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Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm surges and reducing erosion. However, their resilience is being increasingly compromised due to climate change through sea level rises and the greater intensity of storms. This article examines the role of remote sensing tools in studying the impacts of hurricanes on mangrove forests in the coastal United States. Our results show that various remote sensing tools including satellite imagery, Light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been used to detect mangrove damage, monitor their recovery and analyze their 3D structural changes. Landsat 8 OLI (14%) has been particularly useful in long-term assessments, followed by Landsat 5 TM (9%) and NASA G-LiHT LiDAR (8%). Random forest (24%) and linear regression (24%) models were the most common modeling techniques, with the former being the most frequently used method for classifying satellite images. Some studies have shown significant mangrove canopy loss after major hurricanes, and damage was seen to vary spatially based on factors such as proximity to oceans, elevation and canopy structure, with taller mangroves typically experiencing greater damage. Recovery rates after hurricane-induced damage also vary, as some areas were seen to show rapid regrowth within months while others remained impacted after many years. The current challenges include capturing fine-scale changes owing to the dearth of remote sensing data with high temporal and spatial resolution. This review provides insights into the current remote sensing applications used in hurricane-prone mangrove habitats and is intended to guide future research directions, inform coastal management strategies and support conservation efforts. [ABSTRACT FROM AUTHOR]

Mangroves provide numerous ecological, social, and economic benefits that include carbon sequestration, habitat for biodiversity, food, recreation and leisure, income, and coastal resilience. In this regard, mangrove-based carbon market projects (MbCMP), involving mangrove conservation, protection, and restoration, are a nature-based solution (NbS) for climate change mitigation. Despite the proliferation of blue carbon projects, a highly publicized need for local community participation by developers, and existing project implementation standards, local communities are usually left out for several reasons, such as a lack of capacity to engage in business-to-business (B2B) market agreements and communication gaps. Local communities need to be engaged and supported at all stages of the MbCMP development process to enable them to protect their ecological, economic, and social interests as custodians of such a critical ecosystem. In this paper, we provided 15 strategic considerations and recommendations to engage and secure the interests of local communities in the growing mangrove carbon market trade. The 15 considerations are grouped into four recommendation categories: (i) project development and community engagement, (ii) capacity building and educational activities, (iii) transparency in resource allocation and distribution, and (iv) partnerships with local entities and long-term monitoring. We expect our study to increase local participation and community-level ecological, social, and economic benefits from MbCMP by incorporating equitable benefit-sharing mechanisms in a B2B conservation-agreement model. [ABSTRACT FROM AUTHOR]

Mangrove ecotourism is gaining immense popularity in the Gulf Cooperation Council (GCC) countries as a neoliberal conservation tool, and it has contributed significantly to the growth of the tourism sector in the region over the past two decades. However, there is no comprehensive review on the full extent of mangrove ecotourism activities and the contribution to mangrove conservation/restoration and economic growth in the region. A systematic literature review approach was used to examine the evolution of mangrove ecotourism in the GCC countries from 2010 to 2023. A total of 55 articles were retrieved from the Google and Google Scholar search engines, and the Scopus and Web of Science databases were incorporated. We synthesized the results and provided perspectives on the following: (1) the geographical and temporal distribution of studies in relation to mangrove extent, (2) key sites, attractions, and values for mangrove ecotourism activities, (3) the positive and negative impacts of mangrove ecotourism, and (4) existing mangrove conservation and restoration initiatives for the growth of mangrove ecotourism in the GCC countries. The findings underscore the significance of mangrove ecotourism in supporting economic development, protecting coastal ecosystems, and sustaining local livelihoods in the GCC countries. However, this study highlights the crucial need for sustainable coastal environmental management through integrated land use planning and zoning to address the negative impacts of anthropogenic pressures on mangrove ecosystems and ecotourism attractions. The use of remote sensing tools is invaluable in the monitoring of mangrove ecosystems and associated ecotourism impacts for informing evidence-based conservation and restoration management approaches. Thus, harnessing mangrove ecotourism opportunities can help the GCC countries with balancing economic growth, coastal environmental sustainability, and community well-being. [ABSTRACT FROM AUTHOR]

A [2+3] chiral covalent organic cage is produced through a dynamic covalent chemistry approach by mixing two readily available building units, viz. an enantiopure 3,3'‐diformyl 2,2'‐BINOL compound (A) with a triamino spacer (B). The two enantiomeric (R,R,R) and (S,S,S) forms of the cage C are formed nearly quantitatively thanks to the reversibility of the imine linkage. The X‐ray diffraction analysis of cage (S,S,S)‐C highlights that the six OH functions of the BINOL fragments are positioned inside the cage cavity. Upon reduction of the imine bonds of cage C, the amine cage D is obtained. The ability of the cage D to host the 1‐phenylethylammonium cation (EH+) as a guest is evaluated through UV, CD and DOSY NMR studies. A higher binding constant for (R)‐EH+ cation (Ka=1.7 106±10 % M−1) related to (S)‐EH+ (Ka=0.9 106±10 % M−1) is determined in the presence of the (R,R,R)‐D cage. This enantiopreference is in close agreement with molecular dynamics simulation. [ABSTRACT FROM AUTHOR]

A two month old cross-bred Jersey calf weighing 78 kilograms was presented at the District Veterinary Centre, Kannur with the history of non weight bearing lameness on the right forelimb. On clinical examination, the proximal end of fractured right metacarpal was found protruding through the skin on the medial side above the fetlock joint. Radiographic examination confirmed an open long oblique fracture of distal diaphysis of right metacarpal. Bilateral-multiplanar free-form external skeletal fixation technique using polyethylene tube casted with epoxy putty was performed under brachial plexus nerve block and general anaesthesia. Postoperative antibiotics, analgesics and regular dressing were advised. The wound healed completely by two weeks. Animal started bearing weight during 2nd postoperative week and normal weight bearing was observed by the 4th postoperative week. Radiographic union was observed by the 6th postoperative week and the fixator was removed. Mild exuberant granulation was observed on the wound which resolved by the subsequent week following topical application of copper sulphate-potassium permanganate-glycerine paste in the ratio 1:1:5 and the animal had an uneventful recovery. [ABSTRACT FROM AUTHOR]

The rapid growth of urban populations and the need for sustainable urban planning and development has made Unmanned Aerial Vehicles (UAVs) a valuable tool for data collection, mapping, and monitoring. This article reviews the applications of UAV technology in sustainable urban development, particularly in Malaysia. It explores the potential of UAVs to transform infrastructure projects and enhance urban systems, underscoring the importance of advanced applications in Southeast Asia and developing nations worldwide. Following the PRISMA 2020 statement, this article adopts a systematic review process and identifies 98 relevant studies out of 591 records, specifically examining the use of UAVs in urban planning. The emergence of the UAV-as-a-service sector has led to specialized companies offering UAV operations for site inspections, 3D modeling of structures and terrain, boundary assessment, area estimation, master plan formulation, green space analysis, environmental monitoring, and archaeological monument mapping. UAVs have proven to be versatile tools with applications across multiple fields, including precision agriculture, forestry, construction, surveying, disaster response, security, and education. They offer advantages such as high-resolution imagery, accessibility, and operational safety. Varying policies and regulations concerning UAV usage across countries present challenges for commercial and research UAVs. In Malaysia, UAVs have become essential in addressing challenges associated with urbanization, including traffic congestion, urban sprawl, pollution, and inadequate social facilities. However, several obstacles need to be overcome before UAVs can be effectively deployed, including regulatory barriers, limited flight time and range, restricted awareness, lack of skilled personnel, and concerns regarding security and privacy. Successful implementation requires coordination among public bodies, industry stakeholders, and the public. Future research in Malaysia should prioritize 3D modeling and building identification, using the results of this study to propel advancements in other ASEAN countries. [ABSTRACT FROM AUTHOR]

Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant hydraulic systems, can lead to tree mortality events, and may reduce forest diversity, making forests more vulnerable to subsequent forest disturbances, such as forest fires or pest infestations. Although early warning metrics (EWMs) derived using satellite remote sensing data are now being tested for predicting post-drought plant physiological stress and mortality, applications of unmanned aerial vehicles (UAVs) are yet to be explored extensively. Herein, we provide twenty-four prospective approaches classified into five categories: (i) physiological complexities, (ii) site-specific and confounding (abiotic) factors, (iii) interactions with biotic agents, (iv) forest carbon monitoring and optimization, and (v) technological and infrastructural developments, for adoption, future operationalization, and upscaling of UAV-based frameworks for EWM applications. These UAV considerations are paramount as they hold the potential to bridge the gap between field inventory and satellite remote sensing for assessing forest characteristics and their responses to drought conditions, identifying and prioritizing conservation needs of vulnerable and/or high-carbon-efficient tree species for efficient allocation of resources, and optimizing forest carbon management with climate change adaptation and mitigation practices in a timely and cost-effective manner. [ABSTRACT FROM AUTHOR]

Harvested timber and constructed infrastructure over the logging area leave massive damage that contributes to the emission of anthropogenic gases into the atmosphere. Carbon emissions from tropical deforestation and forest degradation are the second largest source of anthropogenic emissions of greenhouse gases. Even though the emissions vary from region to region, a significant amount of carbon emissions comes mostly from timber harvesting, which is tightly linked to the selective logging intensity. This study intended to utilize a remote sensing approach to quantify carbon emissions from selective logging activities in Ulu Jelai Forest Reserve, Pahang, Malaysia. To quantify the emissions, the relevant variables from the logging's impact were identified as a predictor in the model development and were listed as stump height, stump diameter, cross-sectional area, timber volume, logging gaps, road, skid trails, and incidental damage resulting from the logging process. The predictive performance of linear regression and machine learning models, namely support vector machine (SVM), random forest, and K-nearest neighbor, were examined to assess the carbon emission from this degraded forest. To test the different methods, a combination of ground inventory plots, unmanned aerial vehicles (UAV), and satellite imagery were analyzed, and the performance in terms of root mean square error (RMSE), bias, and coefficient of correlation (R2) were calculated. Among the four models tested, the machine learning model SVM provided the best accuracy with an RMSE of 21.10% and a bias of 0.23% with an adjusted R2 of 0.80. Meanwhile, the linear model performed second with an RMSE of 22.14%, a bias of 0.72%, and an adjusted R2 of 0.75. This study demonstrates the efficacy of remotely sensed data to facilitate the conventional methods of quantifying carbon emissions from selective logging and promoting advanced assessments that are more effective, especially in massive logging areas and various forest conditions. Findings from this research will be useful in assisting the relevant authorities in optimizing logging practices to sustain forest carbon sequestration for climate change mitigation. [ABSTRACT FROM AUTHOR]

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management. [ABSTRACT FROM AUTHOR]

The Bumbu River Watershed is an essential source for the drinking and sanitation needs of settlement communities within Lae, Papua New Guinea. However, poor sanitation and waste management practices have led to concerns over the safety and integrity of the watershed's resources. In this study, we explored the effect of these factors on water quality in the Bumbu river and its tributaries using water quality (22 sampling stations), geospatial (degree of urbanisation), and community survey (sanitation and hygiene practices) data. Water Quality Index (WQI) was calculated based on the Canadian Council of Ministers of Environment (CCME) template using pH, Total Dissolved Solids (TDS), conductivity, turbidity, alkalinity, calcium, magnesium, total hardness, mercury, manganese, iron, and Escherichia coli. Using geospatial techniques, principal component analysis, and forward regression analysis, we found that better water quality outcomes coincided with better community health conditions of Crime and Pollution, and better household health outcomes. Land-use itself was not significantly correlated with water quality, but distressingly, we found 19 of 22 water samples to be of "poor" quality, indicating a need for better community water regulation. The methodology and results presented can be used to inform policy decisions at the provincial/national level, and to aid future research activities in other watersheds. [ABSTRACT FROM AUTHOR]

Climate teleconnections (CT) remotely influence weather conditions in many regions on Earth, entailing changes in primary drivers of fire activity such as vegetation biomass accumulation and moisture. We reveal significant relationships between the main global CTs and burned area that vary across and within continents and biomes according to both synchronous and lagged signals, and marked regional patterns. Overall, CTs modulate 52.9% of global burned area, the Tropical North Atlantic mode being the most relevant CT. Here, we summarized the CT-fire relationships into a set of six global CT domains that are discussed by continent, considering the underlying mechanisms relating weather patterns and vegetation types with burned area across the different world's biomes. Our findings highlight the regional CT-fire relationships worldwide, aiming to further support fire management and policy-making. Here the authors find that climate teleconnections modulate ~53 % of the global burned area with both synchronous and lagged signals, and marked regional patterns, with the Tropical North Atlantic mode being the most relevant. [ABSTRACT FROM AUTHOR]

Various series of lanthanide metal–organic networks denoted Ln–Cy (Ln = La, Ce, Pr, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb), were synthesized under solvothermal conditions using potassium cyamelurate (K3Cy) and lanthanide nitrate salts. All obtained materials were fully characterized, and their crystal structures were solved by single-crystal X-ray diffraction. Four types of coordination modes were elucidated for the Ln–Cy series with different Ln3+ coordination geometries. Structural studies were performed to compare the various coordination compounds of the Ln–Cy series. Moreover, the cyamelurate linkers of rich π-conjugated and uncoordinated Lewis basic sites were used as an absorbing chromophore to enhance the luminescence quantum efficiency, the band emission and the luminescence lifetime of the coordinated Ln metal centers. Solid-state UV-visible measurements combined with density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations were performed to further explore luminescent features of the Ln–Cy series and their origins. [ABSTRACT FROM AUTHOR]

Entrance to neurosurgical training is highly competitive. Without proper advice, information and opportunities, talented individuals may be dissuaded from applying. The Neurology and Neurosurgery Interest Group (NANSIG) organises a Careers Day in Neurosurgery every year. Our objective was to assess the overall utility of a neurosurgery careers day and the perceived factors that attract and detract from the specialty, from attendees of the ninth annual neurosurgery careers day. Eighteen-item pre-conference and 19-item post-conference questionnaires were disseminated electronically to conference attendees. Questions aimed to capture: (i) baseline demographics; (ii) previous experience and exposure in neurosurgery; (iii) interest in neurosurgery; (iv) understanding training and a career in neurosurgery; (v) perceived factors of attraction and dissuasion of neurosurgery; and (vi) perceived value, quality and educational purpose of the conference. In total, 77 delegates attended the careers day. Most did not have a formal neurosurgical rotation during medical school (24.7%, n = 19), but almost half had gained neurosurgical experience and presented research work. The careers day increased knowledge of the neurosurgical application process (median Likert score 3/5 to 4/5, p < 0.01), duration of training (72.7–88.3%), and desire to pursue a career in neurosurgery (75.3–81.8%). The most commonly reported factors attracting delegates to neurosurgery were interest in neuroanatomy (80.5%, n = 62), practical skills (64.9%, n = 50), and impact on patients (62.3%, n = 48). The most common dissuasive factors were competition to entry (64.9%, n = 50), long working hours (40.3%, n = 31), and other career interests (35.1%, n = 27). Almost all would recommend the event to a colleague (94.9%, n = 73). Formal undergraduate exposure to neurosurgery is limited. Neurosurgery careers days increase awareness and understanding of the application process and improve interest in a selected cohort. The factors attracting applicants to neurosurgery remain practical links to neuroanatomy, opportunities in neurosurgery for innovation and research, and direct impact on patients. [ABSTRACT FROM AUTHOR]

Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA's mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia's tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests. [ABSTRACT FROM AUTHOR]

Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR.The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open‐source web‐based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting‐edge analyses of properties related to forest ecology and management.We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post‐hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest.treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open‐source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function. [ABSTRACT FROM AUTHOR]

Individual tree attributes, such as stem volume and biomass, are usually predicted by using traditional field-derived allometric models. However, these models are derived from data collected from small areas and lack a level of detail of tree components (e.g., stem, branches, and leaves). Remote sensing techniques such as the Quantitative Structure Modelling (QSM) applied on high-density LiDAR data emerge as a promising solution for obtaining extensive and detailed tree attribute estimates. We used a high-density LiDAR data on board of a Unmanned Aerial Vehicle (UAV) to evaluate the performance of the QSM approach in estimating field-derived individual tree attributes such as the diameter at breast height (dbh), tree height (ht), and volume (v), as well as the stem (SAGB), branch (BAGB), and total (TAGB) aboveground biomass of eucalyptus trees. QSM was used in two different approaches: (i) using dbh and h derived from QSM and then applied into the field-based equations for estimation of volume and (ii) deriving tree volume directly from QSM. In general, all fitted models using the QSM approach were satisfactory, but with a slight tendency of over-estimation of dbh (9.33%), ht (12.40%), v-QSM1 (26.35%), v-QSM2 (26.66%), TAGB (27.08%), SAGB (25.57%), and BAGB (20.08%). Non-significant differences were noticed when estimating the dbh, tree volume, stem, and aboveground biomass. Despite the overestimation, this study indicates that using the QSM approach to estimate individual tree attributes from UAV-LiDAR is a promising alternative to support the decision-making process regarding forest management activities, especially when considering tree architecture and biomass components. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury (TBI) and stroke have devastating consequences and are major global public health issues. For patients that require a cerebral decompression after suffering a TBI or stroke, a decompressive craniectomy (DC) is the most commonly performed operation. However, retrospective non-randomized studies suggest that a decompressive craniotomy (DCO; also known as hinge or floating craniotomy), where a bone flap is replaced but not rigidly fixed, has comparable outcomes to DC. The primary aim of this project was to understand the current extent of usage of DC and DCO for TBI and stroke worldwide. Method: A questionnaire was designed and disseminated globally via emailing lists and social media to practicing neurosurgeons between June and November 2019. Results: We received 208 responses from 60 countries [40 low- and middle-income countries (LMICs)]. DC is used more frequently than DCO, however, about one-quarter of respondents are using a DCO in more than 25% of their patients. The three top indications for a DCO were an acute subdural hematoma (ASDH) and a GCS of 9-12, ASDH with contusions and a GCS of 3-8, and ASDH with contusions and a GCS of 9-12. There were 8 DCO techniques used with the majority (60/125) loosely tying sutures to the bone flap. The majority (82%) stated that they were interested in collaborating on a randomized trial of DCO vs. DC. Conclusion: Our results show that DCO is a procedure carried out for TBI and stroke, especially in LMICs, and most commonly for an ASDH. The majority of the respondents were interested in collaborating on a is a future randomized trial. [ABSTRACT FROM AUTHOR]

Hinge craniotomy (HC) is a technique that allows for a degree of decompression whilst retaining the bone flap in situ, in a 'floating' or 'hinged' fashion. This provides expansion potential for ensuing cerebral oedema whilst obviating the need for cranioplasty in the future. The exact indications, technique and outcomes of this procedure have yet to be determined, but it is likely that HC provides an alternative technique to decompressive craniectomy (DC) in certain contexts. The primary objective was to collate and describe the current evidence base for HC, including perioperative parameters, functional outcomes and complications. The secondary objective was to identify current nomenclature, operative technique and operative decision-making. A scoping review was performed in accordance with the PRISMA-ScR Checklist. Fifteen studies totalling 283 patients (mean age 45.1 and M:F 199:46) were included. There were 12 different terms for HC. The survival rate of the cohort was 74.6% (n = 211). Nine patients (3.2%) required subsequent formal DC. Six studies compared HC to DC following traumatic brain injury (TBI) and stroke, finding at least equivalent control of intracranial pressure (ICP). These studies also reported reduced rates of complications, including infection, in HC compared to DC. We have described the current evidence base of HC. There is no evidence of substantially worse outcomes compared to DC, although no randomised trials were identified. Eventually, a randomised trial will be useful to determine if HC should be offered as first-line treatment when indicated. [ABSTRACT FROM AUTHOR]

The article focuses on intercalated 2D+2D hydrogen-bonded sheets in co-crystals of cobalt salt with 1H,1=H-[3,3=]bipyridinyl-6,6=-dione. Topics inlcude the single-crystal structure reveals that the metal salt and organic ratio, the supramolecular organization of the two components in the co-crystal has dictated by hydrogen bonds complex, and the infrared and powder X-ray diffraction were used to confirm the homogeneity and the phase purity of the bulk crystalline sample.

By examining the crystal structures of the self-assemblies of 1H,1′H-[3,3′]bipyridinyl-6,6′-dione 1 and its coordination structure with Co(II), to form novel CP-671, our study demonstrates the tendency of dipyridones to generate predictable patterns by hydrogen bonding depending on the crystallization conditions and the potential of pyridone ligating groups to design novel coordination polymers with structural diversity. The two structures of 1 elucidated by single-crystal X-ray diffraction show a cyclic dimer and zigzag chain to generate fascinating hydrogen bond frameworks. A two-dimensional coordination polymer structure (CP-671) is obtained by the linkage of 1 with a cobalt cation according to a known coordination mode of the 2-pyridone ligating group. [ABSTRACT FROM AUTHOR]

Background: Trainee research collaboratives (TRCs) have pioneered high quality, prospective 'snap-shot' surgical cohort studies in the UK. Outcomes After Kidney injury in Surgery (OAKS) was the first TRC cohort study to attempt to collect one-year follow-up data. The aims of this study were to evaluate one-year follow-up and data completion rates, and to identify factors associated with improved follow-up rates.Methods: In this multicentre study, patients undergoing major gastrointestinal surgery were prospectively identified and followed up at one-year following surgery for six clinical outcomes. The primary outcome for this report was the follow-up rate for mortality at 1 year. The secondary outcome was the data completeness rate in those patients who were followed-up. An electronic survey was disseminated to investigators to identify strategies associated with improved follow-up.Results: Of the 173 centres that collected baseline data, 126 centres registered to participate in one-year follow-up. Overall 62.3% (3482/5585) of patients were followed-up at 1 year; in centres registered to collect one-year outcomes, the follow-up rate was 82.6% (3482/4213). There were no differences in sex, comorbidity, operative urgency, or 7-day postoperative AKI rate between patients who were lost to follow-up and those who were successfully followed-up. In centres registered to collect one-year follow-up outcomes, overall data completeness was 83.1%, with 57.9% (73/126) of centres having ≥95% data completeness. Factors associated with increased likelihood of achieving ≥95% data completeness were total number of patients to be followed-up (77.4% in centres with < 15 patients, 59.0% with 15-29 patients, 51.4% with 30-59 patients, and 36.8% with > 60 patients, p = 0.030), and central versus local storage of patient identifiers (72.5% vs 48.0%, respectively, p = 0.006).Conclusions: TRC methodology can be used to follow-up patients identified in prospective cohort studies at one-year. Follow-up rates are maximized by central storage of patient identifiers. [ABSTRACT FROM AUTHOR]

Background Perimesencephalic subarachnoid hemorrhage (PMSAH) is a benign subtype with distinct clinical-radiologic features. Digital subtraction angiography (DSA) remains the gold standard investigation for exclusion of a macrovascular cause, although increasingly more clinicians rely solely on CT angiography (CTA). The primary aim of this systematic review was to evaluate the current literature regarding the negative predictive value of CTA. Methods A systematic search in concordance with the PRISMA checklist was performed for studies published between 2000 and 2018. Studies with ≥10 adult patients diagnosed on a non-contrast brain CT with a PMSAH, who underwent a negative CTA and were subsequently subject to a DSA, were included. Simple pooled analysis was performed to inform the negative predictive value (95% CI) of CTA and the risk of DSA- and CTA-related complications. Results Eighteen studies (669 patients) were included. All patients were subject to at least one DSA, the first one mostly performed within 24 hours of CTA (68.6%). 144 patients (21.5%) underwent a second DSA and a third repeat DSA was performed in one patient. The overall negative predictive value of CTA was 99.0% (95% CI 97.8% to 99.5%). The risk of complications following DSA and CTA were 1.35% (3/222) and 0% (0/41), respectively. Conclusions Undertaking a DSA after a negative CTA may not add any further diagnostic value in patients with PMSAH and may lead to net harm. This observation needs to be validated in a large-scale prospective multicenter study with complete case ascertainment and robust data on CTA and DSA complications. [ABSTRACT FROM AUTHOR]

Background: In patients with spontaneous subarachnoid haemorrhage (SAH), a vascular cause for the bleed is not always found on initial investigations. This study aimed to systematically evaluate the delayed investigation strategies and clinical outcomes in these cases, often described as "non-aneurysmal" SAH (naSAH). Methods: A systematic review was performed in concordance with the PRISMA checklist. Pooled proportions of primary outcome measures were estimated using a random-effects model. Results: Fifty-eight studies were included (4473 patients). The cohort was split into perimesencephalic naSAH (PnaSAH) (49.9%), non-PnaSAH (44.7%) and radiologically negative SAH identified on lumbar puncture (5.4%). The commonest initial vascular imaging modality was digital subtraction angiography. A vascular abnormality was identified during delayed investigation in 3.9% [95% CI 1.9–6.6]. There was no uniform strategy for the timing or modality of delayed investigations. The pooled proportion of a favourable modified Rankin scale outcome (0–2) at 3–6 months following diagnosis was 92.0% [95% CI 86.0–96.5]. Complications included re-bleeding (3.1% [95% CI 1.5–5.2]), hydrocephalus (16.0% [95% CI 11.2–21.4]), vasospasm (9.6% [95% CI 6.5–13.3]) and seizure (3.5% [95% CI 1.7–5.8]). Stratified by bleeding pattern, we demonstrate a higher rate of delayed diagnoses (13.6% [95% CI 7.4–21.3]), lower proportion of favourable functional outcome (87.2% [95% CI 80.1–92.9]) and higher risk of complications for non-PnaSAH patients. Conclusion: This study highlights the heterogeneity in delayed investigations and outcomes for patients with naSAH, which may be influenced by the initial pattern of bleeding. Further multi-centre prospective studies are required to clarify optimal tailored management strategies for this heterogeneous group of patients. [ABSTRACT FROM AUTHOR]

In forest ecosystems, many functional processes are governed by local canopy gap dynamics, caused by either natural or anthropogenic factors. Quantifying the size and spatial distribution of canopy gaps enables an improved understanding and predictive modelling of multiple environmental phenomena. For instance knowledge of canopy gap dynamics can help us elucidate time‐integrated effects of tree mortality, regrowth and succession rates, carbon flux patterns, species heterogeneity and three‐dimensional spacing within structurally complex forest ecosystems.Airborne Laser Scanning (ALS) has emerged as a technology that is well‐suited for mapping forest canopy gaps in a wide variety of forest ecosystems and across spatial scales. New technological and algorithmic advances, including ALS remote‐sensing, coupled with optimized frameworks for data processing and detection of forest canopy gaps, are allowing an enhanced understanding of forest structure and functional processes.This paper introduces ForestGapR, a cutting‐edge open source r package for forest gap analysis from canopy height models derived from ALS and other remote sensing sources. The ForestGapR package offers tools to (a) automate forest canopy gap detection, (b) compute a series of gap statistics, including gap‐size frequency distributions and spatial distribution, (c) map gap dynamics (when multitemporal ALS data are available) and (d) convert forest canopy gaps detected into raster or vector layers as per user requirements.As case studies, we run ForestGapR on ALS data collected over four different tropical forest regions worldwide. We hope this new package will enable further research towards understanding the distribution, dynamics and role of canopy gaps not only in tropical forests, but in other forest types elsewhere. [ABSTRACT FROM AUTHOR]

Ordered coordination polymers (CPs) have been an interesting class of materials for scientific and industrial research for the last few decades. However, their availability as well as certain economic and environmental limitations could slow down their use in many applications. Herein, we present room temperature synthesis in water of a series of CPs (four metal–organic polymers MOPs-(1–4) and three mixed metal–organic polymers MMOPs-(5–7)). All MMOPs were found to be isostructural to MOPs as determined by XRD. Remarkably, MOPs-(2 and 3) and MMOPs-(5–7) exhibit switchable chromic behaviour associated with reversible structural transformation which was facilitated by dehydration/rehydration or solvent exchange (MeOH/H2O) processes. Chromic behaviour and its mechanism were investigated using IR, solid-state UV-Vis, XRD, PXRD and TGA indicating the coordination/de-coordination of water molecules to be the key factor that influences the colour changes. These results render the potential application of MOPs and MMOPs as sensor materials. [ABSTRACT FROM AUTHOR]

Background: Incidental discovery accounts for 30% of newly-diagnosed intracranial meningiomas. There is no consensus on their optimal management. This review aimed to evaluate the outcomes of different management strategies for these tumors. Methods: Using established systematic review methods, six databases were scanned up to September 2017. Pooled event proportions were estimated using a random effects model. Meta-regression of prognostic factors was performed using individual patient data. Results: Twenty studies (2130 patients) were included. Initial management strategies at diagnosis were: surgery (27.3%), stereotactic radiosurgery (22.0%) and active monitoring (50.7%) with a weighted mean follow-up of 49.5 months (SD = 29.3). The definition of meningioma growth and monitoring regimens varied widely impeding relevant meta-analysis. The pooled risk of symptom development in patients actively monitored was 8.1% (95% CI 2.7–16.1). Associated factors were peritumoral edema (OR 8.72 [95% CI 0.35–14.90]) and meningioma diameter ≥ 3 cm (OR 34.90 [95% CI 5.17–160.40]). The pooled proportion of intervention after a duration of active monitoring was 24.8% (95% CI 7.5–48.0). Weighted mean time-to-intervention was 24.8 months (SD = 18.2). The pooled risks of morbidity following surgery and radiosurgery, accounting for cross-over, were 11.8% (95% CI 3.7–23.5) and 32.0% (95% CI 10.6–70.5) respectively. The pooled proportion of operated meningioma being WHO grade I was 94.0% (95% CI 88.2–97.9). Conclusion: The management of incidental meningioma varies widely. Most patients who clinically or radiologically progressed did so within 5 years of diagnosis. Intervention at diagnosis may lead to unnecessary overtreatment. Prospective data is needed to develop a risk calculator to better inform management strategies. [ABSTRACT FROM AUTHOR]

Multispectral LiDAR (light detection and ranging) data have been initially used for land cover classification. However, there are still high classification uncertainties, especially in urban areas, where objects are often mixed and confounded. This study investigated the efficiency of combining advanced statistical methods and LiDAR metrics derived from multispectral LiDAR data for improving land cover classification accuracy in urban areas. The study area is located in Oshawa, Ontario, Canada, on the Lake Ontario shoreline. Multispectral Optech Titan LiDAR data over the study area were acquired on 3 September 2014 in a single strip of 3 km2. Using the channels at 1,550 nm (C1), 1,064 nm (C2) and 532 nm (C3), LiDAR intensity data, normalized digital surface model (nDSM), pseudo normalized difference vegetation index (PseudoNDVI), morphological profiles (MP), and a novel hierarchical morphological profiles (HMP) were derived and used as features for the classification. A support vector machine classifier with a radial basis function (RBF) kernel was applied in the classification stage, where the optimal parameters for the classifier were selected by a grid search procedure. The combination of intensity, pseudoNDVI, nDSM and HMP resulted in the best land cover classification, with an overall accuracy of 93.28%. [ABSTRACT FROM AUTHOR]

Objective: Despite modern anti-epileptic drug treatment, approximately 30% of epilepsies remain medically refractory and for these patients, epilepsy surgery may be a treatment option. There have been numerous studies demonstrating good outcome of epilepsy surgery in the short to median term however, there are a limited number of studies looking at the long-term outcomes. The aim of this study was to ascertain the long-term outcome of resective epilepsy surgery in a large neurosurgery hospital in the U.K. Methods: This a retrospective analysis of prospectively collected data. We used the 2001 International League Against Epilepsy (ILAE) classification system to classify seizure freedom and Kaplan-Meier survival analysis to estimate the probability of seizure freedom. Results: We included 284 patients who underwent epilepsy surgery (178 anterior temporal lobe resections, 37 selective amygdalohippocampectomies, 33 temporal lesionectomies, 36 extratemporal lesionectomies), and had a prospective median follow-up of 5 years (range 1–27). Kaplan-Meier estimates showed that 47% (95% CI 40–58) remained seizure free (apart from simple partial seizures) at 5 years and 38% (95% CI 31–45) at 10 years after surgery. 74% (95% CI 69–80) had a greater than 50% seizure reduction at 5 years and 70% (95% CI 64–77) at 10 years. Patients who had an amygdalohippocampectomy were more likely to have seizure recurrence than patients who had an anterior temporal lobe resection (p = 0.006) and temporal lesionectomy (p = 0.029). There was no significant difference between extra temporal and temporal lesionectomies. Hippocampal sclerosis was associated with a good outcome but declined in relative frequency over the years. Conclusion: The vast majority of patients who were not seizure free experienced at least a substantial and long-lasting reduction in seizure frequency. A positive long-term outcome after epilepsy surgery is possible for many patients and especially those with hippocampal sclerosis or those who had anterior temporal lobe resections. [ABSTRACT FROM AUTHOR]

The CO2 adsorption properties of an organic macrocycle, cucurbit[6]uril (CB[6]), have been evaluated through experimental and theoretical studies. Quantum mechanical calculations show that CB[6] is capable of adsorbing the CO2 molecule selectively within its cavity relative to nitrogen. Adsorption experiments at 298 K and at 1 bar pressure gave a CO2 adsorption value of 1.23 mmol g−1 for the unmodified material. Significant enhancements in the CO2 adsorption capacity of the material were experimentally demonstrated through surface modification using physical and chemical methods. Ethanolamine (EA) modified CB[6] provided an excellent sorption selectivity value of 121.4 for CO2/N2 at 323 K and is unique with respect to its discrimination potential between CO2 and N2. The chemical nature of the interaction between CO2 and amine is shown to be the primary mechanism for the enhanced CO2 absorption performance. [ABSTRACT FROM AUTHOR]

Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m x 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery. [ABSTRACT FROM AUTHOR]

Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of – 0.17%, – 0.12% and – 0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume. [ABSTRACT FROM AUTHOR]

Issues with data analysis have recently been highlighted by a reader of our article. These have been addressed with changes to Tables 2&4, as shown below, and Online Resources 5-7. T2 and peritumoral signal are no longer prognostic factors on simple pooled (Online Resource 5) and IPD (Table 4) analyses respectively. In Table 5, the number of patients which informed the outcomes symptom development and intervention were 575 and 947 respectively; 69 developed symptoms (pooled proportion %8.4 [95% CI 2.8-16.7], I2 = 88.9%). These included motor and cognitive deficits (n = 1). We apologise to the readership of the Journal of Neuro-Oncology for these errors and thank the reader for helping us identify them. [ABSTRACT FROM AUTHOR]

Compassion has been one of the greatest virtues of healthcare professionals. In the early phase of the pandemic, a lot of caution was essential, and restrictions were imposed on the hospital visitation of the COVID-19 patients by their family members. The healthcare system was overburdened, and the healthcare workers were apprehensive about the new virus and the rising mortality. Compassion and family-centered care took a step back as survival of the pandemic became the ultimate goal of mankind. "COVID-19 patients admitted to the critical care units, their loved ones and the healthcare professionals caring for these patients took the brunt of the emotional and psychological impacts of the pandemic." However, as we have moved more than a year into the pandemic, knowledge and resources we gained may be leveraged to provide family-centered critical care for COVID-19 patients. Family presence in intensive care units (ICUs) has been associated with higher satisfaction with care, collaboration with the medical team, shared decision-making, reduced delirium, and optimized end-of-life care of COVID-19 patients. The policymakers should review the restrictions, consider a holistic approach, and take appropriate actions to provide safe family-centered critical care for COVID-19 patients. [ABSTRACT FROM AUTHOR]

Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based image analysis (OBIA), and machine learning classification—support vector machine (SVM) and artificial neural network (ANN). Forest features and tree stumps were classified, and the canopy height model was used for detecting forest canopy gaps in the post selective logging region. Stump detection using the integration of TM and OBIA produced an accuracy of 75.8% when compared with the ground data. Forest classification using SVM and ANN methods were adopted to extract other impacts from logging activities such as skid trails, felled logs, roads and forest canopy gaps. These methods provided an overall accuracy of 85% and kappa coefficient value of 0.74 when compared with conventional classifier. The logging operation also caused an 18.6% loss of canopy cover. The result derived from this study highlights the potential use of UAVs for efficient post logging impact analysis and can be used to complement conventional forest inventory practices. [ABSTRACT FROM AUTHOR]

Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented. [ABSTRACT FROM AUTHOR]

Precise assessments of forest species' composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images. [ABSTRACT FROM AUTHOR]

Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and flooding, minimizing the risks of zoonotic disease outbreaks, and providing ecosystem services and livelihood to the indigenous people, in addition to sequestering carbon dioxide for mitigating climate change. Consequently, it is important to explore new methods and technologies that are aiming to upscale and fast-track afforestation and reforestation (A/R) endeavors, given that many of the current tree planting strategies are not cost effective over large landscapes, and suffer from constraints associated with time, energy, manpower, and nursery-based seedling production. UAV (unmanned aerial vehicle)-supported seed sowing (UAVsSS) can promote rapid A/R in a safe, cost-effective, fast and environmentally friendly manner, if performed correctly, even in otherwise unsafe and/or inaccessible terrains, supplementing the overall manual planting efforts globally. In this study, we reviewed the recent literature on UAVsSS, to analyze the current status of the technology. Primary UAVsSS applications were found to be in areas of post-wildfire reforestation, mangrove restoration, forest restoration after degradation, weed eradication, and desert greening. Nonetheless, low survival rates of the seeds, future forest diversity, weather limitations, financial constraints, and seed-firing accuracy concerns were determined as major challenges to operationalization. Based on our literature survey and qualitative analysis, twelve recommendations—ranging from the need for publishing germination results to linking UAVsSS operations with carbon offset markets—are provided for the advancement of UAVsSS applications. [ABSTRACT FROM AUTHOR]

The palm oil industry is one of the major producers of vegetable oil in the tropics. Palm oil is used extensively for the manufacture of a wide variety of products and its production is increasing by around 9% every year, prompted largely by the expanding biofuel markets. The rise in annual demand for biofuels and vegetable oil from importer countries has caused a dramatic increase in the conversion of forests and peatlands into oil palm plantations in Malaysia. This study assessed the area of forests and peatlands converted into oil palm plantations from 1990 to 2018 in the states of Sarawak and Sabah, Malaysia, and estimated the resulting carbon dioxide (CO2) emissions. To do so, we analyzed multitemporal 30-m resolution Landsat-5 and Landsat-8 images using a hybrid method that combined automatic image processing and manual analyses. We found that over the 28-year period, forest cover declined by 12.6% and 16.3%, and the peatland area declined by 20.5% and 19.1% in Sarawak and Sabah, respectively. In 2018, we found that these changes resulted in CO2 emissions of 0.01577 and 0.00086 Gt CO2-C yr−1, as compared to an annual forest CO2 uptake of 0.26464 and 0.15007 Gt CO2-C yr−1, in Sarawak and Sabah, respectively. Our assessment highlights that carbon impacts extend beyond lost standing stocks, and result in substantial direct emissions from the oil palm plantations themselves, with 2018 oil palm plantations in our study area emitting up to 4% of CO2 uptake by remaining forests. Limiting future climate change impacts requires enhanced economic incentives for land uses that neither convert standing forests nor result in substantial CO2 emissions. [ABSTRACT FROM AUTHOR]

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry efficiency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random effects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coefficient (GC). We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random effect variable, and canopy height, crown volume, and crown projected area as fixed effects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus—and similar forest plantations for carbon dynamics and forest product planning. [ABSTRACT FROM AUTHOR]

Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman's correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( r y y ^ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( r y y ^ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance. [ABSTRACT FROM AUTHOR]

Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 square field plots sized 50 × 50 m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross-validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (±sd—standard deviation) predicted AGB stock at the landscape level was 229.10 (±232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd ± 177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R2 with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain. [ABSTRACT FROM AUTHOR]

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments. [ABSTRACT FROM AUTHOR]