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\"Forest Health: An Integrated Perspective is the first book to define an ecologically rational, conceptual framework that unifies and integrates the many sub-disciplines that comprise the science of forest health and protection. This new global approach applies to boreal, temperate, tropical, natural, managed, even-aged, un-even aged and urban forests, as well as plantations. Readers of the text can use real datasets to assess the sustainability of four forests around the world. Datasets for the case studies are at www.cambridge.org/9780521766692, and the text provides stepwise instructions for performing the calculations in Microsoft Excel. Readers can follow along as the editors perform the same calculations and interpret the results. Elevating forest health from a fuzzy concept to an ecologically sound paradigm, this is essential reading for undergraduate and graduate students and professionals interested in forest health, protection, entomology, pathology and ecology\"-- Provided by publisher.

In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

\"This book explores the specifically human dimensions of the problem posed by a new generation of invasive pests and pathogens to tree health worldwide. The growth in global trade and transportation in recent decades, along with climate change, is allowing invasive pests and pathogens to establish in new environments, with profound consequences for the ecosystem services provided by trees and forests, and impacts on human wellbeing. The central theme of the book is to consider the role that social science can play in better understanding the social, economic and environmental impacts of such tree disease and pest outbreaks. Contributions include explorations of how pest outbreaks are socially constructed, drawing on the historical, cultural, social and situated contexts of outbreaks; the governance and economics of tree health for informing policy and decision-making; stakeholder engagement and communication tools; along with more philosophical approaches that draw on environmental ethics to consider 'non-human' perspectives. Taken together the book makes theoretical, methodological and applied contributions to our understanding of this important subject area and encourages researchers from across the social sciences and humanities to bring their own disciplinary perspectives and expertise to address the complexity that is the human dimensions of forest and tree health. Chapter 5 and 11 are open access under a CC BY 4.0 license via link.springer.com\"-- Page 4 of cover.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.

Forest health is an important aspect of sustainable forest management. The practical significance of health assessments of forest ecosystems is becoming more and more prominent because good knowledge about the health level of forests and the causes of unhealthy forests enables the identification of proper actions for enhancing sustainable development of forest ecosystems. This paper evaluated the health status of the forest ecosystem of Mount Taishan using the spatial analysis technique of GIS (Geographic Information System) and local forest inventory data. A comprehensive indicator system that reflects the health status of forestsin the study areawas established. Based on this indicator system, the health level of each sub-compartment of the forests in the study area was assessed. The results show that the high-quality grade forest (80.4 ha) and healthy grade forest (2671 ha) accounted for only 23.5% of the total forest area of Mount Taishan. About 60.5% of Mount Taishan forest was in a sub-health status. The area of unhealthy forests was 1865 ha (accounting for 16% of the total forest area), of which about 98 ha was inextremely unhealthy conditions.Asmore than two-thirds of the forests in Mount Taishan are in a sub-health or unhealthy state, effective measures for improving forest health are in urgent need in the study area.

Global forest area has declined over the past few years, forest quality has declined, and ecological and environmental events have increased with climate change and human activity. In the context of ecological civilization, forest health issues have received unprecedented attention. By improving forest health, forests can better perform their ecosystem service functions and promote green development. This study was carried out in the WuZhi Shan area of Hainan Tropical Rainforest National Park. We employed a decision tree algorithm, a machine learning technique, for our modeling due to its high accuracy and interpretability. The objective weighted method using criteria of importance through intercriteria correlation (CRITIC) was used to determine forest health classes based on survey and experimental data from 132 forest samples. The results showed that species diversity is the most important metric to measure forest health. An interpretable decision tree machine learning model was proposed to incorporate forest health indicators, providing up to 90% accuracy in the classification of forest health conditions. The model demonstrated a high degree of effectiveness, achieving an average precision of 90%, a recall of 67%, and an F1 score of 70.2% in predicting forest health. The interpretable decision tree classification results showed that breast height diameter is the most important variable in classifying the health status of both primary and secondary forests. This study highlights the importance of using interpretable machine learning methods for the decision‐making process. Our work contributes to the scientific underpinnings of sustainable forest development and effective conservation planning. Machine learning can be used to classify whether forest health is healthy or not. This study highlights the importance of using interpretable machine learning methods for the decision‐making process. Our work contributes to the scientific underpinnings of sustainable forest development and effective conservation planning.

The Norway spruce is one of the most important tree species in Europe. This tree species has been put under considerable pressure due to the ongoing impacts of climate change. Meanwhile, frequent droughts and pest outbreaks are reported as the main reason for its dieback, resulting in severe forest cover loss. Such was the case with Norway spruce forests within the Kopaonik National Park (NP) in Serbia. This study aims to quantify, spatially and temporally, forest cover loss and to evaluate the sensitivity of various vegetation indices (VIs) in detecting drought-induced response and predicting the dieback of Norway spruce due to long-lasting drought effects in the Kopaonik NP. For this purpose, we downloaded and processed a large number of Landsat 7 (ETM+), Landsat 8 (OLI), and Sentinel 2 (MSI) satellite imagery acquired from 2009 to 2022. Our results revealed that forest cover loss was mainly driven by severe drought in 2011 and 2012, which was later significantly influenced by bark beetle outbreaks. Furthermore, various VIs proved to be very useful in monitoring and predicting forest health status. In summary, the drought-induced response detected using various VIs provides valuable insights into the dynamics of forest cover change, with implications for monitoring and conservation efforts of Norway spruce forests in the Kopaonik NP.

The planted forest area in Vietnam increased from 3.0 to 4.4 million hectares in the period 2010–2020, but the loss of productivity from pests and diseases continues to be a problem. During this period, frequent and systematic plantation forest health surveys were conducted on 12 native and 4 exotic genera of trees as well as bamboo across eight forest geographic regions of Vietnam. Damage caused by insects and pathogens was quantified in the field and laboratory in Hanoi. The threats of greatest concern were from folivores (Antheraea frithi, Arthroschista hilaralis, Atteva fabriciella, Hieroglyphus tonkinensis, Lycaria westermanni,Krananda semihyalina, and Moduza procris), wood borers (Batocera lineolata, Euwallacea fornicatus, Tapinolachnus lacordairei, Xyleborus perforans, and Xystrocera festiva), sap-sucking insects (Aulacaspis tubercularis and Helopeltis theivora) and pathogens (Ceratocystis manginecans, Fusarium solani, and Phytophthora acaciivora). The number of new and emerging pests and pathogens increased over time from 2 in 2011 to 17 in 2020, as the damage became more widespread. To manage these pests and diseases, it is necessary to further invest in the selection and breeding of resistant genotypes, improve nursery hygiene and silvicultural operations, and adopt integrated pest management schemes. Consideration should be given to developing forest health monitoring protocols for forest reserves and other special-purpose forests.

The BatuTegi KPHL controlled area has relatively high biodiversity, including tree species diversity. Therefore, the health of the KPHL BatuTegi forest can be assessed by considering the diversity of existing tree species and identifying them as a measure of the sustainability of the forest ecosystem. This study aims to clarify the diversity of tree species as an index for assessing the health status of KPHL BatuTegi forests. This study was conducted in the GapoktanHarapanSentosa KPHL Batutegiarea using the Forest Health Monitoring (FHM) method. The result obtained is that the average health of the forest inBatutegi KPHL is moderate at a value of 2.72. Therefore, the health of the BatuTegi KPHL forest is reasonably healthy (stable).