Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

2020 ◽  
Vol 30 (3) ◽  
pp. 514-531
Author(s):  
Paula Lightfoot ◽  
Catherine Scott ◽  
Clare Fitzsimmons
Keyword(s):  
Image Analysis ◽  
Aerial Imagery ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Multi Temporal ◽  
Intertidal Habitats

Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach

2014 ◽  
Vol 150 ◽  
pp. 172-187 ◽  
Author(s):  
Chris M. Roelfsema ◽  
Mitchell Lyons ◽  
Eva M. Kovacs ◽  
Paul Maxwell ◽  
Megan I. Saunders ◽  
...  
Keyword(s):  
Image Analysis ◽  
Analysis Approach ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Multi Temporal ◽  
Seagrass Cover

scholarly journals Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery

2017 ◽  
Vol 17 (10) ◽  
pp. 1823-1836 ◽  
Author(s):  
Karolina Korzeniowska ◽  
Yves Bühler ◽  
Mauro Marty ◽  
Oliver Korup
Keyword(s):  
Image Analysis ◽  
Near Infrared ◽  
Aerial Imagery ◽  
Hazard Mapping ◽  
Snow Avalanches ◽  
Cohen’S Kappa ◽  
Object Based Image Analysis ◽  
Cohen's Kappa ◽  
Object Based ◽  
Normalised Difference Vegetation Index

Abstract. Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km−2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km−2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.

scholarly journals Vegetation mapping using hierarchical object‐based image analysis applied to aerial imagery and lidar data

2019 ◽  
Vol 23 (1) ◽  
pp. 80-93 ◽  
Author(s):  
Kellie A. Uyeda ◽  
Kelsey K. Warkentin ◽  
Douglas A. Stow ◽  
John F. O'Leary ◽  
Rachel A. Snavely ◽  
...  
Keyword(s):  
Image Analysis ◽  
Vegetation Mapping ◽  
Aerial Imagery ◽  
Lidar Data ◽  
Object Based Image Analysis ◽  
Object Based
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