scholarly journals Factors Influencing Modern Wildfire Occurrence in the Mark Twain National Forest, Missouri

2007 ◽  
Vol 31 (2) ◽  
pp. 73-84 ◽  
Author(s):  
Kimberley D. Brosofske ◽  
David T. Cleland ◽  
Sari C. Saunders
Keyword(s):  
Logistic Regression ◽  
Population Density ◽  
Mark Twain ◽  
Easy Access ◽  
Anthropogenic Factors ◽  
National Forest ◽  
Fire Occurrence ◽  
Fire Size ◽  
Wildfire Occurrence ◽  
Factors Influencing

Abstract Understanding relative influences of ecological and anthropogenic factors on wildfire occurrence can assist decisionmakers in allocating fire management resources. We examined the influences of ecological and anthropogenic variables on probability of modern fire occurrence in the Mark Twain National Forest (MTNF), Missouri, using classification and regression tree (CART) and logistic regression analyses. Models were developed for five classes of fire size. Although CART distinguished some effects of fire size on results, logistic regression indicated a single model developed for all fires was sufficient for predictions. Ecological subsection was a dominating influence on fire occurrence for final CART and logistic models, highlighting the potential usefulness of ecosystem classification as a framework for considering factors influencing modern wildfires. Other influential predictors included ecosystem fire resistance; distance to roads, cities, and railroads; road density; mean October precipitation; elevation; median house value; and population density. Wildfires in the MTNF are caused overwhelmingly by arson, which, when combined with our results, suggests that arsonists may seek out flammable fuel types in remote areas with easy access. Within this general anthropogenic fire regime, we found a more subordinate effect of specific human variables (e.g., population density) on modern fire occurrence than did similar studies in the Upper Midwest, perhaps because our study area encompassed primarily federal forestlands with low population density.

scholarly journals Tendencies of Fire Development in the Forests of Ukraine

2020 ◽  
Vol 3 (1) ◽  
pp. 106
Author(s):  
Yevhen Melnyk ◽  
Vladimir Voron
Keyword(s):  
Forest Management ◽  
Forest Fires ◽  
Necessary Conditions ◽  
Climatic Conditions ◽  
Anthropogenic Factors ◽  
Burned Area ◽  
Fire Occurrence ◽  
Management Zones ◽  
Wildfire Occurrence ◽  
Management Zone

Preservation and increase of forest area are necessary conditions for the biosphere functioning. Forest ecosystems in most parts of the world are affected by fires. According to the latest data, the forest fire situation has become complicated in Ukraine, and this issue requires ongoing investigation. The aim of the study was to analyse the dynamics of wildfires in Ukrainian forests over recent decades and to assess the complex indicator of wildfire occurrence in various forest management zones and administrative regions. The average annual complex indicator of fire occurrence, in terms of wildfire number and burned area, was studied in detail in the forests of various administrative regions and forest management zones in Ukraine from 1998 to 2017. The results show that fire occurrence in both the number and area of fires can vary significantly in various forest management zones. There is a very noticeable difference in these indicators in some administrative regions within a particular forest management zone. The data show that the number of forest fires depends not only on the natural and climatic conditions of such regions, but also on anthropogenic factors.

Study on Prediction Model of Karstic Large Spring Water Level Dynamic Coupling Multiple Factors in Jinan

2015 ◽  
Vol 8 (1) ◽  
pp. 309-317 ◽  
Author(s):  
Xing Liting ◽  
Zhou Juan ◽  
Zhang Fengjuan ◽  
Wang Song ◽  
Dou Tongwen ◽  
...  
Keyword(s):  
Prediction Model ◽  
Water Level ◽  
Spring Water ◽  
Anthropogenic Factors ◽  
Karst Water ◽  
Multiple Factors ◽  
Factors Influencing ◽  
Heterogeneous Features ◽  
Dry Seasons ◽  
Natural Decay

In karst regions, due to the heterogeneous features of karst medium, the characteristics of the groundwater flow turn to be of high complexity. Researchers have been seeking proper forecasting methods for karst water dynamic for many years. This paper, taking the spring in Jinan as an example, using regression analysis, analyzed the factors influencing spring water dynamic, and quantitatively evaluated the influencing coefficients of spring water level concerning rainfall, exploitation and recharge as well as the natural decay coefficient of spring water in dry seasons. The prediction model coupling multiple factors was built by investigating natural and anthropogenic factors influencing groundwater level, which could be used for forecasting dynamic of spring water in Jinan. The calculated value of model was highly coincided with the observed value. In consideration of the characteristics of uneven precipitation in Jinan, the suitable zones and volume of artificial recharge were investigated finally, which could help to sustain the spewing of Jinan springs significantly.

scholarly journals Social and environmental correlates of rat complaints in Chicago

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Cecilia A Sánchez ◽  
María Jazmín Rios ◽  
Maureen H Murray
Keyword(s):  
Population Density ◽  
Social Factors ◽  
Home Ownership ◽  
Wealth Inequality ◽  
Building Construction ◽  
Anthropogenic Factors ◽  
Negative Effects ◽  
Environmental Correlates ◽  
Growing Rat ◽  
Commercial Sources

Abstract Urban rats are widely distributed pests that have negative effects on public health and property. It is crucial to understand their distribution to inform control efforts and address drivers of rat presence. Analysing public rat complaints can help assess urban rat distribution and identify factors supporting rat populations. Both social and environmental factors could promote rat complaints and must be integrated to understand rat distributions. We analysed rat complaints made between 2011 and 2017 in Chicago, a city with growing rat problems and stark wealth inequality. We examined whether rat complaints at the census tract level are associated with factors that could influence rat abundance, rats’ visibility to humans, and the likelihood of people making a complaint. Complaints were significantly positively correlated with anthropogenic factors hypothesized to promote rat abundance (restaurants, older buildings, garbage complaints, and dog waste complaints) or rat visibility (building construction/demolition activity), and factors hypothesized to increase the likelihood of complaining (human population density, more owner-occupied homes); we also found that complaints were highest in the summer. Our results suggest that conflicts between residents and rats are mainly driven by seasonal variation in rat abundance and human activity and could be mitigated with strategies such as securing food waste from residential and commercial sources. Accounting for social factors such as population density, construction and demolition activity, and home ownership versus rental can also help cities more accurately predict blocks at higher risk of rat conflicts.

scholarly journals Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 392
Author(s):  
Zige Lan ◽  
Zhangwen Su ◽  
Meng Guo ◽  
Ernesto C. Alvarado ◽  
Futao Guo ◽  
...  
Keyword(s):  
Human Factors ◽  
Large Scale ◽  
Fire Occurrence ◽  
Climate Factors ◽  
Wildfire Occurrence ◽  
Prediction And Prevention ◽  
Data Resolution ◽  
Main Driver ◽  
Landscape Factors ◽  
Modelling Approach

Understanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the topic. China has diverse vegetation types and topography, and has undergone rapid economic and social development, but experiences a high frequency of wildfires, making it one of the ideal locations for wildfire research. We applied the Random Forests modelling approach to explore the main types of wildfire drivers (climate factors, landscape factors and human factors) in three high wildfire density regions (Northeast (NE), Southwest (SW), and Southeast (SE)) of China. The results indicate that climate factors were the main driver of wildfire occurrence in the three regions. Precipitation and temperature significantly impacted the fire occurrence in the three regions due to the direct influence on the moisture content of forest fuel. However, wind speed had important influence on fire occurrence in the SE and SW. The explanation power of the landscape and human factors varied significantly between regions. Human factors explained 40% of the fire occurrence in the SE but only explained less than 10% of the fire occurrence in the NE and SW. The density of roads was identified as the most important human factor driving fires in all three regions, but railway density had more explanation power on fire occurrence in the SE than in the other regions. The landscape factors showed nearly no influence on fire occurrence in the NE but explained 46.4% and 20.6% in the SE and SW regions, respectively. Amongst landscape factors, elevation had the highest average explanation power on fire occurrence in the three regions, particularly in the SW. In conclusion, this study provides useful insights into targeted fire prediction and prevention, which should be more precise and effective under climate change and socio-economic development.

Spatial and temporal characteristics of wildfires in Mississippi, USA

2010 ◽  
Vol 19 (1) ◽  
pp. 14 ◽  
Author(s):  
Katarzyna Grala ◽  
William H. Cooke
Keyword(s):  
Palmer Drought Severity Index ◽  
Severity Index ◽  
The State ◽  
Anthropogenic Factors ◽  
Burned Area ◽  
Drought Severity ◽  
Temporal Characteristics ◽  
Wildfire Occurrence ◽  
Spatial And Temporal Characteristics ◽  
Drought Severity Index

Forests constitute a large percentage of the total land area in Mississippi and are a vital element of the state economy. Although wildfire occurrences have been considerably reduced since the 1920s, there are still ~4000 wildfires each year in Mississippi burning over 24 000 ha (60 000 acres). This study focusses on recent history and various characteristics of Mississippi wildfires to provide better understanding of spatial and temporal characteristics of wildfires in the state. Geographic information systems and Mississippi Forestry Commission wildfire occurrence data were used to examine relationships between climatic and anthropogenic factors, the incidence, burned area, wildfire cause, and socioeconomic factors. The analysis indicated that wildfires are more frequent in southern Mississippi, in counties covered mostly by pine forest, and are most prominent in the winter–spring season. Proximity to roads and cities were two anthropogenic factors that had the most statistically significant correlation with wildfire occurrence and size. In addition, the validity of the Palmer Drought Severity Index as a measure of fire activity was tested for climatic districts in Mississippi. Analysis indicated that drought influences fire numbers and size during summer and fall (autumn). The strongest relationship between the Palmer Drought Severity Index and burned area was found for the southern climatic districts for the summer–fall season.

Predicting the Occurrence of Downy Brome (Bromus tectorum) in Central Oregon

2012 ◽  
Vol 5 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Sara C. P. Lovtang ◽  
Gregg M. Riegel
Keyword(s):  
Logistic Regression ◽  
Great Basin ◽  
Fire Regimes ◽  
National Forest ◽  
Downy Brome ◽  
Department Of Agriculture ◽  
Association Group ◽  
Average Probability ◽  
Regression Techniques ◽  
Successional Pathways

AbstractWhere the nonnative annual grass downy brome proliferates, it has changed ecosystem processes, such as nutrient, energy, and water cycles; successional pathways; and fire regimes. The objective of this study was to develop a model that predicts the presence of downy brome in Central Oregon and to test whether high presence correlates with greater cover. Understory data from the U.S. Department of Agriculture (USDA) Forest Service's Current Vegetation Survey (CVS) database for the Deschutes National Forest, the Ochoco National Forest, and the Crooked River National Grassland were compiled, and the presence of downy brome was determined for 1,092 systematically located plots. Logistic regression techniques were used to develop models for predicting downy brome populations. For the landscape including the eastside of the Cascade Mountains to the northwestern edge of the Great Basin, the following were selected as the best predictors of downy brome: low average March precipitation, warm minimum May temperature, few total trees per acre, many western junipers per acre, and a short distance to nearest road. The concordance index = 0.92. Using the equation from logistic regression, a probability for downy brome infestation was calculated for each CVS plot. The plots were assigned to a plant association group (PAG), and the average probability was calculated for the PAGs in which the CVS plots were located. This method could be duplicated in other areas where vegetation inventories take place.

scholarly journals Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niloufar Nouri ◽  
Naresh Devineni ◽  
Valerie Were ◽  
Reza Khanbilvardi
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Southern Oscillation ◽  
The United States ◽  
Anthropogenic Factors ◽  
Climate Variables ◽  
Term Trend ◽  
Cyclical Variability ◽  
Long Term Trend

AbstractThe annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

PENGARUH KARAKTERISTIK IBU DAN PELAYANAN ANTENATAL CARE TERHADAP KEJADIAN BAYI BERAT LAHIR RENDAH DI KLINIK PRATAMA BERTA TAHUN 2015

2016 ◽  
Vol 1 (2) ◽  
pp. 37-47
Author(s):  
Juliana Ambarita ◽  
Merlina Sinabariba
Keyword(s):  
Logistic Regression ◽  
Antenatal Care ◽  
Pregnant Women ◽  
Birth Spacing ◽  
Observational Research ◽  
Easy Access ◽  
Regression Result ◽  
Multiple Logistic Regression ◽  
Number Of Visits ◽  
Maternity Clinic

Babies born with low birth weight (LBW) are babies born with weight ≤ 2500 grams. The causes of LBW are age, birth spacing, education, antenatal care. Goals : This study aims to analyze the influence of mother characteristics (education, maternal age, age of pregnancy, parity and distance between births) and antenatal care service (ANC) (number of visits and 7T examination components) to events at Primary Maternity Clinic Berta. Methods : This type of research is an observational research using cross sectional design approach. The population is all mothers giving birth at Maternity Clinic Pratama Berta 172 people. The sample amounted to 153 people with simple random sampling technique. Analysis of data with Chi Square and Multiple Logistic Regression (multiple logistic regression). Result : The results showed that the incidence of BBLR of 11.1%. Multiple logistic regression results stated the distance between births (p = 0.027) and the number of visits (p = 0.042) had an effect on the occurrence of LBW. The Exp value (B) of the birth distance is 3.386, so it can be concluded that pregnant women whose birth distance <24 months have 3 times greater probability of baby experiencing LBW and Exp (B) ANC service amounted to 8,496, so it can be concluded that pregnant women His ANC service is not good has a probability 8,496 times larger the baby has LBW. Conclution : For Employees at the Maternity Clinic, Berta provides easy access to ANC services and conducts reproductive health education for pregnant women about the health of pregnant women, the importance of ANC testing during pregnancy and the importance of using long-term contraceptives to regulate birth spacing.

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