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ContextIn the last century European forests are experiencing tree damage and mortality rise and it is expected to continue due to increased disturbances under global change. Disturbances generally creates canopy gaps, which leads to secondary succession, compositional changes and landscape mosaic transformations. Forest gap characterization has traditionally been performed in light-limited tropical and boreal forests, but no studies have been found on water-limited Mediterranean forests. Characterising canopy gaps and their dynamics in Mediterranean forests will help to better understand their dynamics across landscapes under ongoing global change.ObjectivesWe aimed to characterize canopy gaps and quantify their dynamics identifying hotspots of openings and closings in Mediterranean forests.MethodsWe used low density multitemporal airborne LiDAR data between 2010 and 2016, over a large region (Madrid, Spain, 1732.7 km2) with forests ranging from monospecific conifer and broadleaved to mixed forests, to delineate canopy gaps. The characterization was made through its Gap Size Frequency Distribution (GSFD) by forest type and year. We analysed canopy gap dynamics and identified statistically significant hotspots of gap openings and closings in each forest type.ResultsThere were major differences between conifers and broadleaved forest in terms of gap characteristics and GSFD. In general, we found a great dynamism in Mediterranean forests with high rates of forest openings and closings, but a net closing trend. A high spatial heterogeneity was observed finding hotspots of gap openings and closings across the entire study area.ConclusionsWe characterised for the first-time large-scale structure and dynamics of canopy gaps in Mediterranean forests. Our results represents the characterisation of the GSFD of Mediterranean forests and could be considered a benchmark for future studies. The provision of up-to-date periodic maps of hotspots of gap opening, closing and net change help to understand landscape mosaic changes as well as to prioritise forest management and restoration strategies.

Forest canopy gaps are important to ecosystem dynamics. Depending on tree species, small canopy openings may be associated with intra-crown porosity and with space among crowns. Yet, literature on the relationships between very fine-scaled patterns of canopy openings and biodiversity features is limited. This research explores the possibility of: (1) mapping forest canopy gaps from a very high spatial resolution orthomosaic (10 cm), processed from a versatile unmanned aerial vehicle (UAV) imaging platform, and (2) deriving patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. The orthomosaic was imaged from a test area of 240 ha of temperate deciduous forest types in Central Italy, containing 50 forest inventory plots each of 529 m2 in size. Correlation and linear regression techniques were used to explore relationships between patch metrics and understory (density, development, and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, and tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m2) can be faithfully extracted from UAV red, green, and blue bands (RGB) imagery, using the red band and contrast split segmentation. The strongest correlations were observed in the mixed forests (beech and turkey oak) followed by intermediate correlations in turkey oak forests, followed by the weakest correlations in beech forests. Moderate to strong linear relationships were found between gap metrics and understory variables in mixed forest types, with adjusted R2 from linear regression ranging from 0.52 to 0.87. Equally strong correlations in the same forest types were observed for forest habitat biodiversity variables (with adjusted R2 ranging from 0.52 to 0.79), with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely.

Heat accumulation and spring freeze, both strongly influenced by snow cover, are key factors regulating the onset of spring phenology. In forest ecosystems, decreased snow cover due to climate change may differently impact heat accumulation and the occurrence of spring freezes between canopy gap and under the tree canopy, leading to varied phenological responses. In this study, we examined how spring phenology of tree seedling responds to decreased snow cover across microsites and explored whether these responses are species-specific. We conducted a manipulation experiment in a planted forest in northern Hokkaido, Japan, establishing five canopy gap and five under canopy areas, each with control and snow removal plot. Four dominant tree species were planted in each plot. Snow removal significantly advanced budburst and leaf-out in both microsites, with a more pronounced effect observed in the canopy gap. Moreover, snow removal significantly advanced the budburst and leaf-out of all four species in the canopy gap, whereas only Abies sachalinensis showed significantly earlier budburst and leaf-out in the under canopy. Overall, our study demonstrated that projected winter warming led to a greater advancement of spring phenology in tree seedlings in canopy gap compared to under the canopy, with species-specific responses.

Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. Aiming at overcoming the bottleneck issues of canopy gap identification in mountainous forest regions, we constructed a multi-task deep learning model (ES-Net) integrating an edge–semantic collaborative perception mechanism. First, a refined sample library containing multi-scale interference features was constructed, which included 2808 annotated UAV images. Based on this, a dual-branch feature interaction architecture was designed. A cross-layer attention mechanism was embedded in the semantic segmentation module (SSM) to enhance the discriminative ability for heterogeneous features. Meanwhile, an edge detection module (EDM) was built to strengthen geometric constraints. Results from selected areas in Yunnan Province (China) demonstrate that ES-Net outperforms U-Net, boosting the Intersection over Union (IoU) by 0.86% (95.41% vs. 94.55%), improving the edge coverage rate by 3.14% (85.32% vs. 82.18%), and reducing the Hausdorff Distance by 38.6% (28.26 pixels vs. 46.02 pixels). Ablation studies further verify that the synergy between SSM and EDM yields a 13.0% IoU gain over the baseline, highlighting the effectiveness of joint semantic–edge optimization. This study provides a terrain-adaptive intelligent interpretation method for forest disturbance monitoring and holds significant practical value for advancing smart forestry construction and ecosystem sustainable management.

Canopy gaps are a prevalent disturbance form in forest ecosystems that promote forest regeneration and succession by modifying the heterogeneity of the microenvironment. However, a significant knowledge gap exists in comprehending the global-scale impact of canopy gaps on soil nutrient properties, which is related to forest management and conservation tactics. In this study, 518 paired observations derived from 31 peer-reviewed articles were meta-analyzed to evaluate the overall response of soil nutrient properties to canopy gaps. The results showed that canopy gaps increased NO3−–N (+ 22.20%) and MBP (+ 194.17%). The canopy gap decreased the content of TN, MBC, and C:P ratio by 9.27%, 19.58%, and 19.25%, respectively. The size of canopy gaps significantly reduced SOC (−14.37%), MBC (−27.45%), TN (−11.98%), NH4+–N (−65.26%), C:N (−15.77%, −16.02%) and C:P ratio (−28.92%), but significantly increases NO3−–N (+ 37.25%). Hence, it is advisable to establish a critical gap size that caters to the specific soil fertility requirements of various regions for the optimal release of soil nutrients. These findings hold substantial significance for optimizing canopy gap management, comprehensively understanding the impact of canopy gaps on soil nutrient properties, and facilitating decision-making to assess soil fertility following canopy gap disturbances.

Efficient and accurate identification of canopy gaps is the basis of forest ecosystem research, which is of great significance to further forest monitoring and management. Among the existing studies that incorporate remote sensing to map canopy gaps, the object-oriented classification has proved successful due to its merits in overcoming the problem that the same object may have different spectra while different objects may have the same spectra. However, mountainous land cover is unusually fragmented, and the terrain is undulating. One major limitation of the traditional methods is that they cannot finely extract the complex edges of canopy gaps in mountainous areas. To address this problem, we proposed an object-oriented classification method that integrates multi-source information. Firstly, we used the Roberts operator to obtain image edge information for segmentation. Secondly, a variety of features extracted from the image objects, including spectral information, texture, and the vegetation index, were used as input for three classifiers, namely, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). To evaluate the performance of this method, we used confusion matrices to assess the classification accuracy of different geo-objects. Then, the classification results were screened and verified according to the area and height information. Finally, canopy gap maps of two mountainous forest areas in Yunnan Province, China, were generated. The results show that the proposed method can effectively improve the segmentation quality and classification accuracy. After adding edge information, the overall accuracy (OA) of the three classifiers in the two study areas improved to more than 90%, and the classification accuracy of canopy gaps reached a high level. The random forest classifier obtained the highest OA and Kappa coefficient, which could be used for extracting canopy gap information effectively. The research shows that the combination of the object-oriented method integrating multi-source information and the RF classifier provides an efficient and powerful method for extracting forest gaps from UAV images in mountainous areas.

Gap formations in the forest canopy have natural causes, such as bad weather, and anthropic ones, such as sustainable selective extraction of trees and illegal logging, which can already be detected through orbital remote sensing. However, the Amazon region is under frequent cloud cover, which makes it challenging to detect gaps using passive sensors. This study aimed to identify and delimit gaps in the Amazon forest canopy through airborne LiDAR (Light Detection and Ranging) sensor application while testing six different return densities. LiDAR and forest inventory data were obtained over an Amazon rainforest region, defining the minimum area as a forest canopy gap. The point cloud was processed to obtain six return densities with the generation of their respective CHM (Canopy Height Model), which were applied for segmentation and subsequent identification of gap areas and roads. The minimum gap area found was 34 m2, and the Kruskal Wallis test showed no significant difference among the six densities in gap detection; however, road identification decreased as the return density decreased. We concluded that LiDAR data proved promising as point clouds with low return density can be used without impairing gap identification. However, reducing the return density for road identification is not recommended.

Disruptions to historic disturbance and herbivory regimes have altered plant assemblages in forests worldwide. An emerging consensus suggests that these disruptions often result in impoverished forest biotas. This is particularly true for eastern U.S. deciduous forests where large gaps and understory fires were once relatively common and browsers were far less abundant. Although much research has focused on how disturbance and browsers affect tree diversity, far less attention has been devoted to forest understories where the vast majority (>75%) of the vascular species reside. Here we test the hypothesis that the reintroduction of disturbances resembling historic disturbance regimes and moderate levels of ungulate browsing enhance plant diversity. We explore whether once‐common disturbances and their interaction with the top‐down influence of browsers can create conditions favorable for the maintenance of a rich herbaceous layer in a region recognized as a temperate biodiversity hotspot in West Virginia, USA. We tested this hypothesis via a factorial experiment whereby we manipulated canopy gaps (presence/absence) of a size typically found in old‐growth stands, low‐intensity understory fire (burned/unburned), and deer browsing (fenced/unfenced). We tracked the abundance and diversity of more than 140 herb species for six years. Interactions among our treatments were pervasive. The combination of canopy gaps and understory fire increased herbaceous layer richness, cover, and diversity well beyond either disturbance alone. Furthermore, we documented evidence that deer at moderate levels of abundance promote herbaceous richness and abundance by preferentially browsing fast‐growing pioneer species that thrive following co‐occurring disturbances (i.e., fire and gaps). This finding sharply contrasts with the negative impact browsers have when their populations reach levels well beyond those that occurred for centuries. Although speculative, our results suggest that interactions among fire, canopy gaps, and browsing provided a variable set of habitats and conditions across the landscape that was potentially capable of maintaining much of the plant diversity found in temperate forests.

Natural regeneration and forest successional development are influenced by gap formation in forest stands. Nonetheless, there are limited studies that provide quantitative information on the influence of gaps on forest regeneration. We evaluated characteristics of inner and outer canopy gaps and their effects on natural regeneration in 40 canopy gaps in Sitapahar forest reserve of Bangladesh. A total of 50 individuals of 27 gapmaker tree species were found, of which 58% were formed by logging and the rest by natural damages. Elliptical shape represented 53% of the gaps followed by circular and rectangular gaps. The mean area of the outer and inner gaps was 50.1 ± 8.6 and 20.0 ± 3.0 m2, respectively. Gap formation types and shapes did not vary significantly between outer and inner gaps, while the mean gap area in older gaps was significantly higher than in new gaps. In comparison with outer gaps, mean densities of seedlings and saplings in the inner gaps were significantly higher, which is probably because of the closeness to seed trees. The diversity index of regenerating species and their height and collar diameter did not vary significantly between the inner and outer gaps. Positive, but weak relationships of gap area with subcanopy tree density and diversity were found. Since gaps were found dominated by few light-demanding tree species such as Brownlowia elata, Lithocarpus acuminata, Lithocarpus polystachya, and Macaranga denticulate, it is suggested that larger gaps need to be replanted with a combination of light-demanding and shade-tolerant native trees.

Mangroves are among the most ecologically valuable ecosystems of the globe. Reliable remote sensing solutions are required to assist their management and conservation at broad scale. Canopy gaps are part of forests' turnover and rejuvenation, but yet no method has been proposed to map their occurrence and recovery in mangroves. Here, were propose an approach based on a deep learning framework called Mask R‐CNN to achieve automatic detection and delineation of gaps using very‐high‐resolution satellite imagery (<1 m). The Mask R‐CNN combines a series of neural network architectures to identify and delineate gaps, determine their recovery stage, and estimate their morphological attributes. The approach was tested on four mangroves from different regions of the globe with high concentration of gaps of various origins (lightning strikes, oil spills, cutting, pests). The Mask R‐CNN performed well to detect gaps, and accurately delineated gap contours (F1‐score of segmentation ≥0.89). The model also succeeded in distinguishing among five recovery stages of gaps, from their onset to closure (Overall Accuracy = 91.4, Kappa = 0.89). Accurate retrieval of gap area, eccentricity, and compactness – three relevant morphological attributes – were obtained (R2 ≥ 0.83, NRMSE ≤10%). Several sources of confusion and misdelineation were identified. Our approach shows promising transferability to other mangrove sites and optical sensors and could help monitor canopy recovery in mangroves. It also opens promising perspectives for identifying the origin of gaps (natural or human‐induced). It is intended to assist environmental managers and field experts in the management and conservation of these fragile ecosystems. Canopy gaps greatly contribute to mangrove forest turnover and rejuvenation. While gap monitoring is usually performed by field and aerial surveys, we propose a novel method based on deep learning to achieve this automatically from very‐high‐resolution satellite imagery. A multi‐task neural network is applied to the images to accurately detect and delineate gaps, and to determine their recovery stage. Our method proved reliable in various forests of the globe with contrasting characteristics and could therefore improve mangrove conservation and monitoring. Our study also opens perspectives to identify the origin of gaps, including those caused by human activities.