Showing papers in "Forests in 2021"
TL;DR: A novel ensemble learning method is proposed to detect forest fires in different scenarios using two individual learners Yolov5 and EfficientDet and another individual learner EfficientNet, which is responsible for learning global information to avoid false positives.
Abstract: Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.
263 citations
TL;DR: The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selectionand regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation.
Abstract: Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.
63 citations
TL;DR: A novel deep learning framework directly processing the forest point clouds belonging to the four forest types to realize the ITC segmentation and retrieves forest parameters under various forest conditions is performed.
Abstract: Accurate individual tree crown (ITC) segmentation from scanned point clouds is a fundamental task in forest biomass monitoring and forest ecology management. Light detection and ranging (LiDAR) as a mainstream tool for forest survey is advancing the pattern of forest data acquisition. In this study, we performed a novel deep learning framework directly processing the forest point clouds belonging to the four forest types (i.e., the nursery base, the monastery garden, the mixed forest, and the defoliated forest) to realize the ITC segmentation. The specific steps of our approach were as follows: first, a voxelization strategy was conducted to subdivide the collected point clouds with various tree species from various forest types into many voxels. These voxels containing point clouds were taken as training samples for the PointNet deep learning framework to identify the tree crowns at the voxel scale. Second, based on the initial segmentation results, we used the height-related gradient information to accurately depict the boundaries of each tree crown. Meanwhile, the retrieved tree crown breadths of individual trees were compared with field measurements to verify the effectiveness of our approach. Among the four forest types, our results revealed the best performance for the nursery base (tree crown detection rate r = 0.90; crown breadth estimation R2 > 0.94 and root mean squared error (RMSE) 0.88 and RMSE 0.85 and RMSE 0.79 and RMSE < 0.7 m. Our method presents a robust framework inspired by the deep learning technology and computer graphics theory that solves the ITC segmentation problem and retrieves forest parameters under various forest conditions.
61 citations
TL;DR: The results show that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34, a new deep learning model which combines ResNet-34 with transfer learning.
Abstract: In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects Among them, ResNet-34 is used as a feature extractor for wood knot defects At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning After that, the wood knot defect dataset was applied to TL-ResNet34 for testing The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34
55 citations
TL;DR: The use of UAVs in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for "UAV" + "forest".
Abstract: Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV” + “forest”. This result is even more surprising when compared with similar research for “UAV” + “agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely, (1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.
50 citations
TL;DR: The authors conducted a thorough review of recent literature that clearly indicates large direct and indirect impacts of increasing drought conditions on the forests of the Mediterranean Basin, their changes in surface and distribution areas, and the main impacts they have suffered.
Abstract: Forest ecosystems in the Mediterranean Basin are mostly situated in the north of the Basin (mesic). In the most southern and dry areas, the forest can only exist where topography and/or altitude favor a sufficient availability of water to sustain forest biomass. We have conducted a thorough review of recent literature (2000–2021) that clearly indicates large direct and indirect impacts of increasing drought conditions on the forests of the Mediterranean Basin, their changes in surface and distribution areas, and the main impacts they have suffered. We have focused on the main trends that emerge from the current literature and have highlighted the main threatens and management solution for the maintenance of these forests. The results clearly indicate large direct and indirect impacts of increasing drought conditions on the forests of the Mediterranean Basin. These increasing drought conditions together with over-exploitation, pest expansion, fire and soil degradation, are synergistically driving to forest regression and dieback in several areas of this Mediterranean Basin. These environmental changes have triggered responses in tree morphology, physiology, growth, reproduction, and mortality. We identified at least seven causes of the changes in the last three decades that have led to the current situation and that can provide clues for projecting the future of these forests: (i) The direct effect of increased aridity due to more frequent and prolonged droughts, which has driven Mediterranean forest communities to the limit of their capacity to respond to drought and escape to wetter sites, (ii) the indirect effects of drought, mainly by the spread of pests and fires, (iii) the direct and indirect effects of anthropogenic activity associated with general environmental degradation, including soil degradation and the impacts of fire, species invasion and pollution, (iv) human pressure and intense management of water resources, (v) agricultural land abandonment in the northern Mediterranean Basin without adequate management of new forests, (vi) very high pressure on forested areas of northern Africa coupled with the demographic enhancement, the expansion of crops and higher livestock pressure, and the more intense and overexploitation of water resources uses on the remaining forested areas, and (vii) scarcity and inequality of human management and policies, depending on the national and/or regional governments and agencies, being unable to counteract the previous changes. We identified appropriate measures of management intervention, using the most adequate techniques and processes to counteract these impacts and thus to conserve the health, service capacity, and biodiversity of Mediterranean forests. Future policies should, moreover, promote research to improve our knowledge of the mechanisms of, and the effects on, nutrient and carbon plant-soil status concurrent with the impacts of aridity and leaching due to the effects of current changes. Finally, we acknowledge the difficulty to obtain an accurate quantification of the impacts of increasing aridity rise that warrants an urgent investment in more focused research to further develop future tools in order to counteract the negative effects of climate change on Mediterranean forests.
48 citations
TL;DR: The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques.
Abstract: Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.
45 citations
TL;DR: In this article, the authors estimated the economic value of forest carbon stock and carbon value per hectare of forested area based on the price of removing per ton CO2eq in USD from 1990 to 2050.
Abstract: Malaysia has a large extent of forest cover that plays a crucial role in storing biomass carbon and enhancing carbon sink (carbon sequestration) and reducing atmospheric greenhouse gas emissions, which helps to reduce the negative impacts of global climate change. This article estimates the economic value of forest carbon stock and carbon value per hectare of forested area based on the price of removing per ton CO2eq in USD from 1990 to 2050. The economic value of biomass carbon stored in the forests is estimated at nearly USD 51 billion in 2020 and approximately USD 41 billion in 2050, whereas carbon value per hectare forest area is estimated at USD 2885 in 2020 and USD 2388 in 2050. If the BAU scenario of forest loss (converting forests to other land use) continues, the projected estimation of carbon stock and its economic value might fall until 2050 unless further initiatives on proper planning of forest management and ambitious policy implementation are taken. Instead, Malaysia’s CO2 emission growth started to fall after 2010 due to rising forest carbon sink of 282 million tons between 2011 and 2016, indicating a huge potential of Malaysian forests for future climate change mitigation. The estimated and projected value of carbon stock in Malaysian forest biomass, annual growth of forest carbon, forest carbon density and carbon sink would be useful for the better understanding of enhancing carbon sink by avoiding deforestation, sustainable forest management, forest conservation and protection, accurate reporting of national carbon inventories and policy-making decisions. The findings of this study could also be useful in meeting emission reduction targets and policy implementation related to climate change mitigation in Malaysia.
38 citations
TL;DR: In this paper, the authors describe current knowledge gaps in our understanding of moisture in wood and how modification has been and could be used to clarify some of these gaps and further contribute to applications in several related fields of research such as how to enhance the resistance to decomposition, how to improve the performance of moisture-induced wooden actuators, or how to increase the utilization of wood biomass with challenging swelling anisotropy.
Abstract: Moisture plays a central role in the performance of wood products because it affects important material properties such as the resistance to decomposition, the mechanical properties, and the dimensions. To improve wood performance, a wide range of wood modification techniques that alter the wood chemistry in various ways have been described in the literature. Typically, these modifications aim to improve resistance to decomposition, dimensional stability, or, to introduce novel functionalities in the wood. However, wood modification techniques can also be an important tool to improve our understanding of the interactions between wood and moisture. In this review, we describe current knowledge gaps in our understanding of moisture in wood and how modification has been and could be used to clarify some of these gaps. This review shows that introducing specific chemical changes, and even controlling the distribution of these, in combination with the variety of experimental methods available for characterization of moisture in wood, could give novel insights into the interaction between moisture and wood. Such insights could further contribute to applications in several related fields of research such as how to enhance the resistance to decomposition, how to improve the performance of moisture-induced wooden actuators, or how to improve the utilization of wood biomass with challenging swelling anisotropy.
37 citations
TL;DR: In this paper, the degradation, preservation and conservation of waterlogged archaeological wood are discussed with consideration of the effect on the chemical composition of wood, as well as the deposition of sulphur and iron within the structure.
Abstract: This paper reviews the degradation, preservation and conservation of waterlogged archaeological wood. Degradation due to bacteria in anoxic and soft-rot fungi and bacteria in oxic waterlogged conditions is discussed with consideration of the effect on the chemical composition of wood, as well as the deposition of sulphur and iron within the structure. The effects on physical properties are also considered. The paper then discusses the role of consolidants in preserving waterlogged archaeological wood after it is excavated as well as issues to be considered when reburial is used as a means of preservation. The use of alum and polyethylene glycol (PEG) as consolidants is presented along with various case studies with particular emphasis on marine artefacts. The properties of consolidated wood are examined, especially with respect to the degradation of the wood post-conservation. Different consolidants are reviewed along with their use and properties. The merits and risks of reburial and in situ preservation are considered as an alternative to conservation.
34 citations
TL;DR: This review summarizes the available isolation methods and analytical techniques used to identify biologically active compounds present in cinnamon bark and leaves and the influence of these compounds in the treatment of disorders.
Abstract: Cinnamon is an unusual tropical plant belonging to the Lauraceae family. It has been used for hundreds of years as a flavor additive, but it has also been used in natural Eastern medicine. Cinnamon extracts are vital oils that contain biologically active compounds, such as cinnamon aldehyde, cinnamic alcohol, cinnamic acid, and cinnamate. It has antioxidant, anti-inflammatory, and antibacterial properties and is used to treat diseases such as diabetes and cardiovascular disease. In folk medicine, cinnamon species have been used as medicine for respiratory and digestive disorders. Their potential for prophylactic and therapeutic use in Parkinson’s and Alzheimer’s disease has also been discovered. This review summarizes the available isolation methods and analytical techniques used to identify biologically active compounds present in cinnamon bark and leaves and the influence of these compounds in the treatment of disorders.
TL;DR: In this paper, the long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019.
Abstract: Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.
TL;DR: In this paper, a comprehensive review of remote sensing of forest biomass scaled at individual trees has been done, highlighting the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests.
Abstract: Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.
TL;DR: Zhang et al. as discussed by the authors identified ten ecological sources with superior habitat quality, mainly distributed in rural woodlands, in urban green land, and in forest park patches, and identified twenty-five ecological nodes, sixteen ecological fracture points, and sixteen stepping stones to improve the maintenance and construction of the ecological corridor network.
Abstract: The rapid development of urbanization has caused many ecological issues and greatly threatened the sustainable development of human society. The construction of ecological security patterns (ESPs) offers an effective way to balance ecological conservation and urbanization. This study aimed to take the highly urbanized city of Shenzhen, China, as a study area to construct an urban ESP and put forward suggestions for the urban development of ecological security. Ecological sources were identified through the Habitat Quality module in the InVEST model, and ecological corridors, strategic ecological nodes, and stepping-stone patches were extracted based on the minimum cumulative resistance (MCR) model. These elements together constituted the ESP. In particular, with the results of the continuous decline in the overall habitat quality, this study identified ten ecological sources with superior habitat quality, mainly distributed in rural woodlands, in urban green land, and in forest park patches. An optimized pattern for Shenzhen City with one axis, three belts, and four zones is proposed, with the study area divided into an ecological preservation zone, a limited development zone, an optimized development zone, and a key development zone. Moreover, forty-five ecological corridors were extracted and graded into three levels, presenting a spatial pattern of one axis and three belts. The appropriate widths of these ecological corridors were suggested to be between 30 and 60 m in Shenzhen City. In addition, we identified twenty-five ecological nodes, sixteen ecological fracture points, and sixteen stepping stones to improve the maintenance and construction of the ecological corridor network. More generally, this study demonstrates a scientific approach to identifying ESPs based on habitat quality, and can serve as a reference for the planning of urban ecological function regionalization.
TL;DR: In this paper, the authors applied multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE).
Abstract: The extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography, and in those areas the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015–2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, boxplots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the regeneration time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A comparison of the response of 34 SAR variables with official data on wildfires and prescribed fires from the Capital Development Authority revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) was found to be the best variable to detect fire events, although only 50% of the fires were correctly detected. Nonetheless, when the occurrence of fire events according to the SAR variable NSR p_95 was compared to that from the two spectral indices, the SAR variable was found to correctly identify 95% of fire events. The SAR variable NSR p_95 is thus a suitable alternative to spectral indices to monitor the progress of wildfires and assess their severity when there are limitations to the use of optical images due to cloud coverage or smoke, for instance.
TL;DR: Wang et al. as discussed by the authors made a documentation of the specific contents (duration, major aims, geographic coverage and investment), and environmental impacts of these programs from peer-reviewed literature, official reports and journals.
Abstract: Forest ecosystems are in serious trouble globally, largely due to the over-exploitation. To alleviate environmental problems caused by deforestation, China has undertaken a series of key forestry ecological development programs, including the Natural Forest Protection Program (NFPP), the Conversion of Cropland into Forests Program (CCFP), the Desertification Combating Program around Beijing and Tianjing (DCBT), the Key Shelterbelt Development Programs in the Three-North Region and in the Middle and Lower Reaches of the Yangtze River (KSDP) and the Nature Reserve Development Program in Forestry Sector (WCNR). This article aims to make a documentation of the specific contents (duration, major aims, geographic coverage and investment), and environmental impacts of these programs from peer-reviewed literature, official reports and journals. Environmental impact is measured with land area afforested (except the WCNR) and the consequent changes in ecosystem function. Overall, with the huge investment and long-term efforts, these programs have made tremendous progress in increasing vegetative coverage, enhancing carbon sequestration, controlling soil erosion, conservation of biodiversity, etc. For proper implementation and remarkable achievement, a more balanced approach with flexible planning, suitable measures and proper management should be adopted. Meanwhile, the scientific communities need to be more actively involved in execution and assessment of these programs. The environmental impact of the DCBT, the KSDP, and the WCNR deserve more research concern.
TL;DR: Ghana is experiencing an annual D&FD rate of 2%, equivalent to 135,000 hectares loss of forest cover in Ghana as mentioned in this paper, despite many forest policies and interventions to ensure sustainable management and forest use.
Abstract: Globally, forests provide several functions and services to support humans’ well-being and the mitigation of greenhouse gases (GHGs). The services that forests provide enable the forest-dependent people and communities to meet their livelihood needs and well-being. Nevertheless, the world’s forests face a twin environmental problem of deforestation and forest degradation (D&FD), resulting in ubiquitous depletion of forest biodiversity and ecosystem services and eventual loss of forest cover. Ghana, like any tropical forest developing country, is not immune to these human-caused D&FD. This paper reviews Ghana’s D&FD driven by a plethora of pressures, despite many forest policies and interventions to ensure sustainable management and forest use. The review is important as Ghana is experiencing an annual D&FD rate of 2%, equivalent to 135,000 hectares loss of forest cover. Although some studies have focused on the causes of D&FD on Ghana’ forests, they failed to show the chain of causal links of drivers that cause D&FD. This review fills the knowledge and practice gap by adopting the Driver-Pressures-State-Impacts-Responses (DPSIR) analytical framework to analyse the literature-based sources of causes D&FD in Ghana. Specifically, the analysis identified agriculture expansion, cocoa farming expansion, illegal logging, illegal mining, population growth and policy failures and lapses as the key drivers of Ghana’s D&FD. The study uses the DPSIR analytical framework to show the chain of causal links that lead to the country’s D&FD and highlights the numerous interventions required to reverse and halt the ubiquitous perpetual trend of D&FD in Ghana. Similar tropical forest countries experiencing D&FD will find the review most useful to curtail the menace.
TL;DR: In this paper, the feasibility of modified veneer with graphene/Polyvinyl alcohol (Gr/PVA) impregnation mixture to improve its thermal conductivity was investigated and the absorbance and viscosity of the Gr/P VA impregnnation mixtures were observed to expound the Gr /PVA ratio effects on the mixtures.
Abstract: Intending to achieve more green and economical graphene impregnated modified fast-growing poplar wood veneer for heat conduction, this study proposes and investigates the feasibility of modified veneer with graphene/Polyvinyl alcohol (Gr/PVA) impregnation mixture to improve its thermal conductivity. The absorbance and viscosity of the Gr/PVA impregnation mixtures are observed to expound the Gr/PVA ratio effects on the mixtures. Simultaneously, the weight percent gain, chromatic aberration, and thermal conductivity of the modified veneers are measured to determine the impregnation effect and the optimal impregnation formula. Further, the chemical structure, crystallinity, and thermal stability of the optimal sample impregnated with Gr/PVA are tested. The results show that the thermal properties of the Gr/PVA impregnated modified veneer have not all been improved. Still, both the dispersibility of the impregnation mixtures and the impregnation effect is affected by the Gr/PVA ratio. The data shows that the optimal thermal conductivity of modified veneer, which is up to 0.22 W·m−1·K−1 and 2.4 times the untreated one, is dipped by the mixture of 10 wt.% PVA concentration and 2 wt.% MGEIN addition. According to the characterization tests, the crystallinity of the modified veneer reduces, but the thermal stability improves.
TL;DR: In this article, the authors examined the application of the satellite-derived normalized difference vegetation index (NDVI) for detecting the change of extreme precipitation events from 1982 to 2012 by using the Middle and Lower Reaches of the Yangtze River (MLR-YR) in China.
Abstract: Remote sensing has frequently been employed to monitor extreme climatic events, especially droughts, by identifying the anomalies of vegetation activity from the regional to global scale. However, limited research has addressed the performance of remote sensing on detecting extreme precipitation events. By using the Middle and Lower Reaches of the Yangtze River (MLR-YR) in China as an example, this paper examines the application of the satellite-derived normalized difference vegetation index (NDVI) for detecting the change of extreme precipitation events from 1982 to 2012. The performances of three NDVI-based indices, including minimum, mean, and maximum NDVIs, were examined to capture the sensibility of vegetation activity to changes in extreme precipitation events. The results show not only common enhanced trends, but also obvious spatial discrepancies between the intensity and frequency of extreme precipitation events in the MLR-YR. As to its application on terrestrial vegetation, changes in extreme precipitation intensity coincided with that of the vegetation activity, which was represented as the maximum and the minimum NDVIs, especially the maximum NDVI. In addition, similar patterns were found between the standard deviation of the maximum NDVI and the trend of extreme precipitation intensity. Furthermore, the correlation coefficients were relatively greater between the maximum NDVI and extreme precipitation intensity than that of the minimum NDVI. Our results support the hypothesis that maximum NDVI is more suited to capture the response of vegetation activity to extreme precipitation events in the MLR-YR region, in comparison to the other two NDVI indices.
TL;DR: The results of this study show that increasing the working hours of machines instead of increasing their number, increasing the capacity of some facilities instead of establishing new facilities and expanding the transport fleet has a significant impact on achieving predetermined goals.
Abstract: In recent decades, the forest industry has been growingly expanded due to economic conditions, climate changes, environmental and energy policies, and intense demand changes. Thus, appropriate planning is required to improve this industry. To achieve economic, social and environmental goals, a supply chain network is designed based on a multi-period and multi-product Mixed-Integer Non-Linear Programming (MINLP) model in which the objective is to maximize the profit, minimize detrimental environmental effects, improve social effects, and minimize the number of lost demands. In addition, to improve forest industry planning, strategic and tactical decisions have been implemented throughout the supply chain for all facilities, suppliers and machinery. These decisions significantly help to improve processes and product flows and to meet customers’ needs. In addition, because of the presence of uncertainty in some parameters, the proposed model was formulated and optimized under uncertainty using the hybrid robust possibilistic programming (HRPP-II) approach. The e-constraint technique was used to solve the multi-objective model, and the Lagrangian relaxation (LR) method was utilized to solve the model of more complex dimensions. A case study in Northern Iran was conducted to assess the efficiency of the suggested approach. Finally, a sensitivity analysis was performed to determine the impact of important parameters on objective functions. The results of this study show that increasing the working hours of machines instead of increasing their number, increasing the capacity of some facilities instead of establishing new facilities and expanding the transport fleet has a significant impact on achieving predetermined goals.
TL;DR: In this paper, the authors focused on flood mapping using geospatial data and remote sensing and applied object-based image analysis (OBIA) to estimate the extent of flooding in lowland forests in the Republic of Croatia.
Abstract: The impact of floods on forests is immediate, so it is necessary to quickly define the boundaries of flooded areas. Determining the extent of flooding in situ has shortcomings due to the possible limited spatial and temporal resolutions of data and the cost of data collection. Therefore, this research focused on flood mapping using geospatial data and remote sensing. The research area is located in the central part of the Republic of Croatia, an environmentally diverse area of lowland forests of the Sava River and its tributaries. Flood mapping was performed by merging Sentinel-1 (S1) and Sentinel-2 (S2) mission data and applying object-based image analysis (OBIA). For this purpose, synthetic aperture radar (SAR) data (GRD processing level) were primarily used during the flood period due to the possibility of all-day imaging in all weather conditions and flood detection under the density of canopy. The pre-flood S2 imagery, a summer acquisition, was used as a source of additional spectral data. Geographical information system (GIS) layers—a multisource forest inventory, habitat map, and flood hazard map—were used as additional sources of information in assessing the accuracy of and interpreting the obtained results. The spectral signature, geometric and textural features, and vegetation indices were applied in the OBIA process. The result of the work was a developed methodological framework with a high accuracy and speed of production. The overall accuracy of the classification is 94.94%. Based on the conducted research, the usefulness of the C band of the S1 in flood mapping in lowland forests in the leaf-off season was determined. The paper presents previous research and describes the SAR parameters and characteristics of floodplain forest with a significant impact on the accuracy of classification.
TL;DR: This review collects and organized Botryosphaeriaceae occurrences in a single cured dataset, allowing for the first time a complete perspective on species’ global diversity, dispersion, host association, ecological niches, pathogenicity, communication efficiency of new occurrences, and new host–fungus associations.
Abstract: Botryosphaeriaceae-related diseases occur worldwide in a wide variety of plant hosts. The number of studies targeting the distribution, diversity, ecology, and pathogenicity of Botryosphaeriaceae species are consistently increasing. However, with the lack of consistency in species delimitation, the name of hosts, and the locations of studies, it is almost impossible to quantify the presence of these species worldwide, or the number of different host–fungus interactions that occur. In this review, we collected and organized Botryosphaeriaceae occurrences in a single cured dataset, allowing us to obtain for the first time a complete perspective on species’ global diversity, dispersion, host association, ecological niches, pathogenicity, communication efficiency of new occurrences, and new host–fungus associations. This dataset is freely available through an interactive and online application. The current release (version 1.0) contains 14,405 cured isolates and 2989 literature references of 12,121 different host–fungus interactions with 1692 different plant species from 149 countries.
TL;DR: This review paper found that the number of papers addressing forest health issues applying remote sensing techniques have consistently increased, that most of the studies placed their study area in North America and Europe and that satellite-borne multispectral sensors are the most commonly used technology, especially from Landsat mission.
Abstract: Forests are increasingly subject to a number of disturbances that can adversely influence their health. Remote sensing offers an efficient alternative for assessing and monitoring forest health. A myriad of methods based upon remotely sensed data have been developed, tailored to the different definitions of forest health considered, and covering a broad range of spatial and temporal scales. The purpose of this review paper is to identify and analyse studies that addressed forest health issues applying remote sensing techniques, in addition to studying the methodological wealth present in these papers. For this matter, we applied the PRISMA protocol to seek and select studies of our interest and subsequently analyse the information contained within them. A final set of 107 journal papers published between 2015 and 2020 was selected for evaluation according to our filter criteria and 20 selected variables. Subsequently, we pair-wise exhaustively read the journal articles and extracted and analysed the information on the variables. We found that (1) the number of papers addressing this issue have consistently increased, (2) that most of the studies placed their study area in North America and Europe and (3) that satellite-borne multispectral sensors are the most commonly used technology, especially from Landsat mission. Finally, most of the studies focused on evaluating the impact of a specific stress or disturbance factor, whereas only a small number of studies approached forest health from an early warning perspective.
TL;DR: This work evaluates how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests.
Abstract: Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.
TL;DR: Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.
Abstract: This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers’ citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.
TL;DR: Wang et al. as mentioned in this paper employed integrated approaches to characterize the changing patterns and intensities of green space in Shanghai, China from 1990 to 2015, which revealed that both rapid urbanization and greening policies accounted for the spatiotemporal dynamics of UGS.
Abstract: Urbanization has led to the continuous expansion of built-up areas and the ever-growing urban population, threatening the quantity and quality of urban green space (UGS). Exploring the spatiotemporal variations of UGS is substantially conducive to the formulation of land-use policies to protect the ecosystems. As one of the largest megacities all around the world, Shanghai has experienced rapid urbanization in the past three decades. Insights into how UGS changes in response to urbanization and greening policies are essential for guiding sustainable urban development. This paper employed integrated approaches to characterize the changing patterns and intensities of green space in Shanghai, China from 1990 to 2015. The spatiotemporal dynamics of the UGS pattern were derived through four main methods: green space ratio, dynamic change degree (DCD), transition matrix and landscape metrics. The results showed that Shanghai’s green space decreased from 84.8% in 1990 to 61.9% in 2015 while the built-up areas increased from 15.0% to 36.5%. Among the green space sub-types, farmland was largely encroached and fragmented by urban sprawl, especially in the Outer Ring Expressway and Suburban Ring Expressway belts of the city. About 1522 km2 of the green space has transferred into built-up areas, followed by farmland, waterbody, forest, and grassland in descending order. The 2000–2010 period witnessed the strong urban expansion and dramatic changes in UGS, but then the change around 2015 turned down and stable. The landscape pattern metrics showed that the entire green space in Shanghai was growingly fragmented and isolated during the past 25 years. Combined with the green space-related planning and policies issued in 1990–2015, the results revealed that both rapid urbanization and greening policies accounted for the spatiotemporal dynamics of UGS. Based on the results, some implicants to new urban planning and policies of Shanghai were highlighted.
TL;DR: Tannins are soluble, astringent secondary phenolic metabolites generally obtained from renewable natural resources and can be found in many plant parts, such as fruits, stems, leaves, seeds, roots, buds, and tree barks, where they have a protective function against bacterial, fungal, and insect attacks as mentioned in this paper.
Abstract: Tannins are soluble, astringent secondary phenolic metabolites generally obtained from renewable natural resources, and can be found in many plant parts, such as fruits, stems, leaves, seeds, roots, buds, and tree barks, where they have a protective function against bacterial, fungal, and insect attacks. In general, tannins can be extracted using hot water or organic solvents from the bark, leaves, and stems of plants. Industrially, tannins are applied to produce adhesives, wood coatings, and other applications in the wood and polymer industries. In addition, tannins can also be used as a renewable and environmentally friendly material to manufacture bio-based polyurethanes (bio-PUs) to reduce or eliminate the toxicity of isocyanates used in their manufacture. Tannin-based bio-PUs can improve the mechanical and thermal properties of polymers used in the automotive, wood, and construction industries. The various uses of tannins need to be put into perspective with regards to possible further advances and future potential for value-added applications. Tannins are employed in a wide range of industrial applications, including the production of leather and wood adhesives, accounting for almost 90% of the global commercial tannin output. The shortage of natural resources, as well as the growing environmental concerns related to the reduction of harmful emissions of formaldehyde or isocyanates used in the production of polyurethanes, have driven the industrial and academic interest towards the development of tannin-based bio-PUs as sustainable alternative materials with satisfactory characteristics. The aim of the present review is to comprehensively summarize the current state of research in the field of development, characterization, and application of tannin-derived, bio-based polyurethane resins. The successful synthesis process of the tannin-based bio-PUs was characterized by Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), MALDI-TOF mass spectrometry, and gel permeation chromatography (GPC) analyses.
TL;DR: The Air Telang Protected Forest (ATPF) is one of the most dynamic and essential coastal forest landscapes in South Sumatra, Indonesia, because of its location between multiple river outlets, including the Musi catchment as mentioned in this paper.
Abstract: The Air Telang Protected Forest (ATPF) is one of the most dynamic and essential coastal forest landscapes in South Sumatra, Indonesia, because of its location between multiple river outlets, including the Musi catchment—Sumatra’s largest and most dense lowland catchment area. While most ATPF areas are covered by mangroves, these areas have been experiencing severe anthropogenic-driven degradation and conversion. This study aims to evaluate land cover changes and associated carbon emissions in the ATPF over a 35-year period (1985–2020) by utilizing the available Landsat and Sentinel imagery from 1985, 2000, and 2020. Throughout the analysis period, we observed 63% (from 10,886 to 4059 ha) primary and secondary forest loss due to land use change. We identified three primary anthropogenic activities driving these losses, namely, land clearing for plantations and agriculture (3693 ha), coconut plantations (3315 ha), aquaculture (245 ha). We estimated that the largest carbon emissions were caused by coconut plantation conversion, with total carbon emissions of approximately 14.14 Mt CO2-eq. These amounts were almost 4 and 21 times higher than emissions from land clearing and aquaculture, respectively, as substantial soil carbon loss occurs once mangroves get transformed into coconut plantations. While coconut plantation expansion on mangroves could generate significant carbon stock losses and cleared forests become the primary candidate for restoration, our dataset could be useful for future land-based emission reduction policy intervention at a subnational level. Ultimately, our findings have direct implications for current national climate policies, through low carbon development strategies and emission reductions from the land use sector for 2030, as outlined in the Nationally Determined Contributions (NDCs).
TL;DR: In this article, the appropriate distribution area of C. lanceolata (Lamb.) Hook was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041-2060.
Abstract: Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km2, where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing influence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.
TL;DR: A novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN is presented and exhibited competitive performance compared to state-of-the-art methods.
Abstract: Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.