Application of Object Based Image Analysis for Glacial Cirques Detection. Case Study: The Ţarcu Mountains (Southern Carpathians)

Ardelean, Florina; Török-Oance, Marcel; Urdea, Petru; Onaca, Alexandru

doi:10.5775/fg.2067-4635.2011.007.i

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been many remote sensing studies that have implemented OBIA for mapping land cover. These include glacier delineation and debris cover (Ardelean et al, 2011;Rastner et al, 2014;Robson et al, 2015), urban infrastructure (d'Oleire-Oltmanns et al, 2011, agriculture (Forster et al, 2010;Taşdemir et al, 2012), and forestry mapping (Dorren et al, 2003;Guo et al, 2012;Lindguist and D'Annunzio, 2016), to name but a few. The application of OBIA in wetland mapping has not been to the same extent as the disciplines mentioned above in the literature, but is has seen a growth in the last decade with new advances coming through (Harken and Sugumaran, 2005;Mas et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms

Whyte

Ferentinos

Petropoulos

2018

Environmental Modelling & Software

166

106

View full text Add to dashboard Cite

In this work the synergistic use of Sentinel-1 and 2 combined with the System for Automated Geoscientific Analyses (SAGA) Wetness Index in the content of land use/cover (LULC) mapping with emphasis in wetlands is evaluated. A further objective has been to a new Object-based Image Analysis (OBIA) approach for mapping wetland areas using Sentinel-1 and 2 data, where the latter is also tested against two popular machine learning algorithms (Support Vector Machines -SVMs and Random Forests -RFs). The highly vulnerable iSimangaliso Wetland Park was used as the study site. Results showed that two-part image segmentation could efficiently create object features across the study area. For both classification algorithms, an increase in overall accuracy was observed when the full synergistic combination of available datasets. A statistically significant difference in classification accuracy at all levels between SVMs and RFs was also reported, with the latter being up to 2.4% higher. SAGA wetness index showed promising ability to distinguish wetland environments, and in combination with Sentinel-1 and 2 synergies can successfully produce a land use and land cover classification in a location where both wetland and non-wetland classes exist.

show abstract

Section: Introductionmentioning

confidence: 99%

A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms

Whyte

Ferentinos

Petropoulos

2018

Environmental Modelling & Software

166

106

View full text Add to dashboard Cite

show abstract

“…Research on cirques that use a GEOBIA approach are scarce and generic rule sets for detection of cirques do not yet exist. Eisank et al (2010) and Ardelean et al (2011) both used mean curvature derived from digital elevation data for the segmentation of cirques, focusing on the upper divides. Altitudinal thresholds were used in combination with specific context rules as input for segmentation and classification.…”

Section: Introductionmentioning

confidence: 99%

Rule Set Transferability for Object-Based Feature Extraction: An Example for Cirque Mapping

Anders¹,

Seijmonsbergen²,

Bouten³

2015

Photogram Engng Rem Sens

View full text Add to dashboard Cite

Cirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirques may help research on glacial geomorphology and climate change. Our objective was to test the transferability of an object-based rule set for the extraction of glacial cirques, using lidar data and color-infrared orthophotos. In Vorarlberg (W-Austria), we selected one training area with well-developed cirque components to parameterize segmentation and classification criteria. The rule set was applied to three test areas that are positioned in three altitudinal zones. Results indicate that the rule set was successful (81 percent) in the training area and a higher situated area (71 percent). Accuracy decreased in the two lower situated test areas (66 percent and 51 percent). We conclude that rule sets are transferable to areas with a comparable geomorphological history. Yet, significant deviation from the training area requires a different extraction strategy.

show abstract

Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier

Kraaijenbrink

Shea

Pellicciotti

et al. 2016

Remote Sensing of Environment

121

120

View full text Add to dashboard Cite

Application of Object Based Image Analysis for Glacial Cirques Detection. Case Study: The Ţarcu Mountains (Southern Carpathians)

Cited by 4 publications

References 11 publications

A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms

A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms

Rule Set Transferability for Object-Based Feature Extraction: An Example for Cirque Mapping

Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier

Contact Info

Product

Resources

About