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The primary goal of paleoflood hydrology is to estimate the frequency and magnitude of past floods. Botanical evidence, and particularly scars on trees, has been used repeatedly as paleostage indicators to reconstruct peak discharges and flood height. Yet, these reconstructions depend on the presence of visible scars on tree stems which tend to be masked as trees grow older. Here, we estimated flood magnitude using an alternative approach based on growth disturbances in tree-ring series, tree positions and the minimal discharge necessary to submerge the root collar of a tree as estimated by hydraulic modeling. We tested the reliability of this newly developed approach by using the traditional scar-based reconstruction as a benchmark. To this end, we sampled 60 trees showing evidence of flood damage on their stems along a 787-m long segment of the Asco river (Corsica, France). Based on 440 growth disturbances dated in tree-ring series, we reconstructed 28 floods between 1759 and 2020 and 18 during the 20th century. Using the two-dimensional Iber hydraulic model and detailed topographic data of the study site obtained from UAV imagery, we estimated that peak discharges of the 28 reconstructed events ranged between 10 and 210 m3s−1, with 200 m3s−1 being considered as the threshold for extreme floods. Not only do the scar-based and root collar submersion approaches yield similar results, findings are also clearly in line with the sparse information available from historical archives and short gauge station records on past floods. The unprecedented length and depth of the record presented here opens new avenues for climate change and flood impact research., Yihua Zhong was financed by a China Scholarship Council Ph.D. grant. Juan A. Ballesteros Cánovas was partially supported by the Spanish Research Council (EXTreeM project, PID2021-1245730A-100).

In current forestry investigation studies, the research hotspots have tended to concentrate on ascertaining the precision of certain tree parameters. This has resulted in an augmented intricacy of the technique in terms of algorithms and observation instruments. The complexity of the technology and the cost of the equipment make it impossible to use for large-scale forest surveys, for example, a national forest inventory (NFI). The aim of our study was to design a new type of low-cost measuring method that could be utilized in a NFI and in developing countries. Meanwhile, the newly designed method was expected to be able to output certain forest measurement factors without necessitating data processing by NFI field investigators. Based on these objectives, we developed a measuring method that included hardware comprised of two tools. The first tool was an electronic measuring tape that contained a microcontroller unit (MCU) and could automatically record and collaborate with other equipment via wireless protocols. The second tool was a tree stem position mapper that utilized our own designed mechanisms. The results showed that the tree DBH measurements exhibited a 0.05 cm (0.20%) bias and a 0.36 cm (1.45%) root mean square error (RMSE), and the biases on the x-axis and the y-axis of the tree position estimations were −15.92–9.92 cm and −25.90–10.88 cm, respectively, accompanied by corresponding RMSEs of 15.27–29.40 cm and 14.49–34.68 cm. Moreover, an efficiency test determined that the average measurement time per tree was 20.34 s, thus, demonstrating a marked improvement in speed by nearly one-fold compared to the conventional method. Meanwhile, this measurement kit costs less than 150 Euros and is economically suitable for large-scale applications. We posit that our method has the potential to serve as a standard tool in a Chinese NFI and in developing countries in the future.

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10&ndash, 20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases.

Automatic tree identification and position using high-resolution remote sensing images are critical for ecological garden planning, management, and large-scale environmental quality detection. However, existing single-tree detection methods have a high rate of misdetection in forests not only due to the similarity of background and crown colors but also because light and shadow caused abnormal crown shapes, resulting in a high rate of misdetections and missed detection. This article uses urban plantations as the primary research sample. In conjunction with the most recent deep learning method for object detection, a single-tree detection method based on the lite fourth edition of you only look once (YOLOv4-Lite) was proposed. YOLOv4’s object detection framework has been simplified, and the MobileNetv3 convolutional neural network is used as the primary feature extractor to reduce the number of parameters. Data enhancement is performed for categories with fewer single-tree samples, and the loss function is optimized using focal loss. The YOLOv4-Lite method is used to detect single trees on campus, in an orchard, and an economic plantation. Not only is the YOLOv4-Lite method compared to traditional methods such as the local maximum value method and the watershed method, where it outperforms them by nearly 46.1%, but also to novel methods such as the Chan-Vese model and the template matching method, where it outperforms them by nearly 26.4%. The experimental results for single-tree detection demonstrate that the YOLOv4-Lite method improves accuracy and robustness by nearly 36.2%. Our work establishes a reference for the application of YOLOv4-Lite in additional agricultural and plantation products.

The purpose of this study was to analyze the effects of altitude, the position of the trees along a gradient of canopy cover, and the orientation of their crown on leaf traits of 18 deciduous woody species belonging to 10 families in the Fandoglo forest region in Ardabil, North West of Iran. We measured eight leaf traits (leaf width, length, area, thickness, water content, leaf mass per area, specific leaf area, and dry matter concentration) of trees sampled at sites subjected to different light regimes (forest edge, forest understory, and isolated trees). All traits were measured on more than 3600 leaves from 90 trees sampled in two altitudinal ranges (low: 1300-1500 m a.s.l.; high: 1500-1700 m a.s.l.). A two-way ANOVA and t-test for independent samples were applied to test for differences in leaf traits between different altitudes and degree of canopy cover. The results confirmed that species’ leaf traits were more strongly correlated with the altitude and canopy cover rather than the orientation of the crown. No relationship between leaf traits and crown orientation was detected. All leaf traits had significantly higher values at low than at high elevation, indicating that environmental factors such as atmospheric CO2 concentration, temperature, light, irradiance, and wind deeply impact on foliar morphology and function; however, water content and specific leaf area showed an opposite trend. Also, species with different positions along the gradient of canopy cover could have different responses to elevation. Our results indicate that the variation of functional (morphological and physiological) traits in different tree species are affected by altitude and light regime. This might provide a theoretical basis for afforestation and forest management activities in the Fandoghlo forest region.

The present study introduces a method to identify tree stems from terrestrial laser scanning (TLS) data. We focused on forest environments of diverse and layered structure, which were technically characterized by strong occlusion effects with regards to laser scanning. The number and distribution of tree stems are important information for the management of protective forests against natural hazards, for forest inventory, and for ecological studies. Our approach builds upon a three-dimensional (3D) voxel grid transformation of the original point cloud data, followed by two major steps of processing. Firstly, a series of morphological operations removed leaves and branches and left only potential stem segments. Secondly, the stem segments of each tree were combined by a multipart workflow, which uses shape and neighborhood criteria. At the same time, erroneous fragments and noise were removed from the dataset. As a result, each object in the voxel grid was represented by a single connected component referring to one specific tree stem. Testing the method on nine spatially independent plots provided detection rates of 97% for the number and location of stems from mature trees with a diameter >= 12 cm and 84% for smaller trees with a minimum of 130 cm total tree height. In summary, we obtained a dataset covering the number and locations of the stems from both mature and understory trees, while not aiming at a precise reconstruction of the stem shape.