Height Diversity Research Articles

Spaceborne LiDAR and animal-environment relationships: An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem

Animal conservation requires understanding animal-habitat relationships. The integration of novel remote sensing platforms such as Light Detection and Ranging (LiDAR) technology has dramatically improved the resolution of insight when evaluating animal-habitat relationships by characterizing forest structure. However, conventional LiDAR collection (e.g., airborne or terrestrial laser scanning) may be limited by small spatial extents and logistical constraints (e.g., budget) associated with sampling. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission provides an alternative and complement to conventional LiDAR sampling with globally available waveform LiDAR, which is being collected to characterize vertical and horizontal structure of Earth’s forests. Forest carnivores are wide-ranging species occupying forested ecosystems, and are generally associated with vertical and horizontal forest structure for their survival and reproduction. We evaluated patterns in occurrence and habitat use of forest carnivores, which included Pacific martens (Martes caurina), Rocky Mountain red foxes (Vulpes vulpes macroura), and coyotes (Canis latrans) and patterns in occurrence of their prey; American red squirrels (Tamiasciurus hudsonicus) and snowshoe hares (Lepus americanus). Camera trap data were collected during the 2014–2017 winters in the Greater Yellowstone Ecosystem in Wyoming, USA. Our objectives were to (1) combine GEDI samples with multispectral satellite imagery from Landsat 8 to upscale vertical forest structure metrics; (2) assess the relative importance of environmental characteristics influencing occurrence and habitat use of forest-associated predators and prey; and (3) determine if GEDI-derived variables aided our efforts in characterizing animal-environment relationships. We used Random Forest regression models to upscale GEDI samples across our study area and implemented a multi-tiered approach using generalized linear mixed effect models to simultaneously evaluate animal-environment relationships and how GEDI-derived metrics improved the animal-habitat models. GEDI-derived metrics of relative height and foliage height diversity improved our animal-environment models and were among the strongest covariates (effect sizes were 1.3–1.8 times larger than the next closest) in the coyote, red squirrel, and snowshoe hare models. All five species were influenced to some degree by the frequency of rebaiting a camera trap and varying conditions of snow depth. Collectively, our work indicates forest canopy height and complexity variables significantly improved our ability to assess the importance of forest characteristics on forest carnivores and their prey. Indeed, there is an untapped opportunity to enhance animal ecology and conservation planning with continued integration of GEDI information with freely available satellite data to characterize attributes of forest structure across expansive areas.

Integration of VIIRS Observations with GEDI-Lidar Measurements to Monitor Forest Structure Dynamics from 2013 to 2020 across the Conterminous United States

Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at a 1-km resolution for 2013–2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses. The methods developed in this study are expected to enable multi-decadal analysis of forest structure and its dynamics using consistent satellite observations from moderate resolution sensors such as VIIRS onboard JPSS satellites.

Changes in Green Space Use During a COVID-19 Lockdown Are Associated With Both Individual and Green Space Characteristics

Mobility restrictions imposed during the COVID-19 pandemic present a useful study system for understanding the temporal and spatial patterns of green space use. Here, we examine green space characteristics and sociodemographic factors associated with change in frequency of green space use before and during a COVID-19 lockdown in Brisbane, Australia drawing on a survey of 372 individuals. Applying regression analysis, we found that individuals who visited a different green space during lockdown than before tended to decrease their frequency of visits. In contrast, individuals who continued visiting their usual green space during lockdown were more inclined to increase their number of visits. Changes in frequency of green space use were also associated with particular characteristics of their usually visited green space. The presence of blue spaces and accessibility (carparks/public transport) were associated with increased frequency of use while foliage height diversity was associated with reduced frequency of use. We found that females were more likely to change their green space visitation frequency during COVID-19 compared to men and they also reported greater importance of green spaces for social and family interactions and spiritual reasons during COVID-19 compared to before. Males showed greater increases than females in the importance of green space for nature interactions and mental health benefits during the COVID-19 lockdown compared to before. Our results provide key insights for future resilient urban planning and policy that can fulfil a wide range of physical and psychological needs during a time of crisis and beyond.

A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits

Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)—namely, vegetation height, vegetation cover, and vertical structural complexity—to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide.

The formation of "mega-flocks" depends on vegetation structure in montane coniferous forests of Taiwan.

A mixed‐species bird flock is a social assemblage where two or more bird species are moving together while foraging and might benefit from increased foraging efficiency and antipredator vigilance. A “mega‐flock,” which includes flocking species from different vegetation strata, often exhibits high species diversity. Mechanisms for the formation of mega‐flocks have not yet been explored. In this study, we evaluated the influence of vegetation structure and bird species diversity in driving the occurrence of mega‐flocks. We investigated the composition of mixed‐species flocks, local bird communities, and vegetation structure in five vegetation types of two high‐elevation sites in central Taiwan. Mega‐flocks occurred more frequently in pine woodland than later successional stages of coniferous forests. However, species richness/diversity of local bird communities increased along successional stages. Therefore, vegetation variables exhibit more influence on the occurrence of mega‐flocks than local bird communities. Besides foliage height diversity, understory coverage also showed positive effects on flock size of mixed‐species flocks. Our results indicated that pine woodlands with more evenly distributed vegetation layers could facilitate the interactions of canopy and understory flocks and increase the formation of mega‐flocks and thus the complexity of mixed‐species flocks.

Efectos de la ganadería de baja intensidad sobre los anfibios en el Complejo de Humedales Ñeembucú, Paraguay

ABSTRACT Cattle in wetlands impact water quality through waste excretion, which deposits excess nutrients, as well as decreasing the biomass and height of vegetation through trampling and herbivory. Amphibians are sensitive to these changes due to their porous skin and reliance on vegetated microhabitats. Previous studies examining the effect of cattle on amphibians in wetlands report conflicting results, exemplifying the need to avoid overgeneralizations and instead examine specific environments. In the Ñeembucú Wetland Complex in Paraguay, low-intensity cattle ranching is a common practice. This study seeks to understand how the presence of cattle in these operations impacts water chemistry and amphibian species richness, as well as determining which cattle effects (eutrophication, nutrient loading, and decreased vegetation height diversity) have the greatest impact on amphibian communities. We anticipated that increased cattle presence would negatively impact wetland condition, and consequentially lower amphibian species richness. Data was collected over seven weeks at Estancia Santa Ana, a low-intensity cattle ranch in Pilar, Paraguay. Cattle presence was measured through cow footprints and feces densities, while a vegetation survey and water quality testing (nitrate, phosphate, and dissolved oxygen) served as environmental metrics. Pitfall trapping was used to gather information on amphibian species richness and community composition. A Pearson parametric correlation test and Factor Analysis of Mixed Data in R were then used to understand the relationship between variables. Very few statistically relationships were found between variables, and those that existed showed a very weak correlation. This suggests that cow presence does not have as strong of an impact on water quality or amphibian species richness as expected. Terrestrial and aquatic vegetation were found to explain much of the variation among the data, which verifies the importance of amphibians’ microhabitats. While future research comparing generalist and specialist species in the area is necessary, these initial results suggest a hopeful future for collaboration on environmental efforts with low-intensity cattle ranchers.

Fruit Size and Structure of Zoochorous Trees: Identifying Drivers for the Foraging Preferences of Fruit-Eating Birds in a Mexican Successional Dry Forest.

Simple SummaryTropical dry forests are highly threatened by human activities such as agriculture, livestock, and selective logging. These activities have resulted in fragments of tropical dry forest under different successional stages that negatively affect the interaction between plants and fruit-eating birds. We analyzed the consumption of the fruits of zoochorous trees by birds during the dry season in a tropical dry forest and evaluated whether the horizontal and vertical structure of these trees explains fruit consumption. We also related the bird body mass and fruit size removed from zoochorous trees. We found that the tree structure can influence the visitation of fruit-eating birds, and therefore, the number of fruits consumed as the succession progresses. There was a relationship between fruit sizes and bird body mass in the successional gradient. Our results indicate that structural and fruit traits of zoochorous trees drive foraging preferences of fruit-eating birds, whose interactions in different successional stages can help to reverse the negative effects of fragmentation in tropical dry forests of the study area.Tropical dry forests (TDFs) are affected by land-use changes. These modifications impact their composition and arboreal structure, as well as the availability of food for several bird groups. In this study, we evaluated the foraging preferences in zoochorous trees of fruit-eating birds during the dry season of the year in three successional stages (early, intermediate, and mature) of TDFs in southern Mexico. The fruits of these trees are important in the diet of several birds during the dry season, a period during which food resources are significantly reduced in TDFs. We estimated foliar cover (FC) and foliage height diversity (FHD) of zoochorous trees in 123 circular plots. These variables were recognized as proxies of food availability and tree productivity. Foraging preferences were evaluated at the community level, by frugivore type, and by bird species. We evaluated the effect of the structural variables and the fruit size of zoochorous plants on fruit removal by birds and related the bird body mass and fruit size removed in the successional gradient. A total of 14 zoochorous tree species and 23 fruit-eating bird species were recorded along the successional gradient. Intermediate and mature stages showed greater fruit removal. The birds removed mainly B. longipes fruits across the three successional stages. The FHD and fruit size were important drivers in the selection of zoochorous trees and fruit removal by fruit-eating birds. Fruit size and bird body mass were positively related along the successional gradient. The results suggest that fruit removal by fruit-eating birds in the successional gradient can promote the demographic dynamics of several zoochorous tree species, especially of Bursera spp. along the TDFs.

Species diversity and stand structural diversity of woody plants predominantly determine aboveground carbon stock of a dry Afromontane forest in Northern Ethiopia

Aboveground carbon (AGC) stock in forests is affected by several biotic and abiotic factors. Understanding those factors is crucial in managing forests for climate change mitigation and other ecosystem services. This study examined effects of diversity attributes (species and stand structural diversity of woody plants) and topographic attributes (altitude and slope) on AGC stock of a dry Afromontane forest. Data from individual woody plants for estimating AGC were collected from 252 plots (20 × 20 m) established in a systematic grid (2 × 2 km) covering the entire forest area. Woody plant diameter diversity and woody plant height diversity were used as proxies for stand structural diversity. Structural equation modelling was applied to test the effects of the selected attributes on AGC stock, and together they explained 71% of the variation. Stand structural diversity was the most important driving factor for the AGC stock. Woody plant species diversity had both direct and indirect effects on AGC stock, but the indirect effect through its influence on stand structural diversity was more pronounced. Altitude and slope were both negatively but weakly associated with AGC stock. Our results provide insight on the effects of diversity and topographic attributes on AGC stock, which can assist future management of the forest. Enhancing stand structural diversity by means of selection cutting systems and woody plant species diversity by means of enrichment planting can be effective treatments. When considering dry Afromontane forests’ widespread distribution, the potential contribution in carbon sequestration and thereby mitigating climate change is substantial. Hence, the results and management suggestions from the present study may have practical interest for the management of dry Afromontane forests not only in Ethiopia but also in Africa at large.

Impacts of urban-scale building height diversity on urban climates: A case study of Nanjing, China

The rapid development of urbanization is accompanied by the diversification of building heights. It has caused cities to face problem of non-uniform distribution of urban climate parameters (e.g., temperature, velocity), may further leading to urban heat island (UHI) and increasing of building energy consumption etc. Thus, it is of great significance to explore the associated relationships between building height and urban climates from the urban scale by CFD simulation. There is almost a gap on urban scale simulation for both research and engineering because of the complex building diversity and expensive simulation cost. K-means clustering and low-dimensional method based on image recognition were utilized in this work to build the urban geometric model, further improving simulation efficiency. A case study of Nanjing city was selected (about 144 km2 containing 140 thousand buildings). The urban-scale simulation method was verified by experimental data. The numerical results show that the energy consumption of high-rise buildings (HRB) caused by UHI is about 1.2–4.5 %. The UHI intensity of HRB is 0–0.7 °C higher than that of middle-rise buildings (MRB) and low-rise buildings (LRB), while the velocities are similar around three different buildings height. In order to make the pedestrian area (at the height of 1.7 m from the ground) with pleasant urban climates in rapid urbanization process, it is suggested to set the building height between 19 and 50 m. The findings can be beneficial to alleviate the problems of UHI and energy consumption, which have important implications for urban optimization design.

Clarifying the factors affecting Larix gmelinii forest regeneration based on structural equation model

Based on data from 49 plots of natural Larix gmelinii forests in Cuigang Forest Farm of Xinlin Forestry Bureau, Daxing'anling Mountains, China, we used 37 measurable variables that mainly focused on stand non-spatial structure, stand spatial structure, species diversity, soil condition, and site condition to construct the structural equation model of natural regeneration densities and size diversities (i.e., height and ground-diameter). The direct, indirect, and total influence coefficients of each path were quantified to extract the critical and controllable factors that influence regeneration density and diversity of natural L. gmelinii forests, which would help implement sustainable forest management. The results showed that the effects of various latent variables on rege-neration density were following an order as: stand non-spatial structure (-0.410) > species diversity (0.380) > soil condition (0.250) > site condition (0.249) > stand spatial structure (0.197), while the order were changed as: soil condition (0.778) > site condition (0.748) > stand spatial structure (0.684) > stand non-spatial structure (0.287) > forest diversity (0.105), when evaluated on the regeneration diversity. Generally, the critical and controllable factors affecting rege-neration quantity and diversity were soil pH, total potassium concentration, species diversity, tree height diversity, uniform angle index and stand volume per hectare. In the management, suitable thinning treatments or replanting broadleaved trees were recommended for optimizing and adjusting species composition, species diversity, soil pH and nutrition, which would promote natural regene-ration.

The relationship between species and spectral diversity in grassland communities is mediated by their vertical complexity

AbstractAimsThe link between spectral diversity and in‐situ plant biodiversity is one promising approach to using remote sensing for biodiversity assessment. Nevertheless, there is little evidence as to whether this link is maintained at fine scales, as well as to how it is influenced by vegetation's vertical complexity. Here we test, at the community level in grasslands, the link between diversity of the spectral signal (SDiv) and taxonomic diversity (TDiv), and the influence of vertical complexity.MethodsWe used 196 1.5 m × 1.5 m experimental communities with different biodiversity levels. To measure vertical complexity, we quantified height diversity (HDiv) of the most abundant species in the community. TDiv was calculated using the Shannon index based on species cover. Canopy spectral information was gathered using an unmanned aerial vehicle (UAV) mounted with a multi‐spectral sensor providing spectral information via six 10‐nm bands covering the visible and near‐infrared region at a spatial resolution of 3 cm. We measured SDiv in a core area of 1 m ×1 m within the communities as mean Euclidean distance of all pixels in a feature space spanned between the two first components of a PCA calculated for the complete raster stack. We modelled SDiv through mixed‐effect linear models, using TDiv, HDiv, and their interaction as fixed‐effect predictors.ResultsContrary to our expectations, TDiv was negatively linked to SDiv. The diversity in plant height was positively related to SDiv. More importantly, diversity in plant height and TDiv had a significant negative interaction, meaning the more complex the vegetation was in terms of height, the more the SDiv–TDiv relationship became negative.ConclusionsOur results suggest that in order to exploit the SDiv–TDiv link for monitoring purposes, it needs to be contextualized. Moreover, the results highlight that communities’ functional characteristics (i.e. plant height) mediate such a link, calling for new insights into the relation between SDiv and functional diversity.

Species compositional, structural and functional diversity exerts different effects on soil erosion caused by increased rainfall intensity in Chinese tropical forests

High-diversity communities may mitigate the impact of environmental changes on soil erosion owing to the compositional, structural, and functional diversity of their species. In this field study, we explored the relative contributions of these biodiversity components to soil erosion and the mitigating effects of each index under environmental changes (e.g., rainfall intensity). We investigated the compositional (species diversity and evenness), structural (tree diameter and height diversity), and functional diversity (functional richness, evenness, and divergence) of 12 plant communities on Hainan Island, where biodiversity loss is severe. We measured soil erosion in each community using erosion plots under different rainfall intensities. The direction and relative contribution of biodiversity indices to soil erosion were analyzed. Except for the functional evenness, high values of biodiversity indices were associated with significantly reduced soil erosion regardless of rainfall intensity, and the relative contribution of tree height diversity was greater than that of other biodiversity indices. In addition, multiple biodiversity factors alleviated the adverse effects of increased rainfall intensity on soil erosion, but plant structural diversity had a greater positive effect than compositional and functional diversity, predominantly because of tree height diversity. The results confirmed that the consideration of multiple biodiversity components provides an improved understanding of the relationship between biodiversity loss and soil erosion. We emphasize that protection of tree height diversity may not only effectively reduce current soil erosion, but also resist the adverse impact of increased rainfall intensity on soil erosion in the future.

Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

Abstract. Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine-scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA (winter 2016–2017), to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We utilize these descriptors along with topographical metrics in multiple linear and decision tree regressions to investigate snow depth variations under the canopy and in open areas. Canopy, topography, and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity; FHD) along with local-scale processes like wind and topography are highly influential in snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results indicate that FHD can explain up to 27 % of sub-canopy snow depth variation at sites where the effect of topography and wind is negligible. Solar radiation and elevation are the dominant controls on snow depth in open areas. Fine-scale analysis from TLS provides information on local-scale controls and provides an opportunity to be readily coupled with lidar or photogrammetry from uncrewed aerial systems (UASs) as well as airborne and spaceborne platforms to investigate larger-scale controls on snow depth.

Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification

McArthur's foliage height diversity (FHD) has been the gold standard in the determination of structural complexity of forests characterized by LiDAR vertical height profiles. It is based on Shannon's entropy index, which was originally designed to describe evenness in abundances among qualitative typologies, and thus the calculation of FHD involves subjective layering steps which are essentially unnatural to describe a continuous variable (X) such as height. In this contribution we aim to provide a mathematical framework for determining maximum entropy in 3D remote sensing datasets based on the Gini Coefficient of theoretical continuous distributions, intended to replace FHD as entropy measure in vertical profiles of LiDAR heights (1D, X), with extensions to variables expressing dimensions of higher order (2D or 3D, Z ∝ X2 or X3). Then we apply this framework to Boreal forests in Finland to describe landscape heterogeneity with the intention to improve the modelling of forest aboveground biomass (AGB), hypothesizing that LiDAR models of AGB should essentially be different in areas of differing structural characteristics. We carried out a pre-stratification of LiDAR data collected in 2012 using simple rules applied to the L-skewness (Lskew) and L-coefficient of variation of LiDAR echo heights (Lcv; equivalent to the Gini coefficient, GCH), determining a new threshold at GCH = 0.33 as a consequence of the newly developed mathematical proofs. We observed only moderate improvements in terms of model accuracies: RMSDs reduced from 41.7% to 38.9 or 37.0%. More remarkably, we identified critical differences in the metrics selected at each stratum, which is useful to understand what predictor variables are more important for estimating AGB at each area of a forest. We observed that higher LiDAR height percentiles are more relevant at open canopies and heterogeneous forests, whereas closed canopies in homogeneous forests obtain most accurate predictions from a combination of cover metrics and percentiles around the median. Without stratification, the overall model would neglect explained variability in the structural types of lower occurrence, and predictions from a model influenced by structural types of higher occurrence would be biased at those areas. These results are thus useful in terms of improving our understanding on the relationships underlying LiDAR-AGB models.

On the structural complexity of central European agroforestry systems: a quantitative assessment using terrestrial laser scanning in single-scan mode

Agroforestry systems provide important ecosystem functions and services. They have the potential to enrich agricultural monocultures in central Europe with structural elements otherwise absent, which is expected to be accompanied by a surplus of ecosystem functions. Here we used quantitative measures derived from terrestrial laser scanning in single-scan mode to describe the structural complexity, the canopy openness, the foliage height diversity and the understory complexity of four common agroforest systems in central Europe. We accessed silvopasture systems with grazing ponies and cattle as well as fellow deer, short rotation forests with agricultural use between the tree rows, tree orchards with grazing sheep and Christmas tree plantations on which chickens forage. As a reference, we used data for 65 forest sites across Germany, representing different forest types, various dominant tree species, stand ages and management systems. We found that overall stand structural complexity is ranked as follows: forest > silvopasture systems > short rotation forest > tree orchard > Christmas tree plantation. Consequently, if overall structural complexity of an agricultural landscape shall be enriched, there is now strong evidence on how this may be achieved using agroforests. However, if the focus lies on selected structures that serve specific functions, e.g. dense understory to provide animal shelter, specific types of agroforests may be chosen and the ranking in overall structural complexity may be less important.

Living fences decrease the edge effect on nest predation in a tropical dry forest landscape: evidence from an experiment using artificial nests

Agricultural expansion has contributed to the loss and fragmentation of natural habitats and, as a consequence, the edge effect in these habitats has increased, leading to nest predation rates higher at the edge of fragments than in the interior. Living fences in abandoned croplands, however, add a structural component to the landscape which can help to mitigate the effects of disturbance. We used artificial nests to assess whether a living fence at the edge of a fragment of tropical dry forest, immersed in an agricultural landscape, can assuage the edge effect by decreasing predation rates. We identified the predators that attacked nests at several distances from the living fence and related the frequency of predator attacks with plant height, leaf height diversity, and tree and herb coverage. Vegetation structure of the living fence was vertically more complex than that found inside the fragment. 52% of nests were predated and we did not find a significant relationship between predation rates and the proximity of nests to the living fence, suggesting a null edge effect on predation. Birds were the most frequent predators, followed by rodents and carnivorous mammals, with the composition of predators varying among transects. The composition of predators and vegetation structure of the living fence were the factors that explained the null edge effect on nests predation. Our results indicate that the presence of a living fence at the edge of a fragment in agroforestry systems could mitigate the edge effects on nest predation.

The impact of urban morphology and building's height diversity on energy consumption at urban scale. The case study of Dubai

The indoor air temperature and cooling load are highly affected by the surrounding environment. Enhancing outdoor thermal performance leads to a positive impact on energy consumption for cooling purposes, specifically in hot climate countries such as the UAE. The UAE weather characteristics specified as desert climate, however, it is more humid than the other cities with similar climate conditions. It has been found that a limited number of publications concern with the direct impact of urban morphology on the indoor cooling load within local climate conditions of the UAE. Moreover, the integrated and direct impact of height diversity on the indoor cooling load at an urban scale requires more investigation. This study aims to explore the impact of urban morphology, specifically height diversity on the cooling load within local climate conditions of the UAE. Furthermore, it aims to find an optimized cooling consumption prototype of an urban block to emphasize the study novelty. It adopts urban planning and building morphology to optimize the outdoor thermal performance and reflect this enhancement on indoor energy consumption. The results show that the orientation is the most influential factor in the urban block cooling load and energy consumption. It is significantly and directly control the amount of solar radiation and buildings solar gain. The minimum direct solar gain of the base case configuration concerning the orientation is recorded in the N–S with a reduction of 13% compared to the NE-SW orientation. This has resulted in a reduction in cooling load by 6.4% between these two orientations. Further to that, the reduction in outdoor air temperature and solar gain by implementing the building's height diversity resulted in a reduction in cooling load by 4.6% between the base case and the urban block configuration with the building's height diversity.

Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data

Accurate information on the global distribution and the three-dimensional (3D) structure of Earth’s forests is needed to assess forest biomass stocks and to project the future of the terrestrial Carbon sink. In spite of its importance, the 3D structure of forests continues to be the most crucial information gap in the observational archive. The Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) sensor is providing an unprecedented near-global sampling of tropical and temperate forest structural properties. The integration of GEDI measurements with spatially-contiguous observations from polar orbiting optical satellite data therefore provides a unique opportunity to produce wall-to-wall maps of forests’ 3D structure. Here, we utilized Visible Infrared Imaging Radiometer Suite (VIIRS) annual metrics data to extrapolate GEDI-derived forest structure attributes into 1-km resolution contiguous maps of tree height (TH), canopy fraction cover (CFC), plant area index (PAI), and foliage height diversity (FHD) for the conterminous US (CONUS). The maps were validated using an independent subset of GEDI data. Validation results for TH (r2 = 0.8; RMSE = 3.35 m), CFC (r2 = 0.79; RMSE = 0.09), PAI (r2 = 0.76; RMSE = 0.41), and FHD (r2 = 0.83; RMSE = 0.25) demonstrated the robustness of VIIRS data for extrapolating GEDI measurements across the nation or even over larger areas. The methodology developed through this study may allow multi-decadal monitoring of changes in multiple forest structural attributes using consistent satellite observations acquired by orbiting and forthcoming VIIRS instruments.

Deep Learning-Based Diagnosis of Peripheral Artery Disease via Continuous Property-Adversarial Regularization: Preliminary in Silico Study.

This paper presents a novel deep learning-based arterial pulse wave analysis (PWA) approach to diagnosis of peripheral artery occlusive disease (PAD). Naïve application of deep learning to PAD diagnosis can be hampered by the fact that securing a large amount of longitudinal dataset encompassing diverse PAD severity as well as anatomical and physiological variability presents formidable challenge. Training of a deep neural network (DNN) to a small training dataset raises the risk of overfitting the PAD diagnosis algorithm only to the individuals in the training dataset while deteriorating its ability to generalize also to other individuals who may exhibit a large variability in anatomical and physiological characteristics beyond the training dataset. To overcome these obstacles, we propose a continuous property-adversarial regularization (CPAR) approach to robust generalization of a DNN against scarce datasets. Our approach fosters the exploitation of latent features that can facilitate the intended task independently of confounding property-induced disturbances. by regularizing the extraction of disturbance-dependent latent features in the network’s feature extraction layer. By training and testing a deep convolutional neural network (CNN) for PAD diagnosis using scarce virtual datasets, we illustrated that the CNN trained by our approach was superior to a conventionally trained CNN in detecting and assessing the severity of PAD against disturbances originating from diversity in the patients’ height and arterial stiffness: when trained with one-time pulse wave signal measurement at ankle and brachial arteries in a small number of patients, our approach achieved detection accuracy of >90% and severity assessment of 0.83 in r2 value, which were >15% and >40% improvement over conventional approach without CPAR. In addition, we ascertained the advantage of our approach in efficient training and robust generalization of DNN by contrasting it to multi-task learning which promotes the exploitation (as opposed to regularization in CPAR) of disturbance-dependent latent features in fulfilling the intended tasks.

Evaluation of the Effect of Grazing Lands Top Dressing with Inorganic Fertilizer on Biomass Yields for Sustainable Animal Feed Production, Ethiopia: The Case of Participatory Approach

Sub-Saharan livestock production and productivity are very low due to poor quality and inadequate quantity of available feed. Henceforth, livestock production can be improved through good management of natural grasslands and introduction of improved fodder species with the supply of fertilizer and water to maintain high productivity. Pre-extension demonstration of grazing land improvement technologies were conducted in 2017/18 in six Agricultural growth program (AGP II) districts, namely, Boneya Boshe, Wayu Tuka, Guto Gida and Diga from East Wollega and Horro and Guduru districts from Horro Guduru Wallega zone to evaluate, select and popularize farmers’ preferred technologies based on their selection criteria and to create awareness on the importance of the approved technologies. One representative potential peasant association (PA) was selected purposively from each district based on grazing land and livestock population potential and accessibility for field monitoring and visit. Farmers’ selection was done based on interests of farmers in trial management, willingness and ownership of sufficient grazing land to accommodate the trials and gender equality. In each PA, one farmer’s extension research group (FREG) comprising 16 farmers were established to evaluate and select the technologies. Training was given to farmers, DAs and experts. Three treatments/technologies, namely, T1 = Control (farmer practice), T2 = 150 kg/ha urea and T3 = 110 kg/ha urea and 100 kg /ha NPS were evaluated and demonstrated on 4 farmers’ fields on plot size of 400 m 2 in each study districts. The collected data were analyzed using descriptive statistics (mean and standard deviation) and qualitative narrations. The agronomic result showed that T3 performed better in average herbage dry matter yield (12.44 t/ha) followed by T2 (8.71 t/ha) and T1 which gave lower yield (5.5 t/ha). The two treatments/technologies (T3 and T2) had a yield advantage of 55.76 % and 36.83 %, respectively, over the control across the study districts. The overall technology preference score of all districts showed that T 3 (110 kg/ha urea and 100 kg /ha NPS) and T 2 (150 kg/ha urea) were the most preferred technologies and ranked as first and second, respectively, by participants because of high biomass, fast growth habit, early maturing for harvesting, plant height, leafiness and species diversity at all districts. Therefore, the combination of Urea and NPS fertilizer at a rate of 110 kg and 100 kg/ha, respectively, was recommended to be promoted in large scale in the study areas and other places with similar agro-ecologies. Keywords: Demonstration, Evaluation, Grazing land, Herbage dry matter, Technologies DOI: 10.7176/JAAS/70-02 Publication date: January 31 st 2021