Tree Canopy Density Research Articles

Phyto-sociological changes in the Goima Forest, Gujarat due to human disturbances and their implications for conservation

Aim: The study aimed to understand the phytosociological changes by the impact of forest fringe human disturbance on the Goima forest of Valsad district in Gujarat. Methodology: The study area was classified into different tree canopy density and disturbance classes, and ecological indices were calculated for each class following the quadrate survey method. Results: A total of 772 individual plants of 141 species from 53 families were recorded during quadrate sampling, with variations in aspects being especially important predictors of biotic diversity. The study found that the maximum taxa of trees (30), herbs (24), and grasses (7) were reported in the high-disturbance class, while for shrubs (16) and climbers (10), it was in the middle-disturbance class. Dominance (D)was higher in the moderately dense forest as well as in the moderate disturbance class for trees (0.11, 0.13), shrubs (0.24, 0.21), herbs (0.13, 0.12), but for climbers, D was higher in dense forest (0.40) and in moderate disturbance class (0.22), and for grasses, it was higher in the moderately dense forest (0.69) and less disturbed class (0.58). Shannon's index (H) for trees (2.93), herbs (2.85), and grasses (1.68) was maximum in the higher disturbance class, while for shrubs (2.23) and climbers (2.10), it was highest in the low disturbance class. Interpretation: The study highlights that the phytosociology of the Goima forest changes due to human disturbances. The importance of understanding and conserving biodiversity in the Goima forest, especially given its unique flora and its critical role in supporting local communities. The research provides valuable scientific information that can aid in the better management and protection of the forest area. Key words: Canopy class, Goima forest, Human disturbance, Phytodiversity, Species distribution

Promoting equity in urban heat: a greening approach for HOLC's legacy in Houston, Texas

ABSTRACT The lingering influence of the Homeowners’ Loan Corporation (HOLC), a programme established during the 1930s as part of the New Deal, persists in creating a barrier that prevents progress for marginalised communities [Faber, J. 2020. “We Built This: Consequences of New Deal Era Intervention in America’s Racial Geography.” American Sociological Review 85 (5): 739–775. https://doi.org/10.1177/000312242094846]. Particularly notable is the urban heat island phenomenon, currently evident in numerous metropolitan areas nationwide, which disproportionately impacts neighbourhoods that were redlined due to the historical HOLC rankings and subsequent lack of financial investments. This study undertakes a comparative and analytical examination of temperature variations within HOLC-designated zones in Houston, Texas. It employs basic social indicators and evaluates tree canopy density through analysis of variance. Furthermore, spatial regression modelling is employed to mitigate temperature differentials between neighbourhoods characterised by urban tree canopy, especially focusing on the contrast between “A” and “D” zones. In addition, drawing on the statistical parameters derived from estimation, the study's scenario analysis uncovers that a 54% augmentation in tree canopy density within “D” zones would effectively establish thermal equity. As a result, elevating tree canopy density emerges as a strategy to curtail temperature discrepancies while concurrently introducing cooling impacts on surrounding districts. The dataset utilised for the study encompasses satellite-derived urban temperature records, spatially extensive data on tree canopy density, vulnerability insights from the U.S. Census Bureau, and demographic data, including the proportion of African American residents primarily concentrated in “D” graded districts. Thus, this study not only holds potential for enhancing circumstances within marginalised communities but also introduces a pathway to cultivate an enabling environment for all residents.

Assessing Normalized Difference Vegetation Index as a proxy of urban greenspace exposure

The Normalized Difference Vegetation Index (NDVI) is a popular proxy of urban greenspace (UGS). However, it’s unclear how NDVI approximates physical characteristics of UGS in the context of urban health studies, causing ambiguities in translating research findings to UGS management. Therefore, we collected data from Landsat and MODIS satellites and Lidar 3D scans in New York City as of circa 2013, and we evaluated linear and non-linear relationships between NDVI and UGS characteristics. We found that: (1) % UGS was the best predicted UGS characteristic by NDVI (R2: 0.35–0.90, varies by data source and unit of analysis), whereas average tree height was the worst (R2: 0.09–0.46). The predictive power on % canopy cover, tree density, and crown volume density was in a similar range (R2: 0.10–0.67). Prediction improved with finer-resolution NDVI sources and larger units of analysis at the cost of losing useful variations; (2) There was a saturation effect where a linear relationship underestimated UGS characteristics in areas of high NDVI. These areas typically had NAIP-NDVI greater than the range of 0.08–0.25, Landsat-NDVI greater than the range of 0.42–0.65, and MODIS-NDVI greater than the range of 0.49–0.75; (3) Smaller absolute errors from a linear NDVI-UGS relationship were often found in more developed locations. We therefore recommend NDVI as a reliable predictor of UGS coverage and its use in longitudinal studies. Future studies should also consider fine resolution land cover maps and Lidar, which are increasingly available to derive detailed UGS characteristics.

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.

Evaluation of the Contribution of Cemeteries to Urban Ecosystem Services: A Case Study of Edirne

Although green spaces in cities play a critical role in providing ecosystem services, cemeteries are often consideredan overlooked part of urban green spaces. However, in recent years, these areas have come to the forefront due totheir potential to provide a range of important ecosystem services such as conservation of biological diversity,habitat provision, air quality regulation, carbon storage, and water management. This study aims to concretelydetermine the contribution of four cemeteries -Acıçeşme, Buçuktepe, Bademlik and Karaağaç- located within thecentral district boundaries of Edirne, to urban ecosystem services. Using the i-Tree Canopy model, the land coverdistribution, air pollution impacts, and carbon sequestration/storage amounts of these cemeteries were analysed.The results indicate that Buçuktepe cemetery provides the highest ecosystem services due to its large area and densetree canopy. It is estimated that the 64.2% tree canopy cover in Buçuktepe Cemetery removes 365.44 kg of gasesand particulate matter from the air annually, sequesters 16.02 tonnes of carbon and stores 402.21 tonnes of carbon.These results show that cemeteries make a significant contribution to regulating ecosystem services in urban areas.The study highlights the potential for transforming cemeteries into multifunctional landscapes that contribute tourban ecosystem services.

Different diversity patterns of butterfly and moth assemblages between deciduous and evergreen broad-leaf forests

In this study, we compared the diversity and abundance of two groups of Lepidopteran insects—butterflies and moths—across deciduous (DBL) and evergreen broad-leaved (EBL) tree communities in warm temperate forests. Over three years (from 2018 to 2020) at the Wando Arboretum, we used transect and point methods to sample butterflies and light traps to sample moths. Our findings revealed that the total estimated species richness was higher in EBL compared to DBL. Non-metric multidimensional scaling (NMDS) analysis confirmed distinct moth assemblages between the two tree communities, while butterfly assemblages did not exhibit such separation. The seasonal diversity patterns of butterflies in both forests differed significantly, whereas those of moths exhibited a similar trend with a peak in June. Beta diversity components indicated high turnover rates in moth assemblages across both tree communities and high nestedness in butterfly assemblages within the DBL. Although the diet breadth of butterflies and moths did not significantly differ between the two tree communities, the number of butterfly species per host plant was higher in DBL and that of moths was higher in EBL. Considering the ongoing vegetation transition from DBL to EBL, changes in forest structure—such as canopy openness and tree density—may adversely impact insect communities within DBL.

Agroforestry mapping using multi temporal hybrid CNN+LSTM framework with landsat 8 satellite imagery and google earth engine

Agroforestry is indeed a traditional practice followed in tropical countries like India. About 28.43 million hectare area is used for agroforest cultivation. By 2050 India has the mission of increasing the area under agroforestry to 53 million hectares. In this study, we have made an effort to map the agroforest areas using the geospatial tools and hybrid deep learning techniques. The land utilized for cultivation and various agroforestry activities such as rubber, tea, coconut, and banana plantation were classified as forest canopy by the existing classifiers taking the tree canopy density as a parameter. In light of proposing a solution to the issue, we have put forth a multi temporal hybrid deep learning framework which is a fusion of convolutional neural network, a deep neural net and long short term memory network to classify agroforestry distinguishing it from the forest canopy using remote sensing data. The experimentation was carried out in the southern districts of India, and Landsat 8 imagery was used to classify the agroforestry of the study area that includes tea, banana, rubber, coconut, and crop lands. An efficient multi temporal hybrid deep learning framework was designed to classify the agroforest plantation distinguishing it from crop lands and forest clusters. The experimental results of multi temporal hybrid CNN+LSTM outperformed CNN, LSTM, BiLSTM model reducing the error rate with respective accuracy and kappa score of 98.23% and 0.88. The proposed method provides a benchmark to accurately classify and estimate the LULC, particularly mapping the agroforest plantation for other regions across the country.

Critical linkages among floodplain hydrology, geomorphology and ecology along a lowland meandering river, Illinois, USA

AbstractThe ecology of forested floodplains is intricately linked to river hydrology through the frequency, magnitude, timing and duration of floodplain inundation. Spatial variability in inundation characteristics is influenced by the geomorphic template of a floodplain, both in terms of the topography of floodplain features and connectivity of these features to the main river channel. Spatial variability in inundation, in turn, has the potential to produce spatial variability in forest ecological characteristics. This study examines the influence of floodplain geomorphic features on spatial variability in inundation frequency as well as the relationship between these geomorphic features and the ecological characteristics of a floodplain forest. The frequencies of floods of different magnitudes are determined from flow‐duration analysis of over 100 years of discharge data for a lowland meandering river in Illinois, USA. Data on discharge, stage, and topography are then used to calibrate a two‐dimensional hydraulic model of flow across the floodplain at different levels of inundation. Integrating the frequency and inundation data yields mapping of average annual inundation frequency for different parts of the floodplain. Significant differences in inundation frequency correspond to three distinct floodplain geomorphic features: secondary channels (frequency = 12%), closed depressions (frequency = 4%) and the floodplain surface (frequency = 3%). Tree density is similar among the three types of geomorphic features, but tree species composition and canopy density differ significantly between secondary channels and the floodplain surface. The results provide insight into linkages among hydrology, geomorphology and tree characteristics of forested floodplains of lowland meandering rivers.

Perceptions of Tree Risks and Benefits in a Historically African American Neighborhood

An expansive body of research demonstrates the social and ecological benefits of urban forests, although urban tree canopy density tends to be lower than average in areas occupied by marginalized populations. Non-profit organizations and local governments have initiated tree-planting programs; however, some of these programs have encountered local resistance. This study took place in a historically African American neighborhood in the Southern USA with a low tree canopy where residents expressed disinterest in replanting trees following a tree hazard removal campaign led by a local non-profit organization. Employing focus groups and interviews, we explored residents’ environmental attitudes and risk perceptions by asking about the risks and benefits of neighborhood trees and barriers to the enjoyment of them. The material and emotional bonds residents have with the neighborhood informed their preferences about trees and green space. Trees were often viewed as hazards and financial risks, although they were an integral part of residents’ identities for themselves and their community. The findings suggest that neglecting to look at diverse perceptions will challenge a city’s ability to communicate about the urban forest and, therefore, sustainably address disparities in tree benefits and problems.

Importance of plant and fruit traits on the structure of bird seed dispersal networks in different disturbed habitats.

Species functional traits can influence seed dispersal processes and consequently affect species' role in the mutualistic network. Although the effect of animal traits on the structure of the seed dispersal network is well explored, it remains poorly understood how plant and fruit traits contribute to the structure. We here studied the effects of plant and fruit traits on the structure of bird seed dispersal networks across different disturbed habitats in the Meihua Mountain National Nature Reserve, Southeastern China. During the study period, 16, 20, 13, and 15 bird species were recorded foraging on 10, 11, 12, and 8 plant species, resulting in 511, 312, 265, and 201 foraging events in the protected forest, natural forest, village, and bamboo forest, respectively. The composition of these seed dispersal networks is not primarily influenced by a specific group of bulbul species, but rather by the presence of an endangered plant species, Taxus chinensis. As we expected, the structure of the four networks was different among the four disturbed habitats. Furthermore, our results also showed tree height and canopy density were the most important plant traits for structuring the seed dispersal network, while sugar, amylase, dry matter, and alkaloids were identified as significant fruit traits. Overall, our findings highlight the value of integrating trait-based ecology into the framework of the seed dispersal network and provide new insights for mutualistic network conservation in disturbed habitats.

Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts

ContextWildland-urban interface (WUI) areas are facing increased forest fire risks and extreme precipitation events due to climate change, which can lead to post-fire flood events. The city of Flagstaff in northern Arizona, USA experienced WUI forest thinning, fire, and record rainfall events, which collectively contributed to large floods and damages to the urban neighborhoods and city infrastructure.ObjectivesWe demonstrate multi-temporal, high resolution image applications from an unoccupied aerial vehicle (UAV) and terrestrial lidar in estimating landscape disturbance impacts within the WUI. Changes in forest vegetation and bare ground cover in WUIs are particularly challenging to estimate with coarse-resolution satellite images due to fine-scale landscape processes and changes that often result in mixed pixels.MethodsUsing Sentinel-2 satellite images, we document forest fire impacts and burn severity. Using 2016 and 2021 UAV multispectral images and Structure-from-Motion data, we estimate post-thinning changes in forest canopy cover, patch sizes, canopy height distribution, and bare ground cover. Using repeat lidar data within a smaller area of the watershed, we quantify geomorphic effects in the WUI associated with the fire and subsequent flooding.ResultsWe document that thinning significantly reduced forest canopy cover, patch size, tree density, and mean canopy height resulting in substantially reduced active crown fire risks in the future. However, the thinning equipment ignited a forest fire, which burned the WUI at varying severity at the top of the watershed that drains into the city. Moderate-high severity burns occurred within 3 km of downtown Flagstaff threatening the WUI neighborhoods and the city. The upstream burned area then experienced 100-year and 200–500-year rainfall events, which resulted in large runoff-driven floods and sedimentation in the city.ConclusionWe demonstrate that UAV high resolution images and photogrammetry combined with terrestrial lidar data provide detailed and accurate estimates of forest thinning and post-fire flood impacts, which could not be estimated from coarser-resolution satellite images. Communities around the world may need to prepare their WUIs for catastrophic fires and increase capacity to manage sediment-laden stormwater since both fires and extreme weather events are projected to increase.

A Century of Change in a Mature Urban Forest: the Thain Family Forest of the New York Botanical Garden, Bronx, New York

Abstract Urban forests have long been understudied, resulting in a scarcity of long-term data to guide future management. The New York Botanical Garden in New York City has been stewarding the 20 ha Thain Family Forest since 1895. Using inventory data between 1937 and 2021, we document how structure, diversity, and composition of the forest have changed over the past century. Our results show an increase in the proportion and diversity of hardwood species, including oaks, hickories, red maple, black cherry, and American beech, following the loss of a dominant species, eastern hemlock. Despite changes in composition, we find no significant differences in the mean basal area, density, and tree diameter of canopy trees (diameter at breast height [DBH] ≥ 15 cm) from 1937 to 2021. Assessments of small diameter stems (DBH ≥ 2.54 cm) show increasing density, decreasing diameter, and increasing richness from 1985 to 2021, suggesting a period of understory recruitment/regeneration. During this time, native species have been dominant in both overstory and understory strata. These observations mirror trends recorded in hemlock–hardwood and oak–hickory forests region-wide, indicating that, with continued conservation and active management, urban and rural forests may develop in similar patterns and encouraging regional approaches to forest management. Study Implications: Urban forests have long been understudied, resulting in a scarcity of long-term data to guide future management. Over the past century, the 20 ha Thain Family Forest in New York City has experienced a significant change in species composition, with an increase in the proportion and diversity of hardwood species following the loss of eastern hemlock. Despite these changes, forest canopy structure has remained stable. These observations mirror those observed in hemlock–hardwood and oak–hickory forests regionwide, suggesting that with continued conservation and active management, urban and rural forests may develop in similar patterns. The results of this study support continued investment in the research and management of urban forests as a comprehensive part of regional forest management efforts.

TreeDetector: Using Deep Learning for the Localization and Reconstruction of Urban Trees from High-Resolution Remote Sensing Images

There have been considerable efforts in generating tree crown maps from satellite images. However, tree localization in urban environments using satellite imagery remains a challenging task. One of the difficulties in complex urban tree detection tasks lies in the segmentation of dense tree crowns. Currently, methods based on semantic segmentation algorithms have made significant progress. We propose to split the tree localization problem into two parts, dense clusters and single trees, and combine the target detection method with a procedural generation method based on planting rules for the complex urban tree detection task, which improves the accuracy of single tree detection. Specifically, we propose a two-stage urban tree localization pipeline that leverages deep learning and planting strategy algorithms along with region discrimination methods. This approach ensures the precise localization of individual trees while also facilitating distribution inference within dense tree canopies. Additionally, our method estimates the radius and height of trees, which provides significant advantages for three-dimensional reconstruction tasks from remote sensing images. We compare our results with other existing methods, achieving an 82.3% accuracy in individual tree localization. This method can be seamlessly integrated with the three-dimensional reconstruction of urban trees. We visualized the three-dimensional reconstruction of urban trees generated by this method, which demonstrates the diversity of tree heights and provides a more realistic solution for tree distribution generation.

Decrease of deforestation in Protected Areas of Madagascar during the Covid-19 years

Deforestation poses a significant threat to global biodiversity and ecosystem services. This study focuses on estimating the deforestation within Protected Areas (PAs) in Madagascar over a 21- year period from 2001 to 2022. A novel methodology utilizing remote sensing data and specific thresholds of tree canopy density is employed to estimate annual deforestation rates and identify trends and patterns within PAs. The analysis reveals significant deforestation in the PA network over the last decade, particularly in 2014, 2017, 2018, and 2019. Notably, the lowest annual deforestation rates were estimated during the Covid-19 years of 2020 (0.66%), 2021 (0.62%), and the subsequent year in 2022 (0.67%) when considering the entire network of 103 PAs with natural forests from 2013 to 2022.

Enhancing precision flood mapping: Pahang's vulnerability unveiled.

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of 'very high' and 'high' class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

Development of an Electric Variable Air Assist System for Apple Orchard Sprayers

Highlights Electric air assist system was developed with electric fans, pulse width modulation (PWM) controllers, a custom-designed air channel, and a battery. Adjusting PWM duty cycles from 0% to 100% achieved average exit air velocities from 0 to 11.3 m s-1. Spray applications with the system at 50% and 70% power achieved canopy pass through spray coverages from 4.1% to 19.9% and 14.2% to 24.2%, respectively. A multivariable regression model was developed to adjust air assist outputs based on tree canopy density and off-target loss. Abstract. Air assist for apple orchard spray applications is a necessity to deliver pesticides to target crops. However, conventional orchard sprayers use axial fans to provide air assists which are generally designed for tall crops. Thus, these systems have very limited capabilities to control airflows to match canopy densities. An electric air assist system (EAAS) was developed with an electric fans, pulse width modulation (PWM) controllers, a custom-design air channel, and a 400-Ah LiFePO4 battery to address such limitations. The system could ramp up its airflow to nearly 80% of its maximum in 2.5 s, while it took approximately 4 s to reach 100% airflow from no air assist. The EAAS provided airflow of 11.3 and 5.3 m s-1 at the fan outlet and 1 m away, respectively, with 100% duty cycles (DC) of PWM. It was capable of modulating its average air velocities from 0 to 11.3 ms-1 by changing DCs. Spray coverage samples of constant rate applications with different air assist levels were collected from the behind apple tree canopies throughout the 2023 growing season to characterize the potential for off-target spray drift. A multivariable regression model for controlling DCs was developed with the spray coverage data and leaf area index (LAI) to minimize the off-target drift. The EAAS was a promising approach to developing more advanced air assist spraying systems to enable the adjustment of both air and liquid flows based on canopy characteristics to maximize spray depositions on intended targets while minimizing spray drift. Keywords: Air assisted sprayer, Automation, Crop protection, Fan, Penetration, Pesticide, Pulse width modulation, Variable air assistance.

Distribution ofAcer negundoL. in the Karasye-Ozersky forest park of Yekaterinburg

The article is devoted to invasions ofAcer negundoL. 1753 in the forest park area of Yekaterinburg (Russia) based on population (age and vitality structure) and organismal parameters (morphometric indicators). The purpose of the work was to study the invasion of ashleaved maple in the forest park area of Yekaterinburg; the objectives of the study included the following tasks: establishing the ecological-cenotic location of the maple and studying the biological characteristics of the species during its distribution. The objects of the study were eight maple habitats in the Karasye-Ozersky forest park. In ontogenetic development, one life form is identified - a single-stem tree, two periods are defined - pregenerative and generative, and five ontogenetic states. All habitats are young, i.e. are at the initial stage of introduction, however, there are common features of invasion. Distribution begins from open edges, gradually penetrating under the forest canopy, while having high shade tolerance, seed productivity and growth rate,Acernegundoforms multitiered thickets, displacing and complicating the self-restoration of native flora in the Karasye-Ozersky Forest Park. Despite the wide range of distribution of maple in natural and anthropogenic ecosystems, there is an ecological and phytocenotic confinement to it in the forest park, and it is manifested in its distribution mainly in pine forests of mixed herbs with a tree canopy density of 0.5. The established patterns indicate a pronounced expansion of the species and its significant invasive potential.

Deer survey from drone thermal imagery using enhanced faster R-CNN based on ResNets and FPN

Deer surveys play an important role in the estimation of local ecological balance. In the Chitwan National Park of Nepal, the dense tree canopies and tall vegetation often obscure the presence of wild deer, which has a negative effect on the accurate population surveys of wild deer. UAVs equipped with infrared sensors have been increasingly used to monitor wild deer by capturing a lot of images. How to automatically recognize and obtain the number of deer objects from thermal images is becoming an important research topic. Due to the difference between thermal images and true-color images, as well as the variations in deer object sizes in these two types of images, current ready-to-use object detection models, designed for true-color imagery, are ill-suited for the task of detecting small deer objects within thermal imagery. In this paper, an enhanced Faster R-CNN was constructed to detect small deer objects from thermal images, in which a Feature Pyramid Network (FPN) based on a residual network is used to improve feature extraction for small deer objects and multi-scale feature map constrution for the subsequent region proposals searching, bounding box regression, and regions of interest (RoIs) classification. In addition, small-scaled anchor boxes and a multi-scale feature map selection criterion are devised to improve the detection accuracy of small objects. Finally, based on Faster R-CNN, FPN, and different residual networks including ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152, we constructed five object detection models, and evaluated their detection performance by using COCO evaluation matrix. Under the condition of IoU≥0.5, the integration of Faster R-CNN, FPN, and ResNet18 demonstrated to perform better than others. Specifically, The COCO evaluation results revealed an Average Precision (AP) score of 91.6% for all deer objects. Small deer objects (area ≤ 200 pixels) achieved an AP score of 73.6%, medium deer objects (200 < area ≤ 400 pixels) demonstrated an AP score of 93.4%, and large deer objects (area > 400 pixels) achieved the highest AP score of 94.3%. Our research is helpful for effective wild deer monitoring and conservation and can be a valuable reference for the exploration of small object detection from low-resolution thermal images.

A Simulation Study of the Impact of Urban Street Greening on the Thermal Comfort in Street Canyons on Hot and Cold Days

The urban heat island effect has become a widely concerning issue worldwide. Many researchers have made great efforts to improve the summer thermal comfort of urban street canyons by optimizing street greening. Relatively less research has focused on how to improve the thermal comfort of street canyons by optimizing street greening during cold days. Many researchers have proposed strategies to improve the summer thermal comfort of street canyons using road greening. This may have a significant negative impact on the winter thermal comfort of street canyons due to the lack of consideration of the impact on hot and cold days simultaneously, especially when the road green space is mainly composed of evergreen tree species. We aimed to explore the impacts of urban street greening on thermal comfort on hot and cold days at the same time. We used Zhutang West Road in Changsha, China, as an example and built six different models to explore the impacts of the street vegetation types, number of street trees, tree heights, crown widths, and Leaf Area Index on the thermal comfort of the street canyon. In addition, we also considered the impact of different building features and wind directions on the thermal comfort of the street canyon. We employed ENVI-met (version 5.5.1) to simulate different urban street greening models. The results show that the model with a high tree canopy density, tall trees, large and dense crowns, and sufficient building shade has good thermal comfort on hot days (the average physiological equivalent temperature (PET) is 31.1 °C for the study period) and bad thermal comfort on cold days (the PET is 13.3 °C) when it is compared with the other models (the average PETs are 36.2 °C, 31.5 °C, 41.5 °C, 36.2 °C, and 35.5 °C, respectively, on hot days and for other models). In addition, the model with a very large number of short hedges has a positive impact on thermal comfort during hot days (the PET is 31.1 °C). The PET value of another comparable model which does not have hedges is 31.5 °C. Even if the model with a small building area has good ventilation, the small building shade area in the model has a more obvious impact and the model has relatively good thermal comfort during cold days (the PET is 14.2 °C) when it is compared to models with bigger building areas (the PET is 13.9 °C). In summer, when the wind is parallel to the direction of the street canyon, the wind speed in the street canyon is high and the model has relatively good thermal comfort (the PET is 35.5 °C) compared with another model which has different wind direction and lower wind speed at the street canyon (the PET is 36.2 °C). In winter, when the wind is perpendicular to the direction of the street canyon, buildings and trees have a strong windproof effect and this is beneficial to the improvement of thermal comfort (the PET is 15.3 °C for this model and 13.9 °C for another comparable model). This research lays a solid foundation and encourages people to think about the impact of building and tree composition and configuration on the thermal comfort of street canyons during hot and cold days simultaneously.

Prescribed fire lessens bark beetle impacts despite varied effects on fuels 13 years after mastication and fire in a Sierra Nevada mixed-conifer forest

Mastication, thinning, and prescribed fire can help shift fire-prone forests to a structure and composition more resilient to wildfire, but the ability to evaluate treatment effectiveness requires monitoring forest response to disturbances over time after treatment. In this study, we investigated fuel loadings, forest structure, and tree growth to assess treatment legacies thirteen years after mastication, prescribed fire, and surface fuel pull-back from tree boles in Sierra Nevada mixed-conifer forest. An outbreak of bark beetles between 2011 and 2018 impacted all plots but burned treatments had lower overstory mortality relative to unburned treatments which experienced an 80 % decline in live tree biomass. Tree diameter, canopy density, and surface fuel loadings became similar across all treatments during the 13 year time period. Height to live crown remained elevated in burned treatments relative to the control but had lowered by an average of 1.9 ± 0.3 (SE) m across all treatments. Radial tree growth did not have a sustained response to treatment and was greater in burned treatments for only a single year, 2011, several years post-treatment. Prescribed fire decreased duff mass by 56 % relative to unburned treatments but did not affect forest floor bulk density. Downed woody fuel loadings (1-h – 1000-h) were similar across treatments. The invasive Bromus tectorum (cheatgrass) had uniformly high cover across treatments. Whereas treatments were largely ineffective at producing long-term reductions in surface fuel loadings, the enduring reductions in duff loadings and reduced overstory mortality support the long-term utility of prescribed fire. Cumulatively, our results highlight the potential for prescribed fire to reduce overstory mortality and duff loadings in dry mixed-conifer forests, thereby contributing more effectively to fire-resilient characteristics relative to other fuel reduction treatments.