COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.D...COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.展开更多
Urban and community forestry is a specialized discipline focused on the meticulous management of trees and forests within urban,suburban,and town environments.This field often entails extensive civic involvement and c...Urban and community forestry is a specialized discipline focused on the meticulous management of trees and forests within urban,suburban,and town environments.This field often entails extensive civic involvement and collaborative partnerships with institutions.Its overarching objectives span a spectrum from preserving water quality,habitat,and biodiversity to mitigating the Urban Heat Island(UHI)effect.The UHI phenomenon,characterized by notably higher temperatures in urban areas compared to rural counterparts due to heat absorption by urban infrastructure and limited urban forest coverage,serves as a focal point in this study.The study focuses on developing a methodological framework that integrates Geographically Weighted Regression(GWR),Random Forest(RF),and Suitability Analysis to assess the Urban Heat Island(UHI)effect across different urban zones,aiming to identify areas with varying levels of UHI impact.The framework is designed to assist urban planners and designers in understanding the spatial distribution of UHI and identifying areas where urban forestry initiatives can be strategically implemented to mitigate its effect.Conducted in various London areas,the research provides a comprehensive analysis of the intricate relationship between urban and community forestry and UHI.By mapping the spatial variability of UHI,the framework offers a novel approach to enhancing urban environmental design and advancing urban forestry studies.The study’s findings are expected to provide valuable insights for urban planners and policymakers,aiding in creating healthier and more livable urban environments through informed decision-making in urban forestry management.展开更多
Tropical forest cover change along with increasing fragmentation has detrimental effects on the global biodiversity.In the current study change in both forest cover and fragmentation of Koraput district have been asse...Tropical forest cover change along with increasing fragmentation has detrimental effects on the global biodiversity.In the current study change in both forest cover and fragmentation of Koraput district have been assessed in the past three decades(1987-2017)and future decade(2017-2027),which has been modelled using logistic regression showing a gradual decrease in the forest cover and increase in fragmentation.The long term deforestation rates from 1987 to 2017(current period)and from 1987 to 2027(predicted period)were found to be-0.018 and-0.012,respectively.Out of the total geographical area,2027 number of grids(1 km^(2))out of 8856 grids were found to have shown extinction of forest in the study period.The conversion of forested lands into other land uses has been one of the major causes of deforestation in Koraput,especially because of the increasing mining activities and establishment of three major industries namely National Aluminium Company(NALCO),Damanjodi,Hindustan Aeronautics Limited(HAL),Sunabeda and Ballarpur Industries Limited(BILT).The forest fragmentation reveals a negative trend,recording highest conversion from large core fragments to edge(191.33 km2)and the predicted period has also shown the same trend of negative change,which poses serious danger to the structure of the forests.Out of all the landscape matrices calculated,number of patches will increase to 214 in 2027 from 93 in 1987.In the test between geographically weighted regression(GWR)and ordinary least square regression(OLS),GWR was the better fit model for drawing a spatial relationship between forest cover and fragmentation changes.The study confirmed that the forest cover change has impacted the forest fragmentation in the study area.The programmes like REDD+should be implemented along with the experiences of Community Forest Management and the joint forest management should be intensified at community level in order to develop better management practices to conserve habitats in biodiversity rich areas.展开更多
In Mexico, forest fires are strongly influenced by environmental, topographic, and anthropogenic factors. A government-based database covering the period 2000-2011 was used to analyze the spatial heterogeneity of the ...In Mexico, forest fires are strongly influenced by environmental, topographic, and anthropogenic factors. A government-based database covering the period 2000-2011 was used to analyze the spatial heterogeneity of the factors influencing forest fire size in the state of Durango, Mexico. Ordinary least squares and geographically weighted regression models were fit to identify the main factors as well as their spatial influence on fire size. Results indicate that fire size is greatly affected by distance to roads, distance to towns, precipitation, temperature, and a population gravity index. The geographically weighted model was better than the ordinary least squares model. The improvement of the former is due to the influence of factors that were found to be non-stationary. These results suggest that geographic location determines the influence of a factor on fire size. While the models can be greatly improved with additional information, the study suggests the need to adopt fire management policies to more efficiently reduce the effect of anthropogenic factors. These policies may include more training for landowners who use fire for clearing, closure of roads, application of thinning, prescribed burning, and fire breaks in perimeters adjacent to roads.展开更多
基金This research was supported by the UBC APFNet Grant(Project ID:2022sp2 CAN).
文摘COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.
文摘Urban and community forestry is a specialized discipline focused on the meticulous management of trees and forests within urban,suburban,and town environments.This field often entails extensive civic involvement and collaborative partnerships with institutions.Its overarching objectives span a spectrum from preserving water quality,habitat,and biodiversity to mitigating the Urban Heat Island(UHI)effect.The UHI phenomenon,characterized by notably higher temperatures in urban areas compared to rural counterparts due to heat absorption by urban infrastructure and limited urban forest coverage,serves as a focal point in this study.The study focuses on developing a methodological framework that integrates Geographically Weighted Regression(GWR),Random Forest(RF),and Suitability Analysis to assess the Urban Heat Island(UHI)effect across different urban zones,aiming to identify areas with varying levels of UHI impact.The framework is designed to assist urban planners and designers in understanding the spatial distribution of UHI and identifying areas where urban forestry initiatives can be strategically implemented to mitigate its effect.Conducted in various London areas,the research provides a comprehensive analysis of the intricate relationship between urban and community forestry and UHI.By mapping the spatial variability of UHI,the framework offers a novel approach to enhancing urban environmental design and advancing urban forestry studies.The study’s findings are expected to provide valuable insights for urban planners and policymakers,aiding in creating healthier and more livable urban environments through informed decision-making in urban forestry management.
基金the Department of Science and Technology,Govt.of India,DST-INSPIRE for providing fellowship(Sanction No.DST/INSPIRE Fellowship/2015/IF150127 dated 10.04.2015)during the tenure of the research work。
文摘Tropical forest cover change along with increasing fragmentation has detrimental effects on the global biodiversity.In the current study change in both forest cover and fragmentation of Koraput district have been assessed in the past three decades(1987-2017)and future decade(2017-2027),which has been modelled using logistic regression showing a gradual decrease in the forest cover and increase in fragmentation.The long term deforestation rates from 1987 to 2017(current period)and from 1987 to 2027(predicted period)were found to be-0.018 and-0.012,respectively.Out of the total geographical area,2027 number of grids(1 km^(2))out of 8856 grids were found to have shown extinction of forest in the study period.The conversion of forested lands into other land uses has been one of the major causes of deforestation in Koraput,especially because of the increasing mining activities and establishment of three major industries namely National Aluminium Company(NALCO),Damanjodi,Hindustan Aeronautics Limited(HAL),Sunabeda and Ballarpur Industries Limited(BILT).The forest fragmentation reveals a negative trend,recording highest conversion from large core fragments to edge(191.33 km2)and the predicted period has also shown the same trend of negative change,which poses serious danger to the structure of the forests.Out of all the landscape matrices calculated,number of patches will increase to 214 in 2027 from 93 in 1987.In the test between geographically weighted regression(GWR)and ordinary least square regression(OLS),GWR was the better fit model for drawing a spatial relationship between forest cover and fragmentation changes.The study confirmed that the forest cover change has impacted the forest fragmentation in the study area.The programmes like REDD+should be implemented along with the experiences of Community Forest Management and the joint forest management should be intensified at community level in order to develop better management practices to conserve habitats in biodiversity rich areas.
基金funded by the National Polytechnic Institute(IPN)project#SIP 20110943–CONACYT,and COFAA
文摘In Mexico, forest fires are strongly influenced by environmental, topographic, and anthropogenic factors. A government-based database covering the period 2000-2011 was used to analyze the spatial heterogeneity of the factors influencing forest fire size in the state of Durango, Mexico. Ordinary least squares and geographically weighted regression models were fit to identify the main factors as well as their spatial influence on fire size. Results indicate that fire size is greatly affected by distance to roads, distance to towns, precipitation, temperature, and a population gravity index. The geographically weighted model was better than the ordinary least squares model. The improvement of the former is due to the influence of factors that were found to be non-stationary. These results suggest that geographic location determines the influence of a factor on fire size. While the models can be greatly improved with additional information, the study suggests the need to adopt fire management policies to more efficiently reduce the effect of anthropogenic factors. These policies may include more training for landowners who use fire for clearing, closure of roads, application of thinning, prescribed burning, and fire breaks in perimeters adjacent to roads.
