Search Results (3)

The presence of shade-tolerant tree invaders has been recently noted in tropical and temperate forest understories. Maximum growth rate is an important trait for exotic trees becoming invaders in a forest. This study aimed to determine the growth rate of Eriobotrya japonica in a secondary cloud forest in central Veracruz, Mexico. The objectives of this study were to determine wood density, tree ring boundaries and number, and their relationship to diameter at breast height (DBH) and climate data. Tree ring counts were obtained using Python-based software with subsequent visual validation. Growth rates were measured using diametric tape, dendrometric bands, and the pinning method. The number of rings increased with DBH, presenting values ranging from 14 to 27. Tree rings were delimited by fibers that were five times narrower in the ring limit zone than in the intra-ring zone. The growth ring delimitation zones were formed when vascular cambium activity stalled during the relatively dry-cold season (January–February). The growth rate of E. japonica was statistically similar (ca. 0.2 mm yr⁻¹) regardless of the method employed to measure it. Relative growth rate was low (0.02 cm cm⁻¹ yr⁻¹). Wood density (0.76 g cm⁻³) values lay within the upper values for mature forest trees. Eriobotrya japonica is a potential invader, with traits such as high wood density and a relatively low growth rate, which are characteristic of the shade-tolerant tree species. Full article

Resilient cities incorporate a social, ecological, and technological systems perspective through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have compared and combined two of the most common data sources for updating urban tree inventories: Airborne Laser Scanning (ALS) point clouds combined with aerial orthophotographs, and street-level imagery from Google Street View (GSV). Depending on the data source, different methodologies were used to identify the trees. In the first stage, the use of individual tree detection techniques in ALS point clouds was compared with the detection of objects (trees) on street level images using computer vision (CV) techniques. In both cases, a high success rate or recall (number of true positive with respect to all detectable trees) was obtained, where between 85.07% and 86.42% of the trees were well-identified, although many false positives (FPs) or trees that did not exist or that had been confused with other objects were always identified. In order to reduce these errors or FPs, a second stage was designed, where FP debugging was performed through two methodologies: (a) based on the automatic checking of all possible trees with street level images, and (b) through a machine learning binary classification model trained with spectral data from orthophotographs. After this second stage, the recall decreased to about 75% (between 71.43 and 78.18 depending on the procedure used) but most of the false positives were eliminated. The results obtained with both data sources were robust and accurate. We can conclude that the results obtained with the different methodologies are very similar, where the main difference resides in the access to the starting information. While the use of street-level images only allows for the detection of trees growing in trafficable streets and is a source of information that is usually paid for, the use of ALS and aerial orthophotographs allows for the location of trees anywhere in the city, including public and private parks and gardens, and in many countries, these data are freely available. Full article

Assessing forest stand conditions in urban and peri-urban areas is essential to support ecosystem service planning and management, as most of the ecosystem services provided are a consequence of forest stand characteristics. However, collecting data for assessing forest stand conditions is time consuming and labor intensive. A plausible approach for addressing this issue is to establish a relationship between in situ measurements of stand characteristics and data from airborne laser scanning (LiDAR). In this study we assessed forest stand volume and above-ground biomass (AGB) in a broadleaved urban forest, using a combination of LiDAR-derived metrics, which takes the form of a forest allometric model. We tested various methods for extracting proxies of basal area (BA) and mean stand height (H) from the LiDAR point-cloud distribution and evaluated the performance of different models in estimating forest stand volume and AGB. The best predictors for both models were the scale parameters of the Weibull distribution of all returns (except the first) (proxy of BA) and the 95th percentile of the distribution of all first returns (proxy of H). The R² were 0.81 (p < 0.01) for the stand volume model and 0.77 (p < 0.01) for the AGB model with a RMSE of 23.66 m³·ha⁻¹ (23.3%) and 19.59 Mg·ha⁻¹ (23.9%), respectively. We found that a combination of two LiDAR-derived variables (i.e., proxy of BA and proxy of H), which take the form of a forest allometric model, can be used to estimate stand volume and above-ground biomass in broadleaved urban forest areas. Our results can be compared to other studies conducted using LiDAR in broadleaved forests with similar methods. Full article