Rapid damage mapping and loss data collection for natural disasters:Case study from Kaikoura earthquake,New Zealand

On November 14,2016(NZDT),a 7.8 magnitude earthquake struck the northeast coast of the South Island in New Zealand.A tsunami swept onto the coastlines with wave-heights of 2.5 m at Kaikoura.This earthquake is the largest event in the region since a magnitude 7.5 earthquake that occurred 100 km to the northeast in October 1848.The days immediately following a natural disaster are particularly challenging for authorities and aid organisations who need to make decisions relating to deployment and distribution of resources.Rapid Damage Mapping(RDM)is a tool developed by Tonkin+Taylor International Limited(T+TI)whereby integrated disaster mapping information is assembled within the first 24 to 72 hrs of an event.The Committee on Data of the International Council for Science(CODATA)Task Group of Linked Open Data for Global Disaster Risk Research(LODGD)organized ChinaGEOSS portal to access TripleSat and JL-1 satellite images immediately following the devastating Kaikoura earthquake.An internet based Project Orbit portal was set up for use by all response and recovery organisations in New Zealand.While the recent RDM response work was largely reactive in nature,the data set compiled during this work provides a valuable resource,presenting opportunities to apply a more proactive and refined approach to similar RDM work in the future.The recent RDM work provides valuable insight into key vulnerabilities that evolved after the earthquake,and helped to identify more than 10,000 landslips in the area.

Rapid flood inundation mapping by differencing water indices from pre-and post-flood Landsat images

Following flooding disasters,satellite images provide valuable information required for generating flood inundation maps.Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds.We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre-and post-flood satellite images.Values of the Normalized Difference Water Index(NDWI)and Modified NDWI(MNDWI)will be higher in the post-flood image for flooded areas compared to the pre-flood image.Based on a threshold value,pixels corresponding to the flooded areas can be separated from non-flooded areas.Inundation maps derived from differencing MNDWI values accurately captured the flooded areas.However the output image will be influenced by the choice of the pre-flood image,hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years.Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features.Advantages of the proposed technique are that flood impacted areas can be identified rapidly,and that the pre-existing water bodies can be excluded from the inundation maps.Using pairs of other satellite data,several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.

The application of ResU-net and OBIA for landslide detection from multi-temporal Sentinel-2 images

Landslide detection is a hot topic in the remote sensing community,particularly with the current rapid growth in volume(and variety)of Earth observation data and the substantial progress of computer vision.Deep learning algorithms,especially fully convolutional networks(FCNs),and variations like the ResU-Net have been used recently as rapid and automatic landslide detection approaches.Although FCNs have shown cutting-edge results in automatic landslide detection,accuracy can be improved by adding prior knowledge through possible frameworks.This study evaluates a rulebased object-based image analysis(OBIA)approach built on probabilities resulting from the ResU-Net model for landslide detection.We train the ResU-Net model using a landslide dataset comprising landslide inventories from various geographic regions,including our study area and test the testing area not used for training.In the OBIA stage,we frst calculate land cover and image difference indices for pre-and post-landslide multi-temporal images.Next,we use the generated indices and the resulting ResU-Net probabilities for image segmentation;the extracted landslide object candidates are then optimized using rule-based classification.In the result validation section,the landslide detection of the proposed integration of the ResU-Net with a rule-based classification of OBIA is compared with that of the ResU-Net alone.Our proposed approach improves the mean intersection-over-union of the resulting map from the ResU-Net by more than 22%.

Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing classification.Google Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing data.In this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in China.The random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the results.The spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by scoring.Then the top nine features were selected to form an optimal feature subset.Comparing the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature subset.The overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.