Topographic Position Index Based Landform Analysis of Messaria (Ikaria Island, Greece)

Skentos, Athanasios

doi:10.18509/agb.2018.01

Cited by 26 publications

(9 citation statements)

References 10 publications

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“…TPI is a very powerful metric to classify landforms (Weiss, 2001; see also Jenness, 2006, for technical details), providing useful diagnostic elements for a broad array of research fields, for example, geomorphology, hydrology, agronomic sciences, behavioral ecology, forest management, etc. (Copeland & Harrison, 2015; De Reu et al., 2013; Skentos, 2017; Tagil & Jenness, 2008). Due to its effectiveness in subdividing the landscape into morphological categories depending on topography, it can help identify — for instance — the topographic preference or habitat suitability for plants species (Han et al., 2011).…”

Section: Methodsmentioning

confidence: 99%

From glacial refugia to hydrological microrefugia: Factors and processes driving the persistence of the climate relict tree Zelkova sicula

Garfì

Carimi

Fazan

et al. 2021

Ecology and Evolution

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Section: Methodsmentioning

confidence: 99%

From glacial refugia to hydrological microrefugia: Factors and processes driving the persistence of the climate relict tree Zelkova sicula

Garfì

Carimi

Fazan

et al. 2021

Ecology and Evolution

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“…The generation of TWI map was possible following the partition of its values into the next five classes using Natural Breaks method: 3.15–6.1, 6.11–7.78, 7.79–10.21, 10.22–14.5, 14.51–24.59 ( Figure 2 b). Topographic Position Index (TPI) is a mandatory flash-flood predictor which should be involved in the susceptibility related studies because its values emphasize the altitude difference between the location of a specific point and its neighboring area [ 62 ]. This important morphometric indicator was achieved at a spatial resolution of 30 m and its values ranging from −20 to 20 were divided into the next five classes using Natural Breaks method: (−20)–(−3.8), (−3.7)–(−1.1), (−1.1)–1.3, 1.4–4.5, 4.6–20 ( Figure 2 c).…”

Section: Datamentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

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“…Topographic position index (TPI) is a predictor calculated from the DEM, which shows the relative position of a point in the research area in relation to the immediately neighboring regions [50]. The next TPI classes were established using the natural breaks method: −7.8-−1.8; −1.7-−0.5; −0.4-0.5; 0.6-1.9; 2-8.6 (Figure 3c).…”

Section: Flash-flood and Flood Predictorsmentioning

confidence: 99%

Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments

Costache

Bărbulescu

Pham³

2021

Water

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In the present study, the susceptibility to flash-floods and flooding was studied across the Izvorul Dorului River basin in Romania. In the first phase, three ensemble models were used to determine the susceptibility to flash-floods. These models were generated by a combination of three statistical bivariate methods, namely frequency ratio (FR), weights of evidence (WOE), and statistical index (SI), with fuzzy analytical hierarchy process (FAHP). The result obtained from the application of the FAHP-WOE model had the best performance highlighted by an Area Under Curve—Receiver Operating Characteristics Curve (AUC-ROC) value of 0.837 for the training sample and another of 0.79 for the validation sample. Furthermore, the results offered by FAHP-WOE were weighted on the river network level using the flow accumulation method, through which the valleys with a medium, high, and very high torrential susceptibility were identified. Based on these valleys’ locations, the susceptibility to floods was estimated. Thus, in the first stage, a buffer zone of 200 m was delimited around the identified valleys along which the floods could occur. Once the buffer zone was established, ten flood conditioning factors were used to determine the flood susceptibility through the analytical hierarchy process model. Approximately 25% of the total delimited area had a high and very high flood susceptibility.

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Topographic Position Index Based Landform Analysis of Messaria (Ikaria Island, Greece)

Cited by 26 publications

References 10 publications

From glacial refugia to hydrological microrefugia: Factors and processes driving the persistence of the climate relict tree Zelkova sicula

From glacial refugia to hydrological microrefugia: Factors and processes driving the persistence of the climate relict tree Zelkova sicula

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments

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