Contrasting 20-year trends in NDVI at two Siberian larch forests with and without multiyear waterlogging-induced disturbances

The fate of a boreal forest may depend on the trend in its normalized difference vegetation index (NDVI), such as whether the NDVI has been increasing significantly over the past few decades. In this study, we analyzed the responses of two Siberian larch forests at Spasskaya Pad and Elgeeii in eastern Siberia to various waterlogging-induced disturbances, using satellite-based NDVI and meteorological data for the 2000–2019 period. The forest at Spasskaya Pad experienced waterlogging (i.e., flooding events caused by abnormal precipitation) during 2005–2008 that damaged canopy-forming larch trees and increased the abundance of water-resistant understory vegetation. By contrast, the forest at Elgeeii did not experience any remarkable disturbance, such as tree dieback or changes in the vegetation community. Significant increasing NDVI trends were found in May and June–August at Elgeeii (p < 0.05), whereas no significant trends were found at Spasskaya Pad (p > 0.05). NDVI anomalies in May and June–August at Elgeeii were significantly associated with precipitation or temperature depending on the season (p < 0.05), whereas no significant relationships were found at Spasskaya Pad (p > 0.05). Thus, the 20-year NDVI trend and NDVI–temperature–precipitation relationship differed between the two larch forests, although no significant trends in temperature or precipitation were observed. These findings indicate that nonsignificant NDVI trends for Siberian larch forests may reflect waterlogging-induced dieback of larch trees, with a concomitant increase in water-resistant understory vegetation.

imageseg: an R package for deep learning-based image segmentation

Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications. CNNs can perform very well in various tasks, especially for visual tasks and image data. Image segmentation (the classification of all pixels in images) is one such task and can for example be used to assess forest vertical and horizontal structure. While such methods have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. Here, we present R package imageseg which implements a workflow for general-purpose image segmentation using CNNs and the U-Net architecture in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with two models for forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 3288 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1468 understory vegetation images. Overall classification accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively), indicating robustness to variation in input images and good generalization strength across forest types and biomes. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with multiple classes and based on color or grayscale images, e.g. in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond.

Strip Clearcutting Drives Vegetation Diversity and Composition in the Moso Bamboo Forests

Strip clearcutting of Moso bamboo forests in southern China has seen increasing interest as a way of reducing harvesting costs. Previous research has shown that cutting influences the overstory structure and drives changes in the microclimate and soil properties. However, the effects of strip cutting on understory vegetation diversity and composition remain unclear. To better understand the influence of cutting on the understory vegetation, this study compares sites under natural restoration after cut and uncut sites in the Moso bamboo forest. We selected plots that were cut in 2019 (C19) and 2017 (C17), as well as unharvested plots as controls (CK). The results showed that strip clearcutting increased the understory vegetation richness and diversity, and a significant difference (A = 0.23, P = 0.001) existed in the composition of the vegetation between the three treatments. Furthermore, the decrease of soil total phosphorus and total potassium content resulted in the difference in undergrowth vegetation distribution and composition between the uncut plots and the cut plots. Our results suggest that strip clearcutting may not be harmful to biodiversity on a local scale in the Moso bamboo forest. Study Implications: This study demonstrates that strip clearcutting, which is an economically important harvesting method for bamboo, had significant effects on understory vegetation composition and diversity, and understory vegetation has not returned to preharvest levels after two years. The understory vegetation was affected by soil nutrient content and light conditions in the forest. We believe our research has made a significant contribution to the literature because bamboo is commercially important and its sustainable management is needed by many industries. This study highlights the impact of strip cutting on understory vegetation. The retention of understory vegetation characteristics is critical for the sustainable management of these forests, and this study not only demonstrates the dynamics of cut plots recovery but also increases our knowledge of this important species.

Plant Species Classification and Diversity of the Understory Vegetation in Oak Forests of Swat, Pakistan

The forest ecosystem has understory vegetation that plays a vital role in sustaining diversity, providing nutrients, and forming a useful association for developing a balanced ecosystem. The current study provides detailed insights into the plant biodiversity and species classification of the understory vegetation of Swat, Pakistan. The floral diversity of the area was comprised of 58 plant species belonging to 32 families. The physiognomy of the studied area was dominated by herbaceous growth form with 47 species. The dominant life-form class was hemicryptophytes with 19 species (33%), followed by nanophanerophytes with 15 species (26%) and therophytes with 13 species (22%). Of the 58 species, 43 plant species were associated with group III clustered by applying Ward’s agglomerative clustering that indicated wide sociability of the species in the studied oak-dominated forests. Group III had higher species richness (10.3), α-diversity (2.74) and β-diversity (9.85), and Margalef index values (3.95). While the group I had maximum Pielous and Simpson index values of 0.97 and 7.13, respectively. Redundancy analysis revealed that seven variables (i.e., latitude, elevation, clay, wilting point, bulk density, saturation, and electric conductivity) were significantly influential concerning the understory vegetation of oak-dominated forests. The understory vegetation of these forests plays an important role in the forest ecosystem of the region. The present study reveals floral divergence and physiognomic scenario of the unexplored study area, which could be an important reference for future ethnobotanical, phytosociological, and conservational endeavors. Moreover, this information is important to the success of efforts intended to prevent the loss of species diversity in these forests by destroying their natural habitats.

Clumped or regular? the role of thinning pattern on pine growth and soil water content in dense Aleppo pine post-fire stands

The development of silvicultural practices that seek to promote structural heterogeneity is increasingly demanded. This work investigates the effect of thinning spatial pattern on the response to pre-commercial thinning of dense Aleppo pine post-fire stands. On three replicated experimental sites in SE Spain, we applied the following treatments: 600 trees/ha, regular thinning pattern (600R), with residual trees evenly spaced; 600 trees/ha, aggregated thinning pattern (600A), with residual pines arranged in clumps of ∽25 trees with a local within-clump density of 2500 trees/ha; and control treatment, with no thinning applied (> 20,000 trees/ha). We assessed treatment effects on pine growth, size-growth relationships, soil water content, and understory vegetation over the first three years after thinning application. Both regular and aggregated thinning pattern similarly increased pine radial growth. In general, dbh growth rates in response to thinning were faster for smaller trees than for larger trees. The growth rate of pine height was higher for 600R and control than for 600A, indicating a positive effect on height of both low and very high pine densities. We found a near-term positive effect of aggregated pattern on water availability at the stand level, mostly resulting from enhanced soil water content in the canopy gaps. For both thinning patterns, the recovery of understory vegetation was dominated by resprouter species. This study highlights the potential of aggregated thinning patterns to enhance the complexity and heterogeneity of the pine stands without compromising pine growth, which could be of great use to managing pine forests in Mediterranean areas.

Water Dynamics in the Understory of a Pine Plantation Forest After Variable Retention Harvesting

Background: Variable Retention Harvesting (VRH) is a silvicultural technique applied to enhance forest growth, and restore forest stands to closely resemble their natural compositions. This study used sapflow and understory eddy covariance flux measurements to examine the impacts of four different VRH treatments on the dominant components of evapotranspiration including canopy transpiration and water flux from understory vegetation and soil. These VRH treatments were applied to an 83-year-old red pine (Pinus resinosa) plantation forest in the Great Lakes region in Canada and included 55% aggregated crown retention (55A), 55% dispersed crown retention (55D), 33% aggregated crown retention (33A), 33% dispersed crown retention (33D) and unharvested control (CN) plot. Results: Study results showed a positive relationship between thinning intensity and the growth of understory vegetation, and hence enhanced evapotranspiration. The contribution to evapotranspiration from understory vegetation and soil was more pronounced in the dispersed thinning treatments, as compared to the aggregated. Overall, canopy transpiration contributed to 83% of total evapotranspiration in the un-thinned control plot and 55, 58, 30, and 23% for the 55A, 55D, 33A and 33D plots, respectively. The thinning or retention harvesting enhanced the water use efficiency in all treatments.Conclusion: Our results suggest VRH treatments that follow a dispersed harvesting pattern may provide the optimal balance between forest productivity and evapotranspiration or stand water use. Furthermore, a balance of contributions from both the canopy and successional understory vegetation and soil, as observed in the 55% retention harvesting treatment, may increase the resiliency of forest to climate change. These findings will help researchers, forest managers and decision-makers to improve their understanding of thinning impacts on water and carbon exchanges in forest ecosystems and adopt appropriate forest management practices to enhance their carbon sequestration capabilities, water use efficiency and resilience to climate change.