Land subsidence is a global issue and researchers from all over the world are keen to know the causes of deformation and its further influences. This paper reports the findings from time series InSAR (TS-InSAR) results over the Southern Coalfield, Australia using both ALOS-1 PALSAR (Phased Array type L-band Synthetic Aperture Radar) and ENVISAT ASAR (Advanced Synthetic Aperture Radar) datasets. TS-InSAR has been applied to both rural and urban areas with great success, but very few of them have been applied to regions affected by underground mining activities. The TS-InSAR analysis exploited in this paper is based on GEOS-ATSA, and Measurement Point (MP) pixels are selected according to different geophysical features. Three experiment sites with different geological settings within the study zone are analysed: (1) Wollongong city, which is a relatively stable area; (2) Tahmoor town, a small town affected by underground mining activities; and (3) the Appin underground mining site, a region containing multiple underground mining activities. The TS-InSAR results show that the performance of both C-band and L-band is equally good over Wollongong, where the subsidence gradient is not significant and most subsidence rates are between −10 mm∙yr⁻¹ to 10 mm∙yr⁻¹. However, over the Tahmoor and Appin sites, difference in performances has been observed. Since the maximum displacement gradients that can be detected are different for L-band and C-band-based TS-InSAR methods, some rapid changes could cause the TS-InSAR to fail to estimate the correct displacements. It is well known that L-band can perform better than C-band, especially in underground mining regions and mining-affected regions where the deformation rate is much higher than city areas because of its wavelength. Statistical analyses are also conducted to further prove the above statement.

Subsidence from groundwater extraction and underground tunnel excavation has been known for more than a decade in Guangzhou and Foshan, but past studies have only monitored the subsidence patterns as far as 2011 using InSAR. In this study, the deformation occurring during the most recent time-period between 2011 and 2017 has been measured using COSMO-SkyMed (CSK) to understand if changes in temporal and spatial patterns of subsidence rates occurred. Using InSAR time-series analysis (TS-InSAR), we found that significant surface displacement rates occurred in the study area varying from −35 mm/year (subsidence) to 10 mm/year (uplift). The 2011–2017 TS-InSAR results were compared to two separate TS-InSAR analyses (2011–2013, and 2013–2017). Our CSK TS-InSAR results are in broad agreement with previous ENVISAT results and levelling data, strengthening our conclusion that localised subsidence phenomena occurs at different locations in Guangzhou and Foshan. A comparison between temporal and spatial patterns of deformations from our TS-InSAR measurements and different land use types in Guangzhou shows that there is no clear relationship between them. Many local scale deformation zones have been identified related to different phenomena. The majority of deformations is related to excessive groundwater extraction for agricultural and industrial purposes but subsidence in areas of subway construction also occurred. Furthermore, a detailed analysis on the sinkhole collapse in early 2018 has been conducted, suggesting that surface loading may be a controlling factor of the subsidence, especially along the road and highway. Roads and highways with similar subsidence phenomenon are identified. Continuous monitoring of the deforming areas identified by our analysis is important to measure the magnitude and spatial pattern of the evolving deformations in order to minimise the risk and hazards of land subsidence.

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km² (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans.

Beijing City has suffered from groundwater-induced subsidence since the late 1930s and the over-exploration of groundwater could lead to subsidence as much as −12.0 cm yr⁻¹. Previous studies on the ground deformation at Beijing City mainly focused on the period before the year of 2014 when a mega-engineering project was launched to reduce water shortage in Beijing. To study the most recent ground deformation, 19 L-band ALOS-1 PALSAR images (June 2007–January 2011), 24 C-band Sentinel-1 SAR images (June 2015–November 2016) together with 9 ALOS-2 PALSAR acquisitions (September 2014–February 2017) were analysed in this work. Levelling measurements were exploited to verify the ALOS-1-based time series InSAR (TS-InSAR) result while Sentinel-1 and ALOS-2 result were cross-verified with each other. Furthermore, the whole study area was divided into four sub-zones, and the result indicated that the subsidence rates over five townships, Cuigezhuan, Jinzhan, Liyuan, Songzhuang and Yanjiao were accelerating and more attentions should be paid. On the contrary, the town centre of Douge Zhuang township experienced a decreasing trend between these two temporal-periods. Additionally, the time series measurements with respect to five selected measurement points and the profile line along the subsidence hot spots were analysed.

In this article, the condition assessment of a cable-stayed bridge using remote sensing is presented. The displacement influence line (DIL) of the bridge under the live load tests is measured for a discrete number of target points. Three different remote sensing techniques including, laser scanning, terrestrial robotic total station and digital levelling are adopted for this purpose. It is demonstrated that DIL obtained by non-contact system is capable of identifying an emulated damage in an actual operating system. The contribution of the work is fourfold. First, a damage index based on the displacement profile of the bridge under the weigh-in-motion is extracted from the non-contact sensing system. Second, our study compares three different remote sensing techniques, namely, digital levelling, robotic total station and laser scanning and uses the measurements to validate the finite element model. Third, the effectiveness of the proposed method for structural damage identification is validated in a real-world large-scale operating structure. Finally, it is validated that strain-based influence line is highly likely to misidentify damage especially when the location of damage is not in the close proximity of the sensor; however, DIL is a better damage indicator even if damage occurs far from the measurement point.