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Veteran trees are important elements in forests, as well as urban and suburban areas, and represent part of our cultural heritage. However, increasing age also brings a reduction in vitality. Information on tree physiological vitality can be gained by examining ecophysiological traits, such as photosynthesis, stomatal conductance, and leaf water potential. Here, we assess the effects of age on the photosynthesis and water status of 600-year-old pedunculate oak trees (Quercus robur L.) by comparing them with neighbouring 25-year-old trees. While gas exchange measurements indicated lowered photosynthesis in old trees, their maximum rates of Rubisco carboxylation and electron transport were similar to younger trees, suggesting that biochemical limitations to photosynthesis are not the reason behind their reduced vitality. Moreover, there was no difference in light-adapted and dark-adapted chlorophyll fluorescence between old and young trees. In contrast, stomatal conductance (under unlimited soil water availability) was lower, indicating increased stomatal limitations to photosynthesis in veteran trees. On the other hand, high water potential during mild summer drought conditions indicated better access to soil water in old trees, while stomatal conductance in old trees was higher than in young trees at night. A reduced ability to open and close stomata may be one of the reasons for the observed decline in veteran tree vitality, with a lowered ability to regulate stomatal conductance resulting in reduced carbon gain and unnecessarily high water loss. [ABSTRACT FROM AUTHOR]

As the regulator of water and nutrient changes in the active layer after permafrost degradation, root signaling substances affect the plant–soil carbon allocation mechanism under climate warming, which is a key issue in the carbon source/sink balance in permafrost regions. To explore how plant root signaling substances regulate carbon allocation in plants and soils under permafrost degradation, the changes in carbon allocation and root signaling substances in the plants and soils of peatland in different permafrost regions at the time of labeling were studied by in situ 13C labeling experiments. The results showed that the fixed 13C of Larix gemlini, Carex schumidtii, and Sphagnum leaves after photosynthesis was affected by permafrost degradation. In regions with more continuous permafrost, the trend of the L. gemlini distribution to underground 13C is more stable. Environmental stress had little effect on the 13C accumulation of Vaccinium uliginosum. Nonstructural carbohydrates, osmotic regulatory substances, hormones, and anaerobic metabolites were the main root signaling substances that regulate plant growth in the peatlands of the three permafrost regions. The allocation of carbon to the soil is more susceptible to the indirect and direct effects of climate and environmental changes, and tree roots are more susceptible to environmental changes than other plants in isolated patches of permafrost regions. The physical properties of the soil are affected by climate change, and the allocation of carbon is regulated by hormones and osmotic regulators while resisting anoxia in the sporadic regions of permafrost. Carbon allocation in discontinuous permafrost areas is mainly regulated by root substances, which are easily affected by the physical and chemical properties of the soil. In general, the community composition of peatlands in permafrost areas is highly susceptible to environmental changes in the soil, and the allocation of carbon from the plant to the soil is affected by the degradation of the permafrost. [ABSTRACT FROM AUTHOR]

The ecosystem of the water level fluctuation (WLF) zone of the Three Gorges Reservoir (TGR) is highly vulnerable and sensitive due to its unique cyclical flooding and drought conditions. The ecological impact of biological invasion in this area is particularly severe, making it crucial to study the differences in resource utilization strategies between invasive plants (IPs) and native plants (NPs) using functional traits to explore the mechanisms of invasion. We selected the WLF zone of Pengxi River in the TGR area and conducted a multi-scale study along the elevation gradient. The results reveal that at the regional scale, IPs have a larger height and specific leaf area, smaller leaf tissue density, and specific root length compared to NPs, showing a preference for enhancing aboveground resource acquisition over leaf defense capabilities. They allocate more tissue construction resources to their roots to withstand environmental pressures, which may be the key to their successful intrusion, highlighting the role of niche differentiation. On the community scale, the H and SLA of IPs and NPs are positively correlated with elevation, while the LTD of IPs shows a negative correlation. At elevations of 175 m and below, IP and NP exhibit functional convergence, while above 175 m, functional divergence was observed. This indicates that although the different resource utilization strategies are crucial for successful IP invasion, the environmental filtering from periodic floods and drought pressures play a significant role in community assembly in the WLF zone, allowing IP to integrate into habitats with similar functional characteristics already inhabited by NP and establish their own communities. [ABSTRACT FROM AUTHOR]

Accurate comprehension of grassland fires is imperative for maintaining ecological stability. In this study, we propose a novel fire model that incorporates real-time meteorological conditions. Our methodology integrates key meteorological factors including relative humidity, temperature, degree of solidification of combustible materials, and wind speed. These factors are embedded into a comprehensive function that determines both the downwind and upwind spreading speeds of the fire. Additionally, the model accommodates fire spread in the absence of wind by incorporating the direction perpendicular to the wind, with wind speed set to zero. By precisely determining wind speed, the model enables real-time calculation of fire spread speeds in all directions. Under stable wind conditions, the fire spread area typically adopts an elliptical shape. Leveraging ellipse properties, we define the aspect ratio as a function related to wind speed. Consequently, with knowledge of the fire duration, the model accurately estimates the area of fire spread. Our findings demonstrate the effectiveness of this model in predicting and evaluating fires in the Hulunbuir Grassland. The model offers an innovative method for quantifying grassland fires, contributing significantly to the understanding and management of grassland ecosystems. [ABSTRACT FROM AUTHOR]

Lowland tropical rainforests provide an abundance of biodiversity as well as dynamic and stable ecosystems. These forests include tropical peat forests in various locations and forest types that have vegetation structure and composition characteristics, and carbon stocks that still need to be explored more deeply. Research on the structure and the composition of the vegetation and carbon stock in the old secondary peat forest was carried out in the protected areas of Bukit Batu, Riau Province, and Muara Merang, South Sumatra Province. Based on a 1-hectare permanent plot established in Bukit Batu and Muara Merang, 25 subplots of 20 m by 20 m were established in each location for measurement purposes. The results showed that Bukit Batu and Muara Merang had 42 and 36 species belonging to 26 and 20 families, respectively. Bukit Batu had a species diversity index (H') of 2.93, and the dominant tree species were Palaquium xanthochymum with an importance value index (IVI) = 66.27%, Eugenia sp. (IVI = 32.76%), and Litsea sp. (IVI = 18.39%). The Muara Merang location had a species diversity index (H') of 2.82, and the dominant tree species were Eugenia sp. (IVI = 60.88%), Alseodaphne insignis (IVI = 26.34%), and Adenanthera pavonina (IVI = 22.11%). In Bukit Batu, forest stands with a diameter of ≥10 cm contained a biomass of 178.10 tonnes/ha and carbon stock of 83.70 tonnes C/ha, which is equal to 307.20 tonnes CO2/ha. Meanwhile, in Muara Merang, it was 190.41 tonnes/ha and 89.49 tonnes C/ha, which is equal to 328.44 tonnes CO2/ha. This research, especially that in Bukit Batu, Riau Province, enriches the data and information available to date and is very useful in supporting restoration practices in Riau Province's Giam Siak Kecil Biosphere Reserve, which was designated by UNESCO as part of the Man and Biosphere Program. [ABSTRACT FROM AUTHOR]

Wood basic density (WD) plays a crucial role in estimating forest biomass; moreover, improving wood-density estimates is needed to reduce uncertainties in the estimates of tropical forest biomass and carbon stocks. Understanding variations in this density along the tree trunk and its impact on biomass estimates is underexplored in the literature. In this study, the vertical variability of WD was assessed along the stems of large trees that had a diameter at breast height (DBH) ≥ 50 cm from a dense ombrophilous forest on terra firme (unflooded uplands) in Acre, Brazil. A total of 224 trees were sampled, including 20 species, classified by wood type. The average WD along the stem was determined by the ratio of oven-dry mass to saturated volume. Five models were tested, including linear and nonlinear ones, to fit equations for WD, selecting the best model. The variation among species was notable, ranging from 0.288 g cm−3 (Ceiba pentandra, L., Gaertn) to 0.825 g cm−3 (Handroanthus serratifolius, Vahl., S. Grose), with an average of 0.560 g cm−3 (±0.164, standard deviation). Significant variation was observed among individuals, such as in Schizolobium parahyba var. amazonicum (H. ex D.), which ranged from 0.305 to 0.655 g cm−3. WD was classified as low (≤0.40 g cm−3), medium (0.41–0.60 g cm−3), and high (≥0.61 g cm−3). The variability in WD along the stem differs by wood type. In trees with low-density wood, density shows irregular variation but tends to increase along the stem, whereas it decreases in species with medium- and high-density wood. The variation in WD along the stem can lead to underestimations or overestimations, not only in individual trees and species but also in total stocks when estimating forest biomass. Not considering this systematic bias results in significant errors, especially in extrapolations to vast areas, such as the Amazon. [ABSTRACT FROM AUTHOR]

Tropical forest management has a potential role in forest conservation if it is sustainable. This study of a forest under management in Bolivian Amazonia strongly suggests that the management project is not sustainable and that no potential changes in management would be likely to make it so. In a 216.41 ha harvested area, 278 commercial trees from 10 families, 15 genera, and 15 species were measured. The density of commercial species with diameter at breast height (DBH) ≥ 50 cm was 1.28 trees ha−1, and the harvestable commercial volume was 12.40 m3 ha−1. Due to market restrictions, the actual amounts harvested were much lower: 96 trees were harvested with commercial boles totaling 2.7 m3 ha−1. Of the total impact on biomass and carbon (above- and belowground), the logs removed from the area represented only 13.4%, while 86.6% was from losses in the forest as follows: 14.5% from the stumps, crowns, and roots of harvested trees (DBH ≥ 50 cm) plus 72.1% from the trees (DBH ≥ 10 cm) in the forest lost to roads, log landings, and skid tracks and the gap openings caused by felling the harvested trees. The estimated expenses exceeded the gross revenue of the management company (USD 519.15 ha−1), a fact confirmed by the company's subsequent bankruptcy. The project's low harvest intensity reduces the environmental impact per hectare but increases the impact per cubic meter of wood harvested because producing a given volume of wood requires disturbing a larger area and because more kilometers of access roads and skid tracks have to be installed to extract a given volume of wood. Because many costs are fixed regardless of harvest intensity, small harvest volume can render such projects financially unfeasible, increasing the likelihood that they will be abandoned and not provide long-term "sustainable" forest protection. However, this does not mean that higher harvest intensity results in sustainability, as other constraints apply to high-intensity projects. We conclude that conservation alternatives to maintain the forest would be more beneficial than management for timber. [ABSTRACT FROM AUTHOR]

Simple Summary: Cervical cancer occurring during pregnancy represents a rare and challenging situation and requires multidisciplinary care. Both diagnosis and treatment should take into consideration maternal and fetal health, while not compromising the chances for cure. Currently, guidelines for the management of cervical cancer occurring during pregnancy are provided by international consensus meetings with expert panels. However, the current guidelines do not discuss the various approaches that can be found within the literature, such as alternative staging modalities or innovative surgical approaches. Our systematic review, performed with the "Cancer Associé à La Grossesse" (CALG) network, aims to fill the gap on current issues, including both neoadjuvant chemotherapy and surgical strategies to preserve fertility. Cancer during pregnancy is defined as a tumor diagnosed in a pregnant woman or up to 1-year post-partum. While being a rare disease, cervical cancer is probably one of the most challenging medical conditions, with the dual stake of treating the cancer without compromising its chances for cure, while preserving the pregnancy and the health of the fetus and child. To date, guidelines for gynecological cancers are provided through international consensus meetings with expert panels, giving insights on both diagnosis, treatment, and obstetrical care. However, these expert guidelines do not discuss the various approaches than can be found within the literature, such as alternative staging modalities or innovative surgical approaches. Also, the obstetrical care of women diagnosed with cervical cancer during pregnancy requires specific considerations that are not provided within our current standard of care. This systematic review aims to fill the gap on current issues with regards to the management of cervical cancer during pregnancy and provide future directions within this evolving landscape. [ABSTRACT FROM AUTHOR]

Despite their relatively small numbers, large old trees play disproportionately important roles in global biodiversity and ecosystem functions. There is a lack of systematic reviews and quantitative analyses of the accumulated literature. Understanding the research context and evolution could pump prime research and conservation endeavors. Using the comprehensive Web of Science, we applied VOSviewer (1.6.19) and CiteSpace (6.1R2) bibliometric software to examine the large old tree research field in 2000–2022. The queries of the bibliographic database generated quantitative–visual depictions in the form of knowledge maps. The nodes denote research intensity, and inter-node linkages denote the pathways and frequencies of collaborative activities. The research outputs differed significantly in terms of regions, countries, institutions, high-citation articles, productive researchers, hot topics, and research frontiers. Conspicuous spatial disparities were displayed, with the U.S.A., China, and Australia leading in publication counts and a cluster of European countries making considerable collective contributions. The research collaboration demonstrated a dichotomy: European countries networked more by geographical propinquity, and the top three countries connected by long-distance leap-frog jumps. The entrenched discrepancies between the endowed developed domains vis-à-vis the deprived developing domains were clearly expressed. The research productivity progressed through three stages: initial, growth, and flourishing. The leading institutions, researchers, and highly cited papers were recognized. The keyword analysis pinpointed diverse research hotspots: growth dynamics, conservation and management, ecological functions, and environmental response. This study informs recommendations for future research directions and cooperation on longevity mechanisms, evolutionary adaptation, dynamic monitoring, and temporal–spatial patterns. The integrated application of GIS, machine learning, and big data technologies could strengthen research capability. [ABSTRACT FROM AUTHOR]

Assessments of synergies and trade-offs between climate change mitigation and forest biodiversity conservation have focused on set-aside areas. We evaluated a more comprehensive portfolio of silvicultural management adaptations to climate change and conservation measures exemplary for managed European beech forests. Based on the available literature, we assessed a range of common silvicultural management and conservation measures for their effects on carbon sequestration in forest and wood products and for substituting more carbon-intensive products. We complemented this review with carbon sequestration simulations for a typical mountainous beech forest region in Austria. We propose three priority actions to enhance the synergies between climate change mitigation and biodiversity. First, actively increase the proportion of European beech in secondary Norway spruce forests, even though beech will not be unaffected by expected water supply limitations. Secondly, optimize the benefits of shelterwood systems and promote uneven-aged forestry, and thirdly, enhance mixed tree species. Targeted conservation measures (deadwood, habitat trees, and old forest patches) increase the total C storage but decrease the annual C sequestration in forests, particularly in wood products. The establishment of a beech wood market with an extended product portfolio to reduce the use of fuelwood is essential for sustainable climate change mitigation. Since there are limitations in the production of saw timber quality beech wood on low fertility sites, C accumulation, and biodiversity can be emphasized in these areas. [ABSTRACT FROM AUTHOR]

Large, mapped forest research plots are important sources of data to understand spatial and temporal changes in forest communities in the context of global change. Here, we describe the data from the first three censuses of the 16-ha UC Santa Cruz Forest Ecology Research Plot, located in the Mediterranean-climate forest on the central coast of California, USA. The forest includes both mixed-evergreen forest and redwood-dominated forest and is recovering from significant logging disturbances in the early 20th century. Each woody stem with a diameter ≥ 1 cm at 1.3 m was mapped, tagged, identified, and measured, with censuses performed at ~5-year intervals. The first census included just 6 ha (previously described), and the area was then expanded to 16 ha in the second census. We describe the temporal dynamics of the forest in the original 6 ha, as well as the structure and temporal dynamics of the full 16 ha. The community includes 34 woody species, including 4 gymnosperm and 9 angiosperm tree species, 18 species of shrubs, and 3 species of lianas. The community includes eight non-native species, representing less than 0.5% of the stems. More than half the species show greater rates of mortality than recruitments, reflective of a dynamic forest community. Over a decade, the number of living woody stems has declined, but the basal area has increased, reflecting a self-thinning process. [ABSTRACT FROM AUTHOR]

While the use of large tropical trees to predict aboveground biomass (AGB) in forests has previously been studied, the applicability of this approach in arid regions remains unquantified. In the natural forests of arid mountains of Northwestern China, this study collected individual tree data from 105 plots across 11 sites through field measurements. The objective was to assess the feasibility of using large trees for predicting plot AGB in these natural forests of arid mountains. This entailed determining the contribution of large trees, based on which a plot AGB prediction model was constructed. This study also aimed to identify the optimal number of large trees needed for accurate AGB prediction. The findings indicate that within the natural forests of arid mountains, only seven large trees (approximately 12% of the trees in a plot) are necessary to account for over 50% of the plot AGB. By measuring 18 large trees within a plot, this study achieved a precise plot AGB estimation, resulting in a model rRMSE of 0.27. The regression fit R2 for the predicted AGB and the estimated AGB was 0.79, effectively aligning the predicted and measured AGB. In the Tianshan Mountains' natural forests, the prediction model yielded further improvements with an rRMSE of 0.13 and a remarkable regression R2 of 0.92 between predicted and estimated AGB. However, due to variances in tree size distribution and tree species biomass, the Altai Mountains' natural forest was found to be unsuitable for predicting plot AGB using large trees. This study establishes that large trees can effectively represent plot AGB in the natural forests of arid mountains. Employing forest surveys or remote sensing to collect data from a few large trees instead of the entire tree population enables accurate plot AGB prediction. This research serves as the initial quantification of large tree utilization for plot AGB prediction in the natural forests of arid mountains, carrying substantial implications for future arid forest inventories, carbon accounting, and the formulation of prudent conservation strategies. [ABSTRACT FROM AUTHOR]

(1) Background: Satellite monitoring of fire effects is widespread, but often satellite-derived values are considered without respect to the characteristic severity of fires in different vegetation types or fire areas. Particularly in regions with discontinuous vegetation or narrowly distributed vegetation types, such as the state of Utah, USA, specific characterization of satellite-derived fire sensitivity by vegetation and fire size may improve both pre-fire and post-fire management activities. (2) Methods: We analyzed the 775 medium-sized (40 ha ≤ area < 400 ha) and 697 large (≥400 ha) wildfires that occurred in Utah from 1984 to 2022 and assessed burn severity for all vegetation types using the differenced Normalized Burn Ratio. (3) Results: Between 1984–2021, Utah annually experienced an average of 38 fires ≥ 40 ha that burned an annual average of 58,242 ha with a median dNBR of 165. Fire was heavily influenced by sagebrush and shrubland vegetation types, as these constituted 50.2% (17% SD) of area burned, a proportion which was relatively consistent (18% to 79% yr−1). Medium-sized fires had higher mean severity than large fires in non-forested vegetation types, but forested vegetation types showed the reverse. Between 1985 and 2021, the total area burned in fires ≥ 40 ha in Utah became more concentrated in a smaller number of large fires. (4) Conclusions: In Utah, characteristic fire severity differs both among vegetation types and fire sizes. Fire activity in the recent past may serve as an informative baseline for future fire, although the long period of fire suppression in the 20th century suggests that future fire may be more active. Fire managers planning prescribed fires < 400 ha in forests may find the data from medium-sized fires more indicative of expected behavior than statewide averages or vegetation type averages, both of which are weighted to large fires. [ABSTRACT FROM AUTHOR]

Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote-sensing-data-based models are based on supervised learning that requires large and representative human-labeled data for model training, which is costly and time-consuming. The recent introduction of self-supervised learning (SSL) enables models to learn a representation from orders of magnitude more unlabeled data. The success of SSL is heavily dependent on a pre-designed pretext task, which introduces an inductive bias into the model from a large amount of unlabeled data. Since remote sensing imagery has rich spectral information beyond the standard RGB color space, it may not be straightforward to extend to the multi/hyperspectral domain the pretext tasks established in computer vision based on RGB images. To address this challenge, this work proposed a generic self-supervised learning framework based on remote sensing data at both the object and pixel levels. The method contains two novel pretext tasks, one for object-based and one for pixel-based remote sensing data analysis methods. One pretext task is used to reconstruct the spectral profile from the masked data, which can be used to extract a representation of pixel information and improve the performance of downstream tasks associated with pixel-based analysis. The second pretext task is used to identify objects from multiple views of the same object in multispectral data, which can be used to extract a representation and improve the performance of downstream tasks associated with object-based analysis. The results of two typical downstream task evaluation exercises (a multilabel land cover classification task on Sentinel-2 multispectral datasets and a ground soil parameter retrieval task on hyperspectral datasets) demonstrate that the proposed SSL method learns a target representation that covers both spatial and spectral information from massive unlabeled data. A comparison with currently available SSL methods shows that the proposed method, which emphasizes both spectral and spatial features, outperforms existing SSL methods on multi- and hyperspectral remote sensing datasets. We believe that this approach has the potential to be effective in a wider range of remote sensing applications and we will explore its utility in more remote sensing applications in the future. [ABSTRACT FROM AUTHOR]

Specific studies of large trees in giant panda (Ailuropoda melanoleuca) habitats are currently lacking. To fill this research gap, a field survey on large trees in Wanglang Nature Reserve of China was conducted. According to a previous definition of a large tree (DBH ≥ 60 cm), a total of 19 large trees, composed of Abies fargesii var. faxoniana (73.7%), Betula albosinensis (15.8%), and Juniperus saltuaria (10.5%), were found in 50% of 30 10 m × 10 m giant panda habitat plots (contained feces of giant panda). Compared to the habitat where large trees were absent, the giant panda habitat where large trees were present was characterized by larger-sized tree diameter and more bamboo clumps. The mean proportion of large trees in the basal area of the tree layer in habitats where large trees were present was 65.5%. Considering large trees are a scarce resource and play a crucial role in habitat composition and habitat use, large trees and their habitats need to be given priority protection. To achieve more accurate protection, continuous and dynamic monitoring of large trees is needed, and the investigation of large trees should form a part of future national surveys of giant pandas. [ABSTRACT FROM AUTHOR]

A deeper understanding of the influence of fine-scale fuel patterns on fire behavior is essential to the design of forest treatments that aim to reduce fire hazard, enhance structural complexity, and increase ecosystem function and resilience. Of particular relevance is the impact of horizontal and vertical forest structure on potential tree torching and large-tree mortality. It may be the case that fire behavior in spatially complex stands differs from predictions based on stand-level descriptors of the fuel distribution and structure. In this work, we used a spatially explicit fire behavior model to evaluate how the vertical and horizontal distribution of fuels influences the potential for fire to travel from the surface into overstory tree crowns. Our results support the understanding that crown fuels (e.g., needles and small-diameter branchwood) close to the surface can aid in this transition; however, we add important nuance by showing the interactive effect of overstory horizontal fuel connectivity. The influence of fuels low in the canopy space was overridden by the effect of horizontal connectivity at surface fire-line intensities greater than 1415 kW/m. For example, tree groups with vertically continuous fuels and limited horizontal connectivity sustained less large-tree consumption than tree groups with a significant vertical gap between the surface and canopy but high-canopy horizontal connectivity. This effect was likely the result of reduced net vertical heat transfer as well as decreased horizontal heat transfer, or crown-to-crown spread, in the upper canopy. These results suggest that the crown fire hazard represented by vertically complex tree groups is strongly mediated by the density, or horizontal connectivity, of the tree crowns within the group, and therefore, managers may be able to mitigate some of the torching hazard associated with vertically heterogenous tree groups. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) plays an important role in regulating the global carbon cycle and is thus an essential component of ecosystem functioning. In the relationships between biodiversity and ecosystem functioning (BEF), studies have shown that many biotic factors (e.g., species, functional traits, and large trees) and abiotic factors have significant impacts on AGB. However, the relative strength of these affecting factors remains unclear. In this study, we analyzed woody plants (diameter at breast height [DBH] ≥ 1 cm) within a 1.6 ha plot in an old-growth subtropical natural forest in southern China. We used structural equation models to test the effects of tree diversity (species, phylogenetic, functional, and size inequality), functional composition, large trees, and environmental factors (topography, soil nutrients, and understory light) on AGB. Our results indicated that size inequality, the community-weighted mean of maximum DBH (CWM_MDBH), and large trees had significant, positive effects on AGB (p < 0.001), while lower soil phosphorus content was found to promote an increase in AGB. Furthermore, large trees, which were mostly composed of dominant tree species, were the main driver of AGB, and the effect of functional composition (e.g., CWM_MDBH) on AGB was substantially reduced by large trees. We argue that the selection effect plays a key role in regulating BEF relationships in subtropical natural forests and conclude that retaining large-diameter trees and dominant species, along with sustaining a complex stand structure, are key measures for improving productivity. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) is not simply affected by a single factor or a few factors, but also by the interaction between them in complex ways across multiple spatial scales. Understanding the joint effect of stand structural characteristics and climate factors on AGB on large scales is critical for accurate forest carbon storage prediction and sustainable management. Despite numerous attempts to clarify the relationships between stand structural characteristics (tree density/TD, diameter at breast height/DBH, basal area/BA), climate factors (mean annual temperature/MAT, mean annual precipitation/MAP), and AGB, they remain contentious on a large scale. Therefore, we explored the relationships between stand structural characteristics, climate factors, and AGB at a biome level by meta-analyzing datasets contained in 40 articles from 25 countries, and then answered the questions of how stand structural characteristics influence AGB at the biome level and whether the relationships are regulated by climate on a large scale. Through using regression analysis and the establishment of a structural equation model, the results showed that the influence of basal area on AGB at the biome level was more substantial than that of tree density and DBH, and the significant relationship between basal area and AGB was relatively stable regardless of biome variation, but the effects of tree density and DBH was non-negligible within the biome. Climatic factors (e.g., temperature and precipitation), should be considered. Our meta-analysis illustrated the complicated interactions between climate factors, stand structural characteristics, and the AGB of forests, highlighting the importance of climate effects on regulating stand structural characteristics and AGB relationships. We suggest that basal area be preferred and considered in forest sustainable management practice to optimize stand structure for increasing carbon storage potential, with close attention to local climate conditions. Overall, our meta-analysis will crucially aid forest management and conservation in the context of global environmental changes, and provide novel insights and a scientific reference to lead to future carbon storage research on large scales. [ABSTRACT FROM AUTHOR]

Tropical forests are biologically diverse and structurally complex ecosystems that can store a large quantity of carbon and support a great variety of plant and animal species. However, tropical forest structure can vary dramatically within seemingly homogeneous landscapes due to subtle changes in topography, soil fertility, species composition and past disturbances. Although numerous studies have reported the effects of field-based stand structure attributes on aboveground biomass (AGB) in tropical forests, the relative effects and contributions of UAV LiDAR-based canopy structure and ground-based stand structural attributes in shaping AGB remain unclear. Here, we hypothesize that mean top-of-canopy height (TCH) enhances AGB directly and indirectly via species richness and horizontal stand structural attributes, but these positive relationships are stronger at a larger spatial scale. We used a combined approach of field inventory and LiDAR-based remote sensing to explore how stand structural attributes (stem abundance, size variation and TCH) and tree species richness affect AGB along an elevational gradient in tropical forests at two spatial scales, i.e., 20 m × 20 m (small scale), and 50 m × 50 m (large scale) in southwest China. Specifically, we used structural equation models to test the proposed hypothesis. We found that TCH, stem size variation and abundance were strongly positively associated with AGB at both spatial scales, in addition to which increasing TCH led to greater AGB indirectly through increased stem size variation. Species richness had negative to negligible influences on AGB, but species richness increased with increasing stem abundance at both spatial scales. Our results suggest that light capture and use, modulated by stand structure, are key to promoting high AGB stocks in tropical forests. Thus, we argue that both horizontal and vertical stand structures are important for shaping AGB, but the relative contributions vary across spatial scales in tropical forests. Importantly, our results highlight the importance of including vertical forest stand attributes for predicting AGB and carbon sequestration that underpins human wellbeing. [ABSTRACT FROM AUTHOR]

Tropical landscapes comprise a variety of land cover (LC) types with characteristic canopy structure and tree species. Depending on the LC type, large-diameter trees and certain tree species can contribute disproportionately to aboveground biomass (AGB), and these patterns are not described at landscape-level in LC type specific studies. Therefore, we investigated the impact of large trees and tree species on AGB across a range of LC types in Taita Hills, Kenya. Data included 239 field plots from seven LC types: Montane forest, Plantation forest, Mixed forest, Riverine forest, Bushland, Grassland, and Cropland and homestead. Our results show that the contribution of large trees (DBH > 60 cm) on AGB was greatest in Riverine forest, Montane forest and Mixed forest (34–87%). Large trees were also common in Plantation forests and Cropland and homestead. Small trees (DBH < 20 cm) covered less than 10% of the total AGB in all forest types. In Grassland, and Cropland and homestead, smaller DBH classes made a greater contribution. Bushland differed from other classes as large trees were rare. Furthermore, the results show that each LC type had characteristic species with high AGB. In the Montane and Mixed forest, Albizia gummifera contributed 21.1% and 18.3% to AGB, respectively. Eucalyptus spp., exotic species planted in the area, were important in Mixed and Plantation forests. Newtonia hildebrandtii was the most important species in Riverine forests. In Bushland, Acacia mearnsii, species with invasive character, was abundant among trees with DBH < 30 cm. Vachellia tortillis, a common species in savannahs of East Africa, made the largest contribution in Grassland. Finally, in Cropland and homestead, Grevillea robusta was the most important species (>25% of AGB). Our results highlight the importance of conserving large trees and certain species to retain AGB stocks in the landscape. Furthermore, the results demonstrate that exotic tree species, even though invasive, can have large contribution to AGB. [ABSTRACT FROM AUTHOR]

Mature trees play a fundamental role in nature and are crucial to maintaining good air quality in the urban ecosystem where they reduce air pollution, lower the surface temperature, and emit medicinal volatile organic compounds which combine to improve human health and mental wellbeing. From an aesthetic and cultural point of view, they are true living monuments to be preserved. In both rural and city environments, it takes numerous years for trees to become mature enough to have a significant impact on our health and the current global climate changes together with high levels of pollution in urban environments and other anthropic factors such as vandalism constitute important obstacles to new tree growth. This clearly makes existing trees, especially old growth, far more valuable than we often realize. Regardless of their artistic quality and in some instances their positive messages, graffiti are still unacceptable on living organisms, especially older urban trees. They also have a significant environmental impact due to the emissions related to graffiti that are primarily based on anthropogenic volatile organic compounds (VOCs) which contribute to the formation of ground-level ozone. We reviewed the literature on graffiti and paintings applied on tree bark and ultimately found that oil-based paints in particular can damage tree life support systems. We herein also discuss graffiti prevention, the potential impact on human health related with graffiti removal, as well as methods for tree bark cleaning including, as suggested by different urban forestry specialists, the application of citrus-based products for 20–60 min before rubbing and rinsing or multiple 1–2 h treatments, in the case of recent or old graffiti, respectively. [ABSTRACT FROM AUTHOR]

Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan's northwestern province, holds 31% of the country's total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba's total forest land area in 1980 was about 236 km2 and shrank to 152 km2 by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km2/year from 1980 to 2020. The area of woodland forest decreased by 87 km2 (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km2. The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales. [ABSTRACT FROM AUTHOR]

Despite their importance to ecosystem services, wetlands are threatened by pollution and development. Over the last few decades, a growing number of wetland studies employed remote sensing (RS) to scientifically monitor the status of wetlands and support their sustainability. Considering the rapid evolution of wetland studies and significant progress that has been made in the field, this paper constitutes an overview of studies utilizing RS methods in wetland monitoring. It investigates publications from 1990 up to the middle of 2022, providing a systematic survey on RS data type, machine learning (ML) tools, publication details (e.g., authors, affiliations, citations, and publications date), case studies, accuracy metrics, and other parameters of interest for RS-based wetland studies by covering 344 papers. The RS data and ML combination is deemed helpful for wetland monitoring and multi-proxy studies, and it may open up new perspectives for research studies. In a rapidly changing wetlands landscape, integrating multiple RS data types and ML algorithms is an opportunity to advance science support for management decisions. This paper provides insight into the selection of suitable ML and RS data types for the detailed monitoring of wetland-associated systems. The synthesized findings of this paper are essential to determining best practices for environmental management, restoration, and conservation of wetlands. This meta-analysis establishes avenues for future research and outlines a baseline framework to facilitate further scientific research using the latest state-of-art ML tools for processing RS data. Overall, the present work recommends that wetland sustainability requires a special land-use policy and relevant protocols, regulation, and/or legislation. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity on a regional scale of forest biomass is caused by multiple biotic and abiotic factors. However, the contributions of biotic and abiotic factors to the spatial heterogeneity of forest biomass remain unclear. Based on the data of the National Forest Continuous Inventory (NFCI), digital elevation model (DEM), and meteorological data of Guizhou Province in 2015, we studied the spatial heterogeneity of the aboveground forest biomass in Guizhou province and evaluated the contribution rates of its influencing factors using Moran's I, semivariogram, distance-based Moran's eigenvector maps (dbMEMs), and variance partitioning. The results showed that the forest biomass in Guizhou province had strong spatial heterogeneity. Biotic and abiotic factors explained 34.4% and 19.2% of the spatial variation in forest biomass, respectively. Among the biotic factors, the average height of the stand had the greatest influence on forest biomass, while annual precipitation had the greatest influence on forest biomass among abiotic factors. Spatial factors only explained 0.7% of the spatial variation of forest biomass, indicating that the contribution of spatial factors can be explained by some measured abiotic factors. This study provided an effective approach to understand the underlying mechanisms of spatial allocation of forest biomass. [ABSTRACT FROM AUTHOR]

Forest disturbance, such as harvest and fire, can cause a huge amount of carbon emission from soil to the atmosphere. Monitoring forest disturbance at a high spatial resolution is critical for soil carbon modeling. The short-wave infrared bands are important bands in monitoring forest disturbance. However, many high spatial resolution satellites do not contain the short-wave infrared bands in their band designs, and whether the lack of short-wave infrared (SWIR) bands will cause a large influence on forest disturbance monitoring remains unclear. This study aims to evaluate the values of adding SWIR bands in forest disturbance monitoring using the modified continuous monitoring of land disturbance (COLD) approach. Results showed that without the SWIR bands the accuracy of detecting forest disturbance will be reduced by 19–26%. The highest accuracy of modified COLD using the optimal band combination with SWIR bands was 76.3% for monitoring harvest and 86.6% for monitoring fire, while it decreased to 49.8% in detecting harvest and 67.6% in detecting fire without using any SWIR bands. The results demonstrated the importance of adding SWIR bands in forest disturbance monitoring and would guide users to select the satellite data with at least one SWIR band to monitor forest disturbance for improving the soil carbon modeling. [ABSTRACT FROM AUTHOR]

Trait-based approaches to calculate ecological strategy is important for understanding forest succession dynamics. However, the functional traits and ecological strategy change during succession remain poorly understood. We measured key leaf traits and calculated scores for CSR ecological strategy for 13 co-existing species in different successional stages in tropical lowland rain forests. We analyzed the patterns of functional traits and CSR strategies varied along the succession. The relationship between CSR strategy and environmental factors was examined. Our research results are as follows: (1) In older plots, LDMC was generally lower and SLA was generally higher than younger plots with the succession, the functional traits of the successional co-existing tree species changed, and SLA and LDMC showed significant differences. (2) The co-existing tree species' strategies shifted from S/CS and CS strategies to CS/CSR and CS strategies along the succession. (3) Ecological strategies are linked to different combinations of environmental factors across the four successional stages. Our study suggests that the co-existing tree species in different succession stages of tropical lowland rain forests have a high degree of resistance and a conservative ecological strategy, which is significantly related to canopy openness, soil water content, and soil nutrients. [ABSTRACT FROM AUTHOR]

Plant ecological strategies are shaped by long-term adaptation to the environment and are beneficial to plant survival and reproduction. Research is ongoing to better understand how plants best allocate resources for growth, survival and reproduction, as well as how ecological strategies may shift in plant communities over the course of succession. In this study, 12 forest dynamics plots in three different successional stages were selected for study in the tropical lowland rainforest ecosystem of Hainan Island. For each plot, using Grime's competitor, a stress-tolerator, the ruderal (CSR) scheme and using the CSR ratio tool "StrateFy", an ecological strategy spectrum was constructed using functional trait data obtained by collecting leaf samples from all woody species. The ecological strategy spectra were compared across successional stages to reveal successional dynamics. The results showed: (1) The ecological strategy spectra varied among forest communities belonging to three different successional stages. (2) The community-weighted mean CSR (CWM-CSR) strategies shifted with succession: CWM-S values decreased, while the CWM-C and CWM-R values increased. Overall, shifts in plant functional traits occurred slowly and steadily with succession showing complex and diverse trade-offs and leading to variation among the ecological strategy spectra of different successional stages. [ABSTRACT FROM AUTHOR]

The increasing availability of field data presents an opportunity to understand the fundamental ecological relationships and functions of large trees in tropical forests at regional and global scales. However, it is not always clear what the relationships or patterns of diversity and structure are among sites in different biogeographic regions. We evaluated the relationship of the biomass and diameter of the largest trees with a diversity of species and compared, between the sites, the attributes of structure, diversity, and the influence of the 50 hyperdominant species in each site, aiming at the potential formation of groups by sites with characteristics and patterns of similar diversity within biogeographic regions. The average wood density together with the diversity of genera and families are the most important attributes to discriminate biogeographic regions when considering all forest information. Large trees play a fundamental role in forest ecology and seem to express regional environmental characteristics. The upper canopy of tropical forests remains one of the least studied environments in all terrestrial biomes, and is often referred to as "the last biotic frontier" or a "black box," and large trees are also part of this mysterious frontier. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in a range of disciplines. Despite these achievements, only minimal progress has been made in (1) establishing standard operating practices and (2) communicating both the limitations and necessary next steps for future research. In this review of literature published between 2016 and 2022, UAS applications in forestry, freshwater ecosystems, grasslands and shrublands, and agriculture were synthesized to discuss the status and trends in UAS sensor data collection and processing. Two distinct conclusions were summarized from the over 120 UAS applications reviewed for this research. First, while each discipline exhibited similarities among their data collection and processing methods, best practices were not referenced in most instances. Second, there is still a considerable variability in the UAS sensor data methods described in UAS applications in natural resources, with fewer than half of the publications including an incomplete level of detail to replicate the study. If UAS are to increasingly provide data for important or complex challenges, they must be effectively utilized. [ABSTRACT FROM AUTHOR]

This paper provides a review and comparison of strategies to increase forest carbon, and reduce species losses for climate change mitigation and adaptation in the United States. It compares forest management strategies and actions that are taking place or being proposed to reduce wildfire risk and to increase carbon storage with recent research findings. International agreements state that safeguarding biodiversity and ecosystems is fundamental to climate resilience with respect to climate change impacts on them, and their roles in adaptation and mitigation. The recent Intergovernmental Panel on Climate Change report on impacts, mitigation, and adaptation found, and member countries agreed, that maintaining the resilience of biodiversity and ecosystem services at a global scale is "fundamental" for climate mitigation and adaptation, and requires "effective and equitable conservation of approximately 30 to 50% of Earth's land, freshwater and ocean areas, including current near-natural ecosystems." Our key message is that many of the current and proposed forest management actions in the United States are not consistent with climate goals, and that preserving 30 to 50% of lands for their carbon, biodiversity and water is feasible, effective, and necessary for achieving them. [ABSTRACT FROM AUTHOR]

Monitoring long-term forest dynamics can improve our understanding of how tree species change under varying environmental conditions over time. The Nanjenshan Nature Reserve, located in the southernmost part of Taiwan, is covered by natural tropical forest and affected by the northeast monsoon wind in the winter. To explore how monsoon wind shapes the structure of vegetation, we used the long-term data of the Lanjenchi plot (high wind exposure forest) and Nanjenshan plot I (low wind exposure forest) located in the Nanjenshan Nature Reserve. In these two plots, all free-standing trees with diameter at breast height (DBH) ≥ 1 cm were identified, measured, and mapped five times in thirty years (1989–2019). Temporal changes of tree abundance, dominance, recruitment, and mortality were calculated for all species. We recorded 9694 ± 3924 individual ha−1 belonging to 126 species in the high wind exposure forest and 4019 ± 1791 individual ha−1 belonging to 105 species in the low wind exposure forest in the latest census (census 5). From 1989 to 2019, stem density in the high wind exposure forests decreased (from 12,179 ± 6689 to 9694 ± 3924 individual ha−1), while stem density in the low wind exposure forests increased (from 3253 ± 1208 to 4019 ± 1791 individual ha−1). The stem density of the rare species, which survived only in the high wind exposure forest, decreased over time. However, the basal areas of these two forests barely changed during the study periods. Due to environmental conditions, the two forests have developed different forest structures in spaces and time. Species stem density in the high wind exposure forest and diversity could continually decrease and affect the maintenance of species diversity in the monsoon wind-shaped forest. [ABSTRACT FROM AUTHOR]

Turbulent mixing layers are canonical flow in nature and engineering, and deserve comprehensive studies under various conditions using different methods. In this paper, turbulent mixing layers are investigated using large eddy simulation and dynamic mode decomposition. The accuracy of the computations is verified and validated. Standard dynamic mode decomposition is utilized to flow decomposition, reconstruction and prediction. It was found that the dominant-mode selection criterion based on mode amplitude is more suitable for turbulent mixing layer flow compared with the other three criteria based on singular value, modal energy and integral modal amplitude, respectively. For the mixing layer with random disturbance, the standard dynamic mode decomposition method could accurately reconstruct and predict the region before instability happens, but is not qualified in the regions after that, which implies that improved dynamic mode decomposition methods need to be utilized or developed for the future dynamic mode decomposition of turbulent mixing layers. [ABSTRACT FROM AUTHOR]

This study examined the hydrological/meteorological controls on largewildfires > 10,000 acres (40.5 km²) during 2017-2020 in Northern California at spatial and temporal scales of the target wildfires' occurrence or growth. This study used the following simple indices for analysis: Moisture Deficit Index (MDI) computed by dividing vapor pressure deficit by soil moisture, MDIWIND computed by multiplying MDI by horizontal wind speed, and MDIGUST computed by multiplying MDI by wind gust speed. The ignition location MDIWIND and MDIGUST showed larger values on the ignition date in fire-years compared to non-fire-years for most of the target wildfires (95.8% and 91.7%, respectively). The peak timing of MDIGUST, which is to evaluate the integrated effect of dry atmosphere/soil and windy condition, coincided with the ignition date for August Complex Fire 2020, Ranch Fire 2018, Claremont-Bear Fire 2020, and Camp Fire 2018. We also found that August Complex Fire 2020, Claremont-Bear Fire 2020, and Camp Fire 2018 occurred in the areas where MDIGUST became spatially and temporally high. Further, strong relationships were found between burned area sizes of the target wildfires and MDI (R = 0.62, p = 0.002), MDIWIND (R = 0.72, p < 0.001), and MDIGUST (R = 0.68, p < 0.001). Overall, the findings in this study implied the strong effect of dry atmosphere/soil and windy conditions on recent large wildfire activities in Northern California. The findings could contribute to a more temporally and spatially detailed forecast of wildfire risks or a better understanding of wildfires' occurrence and growth mechanisms. [ABSTRACT FROM AUTHOR]

This study investigates the effects of recurrent wildfires on the resilience of a typical Mediterranean ecosystem. It is based on uninterrupted monitoring over 42 years of the avifauna in a cork oak forest that burned three times during this time interval. The monitoring involved two line-transect counts in spring accompanied by the simultaneous and independent estimation of the vegetation cover profile. One of the two transects was initially designed to serve as an unburned control before it also burned during the second fire. Many forest bird species were already present from the first spring postfire due to the rapid regeneration of the canopy. Some open-habitat bird species colonized the burned area during the first 2–4 years after the fire, resulting in an initial phase of high diversity. The postfire bird succession was mainly driven by sedentary species that recolonized the burned area after the first winter, whereas most migratory species present before the fire resettled as early as the first postfire spring, probably because of site tenacity. It was found that the impact of the second fire on avifauna was lower than that of the first or third fire. The return to an avifauna and forest structure successionally equivalent to the prefire control was achieved in about 15 years, which can be considered as the recovery time. Afterwards, both vegetation and avifauna in the burned areas tended to take on more forest characteristics than in the prefire control. These findings suggest that: (i) the recurrence of fire does not necessarily result in the cumulative degradation of the ecosystem at each repetition; (ii) the asymptotic resilience model is not adapted to the case of disturbances in non-mature environments; (iii) the notion of returning to an original undisturbed baseline is illusive in an area that has been under continuous human influence since ancient times. [ABSTRACT FROM AUTHOR]

While closed canopy forests have been an important focal point for land cover change monitoring and climate change mitigation, less consideration has been given to methods for large scale measurements of trees outside of forests. Trees outside of forests are an important but often overlooked natural resource throughout sub-Saharan Africa, providing benefits for livelihoods as well as climate change mitigation and adaptation. In this study, the development of an individual tree cover map using very high-resolution remote sensing and a comparison with a new automated machine learning mapping product revealed an important contribution of trees outside of forests to landscape tree cover and carbon stocks in a region where trees outside of forests are important components of livelihood systems. Here, we test and demonstrate the use of allometric scaling from remote sensing crown area to provide estimates of landscape-scale carbon stocks. Prominent biomass and carbon maps from global-scale remote sensing greatly underestimate the “invisible” carbon in these sparse tree-based systems. The measurement of tree cover and carbon in these landscapes has important application in climate change mitigation and adaptation policies. [ABSTRACT FROM AUTHOR]

Large-diameter trees have mainly been used for timber production in forestry practices. Recently, their critical roles played in biodiversity conservation and maintenance of ecosystem functions have been recognized. However, current forestry policy on the management of large-diameter trees is weak. As China is the biggest consumer of large-diameter timbers, how to maintain sustainable large-diameter timber resources aswell asmaximize ecological functions of the forests is a critical question to address. Here we summarize historical uses, distribution patterns, and management strategies of large-diameter trees in China. We found that large-diameter trees are mainly distributed in old-growth forests. Although China’s forest cover has increased rapidly in the past decades, large-diameter trees are rarely found in plantation forests and secondary forests. We suggest that knowledge of large-diameter trees should be widely disseminated in local forestry departments, especially their irreplaceable value in terms of biodiversity conservation and ecosystem functions. Protection of large-diameter trees, especially those in old-growth forests, is critical for sustainable forestry. Tomeet the increasing demand of large-diameter timbers, plantation forests and secondary forests should apply forest densitymanagement with thinning to cultivate more large-diameter trees. [ABSTRACT FROM AUTHOR]

Thermal dissipation probe (TDP) method (Granier, 1985) is widely used to estimate tree transpiration (i.e., the water evaporated from the leaves) because it is simple to build, easy to install, and relatively inexpensive. However, the universality of the original calibration has been questioned and, in many cases, proved to be inaccurate. Thus, when the TDP is used in a new species, specific tests should be carried out. Our aim was to propose a new method for improving the accuracy of TDP on trees in the field. Small hazelnut trees (diameter at breast height 5 cm) were used for the experiment. The response of TDP sensors was compared with a reference water uptake measured with an electronic potometer system provided with a high precision liquid flow meter. We equipped three stems where we measured the sap flow density, the sapwood area (by using fuchsine), the total tree water uptake (reference), and the main meteorological parameters during summer 2018. Results confirmed that the original Granier’s calibration underestimated the e ective tree transpiration (relative error about 􀀀60%). We proposed a new equation for improving the measurement accuracy within an error of about 4%. The system proposed appeared an easier solution compared to potted trees and particularly suitable for orchards, thus contributing to improve the irrigation management worldwide. [ABSTRACT FROM AUTHOR]

Wildfires, which constitute the most extensive natural disturbance of the boreal biome, produce a broad range of ecological impacts to vegetation and soils that may influence post-fire vegetation assemblies and seedling recruitment. We inventoried post-fire understory vascular plant communities and tree seedling recruitment in the northwestern Canadian boreal forest and characterized the relative importance of fire effects and fire history, as well as non-fire drivers (i.e., the topoedaphic context and climate), to post-fire vegetation assemblies. Topoedaphic context, pre-fire forest structure and composition, and climate primarily controlled the understory plant communities and shifts in the ranked dominance of tree species (***8% and **13% of variance explained, respectively); however, fire and fire-affected soils were significant secondary drivers of post-fire vegetation. Wildfire had a significant indirect effect on understory vegetation communities through post-fire soil properties (**5%), and fire history and burn severity explained the dominance shifts of tree species (*7%). Fire-related variables were important explanatory variables in classification and regression tree models explaining the dominance shifts of four tree species (R² = 0.43-0.65). The dominance of jack pine (Pinus banksiana Lamb.) and trembling aspen (Populus tremuloides Michx.) increased following fires, whereas that of black spruce (Picea mariana (Mill.) BSP.) and white spruce (Picea glauca (Moench) Voss) declined. The overriding importance of site and climate to post-fire vegetation assemblies may confer some resilience to disturbed forests; however, if projected increases in fire activity in the northwestern boreal forest are borne out, secondary pathways of burn severity, fire frequency, and fire effects on soils are likely to accelerate ongoing climate-driven shifts in species compositions. [ABSTRACT FROM AUTHOR]

Tropical forests host at least two-thirds of the world’s flora and fauna diversity and store 25% of the terrestrial above and below ground carbon. However, biodiversity decline due to deforestation and forest degradation of tropical forest is increasing at an alarming rate. Biodiversity dynamics due to natural and anthropogenic disturbances are mainly monitored using established field survey approaches. However, such approaches appear to fall short at addressing complex disturbance factors and responses. We argue that the integration of state-of-the-art monitoring approaches can improve the detection of subtle biodiversity disturbances and responses in changing tropical forests, which are often data-poor. We assess the state-of-the-art technologies used to monitor biodiversity dynamics of changing tropical forests, and how their potential integration can increase the detail and accuracy of biodiversity monitoring. Moreover, the relevance of these biodiversity monitoring techniques in support of the UNCBD Aichi targets was explored using the Essential Biodiversity Variables (EBVs) as a framework. Our review indicates that although established field surveys were generally the dominant monitoring systems employed, the temporal trend of monitoring approaches indicates the increasing application of remote sensing and in -situ sensors in detecting disturbances related to agricultural activities, logging, hunting and infrastructure. The relevance of new technologies (i.e., remote sensing, in situ sensors, and DNA barcoding) in operationalising EBVs (especially towards the ecosystem structure, ecosystem function, and species population classes) and the Aichi targets has been assessed. Remote sensing application is limited for EBV classes such as genetic composition and species traits but was found most suitable for ecosystem structure class. The complementarity of remote sensing and emerging technologies were shown in relation to EBV candidates such as species distribution, net primary productivity, and habitat structure. We also developed a framework based on the primary biodiversity attributes, which indicated the potential of integration between monitoring approaches. In situ sensors are suitable to help measure biodiversity composition, while approaches based on remote sensing are powerful for addressing structural and functional biodiversity attributes. We conclude that, synergy between the recent biodiversity monitoring approaches is important and possible. However, testing the suitability of monitoring methods across scales, integrating heterogeneous monitoring technologies, setting up metadata standards, and making interpolation and/or extrapolation from observation at different scales is still required to design a robust biodiversity monitoring system that can contribute to effective conservation measures. [ABSTRACT FROM AUTHOR]

One of the biggest uncertainties of climate change is determining the response of vegetation to many co-occurring stressors. In particular, many forests are experiencing increased nitrogen deposition and are expected to suffer in the future from increased drought frequency and intensity. Interactions between drought and nitrogen deposition are antagonistic and non-additive, which makes predictions of vegetation response dependent on multiple factors. The tools we use (Earth system models) to evaluate the impact of climate change on the carbon cycle are ill equipped to capture the physiological feedbacks and dynamic responses of ecosystems to these types of stressors. In this manuscript, we review the observed effects of nitrogen deposition and drought on vegetation as they relate to productivity, particularly focusing on carbon uptake and partitioning. We conclude there are several areas of model development that can improve the predicted carbon uptake under increasing nitrogen deposition and drought. This includes a more flexible framework for carbon and nitrogen partitioning, dynamic carbon allocation, better representation of root form and function, age and succession dynamics, competition, and plant modeling using trait-based approaches. These areas of model development have the potential to improve the forecasting ability and reduce the uncertainty of climate models. [ABSTRACT FROM AUTHOR]

Northern regions are experiencing considerable climate change affecting the state of permafrost, peat accumulation rates, and the large pool of carbon (C) stored in soil, thereby emphasizing the importance of monitoring surface C fluxes in different landform sites along a climate gradient. We studied surface net C exchange (NCE) and ecosystem respiration (ER) across different landforms (upland, peat plateau, collapse scar) in mid-boreal to high subarctic ecoregions in the Mackenzie Valley of northwestern Canada for three years. NCE and ER were measured using automatic CO2 chambers (ADC, Bioscientific LTD., Herts, England), and soil respiration (SR) was measured with solid state infrared CO2 sensors (Carbocaps, Vaisala, Vantaa, Finland) using the concentration gradient technique. Both NCE and ER were primarily controlled by soil temperature in the upper horizons. In upland forest locations, ER varied from 583 to 214 g C·m-2·year-1 from mid-boreal to high subarctic zones, respectively. For the bog and peat plateau areas, ER was less than half that at the upland locations. Of SR, nearly 75% was generated in the upper 5 cm layer composed of live bryophytes and actively decomposing fibric material. Our results suggest that for the upland and bog locations, ER significantly exceeded NCE. Bryophyte NCE was greatest in continuously waterlogged collapsed areas and was negligible in other locations. Overall, upland forest sites were sources of CO2 (from 64 g·C·m-2·year-1 in the high subarctic to 588 g C·m-2year-1 in mid-boreal zone); collapsed areas were sinks of C, especially in high subarctic (from 27 g· C· m-2 year-1 in mid-boreal to 86 g· C· m-2 year-1 in high subarctic) and peat plateaus were minor sources (from 153 g· C· m-2 year-1 in mid-boreal to 6 g· C· m-2 year-1 in high subarctic). The results are important in understanding how different landforms are responding to climate change and would be useful in modeling the effect of future climate change on the soil C balance in the northern regions. [ABSTRACT FROM AUTHOR]

Because traditional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, several researchers have presented a co-location decision tree (CL-DT) method that combines the co-location technique with the traditional DT method. However, the CL-DT method only considers the Euclidean distance of neighborhood events, which cannot truly reflect the co-location relationship between instances for which there is a nonlinear distribution in a high-dimensional space. For this reason, this paper develops the theory and method for a maximum variance unfolding (MVU)-based CL-DT method (known as MVU-based CL-DT), which includes unfolding input data, unfolded distance calculations, MVU-based co-location rule generation, and MVU-based CL-DT generation. The proposed method has been validated by classifying remotely sensed imagery and is compared with four other types of methods, i.e., CL-DT, classification and regression tree (CART), random forests (RFs), and stacked auto-encoders (SAE), whose classification results are taken as “true values.” The experimental results demonstrate that: (1) the relative classification accuracies of the proposed method in three test areas are higher than CL-DT and CART, and are at the same level compared to RFs; and (2) the total number of nodes, the number of leaf nodes, and the number of levels are significantly decreased by the proposed method. The time taken for the data processing, decision tree generation, drawing of the tree, and generation of the rules are also shortened by the proposed method compared to CL-DT, CART, and RFs. [ABSTRACT FROM AUTHOR]

This study uses detailed soil and vegetation data collected in a 30-ha old-growth broad-leaved Korean pine forest to study the effect of soil properties on tree community structures. Spatial distribution patterns are simulated using a homogeneous Poisson process (HomP) and a homogeneous Thomas process (HomT). The simulated distributions are compared with the observed ones to explore correlations between certain tree species and several soil elements. The HomP model shows that all tested tree species are significantly correlated with at least one principal component in the upper-layer soil elements. The HomT model shows that only 36.4% of tree species are significantly correlated with the principal component of at least one upper-layer soil element. This result shows that the impact of dispersal limitation is greater than impact of environmental heterogeneity on species spatial distributions. The spatial autocorrelation of species induced by the dispersal limitation will largely conceal the plant-soil relationships caused by the heterogeneity of soil elements. An additional analysis shows that the elements in the upper soil layer which have the greatest impact on community niche structure are Pb, total phosphorus (TP), total nitrogen (TN), Cu, Cr, Zn and available nitrogen (AN). The corresponding elements in the lower soil layers are Pb, TP, Cu, organic carbon (OC), Mn, total potassium (TK) and AN. Different species seem to be complementary regarding the demands on the available soil resources. The results of this study show that the tree species in the different growth groups have different habitat preferences. Compared with subcanopy and shrub species, the canopy species have more significant correlations with the soil elements. [ABSTRACT FROM AUTHOR]

Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA) methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM) and maximum likelihood (MLC) classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions): (1) the layer stacking fusion data; (2) the PCA fusion data; (3) the LiDAR data alone; and (4) the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964) using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the SVM classifier generally performed better than the MLC when classifying multisource data; however, none of the classifiers consistently produced higher accuracies at all spatial resolutions. [ABSTRACT FROM AUTHOR]

Mapping aboveground carbon density in tropical forests can support CO2 emission monitoring and provide benefits for national resource management. Although LiDAR technology has been shown to be useful for assessing carbon density patterns, the accuracy and generality of calibrations of LiDAR-based aboveground carbon density (ACD) predictions with those obtained from field inventory techniques should be intensified in order to advance tropical forest carbon mapping. Here we present results from the application of a general ACD estimation model applied with small-footprint LiDAR data and field-based estimates of a 50-ha forest plot in Ecuador's Yasuní National Park. Subplots used for calibration and validation of the general LiDAR equation were selected based on analysis of topographic position and spatial distribution of aboveground carbon stocks. The results showed that stratification of plot locations based on topography can improve the calibration and application of ACD estimation using airborne LiDAR (R² = 0.94, RMSE = 5.81 Mg ⋅ C⋅ ha-1, BIAS = 0.59). These results strongly suggest that a general LiDAR-based approach can be used for mapping aboveground carbon stocks in western lowland Amazonian forests. [ABSTRACT FROM AUTHOR]

Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through a case study in peatland classification using LiDAR derivatives, we present an analysis of the effects of input data characteristics on RF classifications (including RF out-ofbag error, independent classification accuracy and class proportion error). Training data selection and specific input variables (i.e., image channels) have a large impact on the overall accuracy of the image classification. High-dimension datasets should be reduced so that only uncorrelated important variables are used in classifications. Despite the fact that RF is an ensemble approach, independent error assessments should be used to evaluate RF results, and iterative classifications are recommended to assess the stability of predicted classes. Results are also shown to be highly sensitive to the size of the training data set. In addition to being as large as possible, the training data sets used in RF classification should also be (a) randomly distributed or created in a manner that allows for the class proportions of the training data to be representative of actual class proportions in the landscape; and (b) should have minimal spatial autocorrelation to improve classification results and to mitigate inflated estimates of RF out-of-bag classification accuracy. [ABSTRACT FROM AUTHOR]

In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques. [ABSTRACT FROM AUTHOR]

Location management, which consists of location registration and paging, is essential to provide mobile communication services to mobile stations (MSs). Since MSs riding on a public transportation system (TS) generates significant location registration signaling loads simultaneously when a TS with riding MSs moves between location areas (LAs), group location management was proposed. Under the group location management, an MS performs group registration when it gets on a TS and performs group deregistration when it gets off a TS. Then, only a TS updates its current location when it changes LA, on behalf of all riding MSs. In this paper, movement-based group location management using radio frequency identification (RFID) is proposed, where the MS's getting on and getting off behaviors are detected using RFID and only location update of a TS is carried out if the number of crossed cells from the last updated cell exceeds a predefined movement threshold, on behalf of all riding MSs. Then, we develop an analytical model for the performance analysis of the movement-based group location management and analyze the effects of various parameters on the performance. The results show that the movement-based group location management has reduced signaling cost compared with movement-based individual location management, and optimal performance can be achieved by choosing appropriate movement threshold values. [ABSTRACT FROM AUTHOR]