urbanization has intensified the urban heat island (UHI) effect in Southeast Asian megacities, necessitating effective nature-based solutions. This study evaluates the cooling capacity of diverse green spaces in Khung Bang Kachao (KBK), Thailand, by integrating Google Earth Engine (GEE), field vegetation inventories, and temperature monitoring across 20 plots representing three types: rehabilitation forest, agroforestry, and urban areas. Results demonstrate that mean LST varied significantly across green space types: rehabilitation forests achieved the lowest mean LST (33.86 °C), followed by agroforestry (35.19 °C) and urban areas (40.77 °C). Structural parameters (tree density, crown cover, height, species composition, and NDVI) correlated negatively with LST, with NDVI exhibiting the strongest correlation (r = −0.74). Multivariable modeling revealed NDVI as the sole significant predictor of LST reduction at 10 m and 30 m resolutions, highlighting NDVI as a stronger driver of LST reduction than other structural parameters. In temporal analysis (2020–2024), rehabilitation forest cover contracted sharply from 31.94% to 12.02% due to agricultural conversion, corresponding to lower NDVI and higher LST, potentially exacerbating the localized UHI effect. Furthermore, a weak correlation between LST and air temperature underscores the influence of built environment heat factors. The final model included only Month and Light Intensity. While NDVI indicates LST, satellite-derived LST alone cannot reflect air temperature, requiring both environmental and physical factors in urban heat management.