scholarly journals Correction to: An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

2019 ◽  
Vol 12 (2) ◽  
pp. 275-275 ◽  
Author(s):  
Daniel Hölbling ◽  
Barbara Friedl ◽  
Clemens Eisank
Keyword(s):  
Change Detection ◽  
Detection And Attribution ◽  
Object Based ◽  
Northern Taiwan

scholarly journals An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

2015 ◽  
Vol 8 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Daniel Hölbling ◽  
Barbara Friedl ◽  
Clemens Eisank
Keyword(s):  
Change Detection ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Detection Methods ◽  
Detection And Attribution ◽  
Object Based ◽  
Wide Range ◽  
Northern Taiwan

Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

scholarly journals Climate change detection and attribution in the Ganga-Brahmaputra-Meghna river basins

2021 ◽  
Vol 12 (5) ◽  
pp. 101186
Author(s):  
Chetan Sharma ◽  
Anoop Kumar Shukla ◽  
Yongqiang Zhang
Keyword(s):  
Climate Change ◽  
Change Detection ◽  
River Basins ◽  
Detection And Attribution ◽  
Climate Change Detection

scholarly journals Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

2018 ◽  
Vol 7 (11) ◽  
pp. 441 ◽  
Author(s):  
Zhenjin Zhou ◽  
Lei Ma ◽  
Tengyu Fu ◽  
Ge Zhang ◽  
Mengru Yao ◽  
...  
Keyword(s):  
Coral Reef ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Detection Accuracy ◽  
Reef Environment ◽  
Object Based ◽  
Coral Reef Environment ◽  
Study Sites ◽  
Object Area ◽  
Segmentation Image

Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, which uses QuickBird and WorldView-2 images. The proposed methodological framework includes image fusion, multi-temporal image segmentation, image differencing, random forests models, and object-area-based accuracy assessment. For validation, we applied the method to images of four coral reef study sites in the South China Sea. We compared the proposed OBCD method with a conventional pixel-based change detection (PBCD) method by implementing both methods under the same conditions. The average overall accuracy of OBCD exceeded 90%, which was approximately 20% higher than PBCD. The OBCD method was free from salt-and-pepper effects and was less prone to images misregistration in terms of change detection accuracy and mapping results. The object-area-based accuracy assessment reached a higher overall accuracy and per-class accuracy than the object-number-based and pixel-number-based accuracy assessment.

Object-based rapid change detection for disaster management

2011 ◽  
Author(s):  
Holger Thunig ◽  
Ulrich Michel ◽  
Manfred Ehlers ◽  
Peter Reinartz
Keyword(s):  
Change Detection ◽  
Disaster Management ◽  
Rapid Change ◽  
Object Based

scholarly journals Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

2020 ◽  
Vol 12 (1) ◽  
pp. 174
Author(s):  
Tianjun Wu ◽  
Jiancheng Luo ◽  
Ya’nan Zhou ◽  
Changpeng Wang ◽  
Jiangbo Xi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Data Set ◽  
Label Transfer ◽  
Object Based ◽  
Training Samples ◽  
Map Updating

Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.

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