The definition of strategies for forest restoration projects depends on information of the successional stage of the area to be restored. Usually, classification of the successional stage is carried out in the field using forest inventory campaigns. However, these campaigns are costly, time-consuming, and limited in terms of spatial coverage. Currently, forest inventories are being improved using 3D data obtained from remote sensing. The objective of this work was to estimate several parameters of interest for the classification of the successional stages of secondary vegetation areas using 3D digital aerial photogrammetry (DAP) data obtained from unmanned aerial vehicles (UAVs). A cost analysis was also carried out considering the costs of equipment and data collection, processing, and analysis. The study was carried out in southeastern Brazil in areas covered by secondary Atlantic Forest. Regression models were fit to estimate total height (h), diameter at breast height (dbh), and basal area (ba) of trees in 40 field inventory plots (0.09 ha each). The models were fit using traditional metrics based on heights derived from DAP and a portable laser scanner (PLS). The prediction models based on DAP data yielded a performance similar to models fit with LiDAR, with values of R² ranging from 88.3% to 94.0% and RMSE between 11.1% and 28.5%. Successional stage maps produced by DAP were compatible with the successional classes estimated in the 40 field plots. The results show that UAV photogrammetry metrics can be used to estimate h, dbh, and ba of secondary vegetation with an accuracy similar to that obtained from LiDAR. In addition to presenting the lowest cost, the estimates derived from DAP allowed for the classification of successional stages in the analyzed secondary forest areas.

Urban forests have gained increasing attention as nature-based solutions, yet quantitative information on aboveground biomass and carbon storage in small urban forest remnants remains limited, particularly in small municipalities. This study aimed to quantify aboveground biomass, carbon stocks, and their illustrative economic valuation in an urban Tiny Forest located in southeastern Brazil. A complete tree inventory was conducted across a 2.76-ha area, including all living trees with a diameter at breast height (DBH) ≥ 5 cm. Aboveground biomass was estimated using an allometric equation developed for urban trees, carbon stocks were calculated using a conversion factor of 0.47, and an illustrative economic valuation was derived from a reference price in the voluntary carbon market. The inventory recorded 539 trees belonging to 68 species and 24 botanical families, with native species accounting for 79.0% of the individuals. Total aboveground biomass was estimated at 95.09 Mg (34.45 Mg ha⁻¹), corresponding to 44.69 Mg of carbon (16.19 Mg C ha⁻¹) and 163.88 Mg CO₂e. The illustrative economic valuation of the stored carbon was US$3,349.64. Native species accounted for 86.46% of the estimated carbon stock, with Moquilea tomentosa and Handroanthus impetiginosus among the main contributors. Carbon storage varied with species composition and tree structural characteristics, indicating that abundance alone does not determine species contributions to carbon stocks. These findings demonstrate that small urban forest remnants can represent measurable carbon reservoirs and provide local-scale information to support urban green infrastructure planning. However, the estimates represent carbon stocks at a single point in time and do not establish carbon sequestration rates or potential revenue from certified carbon credits.