Morphometric comparisons between automated and manual karst depression inventories in Apalachicola National Forest, Florida, and Mammoth Cave National Park, Kentucky, USA

Wall, John; Bohnenstiehl, D. R.; Wegmann, Karl W.; Levine, Norman

doi:10.1007/s11069-016-2600-x

Cited by 20 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Power-law frequency-size relations have recently been proposed for sinkholes, considering either their diameter or their area as a measure of size (Galve et al, 2011;Wall & Bohnenstiehl, 2014;Yizhaq et al, 2017), and their exponents have been proposed to vary as new sinkholes develop, grow and coalesce (Yizhaq et al, 2017). The frequencysize relation of topographic depressions in Florida shows a similar behavior (Wall & Bohnenstiehl, 2014). These depressions are used as proxies to karst features, albeit not all of them are sinkholes (Arthur et al, 2007).…”

Section: Karst Sinkholes and Closed Topographic Depressionsmentioning

confidence: 99%

“…Sinkhole maps can be either delineated manually, or by automatically identifying topographic depressions in digital elevation models, which may lead to differences in the resulting inventories (Wall et al, 2017). Here we use the Kentucky (USA) sinkhole database, mapped manually and probably the largest sinkhole data set available, with over 100,000 sinkholes (Paylor et al, 2003); the database of Florida (USA) closed topographic depressions, based on automatic mapping, comprising more than 160,000 depressions (Florida Department of Environmental Protection, 2004); and a compound data set of more than 1000 sinkholes next to the Dead Sea (Yizhaq et al, 2017).…”

Section: Karst Sinkholes and Closed Topographic Depressionsmentioning

confidence: 99%

See 1 more Smart Citation

Power Law Size Distributions in Geoscience Revisited

Corral

González

2019

Earth and Space Science

103

View full text Add to dashboard Cite

The size or energy of diverse structures or phenomena in geoscience appears to follow power law distributions. A rigorous statistical analysis of such observations is tricky, though. Observables can span several orders of magnitude, but the range for which the power law may be valid is typically truncated, usually because the smallest events are too tiny to be detected and the largest ones are limited by the system size. We revisit several examples of proposed power law distributions dealing with potentially damaging natural phenomena. Adequate fits of the distributions of sizes are especially important in these cases, given that they may be used to assess long‐term hazard. After reviewing the theoretical background for power law distributions, we improve an objective statistical fitting method and apply it to diverse data sets. The method is described in full detail, and it is easy to implement. Our analysis elucidates the range of validity of the power law fit and the corresponding exponent and whether a power law tail is improved by a truncated lognormal. We confirm that impact fireballs and Californian earthquakes show untruncated power law behavior, whereas global earthquakes follow a double power law. Rain precipitation over space and time and tropical cyclones show a truncated power law regime. Karst sinkholes and wildfires, in contrast, are better described by truncated lognormals, although wildfires also may show power law regimes. Our conclusions only apply to the analyzed data sets but show the potential of applying this robust statistical technique in the future.

show abstract

Section: Karst Sinkholes and Closed Topographic Depressionsmentioning

confidence: 99%

Section: Karst Sinkholes and Closed Topographic Depressionsmentioning

confidence: 99%

Power Law Size Distributions in Geoscience Revisited

Corral

González

2019

Earth and Space Science

103

View full text Add to dashboard Cite

show abstract

“…Esse tipo de análise quantitativa retira o fator subjetividade nas análises comparativas entre regiões cársticas (Ferrari et al, 1998) e permite revelar dados e informações anteriormente não observadas, possibilitando novas hipóteses (Ford e Williams, 2007). Nesse sentido diversos trabalhos internacionais tratam sobre as formas clássicas ou uso de novas tecnologias de mapeamento de dolinas, análises morfométricas e interpretações ambientais correlatas (Denizman, 2003;Seale et al, 2008;Siart et al, 2009;Faulkner et al, 2013;Kovačič e Ravbar, 2013;Pardo-Igúzquiza et al, 2013;Zhu et al, 2014;Kobal et al, 2015;Aguilar et al, 2016;Gessert, 2016;Keskin e Yılmaz, 2016;Lee et al, 2016;Pardo-Igúzquiza et al, 2016;Telbisz et al, 2016;Wu et al, 2016;Zhu e Pierskalla, 2016;Čeru et al, 2017;Wall et al, 2017;Cahalan e Milewski, 2018;Chen et al, 2018;Harris et al, 2018;Hofierka et al, 2018;Öztürk et al, 2018).…”

Section: Análises Morfométricas De Depressões Cársticasunclassified

“…O LIDAR (Light Detection and Ranging) é capaz de obter informações do terreno mesmo através do dossel, característica útil na aquisição de dados morfométricos de dolinas. Recentemente alguns esforços vêm sendo empregados no sentido da automatização para detecção de depressões, através da comparação de resultados entre as várias bases cartográficas digitais e sistemas "manuais" de mapeamento (Siart et al, 2009;Pardo-Igúzquiza et al, 2013;Carvalho Júnior et al, 2014;Hiruma e Ferrari, 2014;Bauer, 2015;Jeanpert et al, 2016;Wu et al, 2016;Wall et al, 2017;Chen et al, 2018). Tal iniciativa tem como objetivo diminuir o tempo de aquisição e tratamento dos dados, geralmente baseados em fotointerpretação de grandes coleções de imagens, além de reduzir o fator subjetividade inerente ao fotointérprete.…”

Section: Análises Morfométricas De Depressões Cársticasunclassified

Um Panorama Sobre Mapeamentos de Dolinas no Brasil, Feições Elementares do Carste

Ferreira

Uagoda

2020

Rev. Bras. Geog. Fis.

View full text Add to dashboard Cite

O carste se desenvolve sobretudo pela ação da dissolução em subsuperfície, na interação da água com rochas solúveis, especialmente as carbonáticas. Apesar do processo de carstificação nem sempre se apresentar em superfície, a ocorrência de dolinas, feições consideradas características, indica maior proeminência desse fenômeno. Muitos trabalhos internacionais se dedicaram ao mapeamento de dolinas com objetivos diversos, como comparar o nível de carstificação e evolução dos ambientes onde estão inseridas, relacionar o desenvolvimento destas feições com estruturas geológicas ou realizar análises hidrológicas de caráter ambiental. Por outro lado, são poucos os trabalhos com esta temática realizados no Brasil, parte concentrada em regiões cársticas tradicionais, como o Vale do Ribeira-SP, Chapada Diamantina-BA e nos carbonatos de Minas Gerais, e os demais dispersos pelo país, muitos em rochas não carbonáticas. Neste sentido o objetivo deste artigo é realizar uma revisão dos trabalhos sobre mapeamento de dolinas, com enfoque nos aspectos morfométricos e quantificação, visando especialmente elaborar um primeiro panorama sobre como o tema é abordado no Brasil. Observou-se que a maior parte dos estudos nacionais alcançou resultados de frequência (nº/km²) bastante inferiores aos identificados em estudos internacionais. Essa discrepância foi atribuída a alguns aspectos, como a adoção de fontes de dados inadequadas em escala abrangente, a escolha de áreas muito extensas para a análise ou de fato a investigação em áreas pouco propícias a ocorrência de dolinas. An Overview of Doline Mappings in Brazil, Karst's Elemental Features A B S T R A C TKarst develops mainly by the action of dissolution in subsurface, in the interaction of water with soluble rocks, especially carbonates. Although the karsification process does not always appear on the surface, the occurrence of dolines, features considered characteristic, indicates a greater prominence of this phenomenon. Many international works have been devoted to the mapping of dolines with different objectives, such as comparing the level of karsification and evolution of the environments in which they are inserted, to relate the development of these features to geological structures or to perform environmental hydrological analyzes. On the other hand, there are few studies on this subject in Brazil, concentrated part in traditional karst regions such as Vale do Ribeira-SP, Chapada Diamantina-BA and carbonates of Minas Gerais, and the others scattered around the country, many in non-carbonate rocks. In this respect, the objective of this article is to review the work on doline mapping, with a focus on morphometric aspects and quantification, aiming in particular to elaborate a first overview about how the theme is approached in Brazil. It was observed that most of the national studies reached frequency results (n°/km²) much lower than those identified in international studies. This discrepancy was attributed to some aspects, such as the adoption of inadequate data sources in a wide scale, the choice of very extensive areas for analysis or indeed the investigation in areas less favorable to the occurrence of dolines.Keywords: karst; doline; density.

show abstract

“…The advancement of digital photogrammetry and laser altimetry, particularly the improved data resolution, has increased the capability of sinkhole detection (Atzori et al, 2015;Intrieri et al, 2015;Al-Halbouni et al, 2017;Zumpano et al, 2019). The conventional approach is to delineate sinkholes through visual interpretation of digital imagery (Reese and Kochanov, 2003;Seale et al, 2008), while many studies have explored automatic approaches for extracting sinkholes or modeling sinkhole susceptibility (Zhu et al, 2014;Wall et al, 2017;Zumpano et al, 2019). For example, Stocks (2007) used an object-based approach to classify pixels of digital photos as sinkholes, by first grouping image pixels into meaningful objects of different hierarchies based on similar pixel statistics, and then classifying hierarchical objects into different land classes through iterated training processes.…”

Section: Introductionmentioning

confidence: 99%

Sinkhole susceptibility assessment based on morphological, imagery, and contextual attributes derived from gis and imagery data

Qui¹,

Wu²,

Yan³

2020

JCKS

View full text Add to dashboard Cite

This study presents a GIS-based approach to efficiently assess sinkhole susceptibility based on their morphological and contextual attributes derived from GIS and imagery data. Using a 14-km 2 karst area in Nixa, Missouri as the study area, we first applied a sequence of GIS operations to extract sinks (i.e., topographic depressions), from bare-ground digital terrain models. We then derived three types of sink attributes from various GIS and imagery data, including four morphological attributes related to sink size, shape, depth, and terrain ruggedness; three imagery attributes denoting the impervious surface percentage, vegetation growth condition, and seasonal water conditions of sinks; and seven contextual attributes related to land use, population density, neighborhood sink density, hydrological flow, groundwater yield, bedrock depth, and historical sinkhole records. Sinks were ranked by each of the 14 attributes and assigned corresponding susceptibility scores, then combined by specified weights. The results identified high-priority sinkholes for mitigation activities or for further field-based inspection. The proposed computerized approach for sinkhole susceptibility ranking can be effectively used as a first-stage sinkhole examination to maximize the use of limited resources for further comprehensive investigation.

show abstract

Morphometric comparisons between automated and manual karst depression inventories in Apalachicola National Forest, Florida, and Mammoth Cave National Park, Kentucky, USA

Cited by 20 publications

References 32 publications

Power Law Size Distributions in Geoscience Revisited

Power Law Size Distributions in Geoscience Revisited

Um Panorama Sobre Mapeamentos de Dolinas no Brasil, Feições Elementares do Carste

Sinkhole susceptibility assessment based on morphological, imagery, and contextual attributes derived from gis and imagery data

Contact Info

Product

Resources

About