Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.

Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery

The aim of this project is to create high resolution land cover classification as well as tree canopy density maps at a regional level using high resolution spatial data. Modeling and the data manipulation and analysis of LiDAR LAS point cloud dataset as well as multispectral aerial photographs from the National Agriculture Imagery Program (NAIP) were carried out. Using geoprocessing modeling, a land cover map is created based on filtered returns from LiDAR point cloud data (LAS dataset) to extract features based on their class and return values, and traditional classification methods of high resolution multi-spectral aerial photographs of the remaining ground cover for Clarion County in Pennsylvania. The newly developed model produced 7 classes at 10 ft × 10 ft spatial resolution, namely: water bodies, structures, streets and paved surfaces, bare ground, grassland, trees, and artificial surfaces (e.g. turf). The model was tested against areas with different sizes (townships and municipalities) which revealed a classification accuracy between 94% and 96%. A visual observation of the results shows that some tree-covered areas were misclassified as built up/structures due to the nature of the available LiDAR data, an area of improvement for further studies. Furthermore, a geoprocessing service was created in order to disseminate the results of the land cover classification as well as the tree canopy density calculation to a broader audience. The service was tested and delivered in the form of a web application where users can select an area of interest and the model produces the land cover and/or the tree canopy density results (http://maps.clarion.edu/LandCoverExtractor). The produced output can be printed as a final map layout with the highlighted area of interest and its corresponding legend. The interface also allows the download of the results of an area of interest for further investigation and/or analysis.

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Analysis of forest structural complexity using airborne LiDAR data and aerial photography in a mixed conifer–broadleaf forest in northern Japan

Determining forest structural complexity,i.e.,a measure of the number of different attributes of a forest and the relative abundance of each attribute,is important for forest management and conservation.In this study,we examined the structural complexity of mixed conifer–broadleaf forests by integrating multiple forest structural attributes derived from airborne Li DAR data and aerial photography.We sampled 76 plots from an unmanaged mixed conifer–broadleaf forest reserve in northern Japan.Plot-level metrics were computed for all plots using both field and remote sensing data to assess their ability to capture the vertical and horizontal variations of forest structure.A multivariate set of forest structural attributes that included three Li DAR metrics(95 th percentile canopy height,canopy density and surface area ratio) and one image metric(proportion of broadleaf cover),was used to classify forest structure into structural complexity classes.Our results revealed significant correlation between field and remote sensing metrics,indicating that these two sets of measurements captured similar patterns of structure in mixed conifer–broadleaf forests.Further,cluster analysis identified six forest structural complexity classes includingtwo low-complexity classes and four high-complexity classes that were distributed in different elevation ranges.In this study,we could reliably analyze the structural complexity of mixed conifer–broadleaf forests using a simple and easy to calculate set of forest structural attributes derived from airborne Li DAR data and high-resolution aerial photography.This study provides a good example of the use of airborne Li DAR data sets for wider purposes in forest ecology as well as in forest management.

Mapping informal settlement indicators using object-oriented analysis in the Middle East

Mapping informal settlements is crucial for resource and utility management and planning.In 2003,the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals(SDGs).Informal settlement indicators are used as a framework to carry out image analysis,and include vegetation extent,lacunarity of housing structures/vacant land,road segment type and materials,texture measures of built-up areas,roofing extent of built-up areas and dwelling size.Objectbased image analysis(OBIA)methods are recommended to identify informal settlements.This paper documents the application of OBIA to map informal settlements,drawing on the ontology of Kohli et al.(2012)and the indicators of Owen and Wong(2013)for a Middle Eastern city.Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah,Saudi Arabia.Vegetation extent was the most successful indicator detected,with 100% producer accuracy and over 84% user accuracy,followed by the road network,with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies.Lacunarity of housing structures/vacant land was detected well in informal settlements.The texture measure indicator was detected using GLCM_(Ent)(R)with low producer accuracy across all case studies.The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures.The dwellings size indicator generally failed to distinguish formal from informal settlements.Informal and formal were distinguished with an overall accuracy of 83%.This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities.However,a generic ruleset for mapping informal settlements remains elusive,and each indicator requires significant localised‘tuning’.