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Accurate information of the fine-scale spatial patterns of trees and their interactions within a stand is critical for explaining the competition, health and vigor status, and future development of a stand. There are a number of indices which can show such patterns, but the stand spatial structure index is the most important. This index can be quantified based on the spatial information of trees (tree positions) within a stand and has paramount importance in identifying candidate trees to be thinned. This study develops a software tool (algorithm), which can conveniently and accurately estimate the stand spatial structure index. Our proposed algorithm considers the spatial relationships between a reference tree and its four adjacent trees, and computes the three most important stand structure indices (neighborhood comparison, uniform angle index and species mingling) using GIS (ArcEngine) and the C# programming language. The implementation of the developed algorithm (stand spatial structure index) shows that, for any reference tree assumed, its four adjacent trees identified by each of the three stand spatial structure indices (uniform angle index—UAI, mingling—M and neighborhood comparison—NC) are the same, indicating the consistency and accuracy between the three-stand spatial structural indices. For the same tree species in a forest stand, the computational results from each of the spatial structure indices used (UAI, M, NC) are also the same. Thus, the results of this algorithm developed in this study are consistent with that of the Winkelmass1.0 software (a type of software used to simulate stand spatial structure). As this article is based on the GIS technique, the computational results can be visually displayed and implemented on actual maps, making it more convenient and intuitive for forest management. The proposed approach will be useful for accurately identifying the trees to be thinned and helpful for maintaining the vigor stand structure. This study also demonstrates the implementation of the algorithm to the real-world data and proves that the computational process is simple and efficient. The application of this algorithm for the identification of trees to be thinned may help the stakeholders to focus their attention towards multi-functional forest management. The algorithm will also provide an important basis for optimizing thinning and maintaining well-structured forest stands. [ABSTRACT FROM AUTHOR]

Climate change is exacerbating the vulnerability of temperate forests to severe disturbances, potentially increasing tree mortality rates. Despite the significance of this issue, there has been a lack of comprehensive research on tree survival across extensive forest areas under the background of global climate change. To fill this gap, we conducted a detailed analysis of tree survival within a canopy nitrogen and water addition experimental platform in central China, utilizing data from two censuses and evaluating contributing factors. Our findings revealed 283 dead trees within the plots, predominantly of very small diameters (1–10 cm). The distribution of these dead trees varied among subplots, influenced by both biotic and abiotic factors. Notably, three dominant tree species were responsible for 64.8% of the deadwood biomass. The study determined that both the breast diameter and the quantity of dead trees, affected by surrounding trees and environmental conditions, played a critical role in deadwood biomass accumulation. This research offers an in-depth examination of deadwood biomass patterns in a temperate forest, highlighting the need to consider both experiment treatments and abiotic elements like topography in studies of forest ecosystem carbon. The insights gained from this study enhance our understanding of warm temperate forests' role in the global carbon cycle and offer valuable guidance for forest conservation and management strategies. [ABSTRACT FROM AUTHOR]

Canopy volume is a crucial biological parameter for assessing tree growth, accurately estimating forest Above-Ground Biomass (AGB), and evaluating ecosystem stability. Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) are advanced precision mapping technologies that capture highly accurate point clouds for forest digitization studies. Despite advances in calculating canopy volume, challenges remain in accurately extracting the canopy and removing gaps. This study proposes a canopy volume extraction method based on an improved PointNeXt model, fusing ALS and TLS point cloud data. In this work, improved PointNeXt is first utilized to extract the canopy, enhancing extraction accuracy and mitigating under-segmentation and over-segmentation issues. To effectively calculate canopy volume, the canopy is divided into multiple levels, each projected into the xOy plane. Then, an improved Mean Shift algorithm, combined with KdTree, is employed to remove gaps and obtain parts of the real canopy. Subsequently, a convex hull algorithm is utilized to calculate the area of each part, and the sum of the areas of all parts multiplied by their heights yields the canopy volume. The proposed method's performance is tested on a dataset comprising poplar, willow, and cherry trees. As a result, the improved PointNeXt model achieves a mean intersection over union (mIoU) of 98.19% on the test set, outperforming the original PointNeXt by 1%. Regarding canopy volume, the algorithm's Root Mean Square Error (RMSE) is 0.18 m3, and a high correlation is observed between predicted canopy volumes, with an R-Square (R2) value of 0.92. Therefore, the proposed method effectively and efficiently acquires canopy volume, providing a stable and accurate technical reference for forest biomass statistics. [ABSTRACT FROM AUTHOR]

Significant influences on tree growth and forest functionality are attributed to nitrogen (N) addition. However, limited research has been conducted on the effects of N addition on forest spatial structure. In this study, we examined the effects of different N addition methods and concentrations on the stand spatial structure of a deciduous broad-leaved forest over the period 2012 to 2017. Five N addition treatments were implemented: CK (control group without N addition), CN25 (low N concentration added to the canopy), CN50 (high N concentration added to the canopy), UN25 (low N concentration added to the understory), and UN50 (high N concentration added to the understory). The results showed a moderate influence of N addition (CN25, CN50, UN25, UN50) on optimizing the stand spatial structure. CN25, CN50, and UN25 increased the mean values of the mingling degree (M) and neighborhood comparison (U), while decreasing the mean value of the uniform angle index (W), although these effects were not significant. Enhancements in the average value of the crowding degree (C) and comprehensive spatial structure index (CSSI) between 2012 and 2017 were found in all five treatments, demonstrating statistical significance. Assessing the distribution of the stand spatial structure index, CN25, CN50, and UN25 increased the proportion of M at an intensity (M = 0.75) and extreme intensity (M = 1), while decreasing the proportion at zero intensity (M = 0), weak intensity (M = 0.25), and moderate intensity (M = 0.5). A decrease in the proportion of trees was noted when U = 0 (excluding UN50), with no discernible pattern found in the frequency distribution of other values. CN50 and UN25 increased the proportion of W at a moderate level (W = 0.5), while CN25 and UN50 reduced it. No clear pattern was detected in the frequency distributions of other values. All five treatments increased the proportion of C at the maximum level (C = 1), while decreasing the proportions at levels of 0, 0.25, and 0.5 in 2017. Intriguingly, nitrogen addition treatments appeared to optimize the stand spatial structure to some extent and stimulated the growth of trees with larger diameters. Nevertheless, the short duration of the data collection period, spanning only five years, may have influenced the significance of the outcomes, underlining the requirement for extended studies. Conclusively, N deposition adjusted and enhanced the stand spatial structure to various degrees within the research region, providing valuable insights for further optimization of forest management. [ABSTRACT FROM AUTHOR]

Biomass productivity is of great significance for the evaluation of forest quality, which is important for the improvement of forest management. We propose the computational methods of biomass potential productivity (BPP) and biomass realistic productivity (BRP), both of which provide reliable practical guides for predicting forest growth under multi-aged, multi-species, and multi-layered canopy conditions. We used 2222 national forest inventory plots that were measured in four consecutive periods in the Jilin Province for this purpose. We analyzed and verified the computational methods of BPP based on the BRP and evaluated its practical significance. The results showed that growth models of the stand height, stand basal area, and stand biomass of four forest types (pure larch forest, larch broadleaf mixed forest, Mongolian oak pure forest, and Mongolian oak broadleaf mixed forest) fit adequately, BPP was greater than BRP, and this difference decreased with an increasing stand age, suggesting that the potential productivity of the middle-aged and young forest was higher than that of the mature forest, although the difference is minimal. In addition, the realistic productivity of stands with better site quality was close to the potential productivity, which is consistent with the biological significance of the potential productivity of the biomass. The degree of difference between the potential productivity of the biomass and the realistic productivity of biomass also decreases with the decline in site quality, and it can be termed as the potentially improved stand biomass. The BPP model was able to perform well in both the pure and mixed forests. The BRP not only verifies the rationality of the BPP but can be also used to quantify the forest site quality, which is helpful for evaluating forest growth and informed decision making in forestry. [ABSTRACT FROM AUTHOR]