Pub Date : 2022-10-02DOI: 10.3390/geographies2040036
Jhon Lennon Bezerra da Silva, Daiana Caroline Refati, Ricardo da Cunha Correia Lima, A. A. De Carvalho, Maria Beatriz Ferreira, Héliton Pandorfi, Marcos Vinícius da Silva
Thematic maps of land cover and use can assist in the environmental monitoring of semiarid regions, mainly due to the advent of climate change, such as drought, and pressures from anthropic activities, such as the advance of urban areas. The use of geotechnologies is key for its effectiveness and low operating cost. The objective was to evaluate and understand the spatiotemporal dynamics of the loss and gain of land cover and use in a region of the Brazilian semiarid region, and identify annual trends from changing conditions over 36 years (1985 to 2020), using cloud remote sensing techniques in Google Earth Engine (GEE). Thematic maps of land cover and land use from MapBiomas Brazil were used, evaluated by Mann–Kendall trend analysis. The Normalized Difference Vegetation Index (NDVI) was also determined from the digital processing of about 800 orbital images (1985 to 2020) from the Landsat series of satellites. The trend analysis for land cover and use detected, over time, the loss of forest areas and water bodies, followed by the advance of exposed soil areas and urban infrastructure. The modification of native vegetation directly influences water availability, and agricultural activities increase the pressure on water resources, mainly in periods of severe drought. The NDVI detected that the period from 2013 to 2020 was most affected by climatic variability conditions, with extremely low average values. Thematic maps of land cover and use and biophysical indices are essential indicators to mitigate environmental impacts in the Brazilian semiarid region.
{"title":"Techniques of Geoprocessing via Cloud in Google Earth Engine Applied to Vegetation Cover and Land Use and Occupation in the Brazilian Semiarid Region","authors":"Jhon Lennon Bezerra da Silva, Daiana Caroline Refati, Ricardo da Cunha Correia Lima, A. A. De Carvalho, Maria Beatriz Ferreira, Héliton Pandorfi, Marcos Vinícius da Silva","doi":"10.3390/geographies2040036","DOIUrl":"https://doi.org/10.3390/geographies2040036","url":null,"abstract":"Thematic maps of land cover and use can assist in the environmental monitoring of semiarid regions, mainly due to the advent of climate change, such as drought, and pressures from anthropic activities, such as the advance of urban areas. The use of geotechnologies is key for its effectiveness and low operating cost. The objective was to evaluate and understand the spatiotemporal dynamics of the loss and gain of land cover and use in a region of the Brazilian semiarid region, and identify annual trends from changing conditions over 36 years (1985 to 2020), using cloud remote sensing techniques in Google Earth Engine (GEE). Thematic maps of land cover and land use from MapBiomas Brazil were used, evaluated by Mann–Kendall trend analysis. The Normalized Difference Vegetation Index (NDVI) was also determined from the digital processing of about 800 orbital images (1985 to 2020) from the Landsat series of satellites. The trend analysis for land cover and use detected, over time, the loss of forest areas and water bodies, followed by the advance of exposed soil areas and urban infrastructure. The modification of native vegetation directly influences water availability, and agricultural activities increase the pressure on water resources, mainly in periods of severe drought. The NDVI detected that the period from 2013 to 2020 was most affected by climatic variability conditions, with extremely low average values. Thematic maps of land cover and use and biophysical indices are essential indicators to mitigate environmental impacts in the Brazilian semiarid region.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85545287","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 : 2022-09-25DOI: 10.3390/geographies2040035
Yu Wang, Wenhui Wang, Peng Li, Xin Qi, Wenbiao Hu
Thyroid cancer (TC) is the fastest growing cancer in China and has lots of influencing factors which can be intervened to reduce its incidence. In this article, we aimed to identify the risk factors of TC. The regional TC data in 2016 were obtained from the China Cancer Registry Annual Report published by the National Cancer Center (NCC). Univariate correlation analysis and generalized linear Poisson regression analysis were used to determine risk factors for morbidity of TC from the provincial and prefecture levels. High urbanization rate (UR) (RR = 1.109, 95%CI: 1.084, 1.135), high GDP per capita (PGDP) (RR = 1.013, 95%CI: 1.007, 1.018), high aquatic products (RR = 1.047, 95%CI: 1.020, 1.075) and dry and fresh fruit consumption (RR = 1.024, 95%CI: 1.007, 1.040) can increase TC incidence. Therefore, high PGDP, high UR, high aquatic products and dry and fresh fruit consumption were all risk factors for TC incidence. Our results may be helpful for providing analytical ideas and methodological references for the regionalized prevention and control of TC in a targeted manner.
{"title":"Risk Analysis of Thyroid Cancer in China: A Spatial Analysis","authors":"Yu Wang, Wenhui Wang, Peng Li, Xin Qi, Wenbiao Hu","doi":"10.3390/geographies2040035","DOIUrl":"https://doi.org/10.3390/geographies2040035","url":null,"abstract":"Thyroid cancer (TC) is the fastest growing cancer in China and has lots of influencing factors which can be intervened to reduce its incidence. In this article, we aimed to identify the risk factors of TC. The regional TC data in 2016 were obtained from the China Cancer Registry Annual Report published by the National Cancer Center (NCC). Univariate correlation analysis and generalized linear Poisson regression analysis were used to determine risk factors for morbidity of TC from the provincial and prefecture levels. High urbanization rate (UR) (RR = 1.109, 95%CI: 1.084, 1.135), high GDP per capita (PGDP) (RR = 1.013, 95%CI: 1.007, 1.018), high aquatic products (RR = 1.047, 95%CI: 1.020, 1.075) and dry and fresh fruit consumption (RR = 1.024, 95%CI: 1.007, 1.040) can increase TC incidence. Therefore, high PGDP, high UR, high aquatic products and dry and fresh fruit consumption were all risk factors for TC incidence. Our results may be helpful for providing analytical ideas and methodological references for the regionalized prevention and control of TC in a targeted manner.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81597933","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 : 2022-09-22DOI: 10.3390/geographies2040034
D. Danniswari, T. Honjo, K. Furuya
Land surface temperature (LST) is heavily influenced by urban morphology. Building height is an important parameter of urban morphology that affects LST. Existing studies show contradicting results where building height can have a positive or negative relationship with LST. More studies are necessary to examine the impact of building height. However, high accuracy building height data are difficult to obtain on a global scale and are not available in many places in the world. Using the Digital Building Height Model (DBHM) calculated by subtracting the SRTM from AW3D30, this study analyzes the relationship between building height and Landsat LST in two cities: Tokyo and Jakarta. The relationship is observed during both cities’ warm seasons (April to October) and Tokyo’s cool seasons (November to February). The results show that building height and LST are negatively correlated. In the morning, areas with high-rise buildings tend to have lower LST than areas with low-rise buildings. This phenomenon is revealed to be stronger during the warm season. The LST difference between low-rise and mixed-height building areas is more significant than between mixed-height and high-rise building areas.
{"title":"Analysis of Building Height Impact on Land Surface Temperature by Digital Building Height Model Obtained from AW3D30 and SRTM","authors":"D. Danniswari, T. Honjo, K. Furuya","doi":"10.3390/geographies2040034","DOIUrl":"https://doi.org/10.3390/geographies2040034","url":null,"abstract":"Land surface temperature (LST) is heavily influenced by urban morphology. Building height is an important parameter of urban morphology that affects LST. Existing studies show contradicting results where building height can have a positive or negative relationship with LST. More studies are necessary to examine the impact of building height. However, high accuracy building height data are difficult to obtain on a global scale and are not available in many places in the world. Using the Digital Building Height Model (DBHM) calculated by subtracting the SRTM from AW3D30, this study analyzes the relationship between building height and Landsat LST in two cities: Tokyo and Jakarta. The relationship is observed during both cities’ warm seasons (April to October) and Tokyo’s cool seasons (November to February). The results show that building height and LST are negatively correlated. In the morning, areas with high-rise buildings tend to have lower LST than areas with low-rise buildings. This phenomenon is revealed to be stronger during the warm season. The LST difference between low-rise and mixed-height building areas is more significant than between mixed-height and high-rise building areas.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78118280","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 : 2022-09-05DOI: 10.3390/geographies2030033
Jiping Cao, H. Hochmair, Fisal Basheeh
Social media data have been widely used to gain insight into human mobility and activity patterns. Despite their abundance, social media data come with various data biases, such as user selection bias. In addition, a change in the Twitter app functionality may further affect the type of information shared through tweets and hence influence conclusions drawn from the analysis of such data. This study analyzes the effect of three Twitter app policy changes in 2015, 2017, and 2019 on the tweeting behavior of users, using part of London as the study area. The policy changes reviewed relate to a function allowing to attach exact coordinates to tweets by default (2015), the maximum allowable length of tweet posts (2017), and the limitation of sharing exact coordinates to the Twitter photo app (2019). The change in spatial aspects of users’ tweeting behavior caused by changes in user policy and Twitter app functionality, respectively, is quantified through measurement and comparison of six aspects of tweeting behavior between one month before and one month after the respective policy changes, which are: proportion of tweets with exact coordinates, tweet length, the number of placename mentions in tweet text and hashtags per tweet, the proportion of tweets with images among tweets with exact coordinates, and radius of gyration of tweeting locations. The results show, among others, that policy changes in 2015 and 2019 led users to post a smaller proportion of tweets with exact coordinates and that doubling the limit of allowable characters as part of the 2017 policy change increased the number of place names mentioned in tweets. The findings suggest that policy changes lead to a change in user contribution behavior and, in consequence, in the spatial information that can be extracted from tweets. The systematic change in user contribution behavior associated with policy changes should be specifically taken into consideration if jointly analyzing tweets from periods before and after such a policy change.
{"title":"The Effect of Twitter App Policy Changes on the Sharing of Spatial Information through Twitter Users","authors":"Jiping Cao, H. Hochmair, Fisal Basheeh","doi":"10.3390/geographies2030033","DOIUrl":"https://doi.org/10.3390/geographies2030033","url":null,"abstract":"Social media data have been widely used to gain insight into human mobility and activity patterns. Despite their abundance, social media data come with various data biases, such as user selection bias. In addition, a change in the Twitter app functionality may further affect the type of information shared through tweets and hence influence conclusions drawn from the analysis of such data. This study analyzes the effect of three Twitter app policy changes in 2015, 2017, and 2019 on the tweeting behavior of users, using part of London as the study area. The policy changes reviewed relate to a function allowing to attach exact coordinates to tweets by default (2015), the maximum allowable length of tweet posts (2017), and the limitation of sharing exact coordinates to the Twitter photo app (2019). The change in spatial aspects of users’ tweeting behavior caused by changes in user policy and Twitter app functionality, respectively, is quantified through measurement and comparison of six aspects of tweeting behavior between one month before and one month after the respective policy changes, which are: proportion of tweets with exact coordinates, tweet length, the number of placename mentions in tweet text and hashtags per tweet, the proportion of tweets with images among tweets with exact coordinates, and radius of gyration of tweeting locations. The results show, among others, that policy changes in 2015 and 2019 led users to post a smaller proportion of tweets with exact coordinates and that doubling the limit of allowable characters as part of the 2017 policy change increased the number of place names mentioned in tweets. The findings suggest that policy changes lead to a change in user contribution behavior and, in consequence, in the spatial information that can be extracted from tweets. The systematic change in user contribution behavior associated with policy changes should be specifically taken into consideration if jointly analyzing tweets from periods before and after such a policy change.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85507913","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 : 2022-09-02DOI: 10.3390/geographies2030032
Milad Fakhari, J. Raymond, R. Martel, S. Dugdale, N. Bergeron
In summer, salmonids can experience thermal stress during extreme weather conditions. This may affect their growth and even threaten their survival. Cool water zones in rivers constitute thermal refuges, allowing fish to be more comfortable to grow and survive in extreme events. Therefore, identifying and understanding the spatiotemporal variability of discrete thermal refuges and larger scale cooling zones in rivers is of fundamental interest. This study analyzes thermal refuges as well as cooling zones in two salmonid rivers in a subarctic climate by use of thermal infrared (TIR) imagery. The two studied rivers are the Koroc and Berard Rivers, in Nunavik, Quebec, Canada. On the 17 km studied section of the Berard River, four thermal refuges and five cooling zones were detected, covering 46% of the surveyed section of the river. On the 41 km section studied for the Koroc River, 67 thermal refuges and five cooling zones were identified which represent 32% of the studied section of the river. 89% of identified thermal refuges and about 60% of cooling zones are groundwater-controlled. Continuity of permafrost and shape of the river valley were found to be the main parameters controlling the distribution of refuges and cooling zones. These data provide important insights into planning and conservation measures for the salmonid population of subarctic Nunavik rivers.
{"title":"Identification of Thermal Refuges and Water Temperature Patterns in Salmonid-Bearing Subarctic Rivers of Northern Quebec","authors":"Milad Fakhari, J. Raymond, R. Martel, S. Dugdale, N. Bergeron","doi":"10.3390/geographies2030032","DOIUrl":"https://doi.org/10.3390/geographies2030032","url":null,"abstract":"In summer, salmonids can experience thermal stress during extreme weather conditions. This may affect their growth and even threaten their survival. Cool water zones in rivers constitute thermal refuges, allowing fish to be more comfortable to grow and survive in extreme events. Therefore, identifying and understanding the spatiotemporal variability of discrete thermal refuges and larger scale cooling zones in rivers is of fundamental interest. This study analyzes thermal refuges as well as cooling zones in two salmonid rivers in a subarctic climate by use of thermal infrared (TIR) imagery. The two studied rivers are the Koroc and Berard Rivers, in Nunavik, Quebec, Canada. On the 17 km studied section of the Berard River, four thermal refuges and five cooling zones were detected, covering 46% of the surveyed section of the river. On the 41 km section studied for the Koroc River, 67 thermal refuges and five cooling zones were identified which represent 32% of the studied section of the river. 89% of identified thermal refuges and about 60% of cooling zones are groundwater-controlled. Continuity of permafrost and shape of the river valley were found to be the main parameters controlling the distribution of refuges and cooling zones. These data provide important insights into planning and conservation measures for the salmonid population of subarctic Nunavik rivers.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"216 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75601616","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 : 2022-08-29DOI: 10.3390/geographies2030031
E. Feloni, A. Anayiotos, E. Baltas
Urban flooding can cause significant infrastructure and property damage to cities, loss of human life, disruption of human activities, and other problems and negative consequences on people and the local government administration. The objective of this research work is to investigate the relation between urban flood occurrence and potentially flood-triggering factors. The analysis is performed in the western part of Athens Basin (Attica, Greece), where over the past decades several flood events caused human losses and damages to properties and infrastructure. Flood impact is measured by the number of citizen calls for help to the emergency line of the fire service, while potentially influencing factors are several geomorphological characteristics of the area and hydrometeorological indices regarding storms, which were determined with the aid of GIS techniques. The analysis is based on the investigation on binary logistic regression and generalized linear regression models that are used to build relationships between the potentially flood-influencing factors and the flood occurrence/impact for three events that were selected for reasons of comparison. The entire analysis highlights the variations attributed to the consideration of different factors, events, as well as to the different cell size of the grid used in the analysis. Results indicate that, the binary logistic regression model performed for flood occurrence achieves higher predictability, compared to the ability of the model used to describe flood impact.
{"title":"A Spatial Analysis Approach for Urban Flood Occurrence and Flood Impact Based on Geomorphological, Meteorological, and Hydrological Factors","authors":"E. Feloni, A. Anayiotos, E. Baltas","doi":"10.3390/geographies2030031","DOIUrl":"https://doi.org/10.3390/geographies2030031","url":null,"abstract":"Urban flooding can cause significant infrastructure and property damage to cities, loss of human life, disruption of human activities, and other problems and negative consequences on people and the local government administration. The objective of this research work is to investigate the relation between urban flood occurrence and potentially flood-triggering factors. The analysis is performed in the western part of Athens Basin (Attica, Greece), where over the past decades several flood events caused human losses and damages to properties and infrastructure. Flood impact is measured by the number of citizen calls for help to the emergency line of the fire service, while potentially influencing factors are several geomorphological characteristics of the area and hydrometeorological indices regarding storms, which were determined with the aid of GIS techniques. The analysis is based on the investigation on binary logistic regression and generalized linear regression models that are used to build relationships between the potentially flood-influencing factors and the flood occurrence/impact for three events that were selected for reasons of comparison. The entire analysis highlights the variations attributed to the consideration of different factors, events, as well as to the different cell size of the grid used in the analysis. Results indicate that, the binary logistic regression model performed for flood occurrence achieves higher predictability, compared to the ability of the model used to describe flood impact.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73662302","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 : 2022-08-15DOI: 10.3390/geographies2030030
Faith M. Hartley, A. Maxwell, Rick E. Landenberger, Z. J. Bortolot
This study investigates the mapping of forest community types for the entire state of West Virginia, United States, using Global Land Analysis and Discovery (GLAD) Phenology Metrics, Analysis Ready Data (ARD) derived from Landsat time series data, and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study was to explore the use of globally consistent ARD for operational forest type mapping over a large spatial extent. Mean overall accuracy calculated from 50 model replicates for differentiating seven forest community types using only variables selected from the 188 GLAD Phenology Metrics used in the study resulted in an overall accuracy (OA) of 54.3% (map-level image classification efficacy (MICE) = 0.433). Accuracy increased to a mean OA of 64.8% (MICE = 0.496) when the Oak/Hickory and Oak/Pine classes were combined into an Oak Dominant class. Once selected terrain variables were added to the model, the mean OA for differentiating the seven forest types increased to 65.3% (MICE = 0.570), while the accuracy for differentiating six classes increased to 76.2% (MICE = 0.660). Our results highlight the benefits of combining spectral data and terrain variables and also the enhancement of the product’s usefulness when probabilistic predictions are provided alongside a hard classification. The GLAD Phenology Metrics did not provide an accuracy comparable to those obtained using harmonic regression coefficients; however, they generally outperformed models trained using only summer or fall seasonal medians and performed comparably to those trained using spring medians. We suggest further exploration of the GLAD Phenology Metrics as input for other spatial predictive mapping and modeling tasks.
{"title":"Forest Type Differentiation Using GLAD Phenology Metrics, Land Surface Parameters, and Machine Learning","authors":"Faith M. Hartley, A. Maxwell, Rick E. Landenberger, Z. J. Bortolot","doi":"10.3390/geographies2030030","DOIUrl":"https://doi.org/10.3390/geographies2030030","url":null,"abstract":"This study investigates the mapping of forest community types for the entire state of West Virginia, United States, using Global Land Analysis and Discovery (GLAD) Phenology Metrics, Analysis Ready Data (ARD) derived from Landsat time series data, and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study was to explore the use of globally consistent ARD for operational forest type mapping over a large spatial extent. Mean overall accuracy calculated from 50 model replicates for differentiating seven forest community types using only variables selected from the 188 GLAD Phenology Metrics used in the study resulted in an overall accuracy (OA) of 54.3% (map-level image classification efficacy (MICE) = 0.433). Accuracy increased to a mean OA of 64.8% (MICE = 0.496) when the Oak/Hickory and Oak/Pine classes were combined into an Oak Dominant class. Once selected terrain variables were added to the model, the mean OA for differentiating the seven forest types increased to 65.3% (MICE = 0.570), while the accuracy for differentiating six classes increased to 76.2% (MICE = 0.660). Our results highlight the benefits of combining spectral data and terrain variables and also the enhancement of the product’s usefulness when probabilistic predictions are provided alongside a hard classification. The GLAD Phenology Metrics did not provide an accuracy comparable to those obtained using harmonic regression coefficients; however, they generally outperformed models trained using only summer or fall seasonal medians and performed comparably to those trained using spring medians. We suggest further exploration of the GLAD Phenology Metrics as input for other spatial predictive mapping and modeling tasks.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87010151","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 : 2022-08-10DOI: 10.3390/geographies2030029
Vladyslav Zakharovskyi, K. Németh
Spatial scale in modeling is one of the most important aspects of any kind of assessment. This study utilized previously studied assessments of geodiversity through a qualitative–quantitative methodology for geosite recognition. Our methodology was developed based on geodiversity as a complex description of all elements of abiotic nature and processes, influencing it. Based on this definition, geodiversity can be divided into main elements: geology and geomorphology, creating a core of abiotic nature; and additional elements including hydrology, climate, and human influences. We include this description of geodiversity here to emphasize the data which were used in the assessment. The methodology was based on an evaluation system, subject to improvements informed by previous research, and map-based models showing the area of spreading of calculated elements. Except for additional changes in the assessment, this article primarily addresses the problem of scale, by comparing two different methods of scale in the research: grid and non-grid. Grid types of assessment are considered a widely useable method, requiring definitions of areas of research with a potential variety of polygons, and calculating elements inside the cell and applying values to each cell. In contrast, non-grid assessment utilizes the natural borders of all elements (e.g., map view pattern of geological formations), and including them in calculations. The union of layers from different elements creates shapes which highlight regions with the highest values. Hence, the goal of this article is to demonstrate differences between grid and non-grid assessments of geodiversity in Western Samoa. In our results, we compare the methods and emphasize specific tasks most suitable for each method.
{"title":"Scale Influence on Qualitative–Quantitative Geodiversity Assessments for the Geosite Recognition of Western Samoa","authors":"Vladyslav Zakharovskyi, K. Németh","doi":"10.3390/geographies2030029","DOIUrl":"https://doi.org/10.3390/geographies2030029","url":null,"abstract":"Spatial scale in modeling is one of the most important aspects of any kind of assessment. This study utilized previously studied assessments of geodiversity through a qualitative–quantitative methodology for geosite recognition. Our methodology was developed based on geodiversity as a complex description of all elements of abiotic nature and processes, influencing it. Based on this definition, geodiversity can be divided into main elements: geology and geomorphology, creating a core of abiotic nature; and additional elements including hydrology, climate, and human influences. We include this description of geodiversity here to emphasize the data which were used in the assessment. The methodology was based on an evaluation system, subject to improvements informed by previous research, and map-based models showing the area of spreading of calculated elements. Except for additional changes in the assessment, this article primarily addresses the problem of scale, by comparing two different methods of scale in the research: grid and non-grid. Grid types of assessment are considered a widely useable method, requiring definitions of areas of research with a potential variety of polygons, and calculating elements inside the cell and applying values to each cell. In contrast, non-grid assessment utilizes the natural borders of all elements (e.g., map view pattern of geological formations), and including them in calculations. The union of layers from different elements creates shapes which highlight regions with the highest values. Hence, the goal of this article is to demonstrate differences between grid and non-grid assessments of geodiversity in Western Samoa. In our results, we compare the methods and emphasize specific tasks most suitable for each method.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76427599","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 : 2022-07-29DOI: 10.3390/geographies2030028
C. E. Haque, Khandakar Hasan Mahmud, D. Walker
In the field of flood management, risk and loss estimation is a prerequisite to undertake precautionary measures. Among several available tools, the HAZUS model is one of the most effective ones that can assist in the analysis of different dimensions of natural hazards, such as earthquakes, hurricanes, floods, and tsunamis. The flood hazard analysis portion of the model characterizes the spatial variation of flood regimes for a given study area. This research attempts to illustrate how the geoinformatics tool HAZUS can help in estimating overall risk and potential loss and damage due to floods and how this knowledge can guide the decision-making process and enhance community resilience. Examining a case study in the Rural Municipality of St. Andrews in Manitoba, Canada, this study found that both the ‘Quick Look’ and ‘Enhanced Quick Look’ analyses provided robust results. However, for the RM of St. Andrews, which is characterized by differing levels of exposure on the floodplain, and where many new housing starts occur in high-risk flood zones, ‘Enhanced Quick Look’ with spatially explicit building stock is recommended. The case study of the RM of St. Andrews demonstrates that the HAZUS model can predict loss and damage with increasing magnitude of flooding depth. It is thus recognized that the risk and loss estimation tools can be effective means for future flood loss and damage reduction.
{"title":"Understanding Flood Risk and Vulnerability of a Place: Estimating Prospective Loss and Damage Using the HAZUS Model","authors":"C. E. Haque, Khandakar Hasan Mahmud, D. Walker","doi":"10.3390/geographies2030028","DOIUrl":"https://doi.org/10.3390/geographies2030028","url":null,"abstract":"In the field of flood management, risk and loss estimation is a prerequisite to undertake precautionary measures. Among several available tools, the HAZUS model is one of the most effective ones that can assist in the analysis of different dimensions of natural hazards, such as earthquakes, hurricanes, floods, and tsunamis. The flood hazard analysis portion of the model characterizes the spatial variation of flood regimes for a given study area. This research attempts to illustrate how the geoinformatics tool HAZUS can help in estimating overall risk and potential loss and damage due to floods and how this knowledge can guide the decision-making process and enhance community resilience. Examining a case study in the Rural Municipality of St. Andrews in Manitoba, Canada, this study found that both the ‘Quick Look’ and ‘Enhanced Quick Look’ analyses provided robust results. However, for the RM of St. Andrews, which is characterized by differing levels of exposure on the floodplain, and where many new housing starts occur in high-risk flood zones, ‘Enhanced Quick Look’ with spatially explicit building stock is recommended. The case study of the RM of St. Andrews demonstrates that the HAZUS model can predict loss and damage with increasing magnitude of flooding depth. It is thus recognized that the risk and loss estimation tools can be effective means for future flood loss and damage reduction.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"44 9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82720358","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 : 2022-07-13DOI: 10.3390/geographies2030027
Daniel Beene, Su Zhang, C. Lippitt, S. Bogus
Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram–Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application.
{"title":"Performance Evaluation of Multiple Pan-Sharpening Techniques on NDVI: A Statistical Framework","authors":"Daniel Beene, Su Zhang, C. Lippitt, S. Bogus","doi":"10.3390/geographies2030027","DOIUrl":"https://doi.org/10.3390/geographies2030027","url":null,"abstract":"Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram–Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73448343","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：