Presence Of Forest Cover Research Articles

Risk factors for African swine fever spread in wild boar in the Russian Federation

The analysis and assessment of risk factors associated with the occurrence, spread and persistence of African swine fever (ASF) virus in wild boar population are an important tool in determining the strategic measures aimed at eradicating epizootics and mitigating their consequences. A thorough examination of foreign and domestic literature revealed that wild boar population management factors, socio-economic and environmental ones, that mainly account for the density and number of animals were the most significant and associated with the risk of ASF outbreak occurrence in wild animals. In order to identify risk factors for the spread of the disease in wild boar in the Russian Federation Subjects, a regression model was built to examine the relationship between the annual number of ASF outbreaks in wild boar at the municipal raion level, wild boar population density and some other factors for the period between 2007 and 2022. Based on the Subject-level regression modelling results, a positive association between the intensity of the disease outbreaks and wild boar population density was identified in 42.5% of the model regions of the Russian Federation. Other significant factors were the length of roads, the presence of forest cover and outbreaks in domestic pigs. However, on the whole, for all the infected Subjects, the regression model demonstrated the failure of the wild boar population density factor to explain the observed ASF outbreak distribution, and this may be indicative of the existence of other epizootic drivers of the disease spread in the wild. One of such mechanisms may be the persistence of infectious potential in the external environment and in the formed stationary local foci of African swine fever, despite the anti-epizootic measures taken, including the measures aimed at regulating the number of susceptible population – depopulation.

Effects of forests on particle number concentrations in near-road environments across three geographic regions

Trees and other vegetation have been advocated as a mitigation measure for urban air pollution mainly due to the fact that they passively filter particles from the air. However, mounting evidence suggests that vegetation may also worsen air quality by slowing the dispersion of pollutants and by producing volatile organic compounds that contribute to formation of ozone and other secondary pollutants. We monitored nanoparticle (>10 nm) counts along distance gradients away from major roads along paired transects across open and forested landscapes in Baltimore (USA), Helsinki (Finland) and Shenyang (China) − i.e. sites in three biomes with different pollution levels − using condensation particle counters. Mean particle number concentrations averaged across all sampling sites were clearly reduced (15%) by the presence of forest cover only in Helsinki. For Baltimore and Shenyang, levels showed no significant difference between the open and forested transects at any of the sampling distances. This suggests that nanoparticle deposition on trees is often counterbalanced by other factors, including differing flow fields and aerosol processes under varying meteorological conditions. Similarly, consistent differences in high frequency data patterns between the transects were detected only in Helsinki. No correlations between nanoparticle concentrations and solar radiation or local wind speed as affecting nanoparticle abundances were found, but they were to some extent associated with canopy closure. These data add to the accumulating evidence according to which trees do not necessarily improve air quality in near-road environments.

Arthropod biodiversity patterns point to the Mesovoid Shallow Substratum (MSS) as a climate refugium

The determinants of biodiversity patterns in the subterranean habitat called Mesovoid Shallow Substratum (MSS) are not well-understood. In this study, thirty-three scree slopes at high altitudes were selected across the Sierra de Guadarrama National Park in central Spain to investigate the effect of ten environmental variables on the abundance and species diversity of the spider and springtail assemblages from the colluvial MSS. In each locality, a multiperforated PVC tube with a pitfall trap inside was buried up to 1 m deep, and generalized linear models and Mantel tests were used to analyze the effect of mainly climate- and habitat-related variables on the diversity patterns of both taxa. A total of 1143 individual spiders belonging to 54 species and 40 811 springtail individuals belonging to 62 species were collected. The analyses indicated that cold temperatures and the presence of forest cover on the surface significantly enhance richness and abundance in the two taxa. Environmental similarity also had a small positive effect on faunistic similarity. However, the effects of temperature and habitat detected on spider richness and abundance were stronger than on springtails, whereas the reverse was found regarding faunistic similarity. These results indicate that subterranean dwellers respond differently to the same environmental factors, which in turn, points to a different degree of affinity for the MSS. The MSS plays an important role in the survival of high mountain arthropod species, acting as a climate refuge, so the protection of this habitat should be prioritized.

Influence of relief on permanent preservation areas

Many countries have environmental legislation to protecting natural resources on private property. In Brazil, the Brazilian Forestry Code determines specific areas to maintain with natural vegetation cover, known as areas of permanent preservation (APP). Currently, there are few studies that relate topographic variables on APP. In this context, we sought to evaluate the influence of relief on the conservation of areas of permanent preservation (APP) in the areas surrounding Caparaó National Park, Brazil. By using the chi-squared statistical test, we verified that the presence of forest cover is closely associated with altitude. The classes of APP in better conservation status are slopes in addition to hilltops and mountains, whereas APP streams and springs are among the areas most affected by human activities. The most deforested areas are located at altitudes below 1100.00m and on slopes less than 45°. All orientations of the sides were significant for APP conservation status, with the southern, southeastern, and southwestern sides showing the lower degrees of impact. The methodology can be adjusted to environmental legislation to other countries.

Adaptive algorithm for large scale dtm interpolation from lidar data for forestry applications in steep forested terrain

Light Detection and Ranging (lidar) has become a valuable tool in forest inventory because it yields accurate measurements of tree heights. However, tree height can be accurate only if the height of the ground, i. e., the Digital Terrain Model (dtm) is first accurately established.Although great advances have been made in lidar technology over the past decade, filtering lidar data for Digital Terrain Model (dtm) interpolation is still a challenge, especially in steep and complex terrain with forest cover. Several algorithms proposed in the literature address this challenge but their performance deteriorates with the decreasing point density caused by the presence of forest cover and steep slopes. In this paper, we propose a new adaptive algorithm for dtm interpolation from lidar data in steep terrain with forest cover. The algorithm partitions the input data and estimates a section of the dtm by fitting a linear or quadratic trend surface, or uses cubic spline interpolation depending on the complexity of the section of terrain. The performance of the algorithm is tested in three ways: by visual assessment, by comparison of the tree-height estimates produced using the generated dtm with those obtained using field survey, and by use of International Society for Photogrammetry and Remote Sensing (isprs) test data. Test results show that the algorithm can cope well with steep slopes and low lidar point densities, giving a more accurate estimate of average tree height compared to conventional algorithms. The algorithm can be used for dtm extraction in large scale forest inventory projects in challenging environments–complex terrain and low lidar point densities.

The influence of land use patterns on water quality at multiple spatial scales in a river system

AbstractThe influence of land use patterns on water quality in a river system is scale‐dependent. In this study, a four‐order hierarchical arrangement method was used to select water sampling sites and to delineate sub‐basins in the Daliao River Basin, China. The 20 sub‐basins were classified into four spatial scales that represented four different stream orders. Pearson correlation analysis was used to quantify relationships between land use composition and the river's physical‐chemical variables for all samples collected. This analysis showed that the presence of forest cover was associated with higher water quality at the scale of the whole basin. The scale effects of land use patterns on water quality were then examined using stepwise multiple regression analysis that compared different land use types with water quality variables. The results from this analysis showed that urban areas, as opposed to forest areas, became the most significant contributors of water pollutants when scale effects were considered. The influence of urban land cover on water pollution was significantly higher at larger scales. The lack of a significant regression correlation for the forest land use type at smaller scales revealed that forest located upstream of the Daliao River Basin did not provide a buffer for improved water quality. Further analysis showed that this result could be because of disproportionate spatial distributions for forest and urban land use types. The topographic characteristics of sub‐basins, such as average slope (S) and size (A), were determined to be secondary explanatory variables that affected land use impacts on stream water quality. Areas with steep slopes were associated with increased water oxygenation, whereas areas with flatter slopes were associated with higher concentrations of pollutants. These results are significant because they can provide a better understanding of the appropriate spatial scale required for effective river basin management in the future. Copyright © 2013 John Wiley & Sons, Ltd.

Spatial prediction of brushtail possum (Trichosurus vulpecula) distribution using a combination of remotely sensed and field-observed environmental data

Context In New Zealand, the introduced brushtail possum, Trichosurus vulpecula, is a reservoir of bovine tuberculosis and as such poses a major threat to the livestock industry. Aerial 1080 poisoning is an important tool for possum control but is expensive, creating an ongoing need for ever more cost-effective ways of using this technique. Aims To develop geographic information system (GIS) models to better predict spatial variation in the distribution of unmanaged possum populations, to facilitate better targeting of control activities. Methods Relative abundance of possums and their distribution among habitat types were surveyed in a dry high-country area of the northern South Island. Two GIS-based models were developed to predict the relative abundance of possums on trap lines. The first model used remotely sensed (digital) environmental data; the second complemented the remotely sensed data with fine-scale habitat and topographic data collected on the ground. Key results Digital environmental factors and habitat features proved to be key predictors of relative possum abundance. In both GIS models, height above valley floor, presence of forest cover and mean annual temperature were the strongest predictors. Conclusions Predictive maps (projections) of relative possum abundance produced from these models can provide useful decision-support tools for pest-control managers, by enabling possum control to be targeted spatially. Implications Spatially targeted pest control could allow effective control activities for invasive species or disease vectors to be applied at a lower cost for the same benefit.

Hunting practices among the Awá-Guajá: towards a long-term analysis of sustainability in an Amazonian indigenous community

Indigenous Reserves have played an indispensable role in maintaining forest areas in the Neotropics. In the Amazon there is a clear correlation between these reserves and the presence of forest cover; however, the simple presence of uninterrupted vegetation is no guarantee for the conservation of biodiversity, especially where hunting is practiced. This study describes hunting practices among the Awá-Guajá people from 1993 through 1994, also identifying sociocultural, technological, and demographic changes that have influenced their resource acquisition strategies over the last two decades. The data was obtained through ethnographic fieldwork, recording 78 days of foraging returns, with follow-up visits through 2010. This work provides useful information for an effective diachronic analysis of hunting in this community, by revealing foraging patterns of the early to mid-1990s, and describing community transformations over the last two decades in this locale.

The Impact of Snow Model Complexity at Three CLPX Sites

Abstract Many studies have developed snow process understanding by exploring the impact of snow model complexity on simulation performance. This paper revisits this topic using several recently developed land surface models, including the Simplified Simple Biosphere Model (SSiB); Noah; Variable Infiltration Capacity (VIC); Community Land Model, version 3 (CLM3); Snow Thermal Model (SNTHERM); and new field measurements from the Cold Land Processes Field Experiment (CLPX). Offline snow cover simulations using these five snow models with different physical complexity are performed for the Rabbit Ears Buffalo Pass (RB), Fraser Experimental Forest headquarters (FHQ), and Fraser Alpine (FA) sites between 20 September 2002 and 1 October 2003. These models simulate the snow accumulation and snowpack ablation with varying skill when forced with the same meteorological observations, initial conditions, and similar soil and vegetation parameters. All five models capture the basic features of snow cover dynamics but show remarkable discrepancy in depicting snow accumulation and ablation, which could result from uncertain model physics and/or biased forcing. The simulated snow depth in SSiB during the snow accumulation period is consistent with the more complicated CLM3 and SNTHERM; however, early runoff is noted, owing to neglected water retention within the snowpack. Noah is consistent with SSiB in simulating snow accumulation and ablation at RB and FA, but at FHQ, Noah underestimates snow depth and snow water equivalent (SWE) as a result of a higher net shortwave radiation at the surface, resulting from the use of a small predefined maximum snow albedo. VIC and SNTHERM are in good agreement with each other, and they realistically reproduce snow density and net radiation. CLM3 is consistent with VIC and SNTHERM during snow accumulation, but it shows early snow disappearance at FHQ and FA. It is also noted that VIC, CLM3, and SNTHERM are unable to capture the observed runoff timing, even though the water storage and refreezing effects are included in their physics. A set of sensitivity experiments suggest that Noah’s snow simulation is improved with a higher maximum albedo and that VIC exhibits little improvement with a larger fresh snow albedo. There are remarkable differences in the vegetation impact on snow simulation for each snow model. In the presence of forest cover, SSiB shows a substantial increase in snow depth and SWE, Noah and VIC show a slight change though VIC experiences a later onset of snowmelt, and CLM3 has a reduction in its snow depth. Finally, we observe that a refined precipitation dataset significantly improves snow simulation, emphasizing the importance of accurate meteorological forcing for land surface modeling.

Tropical storms and the flood hydrology of the central Appalachians

Flooding from Hurricane Fran is examined as a prototype for central Appalachian flood events that dominate the upper tail of flood peak distributions at basin scales between 100 and 10,000 km2. Hurricane Fran, which resulted in 34 deaths and more than $3.2 billion in damages, made land fall on the North Carolina coast at 0000 UTC, September 6, 1996. By 1200 UTC on September 6, Fran had weakened to a tropical storm, and the center of circulation was located at the North Carolina–Virginia border. Rain bands surrounding the tropical depression produced extreme rainfall and flooding in Virginia and West Virginia, with the most intense rainfall concentrated near ridge tops in the Blue Ridge and Valley and Ridge physiographic provinces. The most severe flooding occurred in the Shenandoah River watershed of Virginia, where peak discharges exceeded the 100‐year magnitude at 11 of 19 U.S. Geological Survey stream‐gaging stations. The availability of high‐resolution discharge and rainfall data sets provides the opportunity to study the hydrologic and hydrometeorological mechanisms associated with extreme floods produced by tropical storms. Analyses indicate that orographie enhancement of tropical storm precipitation plays a central role in the hydrology of extreme floods in the central Appalachian region. The relationships between drainage network structure and storm motion also play a major role in Appalachian flood hydrology. Runoff processes for Hurricane Fran reflected a mixture of saturation excess and infiltration excess mechanisms. Antecedent soil moisture played a significant role in the hydrology of extreme flooding from Hurricane Fran. Land use, in particular, the presence of forest cover, was of secondary importance to the terrain‐based distribution of precipitation in determining extreme flood response.

Measurement and Modeling of Microwave Emission from Forested Snowfields in Michigan

Forest cover was found to confuse the normally inverse relationship between microwave brightness temperature (TB) and snow depth in dry snowpacks in the lower peninsula of Michigan during February of 1979 and 1980. However, even in the presence of forest cover, consisting of a mixture of hardwoods and softwoods, the average Nimbus-7 SMMR 37 GHz vertically and horizontally polarized TBs were 11° and 19° K lower during the study period in February of 1979 versus February of 1980. This is attributed to deeper average snowcover for the study period in 1979 (43.8 cm) as compared to 1980 (9.9 cm) since lower TBs accompany deeper snow under dry snowpack conditions. 37 GHz brightness temperature was correlated with snow depth for the study periods in 1979 and 1980, resulting in positive relationships instead of the expected inverse relationships. Because the snow depth increased with increasing forest cover in the lower peninsula of Michigan, it was assumed that the emissivity of the forest cover overwhelmed that of the snow - both in 1979 and 1980 causing higher TBs in more heavily forested areas. Using a simple model, we removed the effects of the trees from the TB/snow depth relationship. A new value, TBR, residual brightness temperature, derived from the 37 GHz data, was correlated with snow depth for selected time periods in February 1979 and 1980 resulting in inverse relationships of R ≡ .82 and .77 respectively. In addition, SMMR data of August 1979 were correlated with percent forest cover to determine the TB of the study area in the absence of snow. The lower SMMR frequencies (10.69 and 6.6 GHz) showed statistically significant inverse relationships with forest cover during the August study period. These inverse relationships were probably due to increased soil moisture in the more heavily forested areas in the northern half of lower peninsula of Michigan thus causing lower TBs.