Conference Agenda

Satellite and Argo observations of dissolved oxygen responses to “Wind Pump” in the Bay of Bengal

Huabing Xu^{1, 3}, Danling Tang^{1, 3*}, Jinyu Sheng², Yupeng Liu^{1, 3}, Yi Sui²

Effects of tropical cyclones (TCs) “Wind pump” on dissolved oxygen (DO) in subsurface waters (20-200 m) over the Oxygen Minimum Zones (OMZs) in the Bay of Bengal (BoB) are examined based on Argo and satellite data. Five TCs (Hudhud, Five, Vardah, Maarutha and Mora) sweeping the central BoB during 2013-2018 are considered. Our analyses reveal three types of DO temporal variability caused by the storm-induced mixing and upwelling. The first type occurred during TC Hudhud features temporal increases of DO in subsurface waters (37-70 m) caused mainly by intense vertical mixing and downwelling. The second type features DO reductions in subsurface waters respectively after four TCs (Hudhud, Five, Maarutha and Mora) attributed to storm-induced upwelling. The third type occurring during TC Vardah features temporal increases of DO at depths between 40-79 m and decreases at depths between 80-150 m due to the combined effect of strong vertical mixing and upwelling. These three types of DO responses can occur in different areas, depending on TC intensity, translational speed and Ekman pumping. The temporal DO variability is also influenced by the shallow oxycline (58.3±16.7 m), mesoscale eddies and biochemical processes. Due to TC intensification, a pre-existing oceanic cyclonic eddy produced a large upwelling and induced a long time of DO decrease in the subsurface layer. This study suggests three different types of DO responses along TC track in the OMZ, which would help us to evaluate the influence of TC on OMZ.

In studies of upwelling usually data from infrared and optical sensors are used which provide information on the sea surface temperature (SST) and the chlorophyll-a (Chl-a) concentration. In this paper, we show that also synthetic aperture radars (SAR) images can also give valuable contribution to such studies. Upwelling regions become detectable by SAR because they are associated with a reduction of the radar backscatter due to 1) a change of the stability of the air-sea interface or/and 2) the presence of biogenic slicks. While the change of the stability of the air-sea interface due to the presence of cold surface water in the upwelling region causes only a small reduction of the radar backscatter, biogenic cause a very strong reduction, usually of more than 10 dB. In areas of strong upwelling, the biological productivity is high due to increased nutrient supply from lower water levels. The biota living in this area secrete surface active material that ascends to the sea surface and forms there biogenic slicks, which often cover large areas in the form as monomolecular layers. The biogenic slicks damp the short-scale surface waves, which are responsible for the radar backscattering, as strongly as mineral oil films, and thus areas covered with the surface films become visible on SAR images as areas of strongly reduced radar backscatter (dark areas). Biogenic slicks often are entrained in the surface current field of eddies and thus render the structure of eddies visible on SAR images.

This paper focuses on upwelling events off the coast of Namibia (caused by northward directed coastal winds), off the coast of South Africa (caused by intensification of the meandering Agulhas Return Current), off the southern coast of Sicily (caused by strong southeastward directed coastal winds, in particular by the Mistral), and off the north coast of Taiwan (caused by the interaction of the Kuroshio Current with shallow bottom topography). These upwelling events are studied using Sentinel-1 and GF-3 SAR images, Modis SST and Chl-a maps and model data of geostrophic surface currents. We show that this synergism yields new insights into upwelling mechanism. In particular, we show that upwelling events are often associated with the generation of filaments, internal waves, and small-scale eddies, which are detectable by SAR.

Rain is one common phenomenon usually observed on SAR images. Its signatures on C-band images are often composed of very bright patches and adjacent dark patches. It is caused by the radar backscattering or attenuation from rain-induced structures on the sea surface (ring waves, splash products, and turbulence) and the hydrometeors in the atmosphere (liquid water or ice). Although some models have been developed and laboratory experiments have been conducted to explain rain impact at C-band, the vertical non-uniform distribution of raindrops and the presence of ice aloft are often ignored. In this study, we co-analyze Sentinel-1 C-band SAR data together with high temporal and spatial resolution weather radar NEXRAD. NEXRAD provide different precipitation products, including rain rate (1 hour/0.25 km), basic reflectivity (less than 5 min /0.5 km) and hydrometeor classification (less than 5 min/0.25 km). More than 747 SAR images in both co- and cross-polarization have been collocated to provide statistics of NRCS under rain, at 1 km resolution and less than 5 min between SAR and NEXRAD. Our results evidence that NRCS at both VV and VH increase with rain rate, for low to moderate wind regimes. In addition, the very bright patches obtained at both polarizations are found to be in relation with melting ice particles in the atmosphere, by examining the NEXRAD hydrometeor classification. Our analysis also reveals the possible importance of rain impact after the rainfall, with a decrease of the backscatter observed after intense rainfall. This indicates the need to analyze the rain event history to take into account rain effect persistency after the rain event. Finally, examples in the specific case of hurricane are discussed.

.

A semi-automated pocket FerryBox monitoring system was installed on a cargo ship in October 2015 to continuously record in situ chemical oceanographic data along a transect between two cities across the Bohai Strait, China for one year. In 2016, 2 cruises by research vessels that had an automated FerryBox system installed covered the Bohai Strait in the summer and winter. This report summarizes the outcome of this one-year campaign that used FerryBox as a water quality monitoring tool in the Bohai Strait. Temperature, salinity, dissolved oxygen, turbidity and saturation were the 5 parameters selected to describe the seasonal patterns in the Bohai Strait during the one-year period. Evidence of a short-term spring bloom event was found between the FerryBox transects, and the hydrodynamic factors that are the main controlling forces of the water quality in the strait are discussed. The importance of using FerryBox as a valuable and robust tool in future coastal operational observation networks is emphasized

Sand excavation can transport sediments into surrounding waters and thus raise the suspended sediment concentration. However, assessment of the area that is influenced by sand excavation and the strength of this influence is not easy due to the temporal and spatial variability of the background suspended sediment concentrations. Remote sensing can provide data before and after sand excavation activities and thus provides a possibility to estimate the variation in suspended sediment resulting from sand excavation. Sand excavation generally occurs in rivers or estuaries. The medium resolution of ocean color satellite data makes it difficult to obtain appropriate information in estuaries or rivers because of the spatial resolution of the sensors, the narrow spans of the rivers and the effects from the adjacent land. Sand excavation in the Pearl River Estuary has become frequent in recent years due to the development of urbanization in China. The Hong Kong-Zhuhai-Macau Bridge crosses the Pearl River Estuary and is the largest bridge and tunnel project in the world. The suspended sediment generated by upstream sand excavation was doubted to have a significant impact on the suspended sediment in the tunnel region. In this paper, we assessed the impact of upstream sand excavation on the suspended sediment in the Hong Kong-Zhuhai-Macau Bridge construction area using Landsat OLI, ETM+ and TM data. Regional suspended sediment algorithms were developed for Landsat using a symbolic regression method based on data from 25 cruises in the study area from 2003 to 2014. A band shift was conducted on the remote sensing reflectance data from Landsat ETM+ and OLI to produce a time series of suspended sediment that was internally consistent with that of Landsat TM data. The suspended sediment distribution was extracted and used to compare two different conditions, with and without sand excavation. The correlation of suspended sediment in different regions in the surrounding waters, including the correlation between the construction regions and the sand excavation regions, was calculated. Our results indicate that the sand excavation in the upstream region of the Pearl River Estuary has limited impact on the surface suspended sediment concentrations in the Hong Kong-Zhuhai-Macau Bridge tunnel area.

Several decades of glacier recession has led to the formation of large populations of glacial lakes in most glacierised high mountain regions of the world. In many of these regions, enhanced glacier retreat and mass loss has been observed from glaciers which remain in contact with a glacial lake over a prolonged time period. Such negative glacier-lake interactions may heavily influence the long term ice mass loss budget of high mountain regions, and heighten the threat posed by glacial lake outburst floods (GLOFs). Glacier-lake interactions remain scarcely studied in the Himalaya, thus our aim of this work is to quantify the impact of lake expansion on glacier mass loss and glacier retreat across the main Himalayan arc. We generated geodetic mass balance estimates for two time periods (1970s-2000 and 2000-2016) over several regions of the main Himalayan arc, and observed 39-51% greater ice mass loss from lake-terminating glaciers when compared to land-terminating glaciers. Lake-terminating glaciers contributed ~19% of the total ice mass loss across the region, across both time periods, despite comprising only 9% of the glacier population. The mapping of glacier terminus positions over coincident periods shows land-terminating glacier retreat rates of 6.6-12.4 m a^-1, and 16.5-26 m a^-1 for lake-terminating glaciers. Over the later time period, land-terminating glacier length reduced by 8.5%, whereas lake-terminating glacier length reduced by 19.2%. Our results emphasise the role of lake expansion in glacier evolution across the Himalaya, and the requirement to consider glacier-lake interactions in future assessments of glacier recession in the region.

There are more than 5000 glacial lakes > 0.003 km² in the Third Pole region including the Pamir-Hindu Kush-Karakoram-Himalaya and the Tibetan Plateau. Around 30 lakes were identified as being potentially the most dangerous across the Tibetan Plateau. This assessment was based on four core determinates of GLOF hazard, including lake size, watershed area, dam steepness, and topographic potential for ice/rock avalanching. This study found that the potentially most dangerous lakes are located in the central Himalaya, the region where past glacial lake outburst floods (GLOFs) have occurred most frequently. Despite this fact, in-situ measurements relating to lake bathymetries, dam stability, and mother glacier dynamics are still very limited. We selected the most dangerous lakes in the central Himalaya (mainly in Poiqu basin), and mapped lake extents, glacier outlines, their frontal positions and ice flow from optical remote sensing data, and calculated glacier surface elevation change from digital terrain models between 1970s and 2018. Measurements of bathymetries, ground temperature, moisture and heat fluxes at different depths in the lake dam and outwash areas are ongoing. The stability of moraine dams, eventual failure and possible GLOF impacts will be modelled, and observations will be extended over long timescales. The glacial-lake change analysis presented in this study can significantly improve our knowledge of past lake evolution in the central Himalayas and the future GLOF threat.

Meltwater from rock glaciers could provide a relevant contribution to water supply especially in dry regions. Moreover, rock glaciers could have serious hazard potentials when located at or above steep slopes or when damming lakes. Existing investigations about rock glaciers in High Mountain Asia indicate that the landforms are abundant, but information is rare for the Tibetan Plateau and the northern slopes of the Himalaya.

We compiled a rock glacier inventory for the Poiqu basin (~28°17´N, 85°58´E) – central Himalaya/Tibet. The mapping was mainly based on optical Pleaides imagery with 0.5m resolution. Rock glaciers were identified based on their characteristic shape and their surface structure. In addition, we generated a Pleiades DEM and used it for a) creating a hillshade to support rock glacier identification and b) to derive their topographical parameters. Additional information on the occurrence and activity of the rock glaciers was provided by the InSAR technique using ALOS-1 data.

The results of the inventory reveal 370 rock glaciers covering an area of about 21.2 km². The largest one has an area of 0.5 km² and three have an area of more than 0.3 km². The rock glaciers are located between ~3715 m and ~5850 m with a mean altitude of ~5075 m a.s.l.. The mean slope of all rock glaciers is close to 17.4° (min. 6.8°, max. 37.6°). Most of the rock glaciers face towards the Northeast (19%) and West (18.5%). Our study indicates that 147 rock glaciers can be classified as active. We also found rock glaciers damming lakes and rock glaciers located above roads which could threaten the infrastructure in case of instability.

Preliminary results of rock glacier mapping of the same region, which were based on Sentinel 2 images with 10 m resolution and the 8 m High Mountain Asia DEM revealed slightly less rock glaciers (362) in numbers and but indicated a much larger rock glacier area (>40 km²). We conclude, that high resolution data is of utmost importance when creating a rock glacier inventory.

Active rock glaciers (ARGs) and protalus lobes (PTLs) are characteristic periglacial landforms indicating the presence and creeping process of permafrost underground in an alpine environment. However, little information about such landforms has been provided in mountainous western China. In this work, we compiled an inventory including ARGs and PTLs in part of the West Kunlun Shan based on satellite Synthetic Aperture Radar (SAR) interferometry and optical images from Google Earth. Fifteen interferograms generated from ALOS-1 PALSAR images were used for identifying ground movements. Their geomorphic parameters such as aspect, area, altitude, and slope angle, were quantified using the SRTM digital elevation model. Within the 70000 km² study area, we identified 67 ARGs and 22 PTLs. The preliminary results reveal that the mean downslope velocities of the ARGs and PTLs are 79 cm/yr and 25 cm/yr, respectively. The maximum downslope velocity for the ARGs is about 200 cm/yr. The aspects of the landforms vary significantly: 45% of the ARGs are located on northeast-facing slopes while 50% of the PTLs are located on southeast-facing slopes. Our inventory shows the total areas covered by ARGs and PTLs are 14.4 km² and 1.2 km², respectively. The largest ARG has an area of 0.7 km². The ARGs are located between 4100 m and 5600 m and have a mean slope angle of 13°. While the PTLs are in a narrower band, between 4600 m and 5300 m and have a slightly steeper angle of 18°.Compared with the existing rock glacier inventories in High Mountain Asia such as the northern Tien Shan and the Hindu Kush Himalaya, the distribution density of ARGs and PTLs in the western Kunlun Shan is much lower.

Approaches of monitoring earthquakes have evolved from conventional ground-based networks to include space. In the areas of seismology, geology and geophysics, scientists believe that the events leading up to earthquakes goes through a complex process and the process is somehow chaotic. Understanding earthquakes requires a breakthrough from traditional approaches to utilizing advanced technology. In fact, the seismology discipline has expanded the scope of earthquake study from conventional ground-based observations to space. In particular, since the Swarm satellite mission lunched in 2013, they have paved a way to provide a wide range of measurements in space by Vector Field Magnetometer, Absolute Scalar Magnetometer, Electrical Field Instrument, etc. instrumental sensors.

The measurements delivered by the three satellite are very valuable for a range of applications, including earthquake prediction study. However, for more than 5 years, relatively little advancement has been achieved on establishing a systematic approach for detecting anomalies from the satellite measurements for predicting earthquakes before they occur. This report presents a continuous effort, describing essential functional components of a system for automatic anomaly detection. Through a case study we demonstrate the functionality of the system in detecting anomalies, and the process of data processing and analysis along with experience in developing a viable tool for precisely discovering seismic anomalies from the observed data by the Swarm satellites.

Earthquake is one of the most severe natural disasters. More than half of lives lost is caused by earthquake among natural disasters in the world. The earthquake also causes huge economic losses, e.g., about 800 billion RMB were lost during the Wenchuan EQ (Mw=7.8, 2008). Therefore, the governments and scientist of many countries, especially of those with frequent earthquakes, pay great attention to the study of earthquake prediction. It was in the 1960s that the study on the earthquake prediction started and national projects for EQ prediction designed in several countries e.g., China, Japan, USSR and USA (Chen, et al, 2000, Uyeda, 2015).

As well known, the earthquake prediction is a difficult scientific problem in the world. It is often debated and doubtful about that earthquake could be predicted and about whether there is any observable anomalous precursor prior to the earthquake. During the last decades, a lot of example of observable short-term precursors was published letting more scientists considering that there indeed exists precursor before an earthquake and it can be observed only if the reasonably effective method is used. The electromagnetic (EM) method is believed by scientists to be one of the methods that can be used to first reach success in short-term prediction. In this report, we will introduce the first EM observation network built recently using the alternate EM field and its exploitation for monitoring earthquakes. As an example, we will present how to use natural EM source to capture the precursors before the 5.1 Ms Yangbi earthquake in Yunnan province, China, along with a comparative study with the result detected from the Swarm electromagnetic data in the corresponding period of time.

The study is supported by NSFC (41674081,41374077）and NDICC (15212Z0000001). The members of EM group of Institute of Geology, China Earthquake Administration and colleagues in the Yunnan Earthquake Administration joined the construction of the network and data observation.

Since the Swarm satellite constellation launched November 2013, three satellites have delivered immense amounts of geomagnetic field measurements. Currently it is very extremely difficult for researchers to track observed data based on the instrumental sensors, such as Vector Field Magnetometer (VFM), Absolute Scalar Magnetometer (ASM), and conduct anomaly detection, it is imperative to develop more effective data analytics for detecting and discovering abnormalities in the electromagnetic time series data for earthquake studies.

In this report, we propose a Martingale framework which could be adequate for assessing abnormal changes within electromagnetic data streams. The martingale method becomes essential as traditional statistical approach are inappropriate for the high dimensional electromagnetic dataset (Vapnik, 1998). The first step using the framework is to categorically obtain a practical data model in the machine learning standard scenarios through the use of strangeness measures. The strangeness measures establish a way of testing the exchangeability assumption of the dataset using a hypothesis test which drives the martingale process. The Martingale model will also involve the use of machine learning smoothing techniques to reduce noise and other interference efficiently making the framework more sensitive in detecting change point/anomaly. And finally, the model will be evaluated over Swarm electromagnetic data based on the two selected earthquakes, compared against a benchmark method and studied on its effectiveness in detecting abnormal changes before the earthquakes.

The pace of urbanization has been unprecedented. Today, 55 per cent of the world’s population live in cities and another 2.5 billion people is expected to move to urban areas by 2050 (UN, 2018). The UN 2030 Agenda for Sustainable Development gives a prominent role to monitoring the urbanization process. With its synoptic view and large area coverage at regular revisits, satellite remote sensing has been playing a crucial role in urbanization monitoring at regional and global scale. Several methodologies have been developed using Synthetic Aperture Radar (SAR) and/or multispectral imagery to map urban extent globally including the Global Urban Footprint (GUF) and the Global Human Settlement Layer (GHSL). These datasets provide a reliable global map of the urban areas, but they are characterized by low temporal resolution (i.e. every five years) which highlights the need of further research and method development

The objectives of this research are two folds, one is to develop a globally applicable and entirely automatic method to monitor urban footprints using Sentinel-1 SAR and Sentinel-2 MSI dense time series exploiting the Google Earth Engine (GEE) cloud platform, and other is to evaluate derived urban extent for the monitorin of the UN Urban SDG indicator 11.3.1 Ratio of land consumption rate to population growth rate. The innovative aspects of the developed method is to integrate Sentinel-1 and Sentinel-2 dense time series using a totally unsupervised approach. The estimation of the selected urban footprint is performed in several progressive steps. First, the area of interest is divided into mountainous and non-mountainous areas using an available DSM (i.e. SRTM or ALOS World 3D) to take into account the layover and foreshortening of SAR geometric distortions. Then, Sentinel-1 ascending and descending time series are processed in order to enhance the backscatter of stable urban areas and to compute the Sentinel-1 Urban mask using an automatic thresholding procedure. The latest step is to compute a probability urban map combining the Sentinel-1 Urban mask with the Sentinel-2 multi-spectral time series. All available Sentinel-2 images, acquired during the selected sensing period, will be used to compute a cloud-free Sentinel-2 image composite, subsequently we applied a segmentation algorithm to the Sentinel-2 composite, and for each object, we compute several multitemporal spectral indexes statistics (i.e. Min/Max NDVI, Median NDBI, Mean NDWI). Finally, we use a ruleset to estimate the probability urban map combining the Sentinel-1 Urban mask with the computed Sentinel-2 indexes statistics.

To ensure its global applicability, we tested the developed approach in several cities worldwide (i.e. Beijing, Lagos, Milan, Mumbai, New York, Rio and Stockholm) characterized by different urban density and morphologies. We computed the urban footprint in different periods to evaluate the temporal stability of the method and to produce urban footprint time series. The results show that through this method it is possible to obtain high accuracy (kappa higher than 0.85) with respect to the reference data acquired within the EO4Urban project in all cities and in different periods ^[5]. The developed method obtains equal or higher accuracy than GUF and GHSL data in the same area, and a visual comparison shows that the integration of the Sentinel-1 SAR and Sentinel-2 MSI data leads to achieving highly detailed information.

Based on the methodology defined by the UN Habitat, the urban extraction results are being used in the monitoring of Urban SDG indicator 11.3.1 Ratio of land consumption rate to population growth rate in the selected cities. The preliminary results show that timely and reliable urban extraction is essential for the definition of cities and for monitoring Urban SDG indicator 11.3.1.

In the last ten years, the harmonization of rapid urbanization and extremely prosperous economic activity with the air quality and urban land use is the most concerned issue in Chinese urban development policy [1][2]. Accordingly, an urgent and challenging task is to improve the knowledge and understanding of change patterns in human settlements for fast-urbanized megalopolis in South and East Asia. Thanks to the availability of time-series of heterogeneous remote sensing data, it is now possible to explore these changes decoupling those due to urban expansion and those due to increasing economic activities [3]. In this work, we combine multi-temporal the sentinel-1 SAR C-band sensor and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime sensor (also called the Day/Night Band, or DNB) to explore urban change patterns at the geographical scale of Chinese Megalopolis. The joint use of heterogeneous sensor allows discovering more spatial-temporal features and deeper relationships between urban construction and nighttime-based changes, which indirectly reflect the connections between urbanization and economic development.

Three megalopolis, namely the Jingjinji, the Yangtze River Delta and the Pearl River Delta have been selected, which correspond to the currently most developed and the most densely populated portions in P.R.China. First, Sentinel-1 SAR is used to extract urban extents that ensure the focus of our analysis is in built-up areas at the finest spatial resolution for freely available data sets. To handle big-size data over each Megalopolis, defined as a chain of roughly adjacent metropolitan areas, which may be somewhat separated or may merge into a continuous urban region, the critical preprocessing steps and computations are performed in Google Earth Engine (GEE). Then, data-driven unsupervised classification is used to explore change patterns according to a feature space joining the base and the change images. In this way, both the initial state and the temporal change pattern are considered. To ensure the reliability of unsupervised clustering, GMM, K-method, and DBSCAN are adaptively applied to the same feature space. At last, the 2-dimensional vector analysis are given to interpret the clustering results. Consider the resolution difference between the Nighttime light sensor and Sentinel-1 SAR, upscaling is applied to the SAR images to match VIIRS images at 500-meter resolution.

The vector analysis according to the extracted clusters shows that these clusters are interpretable as meaningful temporal and spatial patterns, although the interpretability of the extracted cluster patterns depends on the classifier performance to different feature spaces. According to the most stable clustering results, change patterns of urban construction and nighttime fundamental facilities are clearly differentiated between core urban and suburban areas, and very new development city zone are singled out and highlighted.ß

Ref:

[1] Wang, Shuxiao, et al. "Effectiveness of national air pollution control policies on the air quality in metropolitan areas of China." Journal of Environmental Sciences 26.1 (2014): 13-22.

[2] Wu, Yanyan, Shuyuan Li, and Shixiao Yu. "Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China." Environmental monitoring and assessment 188.1 (2016): 54.

[3] Frolking, Steve, et al. "A global fingerprint of macro-scale changes in urban structure from 1999 to 2009." Environmental Research Letters 8.2 (2013): 024004.

Urban areas have developed mainly against a socio-economic paradigm ignoring to a large extent the environmental impacts Of particular concern for cities in our days, is the lack of balance between the natural, built and socio-economic environments, leading among others, to the degradation of their thermal environment, in particular overheating. Heat islands/spots within the urban ecosystems have negative impact to human health especially for vulnerable groups, increase energy use for cooling and lead to poor city energy efficiency, intensify energy poverty, deteriorate air quality and result in socio-economic problems in general.

While in vegetated areas, evaportranspiration transfers most of the incoming radiation into latent heat, in built up areas sensible heat is generated, which leads to the strong heat load of urban areas. In addition buildings strongly affect the flow patterns of wind and heat, practically keeping the heat close to the ground.

Mitigation plans to counteract overheating are now developed by several cities around the world, in line to the Sustainable Development Goals of the United Nations and the new Covenant of Mayors for Climate and Energy, which recognizes the role of ecosystem-based mitigation in enhancing urban resilience and providing multiple benefits.

Plans also take advantage of Nature Based Solutions (NBS) in an effort to restore the urban ecosystems and achieve the needed balance between the natural, the built and the socio-economic environments.

The scope of this paper is to assess the factors in support of a Planning Support System (PSS) for the design of the appropriate, science and policy wise, NBS so as to restore urban ecosystems, counteract overheating and improve thermal resilience.

Regional land subsidence is an integrated systematic issue related to multidisciplinary and being of global focus, and has been being a serious threat to the urban infrastructure, high-speed railway and the utilization of underground space, and restricting the sustainable development of society. The study of the regional subsidence evolution in Beijing Plain is of great significance: it is necessary to reveal the regional land subsidence evolution pattern under the background of Integration of Beijing-Tianjin-Hebei and the South-to-North Water Diversion. Furthermore, it can help to realize the scientific regulation of regional subsidence and ensure the sustainable development of regional economy and society, which has a special significance and application prospect. Therefore the MT-InSAR method is used to obtain the regional ground subsidence time series information of the study area in three periods: Jun. 2003 ~ Aug. 2010, Oct. 2010 ~ Nov. 2015, and May. 2015 ~ Jun. 2018. Then equations are established based on the time-overlapping information to complete the fusion of multi-platform time series, the inconsistence between different reference points is solved, simultaneously. The results show that, the maximum subsidence values in Beijing Plain are 690.6 mm, 649.2 mm and 411.7 mm during the three periods, with maximum deformation rates of 100.6 mm/a, 130.0 mm/a and 142.3 mm/a, respectively. For the spatial distribution and the evolution of the land subsidence field, the weighted spatial kernel density analysis, profile analysis, trend-surface analysis and profile-gradient analysis are used to analyze the spatial-temporal evolution characteristics of the land subsidence field. In this case, land subsidence in Beijing Plain are thoroughly analyzed overall distribution characteristics and evolution process. Nine subsidence centers are identified and the subsidence centers are connecting to form a main subsidence area in the northern part of Beijing Plain. The spatial clustering degree of the subsidence in the Beijing Plain indicates an overall heterogeneity in spatial. Moreover, the northern subsidence areas spread along the Nankou-Sunhe fault, and is cut into several subsidence centers by active faults, indicating that the regional geological structure has obvious control effect on the spatial distribution of land subsidence areas. The evolution of the subsidence field: the northern subsidence areas spread along the northwest-southeast direction, and then expands to both the east and west sides. Then through the distribution of subsidence areas and groundwater funnel, the InSAR based time series and the monitoring well based groundwater level changes, the correlations in spatial and responses between land subsidence field and groundwater flow field are analyzed. The results show that the subsidence center in the northern Beijing Plain is consistent with the groundwater drop funnel in spatial, with a similar downward trend over the whole observation time. Through the analysis of well based results located in different areas, the long term groundwater exploitation in the northern subsidence area has led to the continuous decline of the water level, resulting in the inelastic and permanent compaction; while for the monitoring wells located outside the subsidence area, the subsidence time series show obvious elastic deformation characteristics as the groundwater level changes.

Cities worldwide experience enhanced heat stress, as a result of the impact of their surface properties and geometry on the surface energy balance. Land surface temperature (LST) from satellite thermal imagery can provide an overall view of extended urban areas, assisting in the identification of thermally vulnerable sites for mitigation responses. Several studies have demonstrated the relationship between LST and vegetation fraction within cities. Here, the Sentinel-3 SLSTR Level-2 LST product is used to examine the synergy between several two- and three-dimensional properties of the urban morphology (e.g. building height, canyon aspect ratio) and LST, for summer conditions. Results, demonstrate that at daytime open impervious urban areas experience higher temperatures than densely built neighborhoods. The contrasting thermal patterns of nighttime imagery reveal the surface heat island development cycle, and indicate the potential caveat of using solely LST as a map based planning tool, due to its dependence on satellite overpass timing. Furthermore, the surface temperature distribution is examined in the context of Local Climate Zones (LCZs); in general, a considerable differentiation among LCZs is found, although local circulations exhibit a stronger control on coastal zones

Benefit from steering antenna beam, TOPS (Terrain Observation by Progressive Scans), the default imaging model of Sentinel-1, can obtain wide coverage with azimuth-invariant SNR and at the same time avoid scalloping. Meanwhile, the introduced Doppler centroid difference higher than 5000 Hz requires a stringent co-registration accuracy to prevent phase discontinuities over burst boundaries. For multi-temporal analysis, it is even harder to achieve the required accuracy. The residual mis-registration can bias the extracted geophysical inversion parameters such as surface deformation. To improve the co-registration accuracy, a co-registration approach based on Floyd-Warshall algorithm and Enhanced Spectral Diversity is proposed. For further improvement in low coherence regions, we present a coherence estimator by combining two consecutive bursts SLC samples. The performance of the presented approach is validated by two low coherence scenes.

PolSAR data has become the significant data source in urban research. However, the widely used methods extract features at the expense of spatial resolution loss. A PolSAR feature optimization approach is addressed. The new method relies on the adaptive selection of homogeneous samples, both polarimetric and spectral characteristics are taken into account. Those homogeneous samples are first applied to suppress the speckle and to refine the feature estimators afterwards. The comparison with the state-of-the-arts using real-world datasets shows that more accurate PolSAR features with well-preserved edges can be obtained over textural regions.

Urbanization is one of the main factors to cause the land use change in the world, the urban area is rapidly expanding with the accelerated process of urbanization, so the knowledge of urban distribution can provide a reliable technical and decision-making basis for urban planning and development potential evaluation. Remote sensing technology has played an active role in the extraction of urban land, and the urban light at night can be used to analyze the city expansion trend and development potential through light intensity and change rate. In this study, cities in Southeast Asia were taken as the research objects, and landsat8 OLI remote sensing data was taken as the main data source. Firstly, spectral characteristics of different objects were analyzed, and the thresholds of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were determined to remove the vegetation and water. The difference between the remaining objects were enlarged after the removal of vegetation and water, the normalized difference building index (NDBI) and the minimum distance classification method were adopted to eliminate the unplanted farmland and bare land. The results showed that this method was simple and capable to extract the impervious land step by step and the accuracy was above 85%. The slope of light change was calculated based on the light data of Defense Meteorological Satellite Program (DSMP) between 2000-2013, in order to analyze the development trend and potential of urban and its surrounding area. The results showed that the urban light index increased at an average annual rate of 0.75 in the built-up area and the surrounding area grew at a high rate of 1.03 per year in the past years from 2000 to 2013, which showed a high density and quick growth rate. It still maintained a strong growth trend and had great potential and space for development.

Over the past two decades, there has been substantial urban growth in Stockholm, Sweden. As a result of accelerating urbanization, Stockholm is now the fastest-growing capital in Europe and it is expected that the Greater Stockholm area will more than double in population by 2045, to 4.5 - 5 million people. The Swedish government has recently taken steps to ensure sustainable management of its green and blue resources in urban areas by requiring all counties to draw up regional plans for their green infrastructure. Using Sentinel-2 and SPOT 5 images, this research investigates the evolution of land cover change in Stockholm County between 2005 and 2015 with a particular focus on what impact urban growth has had on protected green areas, green infrastructure and urban ecosystem service provision.

One scene of Sentinel-2A MSI imagery from 2015 and ten scenes of SPOT 5 imagery from 2005 over Stockholm County were selected for this study. These images are classified into 10 land cover categories using an object-based SVM classifier with spectral, shape and texture features as inputs. The classifications are then used in calculations and comparisons to determine the impact of urban growth in Stockholm between 2005 and 2015, including generation of land cover change statistics, urban ecosystem service provision bundles which include spatial configuration information and evaluation of impact on legislatively protected areas as well as ecologically important habitat networks.

Preliminary results indicate that Urban areas increased by15% or approximately 116 km² while non-urban land cover, mainly agricultural areas and green structure, decreased by just under 4%. The increase in urban areas is just over 2% of the total county land area. More specifically, the results suggest that urban areas may soon overtake agricultural areas to become the second largest land use/cover category in the county landscape after forest. The largest increases in urban areas and significant losses of green structure occurred mainly in the northern and southern outskirts of the county in the rural-urban fringe, with the exception of two municipalities close to Stockholm city which also experienced significant urban growth.

In terms of ecosystem service provision, notable decreases occurred in temperature and global climate regulation, air purification, noise reduction and recreation, place values and social cohesion. Urban areas within a 200m buffer zone around the Swedish EPA’s nature reserves in Stockholm County increased by 16% over the decade, with several examples of new urban areas constructed along the boundary of nature reserves. Further research will include evaluation of important ecological networks in Stockholm county, such as its regional green wedges and broadleaved forest distribution, to see how these have been affected by the urban growth. The results of this study can assist policymakers and planners in their efforts to ensure sustainable urban development and natural resource management for the Stockholm region.

Over the past several decades, the unprecedented pace of urbanization and socioeconomic development in China has placed great anthropogenic pressures on inland surface water quality. In recent years, continuously increasing environmental investments have been undertaken to control pollutant discharge and improve inland water quality across the entire country. However, the quantitative response of water quality to both increasing human pressure and effort is less well understood, particularly for a large-scale region with diverse driving factors. In this study, we use satellite-derived nocturnal radiance signals as proxy measures for both the negative (using the product of lit area and population size) and positive (using the area-weighted magnitude of nighttime lights) effects of human activity to evaluate how water quality changes over time in response to anthropogenic disturbances. Our method clearly demonstrates the extended application of remotely sensed data of nighttime lights with dual actions, particularly in the absence of direct observations of socioeconomic variables due to their consistent, timely and spatially explicit proxy measures for diverse human activities.

.

.