An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study. The Moderate Resolution Imaging Spectroradiometer (MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite (VIIRS)-based nighttime light (NTL) data for robust extraction and mapping of urban built-up areas. The MODIS-based newly proposed Urban Built-up Index (UBI) was combined with NTL data, and the resulting Enhanced UBI (EUBI) was used as a single master image for global extraction of urban built-up areas. Due to higher variation of the EUBI with respect to geographical regions, a region-specific threshold approach was used to extract urban built-up areas. This research provided 500-m-resolution global urban built-up map of year 2014. The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States. The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.

In recent years, the spatial resolution of satellite data has improved with advancements in satellite technology, and acquisition of even more detailed information on the surface of the earth can be expected in the future. Various complications, however, are still associated with retrieval and classification of ground surface information from very-high-resolution satellite data. One important issue is the occurrence of isolated pixels with high local spatial heterogeneity between neighbouring pixels in the classification result. The main objective of this research was to evaluate the effectiveness of two Multiple Classifier System (MCS), which combine the results from different supervised classifiers, as a means for reducing isolated pixels when using high-spatial resolution satellite imagery. The research was conducted in the Tohoku region, where the Great East Japan Earthquake of 11 March 2011 and subsequent tsunami have greatly changed the regional land use and land cover. Multiple input features (five RapidEye spectral channels, five spectral indices, digital elevation model, and slope) were prepared from satellite data and used for the machine learning and validation. Ground truth data belonging to six major land cover classes (forest, shrub/grassland, cropland, urban area, waterbody, bare ground) were collected. Six machine learning classifiers; Random Forest (RF), Support Vector Machines (SVM), K-nearest Neighbours (KNN), XGBoost, Bagging, and Neural Network (NNET), were individually tested for classification accuracy and generation of isolated pixels, and the results were compared with those of the MCS combinations. The XGBoost provided the highest overall accuracy (0.987) and kappa coefficient (0.985) of the individual classifiers, but suffered from a large number of isolated pixels. The MCS by combination weighing the kappa coefficients (MCS kappa), however, produced equivalent overall accuracy (0.989) and kappa coefficient (0.987), but at the same time was able to significantly reduce the number of isolated pixels. This reduction amounted to 30% for all land cover classes. In particular, the number of isolated pixels in the forest class was reduced by 50%. These results clearly show that the MCS kappa is capable of reducing isolated pixels in the classification of very-high-resolution land cover information, without sacrificing classification accuracy. The authors hope that this system will prove effective for monitoring fine-scale changes in land cover, and contribute to conserving landscapes and ecosystem services in the region impacted by the earthquake and tsunami.

Hanoi City of Vietnam changes quickly, especially after its state implemented its Master Plan 2030 for the city’s sustainable development in 2011. Then, a number of environmental issues are brought up in response to the master plan’s implementation. Among the issues, the Urban Heat Island (UHI) effect that tends to cause negative impacts on people’s heath becomes one major problem for exploitation to seek for mitigation solutions. In this paper, we investigate the land surface thermal signatures among different land-use types in Hanoi. The surface UHI (SUHI) that characterizes the consequences of the UHI effect is also studied and quantified. Note that our SUHI is defined as the magnitude of temperature differentials between any two land-use types (a more general way than that typically proposed in the literature), including urban and suburban. Relationships between main land-use types in terms of composition, percentage coverage, surface temperature, and SUHI in inner Hanoi in the recent two years 2016 and 2017, were proposed and examined. High correlations were found between the percentage coverage of the land-use types and the land surface temperature (LST). Then, a regression model for estimating the intensity of SUHI from the Landsat 8 imagery was derived, through analyzing the correlation between land-use composition and LST for the year 2017. The model was validated successfully for the prediction of the SUHI for another hot day in 2016. For example, the transformation of a chosen area of 161 ha (1.61 km²) from vegetation to built-up between two years, 2016 and 2017, can result in enhanced thermal contrast by 3.3 °C. The function of the vegetation to lower the LST in a hot environment is evident. The results of this study suggest that the newly developed model provides an opportunity for urban planners and designers to develop measures for adjusting the LST, and for mitigating the consequent effects of UHIs by managing the land use composition and percentage coverage of the individual land-use type.

Monitoring of water cover and shorelines at a global scale is essential for better understanding climate change consequences and modern human disturbances. The level and turbidity of the surface water, and the background objects in which they interact with, vary significantly at a global scale. The existing water indices applicable to detection and extraction of water cover at local and regional scales cannot work efficiently everywhere in the globe. In this research, a new water index called Superfine Water Index (SWI) was developed for robust detection and discrimination of the surface water at a global scale using MODIS based multispectral data. The SWI was designed in such a way that it provides high contrast between the water and non-water areas. Achieving high contrast is vital for discriminating the surface water mixed with a variety of objects. The sensitivity analysis of the SWI demonstrated its high sensitivity to the surface water compared to the existing water indices. One single-layered global mosaic of a 90-percentile SWI image was used as a master image for global water cover mapping by reducing the large volume of MODIS data available between 2012 and 2014 globally. The random walker algorithm was applied in the SWI image with the support of reference training data for the extraction and mapping of water cover. This research produced an up-to-date global water cover map of the year 2013. The performance of a new map was evaluated with a number of case studies and compared with existing maps. The supremacy of the SWI over the existing water indices, and high performance of the SWI based water map confirmed the reliability of the new water mapping methodology developed. We expect that this methodology can contribute to seasonal and annual change analysis of the global water cover as well.

The damage of buildings and manmade structures, where most of human activities occur, is the major cause of casualties of from earthquakes. In this paper, an improved technique, Earthquake Damage Visualization (EDV) is presented for the rapid detection of earthquake damage using the Synthetic Aperture Radar (SAR) data. The EDV is based on the pre-seismic and co-seismic coherence change method. The normalized difference between the pre-seismic and co-seismic coherences, and vice versa, are used to calculate the forward (from pre-seismic to co-seismic) and backward (from co-seismic to pre-seismic) change parameters, respectively. The backward change parameter is added to visualize the retrospective changes caused by factors other than the earthquake. The third change-free parameter uses the average values of the pre-seismic and co-seismic coherence maps. These three change parameters were ultimately merged into the EDV as an RGB (Red, Green, and Blue) composite imagery. The EDV could visualize the earthquake damage efficiently using Horizontal transmit and Horizontal receive (HH), and Horizontal transmit and Vertical receive (HV) polarizations data from the Advanced Land Observing Satellite-2 (ALOS-2). Its performance was evaluated in the Kathmandu Valley, which was hit severely by the 2015 Nepal Earthquake. The cross-validation results showed that the EDV is more sensitive to the damaged buildings than the existing method. The EDV could be used for building damage detection in other earthquakes as well.