Conference Agenda

In the rapid urbanization process of China, most rural peoples have moved to and lived in cities, making the rapid growing cities occupy lots of farmlands around these cities. On the other hand, the rural houses are abandoned and left in empty instead of transforming as corresponding farmlands. The unequal relationship between the people living and the land use has already seriously hampered the sustainable development of both cities and rural settlements, and affected China’s ideal of building well-off society in all-round way. Rapidly acquiring the information of these abandoned/empty houses in a large scale with low cost is critical for the country to make corresponding policies. And the high spatial resolution remote sensing (HSRI) techniques have a great potential in recognizing these houses because of its ability of recognizing tiny objects at a large scale region.

But currently, the HSRI of this application is limited in a small region because the recognition is still conducted by human interpretation and ground investigation. For applications of large scale region, automatic recognizing algorithm is a critical technique. However, the HSRI presents rare spectral information and plenty spatial information, which makes the well-developed pixel-based automatic classification workflow difficult in acquiring the high level information that whether a house is abandoned or empty. So we have developed an automatic recognizing solution so that the government or other developing countries could share the benefits of remote sensing techniques when making polices to deal the problem of unequal relationship between the people living and the land use in the process of urbanization.

It is hard to directly recognize whether a house is abandoned or empty in the image. However, in the process of human interpretation and ground investigation, we found that the courtyards of empty houses are often full of garbage or grass, thus distinctively different from the houses lived by people. This inspired us that we can recognize the empty house by the ratio between unclean area (garbage and grass) and the courtyard area. Guiding by this fact and the multiscale segmentation of recently popular paradigm Geographic Object-based Image Analysis (GEOBIA), we construct a primary solution and the main procedures are that: (a) acquiring the courtyards vector polygons from the cadastral data; (b) segmenting the image under the constraint of these polygons; (c) classifying the segmented image objects into Clean, Grass, Garbage, etc.; (d) computing the ratio of each courtyard; (e) recognizing the abandoned or empty houses by the ratio of each courtyard. We chose two rural settlements located at the north of Yucheng City, Shandong Province, China, for validating experiments, and acquired the images by Unmanned Aerial Vehicle remote sensing system and got the cadastral data from local government. By comparing the results of our solution and the results interpreted by human and ground investigation, it can be concluded that our solution is promising in dealing this problem.

In the future, we will consider more types of empty houses and experiment them on the high spatial resolution satellite remote sensing images so that it could be applied for large scale region investigation, and thus enable the government could share the techniques benefits when dealing the problems caused by urbanization.

Since 2008, more than half of the world population live in cities, and by 2015, nearly 4 billion people -54 per cent of the world’s population - lived in cities. That number is projected to reach 5 billion by 2030 (UN, 2018). Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution, urban heat island, loss of biodiversity and ecosystem services. Therefore, accurate, timely and consistent information on urban growth patterns is of critical importance to support sustainable development. The objective of this research is to develop novel methodologies to exploit Sentinel-1 SAR and Sentinel-2 MSI time series for monitoring urban changes aiming at globally applicable methods. First, model-based urban change detection method is being developed using multitemporal Sentinel-1 SAR data. Then an integrated approach between Sentinel-1 SAR and Sentinel-2 MSI data will be developed in order not only to detect changes but also to be able to label the different types of changes (e.g., agriculture or forest to urban, old low-rise urban to new high-rise urban, etc.) using a near real-time processing of the Sentinel big data. It is anticipated that urban changes in general, new builtup areas in particular, will be detected in a timely and accurate manner. The urban change information has the potential not only to support sustainable planning at municipal and regional levels, but also contribute to the monitoring objectives of UN Sustainable Developments Goal (SDG) 11: Making cities and human settlements inclusive, safe, resilient and sustainable.

Lakes constitute essential components of global water cycles and serve as important sentinel of environmental changes. During the past few decades, lakes in China have experienced dramatic changes under the influence of both climate change and human activity. Lakes in populated regions are particularly vulnerable to intensive land use/land cover (LULC) changes due to various human activities, e.g., agricultural irrigation, water diversion projects, and urban expansion. In this study, the impacts of LULC changes on the distribution and abundance of lakes in China were investigated by exploiting China Land Use/Cover Dataset (CLUD), remote sensing images, and socio-economic data. We first explored the spatiotemporal change patterns of urban lakes in China’s major cities over the period 1990–2015. The results showed the urban lakes experienced a large reduction in surface area (decreased by 24.22%), which are mainly distributed in the Yangtze Basin, accompanied by a rapid expansion of urban areas. The excessive encroachment in the urban lakes also resulted in increasing landscape fragmentation and decreasing shape complexity. Furthermore, we also investigated the effects of LULC changes on the lakes in the Yangtze Basin, a densely populated region with abundant rainfall, intensive cultivation, and rapid urbanization. Our results revealed the Yangtze Basin experienced rapid lake shrinkage, which was mainly attributed to human-induced alterations from lakes to cropland, fish ponds, and built-up areas, accounting for 34.6%, 24.2%, and 2.5% of the lake area reduction, respectively. Given the increasing vulnerability of these lake resources to anthropogenic activities, understanding the spatiotemporal changes of the lakes and the associated driving factors are issues of increasing concern.

Efficient Urban Change Detection Using Multi-Resolution Remote Sensing Data

for Large Area

Abstract:

Fast change detection and global monitoring are very important to understand large-scale activities in urban areas ate the global level, even though urban area occupies a little part of the surface of the Earth. Human activities have caused global-scale change problems, like global climate change, forest reduction and land deterioration. Urbanization is the most important form of human activities and recent years multi-resolution optical datasets has applied in urban environmental remote sensing. Unlike optical remote sensing, meter-wavelength active echoes can return the structure information of urban areas, like building height, direction and density. Hence, SAR is suitable to monitoring the geometrical and physical changes in the process of urbanization.

Since it is very important to quickly monitor and update any change, here we propose a multi-scale/resolution mapping strategy to explore the characteristics of urbanization, such as the urban structure in the horizontal and vertical directions, and urban extent. Specifically, ASCAT and Nighttime light data with very coarse resolution are used to map large-scale changes. Then, 10-meters resolution SAR images are implemented to focus on more detailed building blocks.

Although scatterometers are designed to actively measure wind speed and direction over the oceans, it also has been used to monitor urban environments [1]. The resolution is approximately 10 km (in the along beam direction) x 25 km (across the beam). This data set (ASCAT Level 1B Full resolution product) is used here to explore urban structural changes, considering the energy of backscattering signal is mainly formed by the dihedral-plane structure of buildings. Also, the Nighttime light data source is also introduced to explore dynamic changes different from those coming from the scattering characteristic of urban areas.

The large-scale change detection approach is quick and efficient but the results are rather coarse. Accordingly, a more detailed survey is needed to focus on the detected changes (i.e. fastening the research by excluding unchanged portions of the urban areas under investigation). Middle-resolution and high-resolution SAR data can be used to detect these detailed changes. In this paper, Sentinel-1A SAR, RADARSAT-2 and ALOS-PolSAR data are considered in selected regions where more detailed information of the change are meant to be detected, exploiting the method recently proposed in [2].

Preliminary results show the effectiveness and feasibility of change extraction at the multiple spatial resolution that have been considered, proving by comparison the expected consistency of changes detected at macro-scale level, and investigated at the micro-scale level. Most changes from macro-scale scatterometer data are distributed around the fringe of cities or urbanized areas. Instead, the more detailed urban structures change reflect the horizontal and vertical urbanization phenomena, including areas with construction, demolition and reconstruction activities. These are recognized as “positive” or “negative” changes and extracted from multi-temporal SAR images.

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

[2] M. Che, P. Gamba, “2- and 3-Dimensional Urban Change Detection with Quad-PolSAR data”, IEEE Geoscience and Remote Sens. Lett., vol. 15, no. 1, pp. 68-72, Jan. 2018.

Abstract：Land subsidence is a slow geological disaster threatening the safety of the public and urban infrastructures. By 2009, more than 50 cities in China have been facing land subsidence problems, among which Beijing is one of the most severely affected. During the last decades, over-exploitation of groundwater has been the main factor for land subsidence in Beijing. Since the South-to-North Water Diversion Project was officially completed on December 24, 2014, the water supplied to Beijing has reached 840 million cubic meters by 2016. After the South Water delivered to Beijing, water shortage was greatly alleviated in Beijing. This study aims to investigate the spatio-temporal dynamics of land subsidence in Beijing before and after the South-to-North Water Diversion Project. Long-time series land subsidence during 2004-2017 were retrieved based on 39 Envisat ASAR images (2004-2010), 27 Radarsat-2 images (2011-2014) and 21 Sentinel-1 images (2015-2017) using PS-InSAR techniques. The paper analyzed the influence of South Water into Beijing on land subsidence in Beijing area, combining with the changes of the groundwater level after south water entering Beijing. The results showed that the maximum annual deformation velocity during the period of 2004-2010, 2011-2014 and 2015-2017 was -126.84 mm/year, -147.57 mm/year and -159.7mm/year, respectively. The leveling measurements are utilized to verify the InSAR results，which demonstrated that the absolute errors of the deformation velocity in the three periods are 0.9-9.8mm/year, 0.6-8.4mm/year and 0.89-5.51mm/year, respectively. The uneven settlement of the regional scale is obvious. By 2017, five subsidence funnel areas, namely Chaoyang-Tongzhou area, Chaoyang Caofang area, Chaoyang Jinzhan area, Changping Beiqijia area, and Haidian Xixiaoying area have been developed. The settlement funnel area extended to northward, east and southeast, and the area of funnels was expanding continuously. Time-series analysis was performed on all PS points with deformation rate faster than -25mm/year. It was found that most (89%) PS points showed increasing surface deformation rate from 2004-2010 to 2011-2014; 62% PS points showed decreasing deformation rate after South-to-North Water Diversion Project (from 2011-2014 to 2015-2017), which were mainly located in the Chaoyang-Tongzhou funnel area and the Changping Beiqijia funnel area. 29% PS points still showed an accelerating settlement during 2015-2017, mainly in the west of the Haidian Xixiaoying and the west of the Changping Shahe, the southwest edge of the Shunyi, the northeast of the Chaoyang and the southeast border of the Tongzhou funnel areas. Regression analysis between the time-series cumulative displacement and groundwater level at the second confined aquifer at four observation wells showed that land subsidence agreed well with groundwater level depth (R²>0.67), indicating the major control of groundwater on subsidence processes. According to the groundwater level map of 2012, 2014 and 2016, it was found that the groundwater level decreased obviously in 2014 compared with 2012, and recovered in 2016, , especially in the eastern and northern part of Chaoyang District,and the location of the subsidence funnel area was basically coincided with the groundwater funnel area. In summary, South-to-North Water Diversion Project has contributed to the mitigation of the ground subsidence in Beijing.
Keywords：land subsidence; South-to-North Water Diversion Project; PS-InSAR; Beijing

Urbanization affects considerably the thermal environment of cities and influence the spatial and temporal consumption of energy for heating and cooling. The increase of impervious surfaces alongside with the reduction of vegetated areas lead to increased air and surface temperatures. Remote sensing data is suitable for up-to-date urban land use mapping and for the assessment of the thermal environment of urban areas.

In this study a statistical approach is developed on the basis of satellite data in the visible and thermal infrared parts of the spectrum (for land use/land cover and land surface temperature respectively) in order to identify the areas where maximum and minimum temperature values are observed. The approach is tested to compact urban agglomerations to assess its validity.

The recognition of such areas is important as they reflect areas where immediate interventions are necessary to ameliorate the thermal environment (for instance by introducing nature based solutions), whereas the knowledge of their spatial distribution and temporal variations is needed for smart urbanization and smart governance practices.

Spatial information on the extent and expansion of built-up land cover is a valuable indicator for global and regional ecosystems and can inform effective policy alternatives for sustainable development. Optical remote sensing has proven to be a powerful tool to capture such information, although it suffers from limitations, especially where there is frequent cloud cover. The increased availability of synthetic aperture radar imagery (SAR) offers an additional means for assessing the surface features and monitoring land cover dynamics. However, its application in the built environment is not yet fully exploited due to the speckle nature and limited radiometric resolution. In this paper, we demonstrate a methodology for monitoring built-up land and revealing its expansion at a regional scale by taking advantage of the individual strengths of both radar and optical remote sensing data. The new method takes radiometric, interferometric, spectral, temporal and spatial-contextual signatures into account to resolve the ambiguities between natural/built-up lands and stable/changed areas by: (1) constraining the study extent to built-up areas using spectral information of optical datasets based on the Bayes theory; (2) integrating radiometric and interferometric information in a SAR stack with the spatial-contextual information in ancillary optical data, to detect accumulative change under a Markov random field. We test the method in two rapidly expanding regions in Nanjing city in China. Based on validation data from independent optical data and in-situ campaigns, the overall accuracy of change detection is high in both test sites, up to 82.9% and 85.5%, respectively. The small commission error (around 10%) for the changed class shows the potential of this method to pinpoint regional expansion without knowledge of any events on the ground, even in richly textural scenes. The results also prove the suitability of the approach for detecting the gradual changes in the built environment that cannot be captured from bi-temporal SAR data in previous studies.

The discomfort index (DI) is an important indicator that measures human heat sensation for different climatic conditions. Currently, the DI of a city is usually calculated using a few meteorological stations and hence does not accurately represent various thermal discomfort states of the city as a whole, especially in the event that the discomfort states vary depending such urban characteristics as urban density, % of greenery, aspect ratio, etc. This is a considerable drawback taken the importance of the index for assessing the quality of life within urban agglomerations and thus facilitating measures for smart urbanization.

In this study a technique to produce fine-scale DI maps is proposed and applied accordingly. The technique is based on the combination of Sentinel-2 and Landsat 8 images with in-situ measured meteorological data. The DI map clearly reveals the spatial details of the DI in different locations of the city and thus supports focused interventions with the potential to support the smart operation of a city.

In recent decades, the great development of radar remote sensing provides the opportunities for land cover mapping at larger extent. However, in the region with rich textures heterogeneous land covers exist and intermingle over short distances, relatively few studies have analyzed the potential of SAR datasets. Current studies focus more on the improvement of classifiers or multi-source data fusion. Radar image resolution and parameter estimation accuracy are not considered, thus structural features in a SAR image cannot be accurately described in details.

Covariance matrix is fundamental for the full exploitation of InSAR capabilities and widely applied in data processing. Present researches are mainly based on parameter estimation of the single element in covariance matrix without consideration of complex statistical inference. Conversely, these methods try to mitigate the source of errors at the cost of the increase of constraints. As a result, it is usually difficult to achieve the satisfied results in the real world when the assumptions are broken.

To solve this problem and further extend previous research into remote monitoring of urban environments, this study highlights the impact of the InSAR parameters on land cover mapping under the framework of InSAR covariance matrix estimation. More concretely, we will quantitatively evaluate the influences of the quality of the input variables, the classifiers and the information fusion on classification accuracy respectively, and show that the overall accuracy depends strongly on the error mitigation of input variables. On this basis, a methodology that fuses multitemporal SAR dataset for land cover mapping over scenes with rich textures will be proposed. The objective of the study is that we can obtain a full resolution land cover map with higher accuracy and simultaneously evaluate changed areas caused by urban extension. This research will be very useful for many populated cities especially the fast growing cities in Mainland China.

The Hengqin Island of Zhuhai City, Guangdong Province in Pearl River Delta is chosen as a typical experimental area. InSAR classification results with the traditional method and the exact InSAR parameter estimation method are compared. It is shown that the more accurate parameter estimation is as high as 10% and 9% for the overall classification accuracy and Kappa coefficient compared with the measured data.

This method will improve the accuracy of InSAR parameter estimation and simultaneously preserving the resolution of the image particularly over rich texture areas. It will also be very useful to monitor natural distribution over complicated scenes with larger extent where the region of interest is intricate. Therefore, the research proposed has both scientific and practical values.