Pub Date : 2024-06-10DOI: 10.14712/23361980.2024.7
T. Galia, V. Škarpich, Adriana Holušová, Jan Hradecký
Gravel and sandy bars constitute critical components of river channel morphology, yet their morphodynamics in large, heavily regulated rivers during periods without significant flows remain poorly understood. This study investigates changes in surface heterogeneity and sediment sizes through a two-year field monitoring program, focusing on the frontal, central, and distal sections of four bars along the Elbe River in Czechia. Despite the absence of high-flow events reaching at least a one-year recurrence interval, observable changes in surface heterogeneity and sediment sizes were noted across all bars. However, the changes did not follow a uniform pattern; individual bars and their sections exhibited varying degrees of surface sediment coarsening or fining, alongside increases or decreases in surface heterogeneity. These findings highlight the necessity for site-specific management strategies for individual bars within such human-impacted rivers, recognizing their value as ecological hotspots. Furthermore, the methodology presented in this study may serve as a blueprint for the cost-effective monitoring of bar dynamics in channelized river sections.
{"title":"Short-term geomorphic adjustments of bars in the Elbe, a large regulated river in Czechia","authors":"T. Galia, V. Škarpich, Adriana Holušová, Jan Hradecký","doi":"10.14712/23361980.2024.7","DOIUrl":"https://doi.org/10.14712/23361980.2024.7","url":null,"abstract":"Gravel and sandy bars constitute critical components of river channel morphology, yet their morphodynamics in large, heavily regulated rivers during periods without significant flows remain poorly understood. This study investigates changes in surface heterogeneity and sediment sizes through a two-year field monitoring program, focusing on the frontal, central, and distal sections of four bars along the Elbe River in Czechia. Despite the absence of high-flow events reaching at least a one-year recurrence interval, observable changes in surface heterogeneity and sediment sizes were noted across all bars. However, the changes did not follow a uniform pattern; individual bars and their sections exhibited varying degrees of surface sediment coarsening or fining, alongside increases or decreases in surface heterogeneity. These findings highlight the necessity for site-specific management strategies for individual bars within such human-impacted rivers, recognizing their value as ecological hotspots. Furthermore, the methodology presented in this study may serve as a blueprint for the cost-effective monitoring of bar dynamics in channelized river sections.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.14712/23361980.2024.5
Munazza Afreen, Fazlul Haq, Bryan G. Mark
The rapid deglaciation in the Upper Indus Basin (UIB) significantly impacts local landscapes, watersheds, and basin-wide hydrology. While creating new opportunities, such as emerging landscapes and hydrological changes, deglaciation simultaneously heightens the risk of glacio-hydrological hazards in adjacent and downstream regions. With limited available land for agriculture and settlements, communities around glaciers expand human activities toward newly formed floodplains and deglaciating valleys, necessitating a comprehensive understanding of associated risks and vulnerabilities. This study employs Geographical Information System (GIS) and Remote Sensing products for a multicriteria hazards susceptibility assessment in the Shigar Valley, located in the downstream of major Himalayan glaciers – the Baltoro (63 km) and Biafo (67 km) glaciers. The research reveals that 28.3% of the valley is highly susceptible to multiple hazards, emphasizing the urgency of informed decision-making in the region. Only 0.03% area lies in the very low susceptible category, 9.7% in the low susceptible, 60.6% in the moderately susceptible, and 1.04% in the very highly susceptible categories. These findings highlight the need for proactive measures, adaptive strategies, and sustainable development in the Shigar Valley to mitigate the escalating risks posed by deglaciation and changing hydrological patterns.
{"title":"Hazards profile of the Shigar Valley, Central Karakoram, Pakistan: Multicriteria hazard susceptibility assessment","authors":"Munazza Afreen, Fazlul Haq, Bryan G. Mark","doi":"10.14712/23361980.2024.5","DOIUrl":"https://doi.org/10.14712/23361980.2024.5","url":null,"abstract":"The rapid deglaciation in the Upper Indus Basin (UIB) significantly impacts local landscapes, watersheds, and basin-wide hydrology. While creating new opportunities, such as emerging landscapes and hydrological changes, deglaciation simultaneously heightens the risk of glacio-hydrological hazards in adjacent and downstream regions. With limited available land for agriculture and settlements, communities around glaciers expand human activities toward newly formed floodplains and deglaciating valleys, necessitating a comprehensive understanding of associated risks and vulnerabilities. This study employs Geographical Information System (GIS) and Remote Sensing products for a multicriteria hazards susceptibility assessment in the Shigar Valley, located in the downstream of major Himalayan glaciers – the Baltoro (63 km) and Biafo (67 km) glaciers. The research reveals that 28.3% of the valley is highly susceptible to multiple hazards, emphasizing the urgency of informed decision-making in the region. Only 0.03% area lies in the very low susceptible category, 9.7% in the low susceptible, 60.6% in the moderately susceptible, and 1.04% in the very highly susceptible categories. These findings highlight the need for proactive measures, adaptive strategies, and sustainable development in the Shigar Valley to mitigate the escalating risks posed by deglaciation and changing hydrological patterns.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.14712/23361980.2024.2
Arundhuti Patangia, Bimal K. Kar
This article analyses the patterns of inter-state migration (both inward and outward migration within the country) in India’s northeast states of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland and Tripura. While most of the previous studies of population migration in India were related to international migration, this article focuses on the analysis of trends and spatial variation of inter-state inward and outward migration and associated rural-urban and male-female differentials in the region. The analysis is primarily based on the Census of India data for 2001 and 2011, because the 2021 Census has not been yet conducted in the country.
{"title":"The nature, dimensions, causes and implications of in and out migration in North-East India","authors":"Arundhuti Patangia, Bimal K. Kar","doi":"10.14712/23361980.2024.2","DOIUrl":"https://doi.org/10.14712/23361980.2024.2","url":null,"abstract":"This article analyses the patterns of inter-state migration (both inward and outward migration within the country) in India’s northeast states of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland and Tripura. While most of the previous studies of population migration in India were related to international migration, this article focuses on the analysis of trends and spatial variation of inter-state inward and outward migration and associated rural-urban and male-female differentials in the region. The analysis is primarily based on the Census of India data for 2001 and 2011, because the 2021 Census has not been yet conducted in the country.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140724647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-21DOI: 10.14712/23361980.2024.1
Perla Lorena Romero-Gaeta, I. Alcántara-Ayala
The COVID-19 pandemic had a significant impact on the inhabitants of Mexico City. With over 9 million people living in 16 districts, infections and mortality rates varied greatly. In this article, demographic and socio-economic factors were analyzed to determine vulnerability and exposure to COVID-19 during the crisis from 27 February 2020 to 10 May 2021. The study revealed that mortality and infections were distributed differently across the districts of Mexico City. The districts with the most confirmed cases did not necessarily have the highest death rates. Many deaths were linked to age and comorbidities, such as hypertension, diabetes, and obesity. Poverty, overcrowding, the lack of space, and basic services contributed to vulnerability and exposure to the disease. Inequalities in the city’s development over time resulted in varying degrees of vulnerability and exposure to COVID-19, leading to different patterns of infections and deaths across the districts. The prevalence of infections in the city´s southwestern districts can be attributed to the combination of marginalization, poverty, and inadequate services. Conversely, the northwest areas of the city, with a higher concentration of elderly residents, experienced a greater number of fatalities.
{"title":"The COVID-19 disaster in Mexico City: Exploring risk drivers at the local scale","authors":"Perla Lorena Romero-Gaeta, I. Alcántara-Ayala","doi":"10.14712/23361980.2024.1","DOIUrl":"https://doi.org/10.14712/23361980.2024.1","url":null,"abstract":"The COVID-19 pandemic had a significant impact on the inhabitants of Mexico City. With over 9 million people living in 16 districts, infections and mortality rates varied greatly. In this article, demographic and socio-economic factors were analyzed to determine vulnerability and exposure to COVID-19 during the crisis from 27 February 2020 to 10 May 2021. The study revealed that mortality and infections were distributed differently across the districts of Mexico City. The districts with the most confirmed cases did not necessarily have the highest death rates. Many deaths were linked to age and comorbidities, such as hypertension, diabetes, and obesity. Poverty, overcrowding, the lack of space, and basic services contributed to vulnerability and exposure to the disease. Inequalities in the city’s development over time resulted in varying degrees of vulnerability and exposure to COVID-19, leading to different patterns of infections and deaths across the districts. The prevalence of infections in the city´s southwestern districts can be attributed to the combination of marginalization, poverty, and inadequate services. Conversely, the northwest areas of the city, with a higher concentration of elderly residents, experienced a greater number of fatalities.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.14712/23361980.2023.18
Christopher Gomez, Jiaqi Liu, Jing Wu, Frans C. Persendt, B. Bradák, Yousefi Saleh, D. Hadmoko
In this article, desertification and dune progression over vegetation was quantified using remote sensing data. However, vegetation buried under sand blowout could not be counted using this method. Therefore, to estimate the extent of buried vegetation, a GPR campaign was conducted over the coastal sand-dune of Tottori Prefecture (Japan) in combination with a high-resolution topographic UAV-based survey of the topography. The results show that buried vegetation exists underneath sand-blowout, especially near the dune ridges, and can extend from 20 to 30 meters further than the estimate based on airborne remote sensing. Furthermore, the presence of palaeo-vegetation in palaeodune layers also provides the information on the long-term evolution of sand dunes, which can be used to reconstruct Quaternary coastal environments.
{"title":"Improving vegetation spatial distribution mapping in arid and on coastal dune systems using GPR in Tottori Prefecture (Japan)","authors":"Christopher Gomez, Jiaqi Liu, Jing Wu, Frans C. Persendt, B. Bradák, Yousefi Saleh, D. Hadmoko","doi":"10.14712/23361980.2023.18","DOIUrl":"https://doi.org/10.14712/23361980.2023.18","url":null,"abstract":"In this article, desertification and dune progression over vegetation was quantified using remote sensing data. However, vegetation buried under sand blowout could not be counted using this method. Therefore, to estimate the extent of buried vegetation, a GPR campaign was conducted over the coastal sand-dune of Tottori Prefecture (Japan) in combination with a high-resolution topographic UAV-based survey of the topography. The results show that buried vegetation exists underneath sand-blowout, especially near the dune ridges, and can extend from 20 to 30 meters further than the estimate based on airborne remote sensing. Furthermore, the presence of palaeo-vegetation in palaeodune layers also provides the information on the long-term evolution of sand dunes, which can be used to reconstruct Quaternary coastal environments.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138963605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.14712/23361980.2023.16
Andrea Lešková, A. Vaishar
This article focuses on the typology of countryside architectonical forms in the region of South-Moravia in southeastern Czechia and on the expression of village identity through architecture in case study villages. Original folk architecture has been altered by new types of constructions built in rural areas since the 1950s, followed by a more recent wave of new architectural forms that have developed since the 1990s. The number of architectural types in case study villages was predominantly calculated using the panoramic sceneries on mapy.cz. The coefficients of countryside identity were allocated to architectural types based on basic folk house features. The value of countryside identity is higher in smaller villages except for suburbanized settlements of the regional capital of Brno.
{"title":"The typology of countryside architectonical forms in South-Moravia, a region of Czechia","authors":"Andrea Lešková, A. Vaishar","doi":"10.14712/23361980.2023.16","DOIUrl":"https://doi.org/10.14712/23361980.2023.16","url":null,"abstract":"This article focuses on the typology of countryside architectonical forms in the region of South-Moravia in southeastern Czechia and on the expression of village identity through architecture in case study villages. Original folk architecture has been altered by new types of constructions built in rural areas since the 1950s, followed by a more recent wave of new architectural forms that have developed since the 1990s. The number of architectural types in case study villages was predominantly calculated using the panoramic sceneries on mapy.cz. The coefficients of countryside identity were allocated to architectural types based on basic folk house features. The value of countryside identity is higher in smaller villages except for suburbanized settlements of the regional capital of Brno.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138974794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.14712/23361980.2023.17
Peter Deďo, David Hána
The aim of this article is to analyze the capital drain among individual European Union (EU) member states and its cohesive and political consequences. Since the capital drain has not yet been calculated at the individual country level, the methodological part of this article delves into this calculation in more detail. Between 1999 and 2018, Ireland and Luxembourg had the highest capital drain due to their tax haven policies. Apart from these extremes, Czechia experienced the largest capital drain during this period. Inequalities among EU member states were gradually decreasing in terms of gross domestic product and gross national disposable income, suggesting that the EU’s cohesion policy has partially been successful in reducing inequalities among EU countries. However, capital drain and its populist interpretations may become a significant political problem for the most negatively affected countries.
{"title":"Consequences of capital drain among EU member states","authors":"Peter Deďo, David Hána","doi":"10.14712/23361980.2023.17","DOIUrl":"https://doi.org/10.14712/23361980.2023.17","url":null,"abstract":"The aim of this article is to analyze the capital drain among individual European Union (EU) member states and its cohesive and political consequences. Since the capital drain has not yet been calculated at the individual country level, the methodological part of this article delves into this calculation in more detail. Between 1999 and 2018, Ireland and Luxembourg had the highest capital drain due to their tax haven policies. Apart from these extremes, Czechia experienced the largest capital drain during this period. Inequalities among EU member states were gradually decreasing in terms of gross domestic product and gross national disposable income, suggesting that the EU’s cohesion policy has partially been successful in reducing inequalities among EU countries. However, capital drain and its populist interpretations may become a significant political problem for the most negatively affected countries.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-21DOI: 10.14712/23361980.2023.15
Petr Horák
This paper aims to explore the influence of related variety on direct state-supported R&D cooperation across various geographical levels to understand regional performance differentiation and economic base restructuring in Czechia by employing Frenken et al.’s (2007) methodological approach to calculate a related and unrelated variety for all NACE and NACE C-Manufacturing. Findings indicate that the city of Prague has the highest unrelated and related variety, followed by the cities of Brno, Ostrava, and Pilsen. Calculation just for C-Manufacturing changes the ordering significantly. Furthermore, intra-regional and extra-regional pairwise R&D cooperation in joint projects is calculated. The cluster analysis of Czech microregional data (SO ORP) reveals patterns such as emerging collaborators and collaboration powerhouses. Linear regression analyses established a strong positive association between R&D collaboration intensity and related variety, while a negative link was observed with unrelated variety. Similar relationships were observed in the manufacturing sector (NACE-C).
本文采用 Frenken 等人（2007 年）的方法，计算了所有 NACE 和 NACE C 制造业的相关和非相关品种，旨在探讨相关品种在不同地理层次上对国家直接支持的研发合作的影响，以了解捷克的地区绩效差异和经济基础结构调整。结果表明，布拉格市的非相关和相关品种最多，其次是布尔诺市、俄斯特拉发市和比尔森市。仅计算 C 制造业，排序就发生了显著变化。此外，还计算了联合项目中的区内和区外成对研发合作。对捷克微观地区数据（SO ORP）的聚类分析揭示了新兴合作者和合作强国等模式。线性回归分析表明，研发合作强度与相关品种之间存在密切的正相关关系，而与非相关品种之间则存在负相关关系。在制造业（NACE-C）中也观察到了类似的关系。
{"title":"Related variety and state-sponsored R&D collaboration: a geographical and industrial analysis in Czechia","authors":"Petr Horák","doi":"10.14712/23361980.2023.15","DOIUrl":"https://doi.org/10.14712/23361980.2023.15","url":null,"abstract":"This paper aims to explore the influence of related variety on direct state-supported R&D cooperation across various geographical levels to understand regional performance differentiation and economic base restructuring in Czechia by employing Frenken et al.’s (2007) methodological approach to calculate a related and unrelated variety for all NACE and NACE C-Manufacturing. Findings indicate that the city of Prague has the highest unrelated and related variety, followed by the cities of Brno, Ostrava, and Pilsen. Calculation just for C-Manufacturing changes the ordering significantly. Furthermore, intra-regional and extra-regional pairwise R&D cooperation in joint projects is calculated. The cluster analysis of Czech microregional data (SO ORP) reveals patterns such as emerging collaborators and collaboration powerhouses. Linear regression analyses established a strong positive association between R&D collaboration intensity and related variety, while a negative link was observed with unrelated variety. Similar relationships were observed in the manufacturing sector (NACE-C).","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139250718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.14712/23361980.2023.14
Daniel Bicák
Machine learning algorithms are widely used methods in geographical research. However, these algorithms are not properly exploiting the underlying spatial relationships present in the geographical data. One of the approaches, which addresses this problem, is based on an ensemble of local models, which are constructed from samples in close proximity to the location of prediction. This concept was applied to the Random Forest (RF) algorithm, creating a Geographical Random Forest (GRF). This study aims to further develop GRF by tuning the spatial parameters for each location in case of agricultural drought. In addition to tuning, the explanatory property of RF within the framework GRF is explored. Four machine learning models were constructed; regular RF, regular RF with spatial covariates, GRF, and GRF with the tuning of spatial parameters. Models were evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Although the decrease in RMSE in this very case is relatively small, the method may provide higher improvement with different datasets.
{"title":"Tuning spatial parameters of Geographical Random Forest: the case of agricultural drought","authors":"Daniel Bicák","doi":"10.14712/23361980.2023.14","DOIUrl":"https://doi.org/10.14712/23361980.2023.14","url":null,"abstract":"Machine learning algorithms are widely used methods in geographical research. However, these algorithms are not properly exploiting the underlying spatial relationships present in the geographical data. One of the approaches, which addresses this problem, is based on an ensemble of local models, which are constructed from samples in close proximity to the location of prediction. This concept was applied to the Random Forest (RF) algorithm, creating a Geographical Random Forest (GRF). This study aims to further develop GRF by tuning the spatial parameters for each location in case of agricultural drought. In addition to tuning, the explanatory property of RF within the framework GRF is explored. Four machine learning models were constructed; regular RF, regular RF with spatial covariates, GRF, and GRF with the tuning of spatial parameters. Models were evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Although the decrease in RMSE in this very case is relatively small, the method may provide higher improvement with different datasets.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135161161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.14712/23361980.2023.13
Bui B. Thien, Vu T. Phuong, Akinola A. Komolafe
Deforestation in the tropics continues inexorably with severe implications for biodiversity conservation, climate regulation and ecosystem services. This study investigated variation in forest cover in Thua Thien Hue province, Vietnam, using the Landsat Thematic Mapper and Operational Land Imager satellite images over the period 1989–2021. Imageries were classified using the maximum likelihood classification technique for the years 1989, 2006, and 2021 and were evaluated for accuracy using the kappa coefficient for each year. Furthermore, forest cover losses and gains were evaluated using the Normalized Difference Vegetation Index and Soil Adjusted Vegetation Index, which were compared with the output of the supervised classification. Results showed that the forest cover of Thua Thien Hue province has drastically declined over the years. The forest cover, which was estimated at 68.88% (3461.46 km2) of the total land area in 1989, increased to 69.04% (3469.51 km2) in 2006 and subsequently decreased to 57.55% (2891.81 km2) in 2021. Severely reduced forest cover is often associated with the expansion of agriculture on the forest edge; other contributing factors include logging, illegal production land, and forest fires. Overall, our results show the necessity of forest management, rational land-use planning policy, and increased community awareness of conservation and sustainable development of forest resources in the study area in the future.
{"title":"Assessment of forest cover and forest loss using satellite images in Thua Thien Hue province, Vietnam","authors":"Bui B. Thien, Vu T. Phuong, Akinola A. Komolafe","doi":"10.14712/23361980.2023.13","DOIUrl":"https://doi.org/10.14712/23361980.2023.13","url":null,"abstract":"Deforestation in the tropics continues inexorably with severe implications for biodiversity conservation, climate regulation and ecosystem services. This study investigated variation in forest cover in Thua Thien Hue province, Vietnam, using the Landsat Thematic Mapper and Operational Land Imager satellite images over the period 1989–2021. Imageries were classified using the maximum likelihood classification technique for the years 1989, 2006, and 2021 and were evaluated for accuracy using the kappa coefficient for each year. Furthermore, forest cover losses and gains were evaluated using the Normalized Difference Vegetation Index and Soil Adjusted Vegetation Index, which were compared with the output of the supervised classification. Results showed that the forest cover of Thua Thien Hue province has drastically declined over the years. The forest cover, which was estimated at 68.88% (3461.46 km2) of the total land area in 1989, increased to 69.04% (3469.51 km2) in 2006 and subsequently decreased to 57.55% (2891.81 km2) in 2021. Severely reduced forest cover is often associated with the expansion of agriculture on the forest edge; other contributing factors include logging, illegal production land, and forest fires. Overall, our results show the necessity of forest management, rational land-use planning policy, and increased community awareness of conservation and sustainable development of forest resources in the study area in the future.","PeriodicalId":41831,"journal":{"name":"AUC Geographica","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135216438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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