Loss Of Forest Area Research Articles

Cambio de uso de suelo y variabilidad climática en Chiautzingo, Puebla, México

La modificación de la cobertura vegetal es evidente y creciente, las causas principales son las diferentes actividades socioeconómicas que desarrolla la población. El clima es un factor esencial en la distribución de patrones bióticos de los ecosistemas y, en el mismo sentido, los ecosistemas, a través de su cobertura, pueden incidir en la variabilidad climática. Los estudios que consideran el cambio de uso de suelo, la vegetación y el clima son relevantes para determinar los procesos relacionados con la degradación del suelo, pérdida de biodiversidad y variabilidad climática. En este estudio se analizó el cambio de uso del suelo y la variabilidad climática para el periodo 1984-2018 en el municipio de Chiautzingo, Puebla, México a partir de imágenes de satélite Landsat en Sistemas de Información Geográfica. Se analizaron datos históricos de tres estaciones climatológicas y mediante una encuesta a productores, se recabo información sobre la percepción tanto del fenómeno del cambio de uso del suelo como del de variabilidad climática. El cambio de uso del suelo está relacionado con la pérdida de superficie forestal, con una tasa de cambio anual de -0.57 %. El incremento de la superficie destinada a agricultura protegida fue de 310.58 hectáreas, lo cual ejerce presión fuerte sobre los recursos bióticos y abióticos. También se encontró que existe variabilidad climática, la cual es percibida por la población. Es necesario contar con este análisis para identificar las áreas que tienen mayor degradación ambiental, y como base para la planificación del uso y manejo de los recursos naturales.

MONITORING AREA OF FOREST LOSS OIL PRODUCTION TERRITORY OF TOMSK REGION USING SATELLITE DATA

The analysis of the dynamics of the areas of forest loss on the left bank of the Kargasoksky and Pavel districts based on the Global Forest Watch (GFW) and the atlas of fires is carried out. It is shown that the greatest loss of forests occurred due to fires. The loss of forests is associated with the development of deposits in the left bank of the Kargasoksky district - 432 km2 and in the left bank of the Parabelsky district - 106 km2, which makes up only 9 and 4% of the total loss of forests.

Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India

Rapid population growth and its corresponding effects like the expansion of human settlement, increasing agricultural land, and industry lead to the loss of forest area in most parts of the world especially in such highly populated nations like India. Forest canopy density (FCD) is a useful measure to assess the forest cover change in its own as numerous works of forest change have been done using only FCD with the help of remote sensing and GIS. The coupling of binary logistic regression (BLR), random forest (RF), ensemble of rotational forest and reduced error pruning trees (RTF-REPTree) with FCD makes it more convenient to find out the deforestation probability. Advanced vegetation index (AVI), bare soil index (BSI), shadow index (SI), and scaled vegetation density (VD) derived from Landsat imageries are the main input parameters to identify the FCD. After preparing the FCDs of 1990, 2000, 2010 and 2017 the deforestation map of the study area was prepared and considered as dependent parameter for deforestation probability modelling. On the other hand, twelve deforestation determining factors were used to delineate the deforestation probability with the help of BLR, RF and RTF-REPTree models. These deforestation probability models were validated through area under curve (AUC), receiver operating characteristics (ROC), efficiency, true skill statistics (TSS) and Kappa co-efficient. The validation result shows that all the models like BLR (AUC = 0.874), RF (AUC = 0.886) and RTF-REPTree (AUC = 0.919) have good capability of assessing the deforestation probability but among them, RTF-REPTree has the highest accuracy level. The result also shows that low canopy density area i.e. not under the dense forest cover has increased by 9.26% from 1990 to 2017. Besides, nearly 30% of the forested land is under high to very high deforestation probable zone, which needs to be protected with immediate measures.

Accounting for twenty-first-century annual forest loss in the Atlantic Forest of Brazil using high-resolution global maps

ABSTRACTDeforestation in the Brazilian Atlantic Forest has been mapped since 1986 by SOS Mata Atlântica in partnership with the National Institute for Space Research under the project named Atlas dos Remanescentes Florestais da Mata Atlântica (SOSMA). The mapping results depict a worrying scenario of over-fragmentation and reduction of forest cover. The limited information provided, however, may underestimate deforestation in this Brazilian biome. We analysed the use of new high-resolution global deforestation datasets to quantify 10 years of deforestation in a heterogeneous landscape, where forest fragments are immersed in an agricultural and urban matrix. We set up a framework based on high-resolution satellite images available on Google Earth to visually inspect whether the areas of Global Forest Change (GFC) and SOSMA considered to be forest loss corresponded to actual deforestation. Results showed that no more than 21% of the forest loss area of the GFC were considered deforestation of Atlantic Forest remnants. More than 55% of the mapped patches (counts) were considered misclassifications. Misclassifications varied throughout the years, revealing the absence of a clear pattern, and they were considered dependent on the year. Forest plantation, forest regeneration, and grassland were the main sources of misclassifications. Despite errors, GFC accounted for significantly more deforestation than the official Brazilian mapping of the Atlantic Forest (SOSMA), showing that many small forest fragments that are important for maintaining biodiversity are being lost with little or no concern about it. We suggest that instead of sustaining an entire national programme that relies on acquiring and processing imagery, it would be better to work alongside a global programme such as the GFC by providing visual inspection, cross-validation, local knowledge, and further improvements.

Diagnóstico del uso del suelo y vegetación en la microcuenca Tula, México

Los cambios en el uso de suelo y la cobertura vegetal son el componente principal en el deterioro a escala global, regional y local, y han sido documentados como el segundo problema ambiental a nivel global. En México, hay pocas políticas de planificación en el desarrollo territorial de los municipios y una deficiente toma de decisiones en el manejo de los recursos naturales, ejemplo de ello es la microcuenca Tula. El trabajo de investigación tuvo como objetivo, diagnosticar la situación actual y los cambios en la cobertura y uso de suelo de la microcuenca Tula, México por medio del análisis espacial, para conocer sus principales procesos de cambio, con el propósito de diseñar estrategias que permitan un manejo sustentable de sus recursos naturales. El análisis espacial se realizó, a partir de imágenes satelitales Landsat (1992, 2000 y 2017) mediante sistemas de información geográfica. Para identificar los cambios se aplicaron tabulaciones cruzadas y se estimaron las tasas de cambio. En el periodo analizado (25 años), el área agrícola se incrementó 33% y una pérdida de superficie forestal de 22%. Las tasas de cambio y probabilidad de permanencia indicaron que de continuar con la tendencia actual seguirán incrementándose las áreas agrícolas y los cuerpos de agua, así como el abandono de algunas superficies debido a su baja productividad.

Time Series Analysis of Land Cover Using Remote Sensing Technology

The observation of land use/land cover (LULC) is essential as it allows humans to investigate the alteration of land which occurs over a period of time. This is to allow mankind to have a proper management of the earth resources and well planned development. One way to observe LULC is by using remote sensing technology since it provides continuous data monitoring of the earth’s surface. This study is carried out at Bandar Meru Raya, Ipoh, Perak and aimed to quantify the LULC changes, especially in urban areas which occurred within two decades from 1995 to 2005 and from 2005 to 2015. Maximum likelihood supervised classification was performed on three Landsat satellite imageries using ERDAS Imagine 2014 software. The images were classified into 4 general LULC categories namely forest, development area, green area, and water bodies. The results indicated that a considerable amount of forested area was decreasing by 183.12 hectares within the last 20 years while development area gained a total of 157.12 hectares. This LULC changes showed a serious loss of trees and this sort of change tends to affect the nature’s ecosystem. Therefore, by quantifying the loss of forest area will enable authorities to oversee and plan a better management of trees for future in urban environments. Such management plan is necessary in order to maintain the importance of trees towards nature and community.

Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China

Accurate acquisition of the spatiotemporal distribution of urban forests and fragmentation (e.g., interior and intact regions) is of great significance to contributing to the mitigation of climate change and the conservation of habitat biodiversity. However, the spatiotemporal pattern of urban forest cover changes related with the dynamics of interior and intact forests from the present to the future have rarely been characterized. We investigated fragmentation of urban forest cover using satellite observations and simulation models in the Nanjing Laoshan Region of Jiangbei New Area, Jiangsu, China, during 2002–2023. Object-oriented classification-based land cover maps were created to simulate land cover changes using the cellular automation-Markov chain (CA-Markov) model and the state transition simulation modeling. We then quantified the forest cover change by the morphological change detection algorithm and estimated the forest area density-based fragmentation patterns. Their relationships were built through the spatial analysis and statistical methods. Results showed that the overall accuracies of actual land cover maps were approximately 83.75–92.25% (2012–2017). The usefulness of a CA-Markov model for simulating land cover maps was demonstrated. The greatest proportion of forest with a low level of fragmentation was captured along with the decreasing percentage of fragmented area from 81.1% to 64.1% based on high spatial resolution data with the window size of 27 pixels × 27 pixels. The greatest increase in fragmentation (3% from 2016 to 2023) among the changes between intact and fragmented forest was reported. However, intact forest was modeled to have recovered in 2023 and restored to 2002 fragmentation levels. Moreover, we found 58.07 km2 and 0.35 km2 of interior and intact forests have been removed from forest area losses and added from forest area gains. The loss rate of forest interior and intact area exceeded the rate of total forest area loss. However, their approximate ratio (1) implying the loss of forest interior and intact area would have slight fragmentation effects on the remaining forests. This analysis illustrates the achievement of protecting and restoring forest interior; more importantly, excessive human activities in the surrounding area had been avoided. This study provides strategies for future forest conservation and management in large urban regions.

Forest degradation impacts on carbon stocks, tree density and regeneration status in banj oak forests of Central Himalaya

AbstractThe oak forests of the Himalaya are an important repository of carbon. Forest degradation, in terms of loss in biomass and carbon, is a more pressing problem than loss of forest area in the Himalaya. The altitudinal gradient between 1,000 m and 2,200 m is dominated by chir pine and banj oak forest. As a result of small‐scale chronic disturbances the structure and function of banj oak forest is rapidly changing, leaving few pristine patches. Forty permanent plots of 400 m2 in four disturbance categories of Central Himalayas (1,400 m–2,300 m asl) were studied over 3 years (2015, 2016 and 2017) to understand the impact of disturbance on banj oak forests. The study indicates that tree biomass declined by 62% from undisturbed to degraded forests, carbon sequestration rate declined by 73% and was maximum for Moderately disturbed‐A forests, total soil carbon dropped by 79% and mycorrhizal sporocarps declined by 78% from undisturbed to degraded forests. There was an overall decline in tree density by 56% from undisturbed forests to degraded forests. In degraded forests, the invasion of pine, a species tolerant of disturbance, helps in maintaining the forest status but the population structure changes and significant decline in carbon sequestration rate and total biomass stocks (above ground biomass and below ground biomass) was observed. The study indicates that moderate level disturbance promotes better carbon sequestration rates, tree density and the survival of seedlings.

A spatial pattern analysis of forest loss in the Madre de Dios region, Peru

Over the past decades, the Peruvian Amazon has experienced a rapid change in forest cover due to the expansion of agriculture and extractive activities. This study uses spectral mixture analysis (SMA) in a cloud-computing platform to map forest loss within and outside indigenous territories, protected areas, mining concessions, and reforestation concessions within the Madre de Dios Region in Peru. The study area is focused on key areas of forest loss in the western part of the Tambopata National Reserve and surrounding the Malinowski River. Landsat 8 Operational Land Imager and Landsat 7 Enhanced Thematic Mapper Plus surface reflectance data spanning 2013–2018 were analyzed using cloud-based SMA to identify patterns of forest loss for each year. High-resolution Planet Dove (3m) and RapidEye (5m) imagery were used to validate the forest loss map and to identify the potential drivers of loss. Results show large areas of forest loss, especially within buffer zones of protected areas. Forest loss also appears in the Kotsimba Native Community within a 1 km buffer of the Malinowski River. In addition to gold mining, agriculture and pasture fields also appear to be major drivers of forest loss for our study period. This study also suggests that gold mining activity is potentially not restricted to the legal mining concession areas, with 49% of forest loss occurring outside the mining concessions. Overall accuracy obtained for the forest loss analysis was 96%. These results illustrate the applicability of a cloud-based platform not only for land use land cover change detection but also for accessing and processing large datasets; the importance of monitoring not only forest loss progression in the Madre de Dios, which has been increasing over the years, especially within buffer zones, but also its drivers; and reiterates the use of SMA as a reliable change detection classification approach.

Land Use Changes and Their Perception in the Hinterland of Barranquilla, Colombian Caribbean

The coastal strip of the western peri-urban area of Barranquilla in the Atlántico Department (Colombia) is experiencing changes in human-environment interactions through infrastructure, residential, and tourism projects in a vulnerable landscape. In the hilly area, fragments of biodiverse tropical dry forest still exist in various states of conservation and degradation. To understand the interrelated social, economic, and ecological transformations in the area, we analyzed land use change on the local scale including the local community’s perception, because the local community is a key actor for sustainable land use. For the analysis of the interrelated social, economic, and ecological processes, we combined visual interpretation of high-resolution satellite imagery, on-site field land use mapping, and a spatial statistical analysis of the distribution of land use classes with in-depth interviews and a participatory GIS workshop, thus benefitting from the complementary methodological strengths of these approaches. The case study is the rural community of El Morro, which exhibits the typical social, economic, and ecological changes of the coastal strip of the western peri-urban area of Barranquilla. The local community perceives a continuous loss of forest area, but observations from on-site field mapping cannot confirm this linear trend. We observed a gradual replacement of traditional land uses such as smallholder agriculture, charcoal production, and cattle breeding by services for tourism, gated community projects for urban dwellers, and infrastructure projects; these spatial developments have several characteristics of rural gentrification. We conclude that the drivers of environmental degradation have changed and the degradation increased. The development projects of external companies have been rejected by the local community and have induced environmental consciousness among community members. Thus, the local community has become an advocate for sustainable land use in the study area.

Global patterns of forest loss across IUCN categories of protected areas

Forests are under increasing pressure globally and the establishment of protected areas has long been used as a conservation tool to preserve them. Seven categories of protected areas have been defined by the International Union for Conservation of Nature (IUCN) with different management objectives and protection levels. However, recent studies raised questions over whether protected areas are effective in preventing ecosystem degradation and whether IUCN categories vary in their effectiveness. In this study, we analysed forest loss and trends between 2001 and 2014 within IUCN protected areas at a global scale and within sixteen Intergovernmental Platform for Biodiversity and Ecosystem services (IPBES) subregions, relevant for international policy. As habitat protection can be driven by the location of protected areas and as the amount of forest within protected sites is highly unequal, we reported the forest loss integrating the proximity of roads and population, as well as the amount of initial forest in 2000. Our results show that worldwide, the highest protection categories experienced less forest loss than those allowing more human intervention, although this result was reversed in three IPBES subregions. Moreover, in four subregions there was more forest loss within protected areas than outside. We also found accelerating rates of forest loss in protected areas across all IUCN categories, more pronounced in the highest protection IUCN categories. Our results highlight the importance of moving the discussion of the post-2020 biodiversity framework for protected areas beyond simple general areal targets and that areas with poor implementation effectiveness should benefit from additional support.

Forest loss shapes the landscape suitability of Kyasanur Forest disease in the biodiversity hotspots of the Western Ghats, India.

Anthropogenic pressure in biodiversity hotspots is increasingly recognized as a major driver of the spillover and expansion of zoonotic disease. In the Western Ghats region of India, a devastating tick-borne zoonosis, Kyasanur Forest disease (KFD), has been expanding rapidly beyond its endemic range in recent decades. It has been suggested that anthropogenic pressure in the form of land use changes that lead to the loss of native forest may be directly contributing to the expanding range of KFD, but clear evidence has not yet established the association between forest loss and KFD risk. The current study sought to investigate the relationship between KFD landscape suitability and both forest loss and mammalian species richness, to inform its epidemiology and infection ecology. A total of 47 outbreaks of KFD between 1 January 2012 and 30 June 2019 were modelled as an inhomogeneous Poisson process. Both forest loss [relative risk (RR) = 1.83; 95% confidence interval (CI) 1.33-2.51] and mammalian species richness (RR = 1.29; 95% CI 1.16-1.42) were strongly associated with increased risk of KFD and dominated its landscape suitability. These results provide the first evidence of a clear association between increasing forest loss and risk for KFD. Moreover, the findings also highlight the importance of forest loss in areas of high biodiversity. Therefore, this evidence provides strong support for integrative approaches to public health which incorporate conservation strategies simultaneously protective of humans, animals and the environment.

Impact of the Accuracy of Land Cover Data sets on the Accuracy of Land Cover Change Scenarios in the Mono River Basin, Togo, West Africa

Knowledge about land use and land cover (LULC) dynamics is of high importance for a number of environmental studies including the development of water resources, land degradation and food security. Often, available global or regional data sets are used for impact studies, although they have not been validated for the area of interest. Validation is especially required if data are used to set up a land change model predicting future changes for management purposes. Therefore, three different LULC maps of the Mono River Basin in Togo were evaluated in this study. The analyzed maps were obtained from three sources: CILSS (2 km resolution), ESA (300 m), and Globeland (30m) datasets. Validation was performed using 1,000 reference points in the watershed derived from satellite images. The results reveal CILSS as the most accurate data set with a Kappa coefficient of 68% and an overall accuracy of 83%. CILSS data shows a decrease of savanna and forest whereas an increase of cropland over the period 1975 to 2013. The increase of cropland area of 30.97% from 1975 to 2013 can be related to the increase in population and their food demand, while the losses of forest area and the decrease of savanna are further amplified by using wood as energy sources and the lack of forest management. The three datasets were used to simulate future LULC changes using the Terrset Land Change Modeler. The validation of the model using CILSS data for 2013 showed a quality of 50.94%, it is only 40.04% for ESA and 20.13% for Globeland30. CILSS data was utilized to simulate the LULC distribution for the years 2020 and 2027 because of its satisfactory performances. The results show that a high spatial resolution is not a guarantee of high quality. The results of this study can be used for impact studies and to develop management strategies for mitigating negative effects of land use and land cover change.

Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017

The data, information and knowledge on the tropical forest area and its dynamics in the Brazilian Amazon remain contentious. We use time-series satellite images to quantify annual forest area, loss and gain in the Brazilian Amazon during 2000–2017. We find that forest area was ~15% higher than the estimate by the official Brazilian forest dataset (PRODES), but annual forest-loss rates were twice the PRODES estimates (~0.027 × 106 km2 yr–1 during 2001–2016). Forest-loss rates increased again after 2013. The El Nino and drought year (2015/2016) drove large forest area loss. The cumulative forest-loss area within the protected areas (which include ~50% of forests in the region) was ~11% of the total forest-loss area, which highlights the roles of protected areas in forest conservation. A new estimation of forest cover and loss finds more area but also double the loss rates in recent years, relative to previous estimates. Deforestation increased after a low peak in 2013.

Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000‐2017 Landsat time-series

Spatially and temporally consistent vegetation structure time-series have great potential to improve the capacity for national land cover monitoring, to reduce latency and cost of international reporting, and to harmonize regional land cover characterizations. Here we present a semi-automatic, operational algorithm for mapping and monitoring of woody vegetation canopy cover and height at a regional scale using freely available Landsat time-series data. The presented algorithm employs automatic data processing and mapping using a set of lidar-based vegetation structure prediction models. Changes in vegetation cover are detected separately and integrated into the structure time-series. Sample-based validation and inter-comparison with existing datasets demonstrates the spatial and temporal consistency of our regional data time-series. The dataset reliably reflects changes in tree cover (tree cover loss user's accuracy of 0.84 and producer's accuracy of 0.75) and can serve as a tool to map annual forest extent (user's accuracy of 0.98 and producer's accuracy of 0.81 for 10% canopy cover threshold to define the forest class). The tree height estimates are consistent with a GLAS-based global map (mean average error of 3.7 m, the correlation coefficient of 0.92 and the R2 of 0.85). The algorithm was prototyped within the Lower Mekong region where it revealed an intensive woody vegetation dynamic. Of the year 2000 forest area (defined using canopy cover threshold of 10% and tree height threshold of 5 m), 9.4% was deforested by the year 2017, and 16.6% was affected by stand-replacement disturbance followed by reforestation. The average annual area of stand-level forest disturbance within the region was 2.34 Mha, and increased by 34% from 2001 (1.85 Mha) to 2017 (2.48 Mha). Total forest area decreased by 6.2% within the region, and 11.1% of year 2000 primary forest area was lost by 2017. At the national level, Cambodia demonstrated the highest rate of deforestation, with a net forest area loss of 22.5%. We estimated that 21.3% of 2017 forest cover had an age of 17 years or less, illustrating the intensive forest land uses within the region. The time-series product is suitable for mapping annual land cover and inter-annual land cover change using customized class definitions. The regionally-consistent data are publicly available for download (https://glad.umd.edu/), and online analysis (https://rlcms-servir.adpc.net/en/forest-monitor/), and serve as an input to the SERVIR-Mekong Regional Land Cover Monitoring System.

Assessing Hydrological Ecosystem Services in a Rubber-Dominated Watershed under Scenarios of Land Use and Climate Change

Land use and climate change exert pressure on ecosystems and threaten the sustainable supply of ecosystem services (ESS). In Southeast-Asia, the shift from swidden farming to permanent cash crop systems has led to a wide range of impacts on ESS. Our study area, the Nabanhe Reserve in Yunnan province (PR China), saw the loss of extensive forest areas and the expansion of rubber (Hevea brasiliensis Müll. Arg.) plantations. In this study, we model water yield and sediment export for a rubber-dominated watershed under multiple scenarios of land use and climate change in order to assess how both drivers influence the supply of these ESS. For this we use three stakeholder-validated land use scenarios, varying in their degree of rubber expansion and land management rules. As projected climate change varies remarkably between different climate models, we combined the land use scenarios with datasets of temperature and precipitation changes, derived from nine General Circulation Models (GCMs) of the Fifth Assessment Report of the IPCC (Intergovernmental Panel on Climate Change) in order to model water yield and sediment export with InVEST (Integrated Valuation of Ecosystem Services and Trade-offs). Simulation results show that the effect of land use and land management decisions on water yield in Nabanhe Reserve are relatively minor (4% difference in water yield between land use scenarios), when compared to the effects that future climate change will exert on water yield (up to 15% increase or 13% decrease in water yield compared to the baseline climate). Changes in sediment export were more sensitive to land use change (15% increase or 64% decrease) in comparison to the effects of climate change (up to 10% increase). We conclude that in the future, particularly dry years may have a more pronounced effect on the water balance as the higher potential evapotranspiration increases the probability for periods of water scarcity, especially in the dry season. The method we applied can easily be transferred to regions facing comparable land use situations, as InVEST and the IPCC data are freely available.

Multiscale analysis of patch and landscape characteristics of forest fragmentation in Liaoning Province, China

Measuring dynamic changes in forest fragmentation is essential for protecting natural resources as part of regional land use planning. We investigated spatiotemporal dynamics of patch and landscape characteristics of forest fragmentation in Liaoning, China, from 2000 to 2010 by multiscale analysis based on the neighbor rule and landscape metrics. Several policy recommendations were proposed to prevent widespread loss of regional forest land. From 2000 to 2010, the area of forest loss decreased; forest patches decreased in size, subdivided, and disappeared in Liaoning at the patch level. During the study period, the transformation of forest land in most eastern counties remained in the middle phase of land transformation, with shrinkage being the main process of forest fragmentation (accounting for approximately 61.59% of lost forest area in 2000–2005 and 51.33% in 2005–2010). In contrast, the transformation of forest land in most western counties was in the later phases, with subdivision and attrition being the main processes. The areas of the four fragmentation processes decreased with increasing elevation, slope, and distance from residential lands, rivers, and roads. In most southern counties of Liaoning, forest fragmentation intensified at the landscape level. From 2000 to 2005, forest landscape patches close to rivers became irregular, isolated, and divided. However, these fragmentation metrics were not significantly related to physical, socioeconomic, and other spatial factors at the landscape level during the entire study period. Differences observed across multiple scales and levels may be used to monitor ecological processes and protect natural resources in China.

Pathogens-The Hidden Face of Forest Invasions by Wood-Boring Insect Pests.

Forests are one of the most important global vegetation types, serving functions from supporting healthy watershed, to wildlife habitat, and economic industries through their harvesting and tourism value. Understanding and mitigating threats to these resources is therefore of great societal importance. Forest pests are among the most pertinent and obvious threats to forest health, with their impacts amplified by contemporary issues such as climate change and global trade (Allen et al., 2010; Wingfield et al., 2016). In particular, recent increases in the frequency and severity of insect outbreaks have led to the loss of significant forest areas from across the globe (Roy et al., 2014) and highlighted the need for rigorous research to understand the underlying basis of their impacts. Introduced wood-boring beetles have been particularly damaging, which are often released from competitors and predators in their new environments, and target healthy trees without evolved resistance. In their native range, they typically colonize only dead or dying trees and are, therefore, not recognized as harmful. Recent examples of invasive pests include the emerald ash borer (Agrilus planipennis), which primarily attacks ash trees, the redbay ambrosia beetle (Xyleborus glabratus) infesting members of the laurel family (Lauraceae), and the Asian longhorned beetle (Anoplophora glabripennis) and polyphagous shot hole borer (PSHB), which are capable of infesting a wide range of tree species (Baranchikov et al., 2008; Fraedrich et al., 2008; Haack et al., 2010; Eskalen et al., 2013; Paap et al., 2018). Since their accidental introductions from Asia, these aggressive pests have been implicated in the mortality of millions of trees in Europe and North America, and the potential for the further spread of these and other pest species poses a major threat to the health of global forests. While forest insect pests have understandably attracted strong research attention, especially in countries where forestry is a major economic industry, far less understood are the pathogens they carry and how these contribute to forest damage, especially under climate change. For instance, the redbay ambrosia beetle, PSHB and longhorned beetles described above have each been found to carry assemblages of fungi, including pathogenic species (Fraedrich et al., 2008; Linnakoski et al., 2018; Paap et al., 2018). Here we focus on fungal pathogens vectored by wood-boring insect pests, and argue that they can amplify the negative effects of these pests and cause significant forest damage in their own right (Fraedrich et al., 2008; Ploetz et al., 2013). The purpose of this opinion article is to shed light on these implications, discuss the mechanisms underlying their interactions with host trees, and highlight the research required to resolve gaps in knowledge and progress understanding of this topic.

Institutional Constraints and the Forest Transition in Tropical Developing Countries

The forest transition describes a reversal or turnaround in long-run land-use trends for a country or region from a period of net forest area loss to net gain. Following North, this paper constructs a model to show that effective institutions may impose additional costs on a representative economic agent converting forests to agriculture in a tropical developing country. The main outcome to emerge from the model is that an improvement in institutions accelerates the onset of a forest transition. This hypothesis is tested by examining the probability of a forest transition occurring for 101 tropical developing countries over 1990 to 2015. The empirical results suggest that the hypothesis cannot be fully rejected. However, not all the institutional indicators analyzed affect the likelihood of the forest transition, and one economy-wide indicator appears to delay the transition. Nevertheless, the results illustrate the importance of understanding how institutional indicators affect the decisions of economic agents concerning land clearing as well as forest management practices and policies.

Analysis of coastline change in relation to meteorological conditions and human activities in Ca mau cape, Viet Nam

Ca Mau Cape is a part of Ngoc Hien district and is the southernmost tip of inland Vietnam. The district is highly vulnerable to coastal processes induced by natural and human factors that play important role in shaping and controlling the coastline. This study investigated the coastline change over 20 years, 1990–2010, in relation to meteorological conditions and human interventions using the land-use map (Landsat TM) and questionnaire survey. During this period, Ngoc Hien lost 2206 ha of coastal zone due to erosion. Most of the erosion occurred in the communes located in the east coast side. Remarkable changes in land-use/land-cover patterns were observed with a substantial reduction in the forest and increase in the aquaculture farming land. The results of the technical data analysis and questionnaire survey showed that both meteorological conditions and human interventions were interacted to cause the changes. Although the impacts of individual factors could not be readily quantified, the most important factors causing the coastline changes were forest area loss, river dredging, aquaculture and infrastructure development. The scientific information obtained in this study can be used to develop sustainable coastal management strategies for the study area.