XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/8: Land]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: GULLIES, GOOGLE EARTH AND THE GREAT BARRIER REEF: A REMOTE SENSING METHODOLOGY FOR MAPPING GULLIES OVER EXTENSIVE AREAS U. Gilad, R. Denham and D. Tindall

Document type:: Multivolume work

Structure type:: Chapter

3. DATA AND METHODS Google Earth was used to examine the occurrence of gullies in randomly selected sites, and also as a tool for mapping gully extents. This data was then imported into a GIS and examined against several raster and vector data layers representing various landscape variables such as slope (derived from SRTM DEM), Foliage Projective Cover (FPC) (Armston et al., 2009), land clearing (Muir et al., 2011), 1:100,000 vector drainage line, geology, soils, and regional ecosystems (DSEWPC, 2011). Imagery on Google Earth enabled the identification of gullies; however, this was only possible over about a third of the Burdekin where Google Earth has Quickbird imagery coverage (<1 m pixel resolution). In the remaining 70% SPOT imagery (-2.5m pixel resolution) was found not to have sufficient resolution to identify gullies with high certainty (Figure 2). Random training site Random validation site | [7] Quickbird imagery A Hj] SPOT imagery 0 km 50 I——À | Figure 2. Google Earth imagery coverage and training and validation sites. Five hundred and eighty two training sites (circle, 28.3 ha each) were randomly generated for areas in the Burdekin where Quickbird or GeoEye imagery were available on Google Earth (Figure 2). Sites were visually inspected on the imagery for the presence or absence of gullies. 104 training sites had gullies present, 433 training sites had no evidence of gullies or gullies were not visible. Forty five training sites were removed from the data set as determination of presence or absence was uncertain due to vegetation or similarity to other erosion features. Landscape variables were recorded at each gullied site to create a set of conditional classes representing the probability of gullies. Further assumptions were made about presence or International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia absence of gullies based on information provided by local experts and further visual inspection of imagery and field observations. These conditions were then applied across the Burdekin to create a binary map of no-gully areas and gully sensitive areas. Validation of the results was undertaken by field observation, expert local knowledge and the examination of a further 456 validation sites (0.25 ha squares) on Google Earth (Figure 2). The validation sites were smaller than the training sites in order to prevent gully overestimation. This is believed to have been the case with the training sites as these were classified as 'gully' even if gullies composed only a small portion of their area. In line with this assumption, gullies were identified on only 12 validation sites whereas 444 validation sites had no gullies or gullies were not visible. Knowledge of no-gully areas allowed targeted mapping on areas where gullies are more likely to occur. The mapping (Figure 3) was conducted in the form of manual digitizing on Quickbird imagery on Google Earth using the polygon tool and then importing vectors into ArcGIS. Overall, more than 5100 gullies were digitized over an area of more than 3500 km?. These data were used to validate and further refine the abilities to locate gullies within the gully sensitive area. White polygons indicate gullies. All other areas classified as having no gullies. Background imagery from Google Earth. The 5100 mapped gullies were then used as training data to examine a group of additional potential explanatory topographic variables derived from the SRTM 1" DEM (~30 m spatial resolution). The efficiency of the explanatory variables in predicting gully occurrence was assessed by calculating the Area Under the Curve (AUC). The AUC provides an effective measure of how much of the response variables distribution (in this case gully presence) is explained by a particular explanatory variable. Most variables offered a higher than random, yet still modest gully prediction ability (AUC 60-70%, compared with AUC of 50% for a random prediction). A further test using a multivariate logistic regression model combining several layers did not show significantly improved results. The variable that showed highest correlation with gully occurrence was elevation above drainage line, which had an AUC of 80% (Fig prot area Cumulative mapped gullies (%) pres nto of s hig] met obs hig] assi the no- Wit] pre for con det: Init froi Or ext on less

Access restriction

Copyright

fullscreen: Technical Commission VIII (B8)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation