Canopy Cover Density Research Articles

Spatio-temporal Analysis of Agroforestry Systems in Hotan Using Multi-source Remote Sensing and Deep Learning

In the Hotan region of Xinjiang, where arable land is scarce, an agroforestry system integrating walnut trees with crops has been implemented to maximize land-use efficiency. While this dense planting enhances land use, it also limits the availability of light to the understory crops, potentially impacting their yield and quality. To address this issue and enhance the system's sustainability and productivity, precise delineation of the planting structure is critical. This study proposes a novel framework that leverages multi-source remote sensing data combined with advanced deep learning techniques to analyze the agroforestry planting structure. The methodological approach consists of three key phases. First, an instance segmentation model was employed to extract farmland parcels from high-resolution imagery, providing a basis for vegetation classification. Next, a time series model using irregular satellite image time series (irSITS) tracked the growth dynamics of the vegetation. Finally, the spatial planting structure of the walnut trees was quantified using the D-LinkNet model, integrated with a template filling algorithm.The results demonstrated a classification accuracy of 97.85% in extracting parcel-level planting structures, identifying 42,955 farmland parcels, including 21,153 intercropped parcels. The temporal and spatial characteristics of the agroforestry system were then analyzed, leading to a grading of canopy cover and walnut tree density within the intercropped areas. This comprehensive spatiotemporal planting structure offers a valuable foundation for informed local agricultural policy adjustments. In conclusion, this approach advances the understanding of complex agroforestry systems and provides a robust scientific basis for optimizing intercropping practices, contributing to sustainable agricultural development in arid regions.

Modeling and spatialization of biomass and carbon stock using unmanned Aerial Vehicle Lidar (Lidar-UAV) metrics and forest inventory in cork oak forest of Maamora

Recently, the concerns about climate change have heightened the need for effective methods for estimating and mapping Biomass and Carbon stock at local, national, continental, and global scales. Reliable Biomass and Carbon stock quantification and spatialization is a challenge, especially in degraded Mediterranean Cork oak forest. To estimate and map Biomass (Btree−Total) and Carbon stock (Cst−total), we explored an improved approach using extracted metrics collected by Lidar-UAV (unmanned aerial vehicles Lidar), combined with forest inventory data. We approach three types of models for data analysis: Simple linear regression, multiple linear regressions, and stepwise multiple linear regression. The best Biomass and Carbon stock model fit is the Stepwise multiple linear regressions, involving the following metrics: maximum elevation, canopy cover and point cloud density and intensity. Our finding provides a quantification and spatialization Biomass and Carbon stock model based on Lidar-UAV metrics in Cork Oak Mediterranen forest and the results confirm the degraded state of Maamora Forest with a Biomass and Carbon stock relatively medium to low.

Fishers (Pekania pennanti) are forest structure specialists when resting and generalists when moving: behavior influences resource selection in a northern Rocky Mountain fisher population

BackgroundStudies of animal habitat selection are important to identify and preserve the resources species depend on, yet often little attention is paid to how habitat needs vary depending on behavioral state. Fishers (Pekania pennanti) are known to be dependent on large, mature trees for resting and denning, but less is known about their habitat use when foraging or moving within a home range.MethodsWe used GPS locations collected during the energetically costly pre-denning season from 12 female fishers to determine fisher habitat selection during two critical behavioral activities: foraging (moving) or resting, with a focus on response to forest structure related to past forest management actions since this is a primary driver of fisher habitat configuration. We characterized behavior based on high-resolution GPS and collar accelerometer data and modeled fisher selection for these two behaviors within a home range (third-order selection). Additionally, we investigated whether fisher use of elements of forest structure or other important environmental characteristics changed as their availability changed, i.e., a functional response, for each behavior type.ResultsWe found that fishers exhibited specialist selection when resting and generalist selection when moving, with resting habitat characterized by riparian drainages with dense canopy cover and moving habitat primarily influenced by the presence of mesic montane mixed conifer forest. Fishers were more tolerant of forest openings and other early succession elements when moving than resting.ConclusionsOur results emphasize the importance of considering the differing habitat needs of animals based on their movement behavior when performing habitat selection analyses. We found that resting fishers are more specialist in their habitat needs, while foraging fishers are more generalist and will tolerate greater forest heterogeneity from past disturbance.

Canopy cover and soil moisture influence forest understory plant responses to experimental summer drought.

Extreme droughts are globally increasing in frequency and severity. Most research on drought in forests focuses on the response of trees, while less is known about the impacts of drought on forest understory species and how these effects are moderated by the local environment. We assessed the impacts of a 45-day experimental summer drought on the performance of six boreal forest understory plants, using a transplant experiment with rainout shelters replicated across 25 sites. We recorded growth, vitality and reproduction immediately, 2 months, and 1 year after the simulated drought, and examined how differences in ambient soil moisture and canopy cover among sites influenced the effects of drought on the performance of each species. Drought negatively affected the growth and/or vitality of all species, but the effects were stronger and more persistent in the bryophytes than in the vascular plants. The two species associated with older forests, the moss Hylocomiastrum umbratum and the orchid Goodyera repens, suffered larger effects than the more generalist species included in the experiment. The drought reduced reproductive output in the moss Hylocomium splendens in the next growing season, but increased reproduction in the graminoid Luzula pilosa. Higher ambient soil moisture reduced some negative effects of drought on vascular plants. Both denser canopy cover and higher soil moisture alleviated drought effects on bryophytes, likely through alleviating cellular damage. Our experiment shows that boreal understory species can be adversely affected by drought and that effects might be stronger for bryophytes and species associated with older forests. Our results indicate that the effects of drought can vary over small spatial scales and that forest landscapes can be actively managed to alleviate drought effects on boreal forest biodiversity. For example, by managing the tree canopy and protecting hydrological networks.

Forest floor environment overrules global change treatment effects on understorey communities in a mesocosm experiment.

Light availability profoundly influences plant communities, especially below dense tree canopies in forests. Canopy disturbances, altering forest floor light conditions, together with other environmental changes such as climate change, nitrogen deposition and legacy effects from previous land-use will simultaneously impact forest understorey communities. Yet, knowledge on the individual effects of these drivers and their potential interactions remains scarce. Here we performed a forest mesocosm experiment to assess the influence of warming, illumination (simulating canopy opening), nitrogen deposition and soil land-use history (comparing ancient and post-agricultural forest soil) on understorey community composition trajectories over a 7-year period. Strikingly, understorey communities primarily evolved in response to the deeply shaded ambient forest conditions, with experimental treatments exerting only secondary influences. The overruling trajectory steered all mesocosms towards slow-colonizing forest specialist communities dominated by spring geophytes with lower nutrient-demand. The illumination treatment and, to a lesser extent, warming and agricultural land-use legacy slowed down this trend by advancing fast-growing resource-acquisitive generalist species. Warm ambient temperatures induced thermophilization of plant communities in all treatments, including control plots, towards higher dominance of warm-adapted species. Nitrogen addition accelerated this thermophilization process and increased the community light-demand signature. Land-use legacy effects were limited in our study. Our findings underscore the essential role of limited light availability in preserving forest specialists in understorey communities and highlight the importance of maintaining a dense canopy cover to attenuate global change impacts. It is crucial to integrate this knowledge in forest management adaptation to global change, particularly in the face of increasing demands for wood and wood products and intensified natural canopy disturbances.

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion.

Diameter and height are crucial morphological parameters of banana pseudo-stems, serving as indicators of the plant's growth status. Currently, in densely cultivated banana plantations, there is a lack of applicable research methods for the scalable measurement of phenotypic parameters such as diameter and height of banana pseudo-stems. This paper introduces a handheld mobile LiDAR and Inertial Measurement Unit (IMU)-fused laser scanning system designed for measuring phenotypic parameters of banana pseudo-stems within banana orchards. To address the challenges posed by dense canopy cover in banana orchards, a distance-weighted feature extraction method is proposed. This method, coupled with Lidar-IMU integration, constructs a three-dimensional point cloud map of the banana plantation area. To overcome difficulties in segmenting individual banana plants in complex environments, a combined segmentation approach is proposed, involving Euclidean clustering, Kmeans clustering, and threshold segmentation. A sliding window recognition method is presented to determine the connection points between pseudo-stems and leaves, mitigating issues caused by crown closure and heavy leaf overlap. Experimental results in banana orchards demonstrate that, compared with manual measurements, the mean absolute errors and relative errors for banana pseudo-stem diameter and height are 0.2127 cm (4.06%) and 3.52 cm (1.91%), respectively. These findings indicate that the proposed method is suitable for scalable measurements of banana pseudo-stem diameter and height in complex, obscured environments, providing a rapid and accurate inter-orchard measurement approach for banana plantation managers.

Analisis Komparatif Tutupan Mangrove Menggunakan Citra Landsat 9 dan Sentinel 2A di Desa Labuan Tereng Kabupaten Lombok Barat

Labuan Tereng Village is a place where mangrove vegetation grows and develops. The location close to the harbor sheet makes the mangrove ecosystem damaged by port activities. High port activity can disrupt the condition of mangrove ecosystem development in Labuan Tereng Village. This study was conducted to evaluate mangrove health by looking at canopy cover or canopy density using the Hemispherical Photography method analyzed using Landsat 9 and Sentinel 2A to assess the extent of relationship between mangrove canopy cover and images. The findings revealed that the mean mangrove cover density value of 66% which is included in the medium category. Statistical analysis of several vegetation indexes with mangrove cover obtained the highest linear regression results in the SAVI model with a value of 0,41 for Landsat 9 images and 0,65 for Sentinel 2A images. The results of image data analysis on Sentinel 2A show a smaller pixel size value and contain more pixels so as to produce better and complex data analysis when compared to Landsat 9 imagery.

Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms

Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and remotely sensed data from Landsat 8 and LiDAR, we analyzed the effects of structural and functional vegetation traits and environmental factors on burn severity. This analysis revealed that the difference normalized burn ratio (dNBR) was a more reliable indicator of burn severity compared to the relative dNBR (RdNBR). Stepwise regression identified pre-fire normalized difference vegetation index (NDVI), canopy cover, and tree density as significant variables across all land cover types that explained burn severity, suggesting that denser areas with higher vegetation greenness experienced more severe burns. Interestingly, residuals between the actual and estimated dNBR were lower in herbaceous zones compared to denser forested areas at similar elevations, suggesting potentially more predictable burn severity in open areas. Spatial analysis using Geary’s C statistics further revealed a strong negative autocorrelation: areas with high burn severity tended to be clustered, with lower severity areas interspersed. Overall, this study demonstrates the potential of readily available remote sensing data to predict potential burn severity values before a fire event, providing valuable information for forest managers to develop strategies for mitigating future wildfire damage.

Impact of Establishment Techniques and Weed Management Strategies on Chemical Properties of Soil in Wet Direct-seeded Winter Rice

Aim: The aim of the experiment was to check the effect of establishment techniques and weed management strategies on chemical properties of soil. Study Design: The design of the experiment was split plot design. Place and Duration of Study: Assam Agricultural University-Assam Rice Research Institute, Titabar, Assam, India, during the sali season of 2022-2023. Methodology: A total of three establishment techniques viz., broadcasting , drum seeding and line sowing in main plot and six different weed management practices namely hand weeding at 20, 40 and 60 DAS , pyrazosulfuron-ethyl @ 30 g/ha at 2 DAS fb bispyribac-Na @ 25 g/ha at 25 DAS, pyrazosulfuron-ethyl @ 30 g/ha at 2 DAS fb mechanical weeding at 40 and 60 DAS, pyrazosulfuron-ethyl @ 30 g/ha at 2 DAS fb bispyribac-Na @ 25 g/ha at 25 and 45 DAS , weed free check and weedy check in sub plot , respectively. Where DAS=Days after sowing and fb=followed by. Results: Results revealed that among various establishment techniques, drum seeding technique resulted in the lowest available nitrogen (237.87 kg/ha), phosphorus (21.67 kg/ha) and potassium (150.15 kg/ha) in the soil, respectively after harvest. Moreover, under various weed management practices, pyrazosulfuron-ethyl @ 30g/ha at 2 DAS fb bispyribac-Na @ 25 g/ha at 25 and 45 DAS showed lowest nitrogen (236.87 kg/ha), phosphorus (21.37 kg/ha) and potassium (149.23 kg/ha) in the soil, respectively after harvest which was next to weed free check. Conclusion: Combined application of drum seeding along with pyrazosulfuron-ethyl @ 30g/ha at 2 DAS fb bispyribac-Na @ 25 g/ha at 25 and 45 DAS can facilitate optimum plant population of crop with dense canopy cover, resulting in higher uptake of essential minerals and nutrients by the crop, enhancing the overall yield of rice while minimizing weed population throughout the growing period.

A telepítési sűrűség és a lombkorona-borítottság vizsgálata budapesti szabadtereken

Trees in public open spaces are an important part of the urban green space system, which has to adapt to increasingly difficult conditions due to urbanisation, climate change and human activity. Green spaces are a major factor in improving the microclimate, helping to protect biodiversity and playing an important aesthetic role, so they need to be preserved and enhanced. In our country, since the 2000s, a large number of studies and civil initiatives have highlighted the importance of green spaces, but typically from a quantitative approach. However, improving the quality of planting is an equally important task. Our research assesses the canopy cover of trees in open spaces in Budapest in terms of planting density, with a focus on the planting distance of trees in certain squares and streets and the resulting cover values. In our studies, we have considered the quantitative and qualitative variation in crown canopy cover at the time of planting and in maturity, so we can formulate ideal planting suggestions based on the environment, requirements and characteristics (crown shape, growth vigour) of each taxa. In the series of case studies, we first studied the woody species of one of the most important intermodal nodes of Budapest, Móricz Zsigmond Square and Bartók Béla Street. The canopy cover of the square reaches the ideally defined minimum canopy cover of 25-30% in 5-7 years. Considering canopy cover and planting density, it was found that in 2023 the canopy cover of the trees in the Móricz Zsigmond square averages 13%, while at maturity it will cover more than 50%. This means that about half of the tree crowns will be able to have the highest potential impact in terms of ecosystem services.

Forest Canopy Density Monitoring by Using Geospatial Techniques: A Study of Gadchiroli District, Maharashtra, India

Forest cover is a crucial part of the environment. It makes an essential contribution to the socio-economic and environmental welfare of the Nation. However, these forests are seriously threatened by deforestation, increased mining activity, population growth, uncontrolled urbanisation, a developing tendency of industrialisation, agricultural land purpose, shifting cultivation, effects on soil, water, and biodiversity, unsustainable forms of human activities and others. As a result, developing strategies to promote sustainable forest management, prevent desertification, prevent soil erosion, and halt environmental degradation is essential. Remote Sensing has enabled humans to observe and obtain information about the earth's surface with spatiotemporal changes. The Indian state of Maharashtra's Gadchiroli district is used as a study region. This study investigates forest canopy density and the spatiotemporal changes in forests. The geographical pattern of forest canopy density is displayed by several indices using data from Landsat 5 and Landsat 8 at 30 m spatial resolution. Try to make the study more relevant in the contemporary world. The research area's forest cover has changed through time, as shown by several multi-temporal data sets (1989 and 2019). The results revealed that between 1989 and 2019, forest canopy cover and forest density decreased. It indicated that over 30 years, 1045.51 sq. km of land had degraded. The amount of highly dense forest has decreased significantly over the research period, whereas the non-forest area has been gradually growing for the past 30 years.

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

Imprint of selective logging on seed regeneration of Brachystegia spiciformis Benth. in dry miombo woodland in Zimbabwe

Woodlands that have been affected by selective logging are ubiquitous in southern Africa but how this harvesting practice impacts seed regeneration of dominant tree species remains unclear. To address this deficiency, the study assessed the effects of different selective logging intensities on post-dispersal seed predation and seedling recruitment of Brachystegia spiciformis Benth. in Ruzawi Forest, Zimbabwe. Data were collected in blocks that were subjected to selective logging intensities of 0 % (00 L, unlogged), 5 % (05 L), 10 % (10 L), and 15 % (15 L). Ten years after logging, three transects (200×30 m) were laid in each block and five main plots (20×20 m) were laid in each transect, making a total of 60 plots. Low-intensity selective logging of 5 % did not cause any significant changes to seed predation levels, densities of true seedlings, seedling sprouts and root suckers, and soil nutrient concentrations. Seed predation was significantly greater in 10 L and 15 L sites than in unlogged and 05 L sites. As logging intensity increased, the relative importance of sexual reproduction decreased and that of vegetative reproduction increased. Canopy cover and seedling density decreased as logging intensity increased but a reverse trend was observed with grass biomass and root sucker density. Grass biomass was negatively correlated with the density of true seedlings and seedling sprouts. Soil organic C, N, P, and K were all significantly reduced in 10 L and 15 L sites than in unlogged and 5 L sites. Our results suggest that unless selective logging is practiced at very low harvest intensities, it can create opportunities for the growth of dense grass swards, potentially promoting rodent seed predation and limiting seedling recruitment. This study recommends effective use of low-impact selective logging practices as well the adoption of woodland management policies that prioritize sustainable utilization practices.

Eurasian woodcock (Scolopax rusticola) in intensively managed Central European forests use large home ranges with diverse habitats

Modern management of Europe's forests should strive for a delicate balance between efficient production, which is their primary purpose, and forest management practices that conserve and support biodiversity. This requires detailed knowledge of the ecology of forest-dwelling species, including their site-specific behavior and habitat preferences. One such species is the Eurasian woodcock (Scolopax rusticola), an important game bird that breeds throughout the Palearctic region. Despite its importance, our knowledge of woodcock ecology is limited, and collecting reliable data is challenging due to the species' cryptic behavior. Here, we used high-precision GPS-GSM telemetry to determine home range size and to investigate habitat selection in male Eurasian woodcocks inhabiting Central European forests. Using Bayesian mixed-effects models corrected for spatial autocorrelation, we compared several habitat characteristics (including dominant tree and herb species) of sites where woodcocks occurred with randomly selected sites within their home ranges. Our results show that woodcock in Central Europe move over much larger home ranges during the breeding season (median 7.26 km2) than previously reported in other parts of their breeding range, although they regularly use only small part of this large area (median 1.09 km2). Within their home range, woodcocks used a wide range of habitats, including intensively managed conifer monocultures, but preferred deciduous forests, particularly those dominated by European white birch (Betula pendula). Specifically, 24 % of the plots with woodcock were dominated by birch, compared to only 12 % of the randomly selected plots. Importantly, regardless of dominant tree species, woodcocks preferred sites with dense vegetation cover, but good terrain traversability. During the day, woodcocks preferred sites with dense canopy cover, while at night they preferred more open areas.However, in contrast to previously published results elsewhere, these open areas were more likely to be sparse forest (73%) than grasslands or non-forest cropland (24%), and were characterized by less continuous canopy cover, with a median of only 40%.Our study highlights the importance of specific microhabitats, such as sites with dominant representation of commercially non-target broadleaved species (birch), or sites with sparse canopy cover but denser understorey vegetation. Appropriate management practices that promote such specific microhabitats would have a positive impact on populations of woodcock and other forest species inhabiting commercial forests in Central Europe.

Where are the large trees? A census of Sierra Nevada large trees to determine their frequency and spatial distribution across three large landscapes

Large trees (≥76.2 cm/≥30″ DBH) and especially very large trees (≥101.6 cm/≥40″ DBH) are key structures of Sierra Nevada forests for their ecological function, habitat, and carbon storage. Many of these trees have been lost to historic harvest and more recently to drought and wildfires. Understanding the current frequency and distribution of these large trees is essential to understanding their ecological contribution and management needs. We used airborne lidar to census large trees across three Sierra Nevada landscapes (cumulatively 396 K ha) in lower (dominated by ponderosa pine and mixed conifer) and upper (dominated by red fir) montane forest zones. We used data from a network of Forest Inventory and Analysis (FIA) plots to interpret our lidar-based results for large tree frequency, species, and ages. The lidar data identified > 8 M large and > 2.7 M very large trees, and their mean densities were similar to those from FIA data. Large portions of our study areas had either no or low densities (<20) of large trees per hectare. We found that large and very large tree concentrations were spatially aggregated with most in denser patches containing 20 to 50 + large trees per hectare. Depending on the study area, these often sizable (>1000 ha) patches of dense large trees can cover 20% to 40% of the landscape. (Patches of denser very large trees cover less of the landscape, typically 5% to 10%). However, these large patches are rarely simple blocks. Instead, they typically form complex amorphous matrices interspersed with patches of forests containing shorter trees or non-forest cover. Crucially, almost all large trees were in stands with high canopy cover, suggesting horizontal fuel continuity and low resilience to future wildfires. For lower montane large trees, canopy cover versus large tree density showed almost a unimodal response with canopy cover of 60% to 80% for locations with > 20 large trees per ha. For upper montane large trees, canopy cover versus large tree density showed a more linear relationship for all three study areas. High levels of canopy cover, especially for lower montane forests, suggest settings in which infilling following decades of fire suppression have created overly dense stands with lower resilience to drought and wildfire. Other studies have documented substantial recent losses of these large trees to both factors. The high canopy cover within which almost all large trees exist emphasizes the need for treatment almost everywhere that large trees are present for lower montane forests. This likely will require treatments both within the stands that contain large trees and across the landscapes in which they are found.

Exploring the landscape scale influences of tree cover on crop yield in an agroforestry parkland using satellite data and spatial statistics

Trees in agroforestry parklands influence crops both through competitive and facilitative mechanism, but the effects are challenging to disentangle due to the complexity of the system with high variability in tree cover structure and species diversity and crop combinations. Focusing on a landscape in central Burkina Faso dominated by Vitellaria paradoxa and Parkia biglobosa, this paper examines how tree cover influences crop yield at landscape scale using satellite data and spatial statistics. Our analysis is based on data from 2017 to 2018 with differences in rainfall to assess the stability in identified relationships. Our findings showed that tree canopy cover and tree density inside the fields tended to decrease crop yield because of competition, but also that these variables when considering the surrounding landscape exerted an opposite effect because of their buffering effects. The explanatory variables representing soil properties did have limited effects on crop yield in this study. These patterns were consistent during the two years of monitoring. Overall, our results suggest that farmers in this area might manage the tree cover in a way that optimizes sustainable yields as canopy cover and tree density in most parklands is below the limits identified here where competition outweight the facilitative effects.

Determinants of Aboveground Carbon Storage of Woody Vegetation in an Urban–Rural Transect in Shanghai, China

Carbon storage of urban woody vegetation is crucial for climate change mitigation. Biomass structure and species composition have been shown to be important determinants of carbon storage in woody vegetation. In this study, allometric equations were used to estimate the aboveground carbon storage of urban woody vegetation along an urban–rural transect in Shanghai. A random forest model was developed to evaluate the importance scores and influence of species diversity, canopy cover, species evenness, and tree density on aboveground carbon storage. The results showed that tree density, canopy cover, species diversity, species evenness, and aboveground carbon storage of urban woody vegetation vary with the degree of urbanization and urban–rural environment. In addition, the Bayesian optimization algorithm optimized the random forest model parameters to enhance model accuracy, and good modeling results were demonstrated in the study. The R2 was at 0.61 in the testing phase and 0.78 in the training phase. The root mean square errors (RMSEs) were 0.84 Mg/ha of carbon in the testing phase and 0.57 Mg/ha in the training phase, which is indicative of a low error of the optimized model. Tree species diversity, canopy cover, species evenness, and tree density were found to correlate with aboveground carbon storage. Tree density was the most important contributor, followed by species diversity and canopy cover, and species evenness was the least effective for aboveground carbon storage. Meanwhile, the results of the partial dependence analysis indicated the combination of factors most conducive to aboveground carbon storage at a tree density of 2200 trees/ha, canopy cover of 50%, species diversity of 1.2, and species evenness of 0.8 in the transect. The findings provided practical recommendations for urban forest managers to adjust the structure and composition of woody vegetation to increase carbon storage capacity and reduce greenhouse gas emissions.

Fire severity influences large wood and stream ecosystem responses in western Oregon watersheds

BackgroundWildfire is a landscape disturbance important for stream ecosystems and the recruitment of large wood (LW; LW describes wood in streams) into streams, with post-fire management also playing a role. We used a stratified random sample of 4th-order watersheds that represent a range of pre-fire stand age and fire severity from unburned to entirely burned watersheds to 1) determine whether watershed stand age (pre-fire) or fire severity affected riparian overstory survival, riparian coarse wood (CW; CW describes wood in riparian areas), LW, or in-stream physical, chemical, and biological responses; and 2) identify relationships of LW with riparian vegetation and in-stream physical, chemical, and biological factors.ResultsAt higher fire severities, LW and CW diameter was smaller, but volume did not change in the first year post-fire. Larger size of CW in riparian areas versus LW in streams suggests potential future recruitment of larger-diameter wood into streams from riparian zones in severely burned watersheds. Fire severity exerted strong control on stream responses across watersheds, explaining more of the variation than stand age. At higher fire severities, riparian tree mortality, salvage logging, light, dissolved organic matter (DOM) concentrations, and fish densities were higher, whereas canopy cover, LW diameter, macroinvertebrate diversity, and amphibian density were lower. In watersheds with older stand ages, elevation and mean annual precipitation were greater but mean annual temperature, specific ultra-violet absorption at 254 nm, and phosphorus concentrations were lower. Overstory mortality in burned riparian areas was lower for red alder (12%) than western redcedar (69%).ConclusionsOur results link forested streams, fire, and LW by identifying key relationships that change with fire severity and/or watershed stand age. Severe fires burn more overstory riparian vegetation, leading to increased light, DOM concentrations, and macroinvertebrate and fish densities, along with reduced canopy cover, LW diameter, macroinvertebrate diversity, and amphibian densities. We highlight an important function of red alder in riparian zones—as a fire-resistant species, it may help facilitate a more rapid recovery for streams in fire-prone landscapes. Continued comprehensive aquatic and riparian ecosystem monitoring of these watersheds will aid in understanding long-term effects of post-fire management activities (salvage logging) on aquatic ecosystems.

Vegetation type change in California’s Northern Bay Area: A comparison of contemporary and historical aerial imagery

The North Bay area of California is a populous and ecologically diverse area that has experienced significant changes in the past century, as well as a series of recent wildfires, after over a century of fire suppression practices. While much research has been conducted quantifying drivers and patterns of vegetation change in conifer-dominated ecosystems, and how such changes have influenced current trends in fire behavior, studies of similar focus and scale are rarer in non-conifer ecosystems, including mixed-hardwood forests or shrub-dominated ecosystems in central and coastal Northern California. This is despite the fact that ecosystems other than conifer forests make up the majority of area burned in California wildfires. As such, expanding research focused on patterns of large-scale vegetation change as a possible driver of this trend in this area is a priority. In this study, we sought to map the overall extent and patch sizes of broad vegetation classes across a 52,000 ha study area based on historical (1948) and contemporary (2014) aerial imagery and to investigate shifts in vegetation patterns, as well as potential pathways and drivers of detected changes. We classified vegetation types through segmenting our imagery into homogenous polygons, and assigning broad vegetation categories using a random forest algorithm. We then analyzed patterns of change using spatial statistics and conditional inference tree analyses. We detected a large increase (12%) in the relative landscape proportion and average patch size of the forest class, characterized by dense tree canopy cover. Woodlands and shrub patches were most susceptible to type change, with the majority (57% and 65%, respectively) of converted areas subsequently identified as denser forest stands. By contrast, herbaceous and forest patches were most persistent. We additionally found that disturbance history, specifically whether an area burned or not, and topographic variables, including elevation and aspect, were important influences on the likelihood of vegetation persistence, while slope and water availability were not. Historical aerial imagery, which provides fine resolution and accurate data over a large spatial scale, is a useful tool for detecting landscape-scale vegetation shifts in ecosystems where widespread vegetation monitoring was not common historically. The marked increase in dense forest we detected, specifically due to the conversion of large areas of shrub and woodland vegetation may correspond to higher surface and ladder fuel load and continuity, and potentially higher wildfire risk. Fuel reduction treatments typically implemented in conifer-dominated forests may also be warranted in these mixed hardwood forests. However, more research is needed to understand drivers of change in non-conifer-dominated ecosystems in California and how such change influences wildfire behavior.

Using radio frequency identification (RFID) technology to characterize nest site selection in wild Japanese tits Parus minor

Selecting a suitable nest site is critical to the survival and reproduction of birds. Prospecting allows individuals to gather information on the local quality of potential future breeding sites, which may help them make the best nest site selection decision. However, few studies have focused on the direct links between the prospecting activity of breeders and subsequent nest site selection. In this study, we investigated the prospecting pattern of Japanese tits Parus minor during the pre‐breeding period of the first breeding attempt and whether nest site characteristics influence their nest box visiting behaviour and occupied nest site. We used radio frequency identification (RFID) to track the movements of Japanese tits visiting nest boxes and compared nest site characteristics between visited and unvisited (control) nest boxes, as well as between visited and occupied nest boxes. We found that Japanese tits started visiting nest boxes approximately 20 days before breeding, visited an average of six nest boxes and eventually chose the most visited nest box for breeding activities. Japanese tits were more likely to visit nest boxes that had less canopy cover and lower shrub density but a greater total number of surrounding trees and ultimately chose breeding nest boxes with a smaller entrance inclination, in nesting trees with a larger diameter at breast height (DBH) which were surrounded by trees with a larger DBH. Our results suggest that Japanese tits visit several potential breeding sites before choosing breeding nest boxes and that nest site characteristics can influence their prospecting activity and nest site selection.