Conference Agenda

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 weather stations and hence does not accurately represent various thermal discomfort states of the city as a whole. 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 operation of a city as smart.

Urban land surface temperature (LST) is an important parameter. However, due to the contradiction between the temporal and spatial resolution of thermal infrared remote sensing data, it’s difficult to obtain high temporal- spatial resolution LST data simultaneously. In order to solve this problem, this author considered multiple city characteristical parameters and used a simple model that can adjust the spatial distribution of scaling factors, combining with the SVM model to establish a new surface temperature spatial downscaling method which was suitable for city surface, named A-SVM.

In this paper, Beijing was chose as research area. The author divided the study into four steps. Firstly, correlation analysis and PCA (Principal Component Analysis) were used to select the parameters strongly related-to LST but independent respectively as the regression kernels (scaling factors) to do downscaling regression. The result shows that there are 6 factors were selected to do SVM regression, namely NDVI, UI, MNDWI, VAP, BAP and WAP. Screening of scale factors is the basis of SVM and A-SVM methods.

Secondly, three methods were used to verify the accuracy of the A-SVM model, which are direct resampling, TsHARP method and SVM model. The direct resampling method is a strategy that does not use any auxiliary data to directly resample a low-resolution image to a high spatial resolution. The TsHARP method is an improvement to the DisTrad quadratic model, using the negative correlation between NDVI and LST, and its basic assumption is that the linear relationship between LST and NDVI is scale invariant. SVM is a newer statistical learning theory proposed by Vapnik. Using the SVM model for low resolution to high resolution, replacing the low-resolution scale factors to high-resolution scale factors and further adding the model estimation error，high-resolution LST can be obtained.

There is a simple model to dynamically adjust the scale factor spatial distribution according to the Standard Deviation of scale factors from high resolution and low resolution LST. A-SVM model was built based on this method and SVM model. By considering the relationship between high-resolution and low-resolution images, the purpose of the model-building is to make the remote-sensing indices of the high spatial resolution image have the same spatial distribution as those of the low spatial resolution image, which will reduce the downscaling estimation error of different resolutions.

Finally, the author got the results of retrieved LST from four methods and the scatter diagram of estimated 120m LST from four methods against the LST received from TM. The 120m LST which was directly retrieved from TM thermal infrared band was selected as the real LST and verification data. Results shows that the improved method could obtain ideal downscaling results, whose RMSE was 1.82 K, R² was 0.85, and ERGAS index achieved 0.07, and it was superior to the direct resampling method, the classical method TsHARP and the SVM method whose scaling factors were unadjusted.

Through this research, this author got two important conclusions:

(1)To reflect the complex characteristics of urban surface, multiple related parameters with LST including NDVI, UI, MNDWI,VAP,BAP,WAP were chosen as the scaling factors based on the correlation and PCA analysis, which can partly show the change of urban LST and avoid redundancy phenomenon between each other.

(2) Based on the 6 kinds of scaling factors, a new adjusted-SVM spatial downscaling method for urban land surface (A-SVM) was developed combining the SVM model with a space distribution adjustment model, which could avoid the ideal assumptions that the relation between scaling factors and LST was invariant. The validation result shows A-SVM method can get the better prediction precision.

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 order to identify the areas where maximum and minimum temperature values are observed; the approach is tested for the city of Athens, Greece. The recognition of such areas is important as they reflect areas where immediate interventions are necessary to ameliorate the thermal environment, whereas the knowledge of their spatial distribution and temporal variations is needed for smart urbanization.

This abstract presents a detailed application of feature extraction methods on PolSAR records for built-up area identification. This work aims at providing a thorough description of the multivariate patterns insisting on roll-variant and roll-invariant quad-POLSAR features.

Indeed, the detection and classification of urban areas in the instantaneous field of view can take a great advantage from processing the whole amount of records and parameters obtained from POLSAR images. In fact, multivariate processing is used to deliver a thorough understanding of the relationships and regularities that can be retrieved over the selected areas for each target class. Hence, the rationale for employing both roll-variant and roll-invariant features is the intimate involvement of all the attributes acquired by POLSAR remote sensing with the anthropogenic extents distribution, which is used to provide a complete characterization of the built-up areas and of the hidden regularities lying within the records as well.

In order to provide such a multivariate analysis for feature extraction, two methods which aim at retrieving a solid recognition of significant patterns within the dataset have been taken into account. First, we employed a method named Hierarchical Binary Decision Tree (HBDT) [1], which combines different processing chains (made by a feature selection and a classification step) and automatically adapts to the spatial (and spectral) properties of the classes available in the scene. Typically, in homogeneous decision trees, at each node the same algorithm is used for separation between groups of classes. In our case, the most useful processing chain, composed by the most suitable feature set and the most efficient classifier, is selected per each node. This selection is performed by computing an intermediate accuracy assessment for each processing chain. Only the feature set (or its most noiseless subset) and classifier pair that produces the best result is selected and assigned to the node. The procedure is then repeated removing the already identified class/classes until all the nodes are identified.

Moreover, we considered a method based on mutual information maximization to explore the dataset in order to detect relevant patterns for built-up area extraction [2]. Specifically, the method that has been developed aims at providing a identifying affinity patterns, which better describe each class within the considered dataset. Good classification performances are obtained by selecting patterns fulfilling the Pareto optimality and by properly modeling a combination of the information provided by each pattern. Since the proposed approach is completely data driven and relies on information theory-based quantities, it is very flexible and totally independent from the statistics of the classes, and allows exploring datasets consisting of heterogeneous features.

Two datasets acquired by Radarsat-2 Quad-PolSAR sensor over the San Francisco area have been considered for testing. Specifically, they consist of 28 and 113 roll-invariant and roll-variant features, respectively. The aforementioned algorithms have been used to investigate the POLSAR records in order to obtain a precise characterization of the patterns that describe anthropogenic extents in the considered area. In order to accurately assess the detection and classification performance of the aforesaid methods, we analyzed the considered datasets by means of algorithms relying on a different approach. Specifically, we used methods based on ensemble learning [3] (such as random cluster ensemble and recursive feature elimination scheme) in order to explore the relationships among the data. Results show that the methods relying on the multiple feature pattern recognition are able to provide accurate extraction of built-up areas over both the considered datasets. Indeed, they are able to outperform existing algorithms while guaranteeing acceptable computational complexity costs, so that they represent a valid option for POLSAR feature extraction applied to identification of built-up areas.

Land subsidence induced by excessive groundwater withdrawal has caused serious social, geological, and environmental problems in Beijing. Rapid increases in population and economic development have aggravated the situation. Monitoring and prediction of ground settlement is important to mitigate these hazards. In this study, we combined persistent-scatterer interferometric synthetic aperture radar (PS-InSAR) with Grey system theory to predict the evolution of land subsidence in the Beijing plain. Land subsidence during 2003–2014 in the Beijing plain was determined based on 39 ENVISAT Advanced Synthetic Aperture Radar (ASAR) images and 27 RadarSat-2 images. Results were consistent with global positioning system (GPS), leveling measurements at the point level and TerraSAR-X subsidence maps at the regional level. It was demonstrated that the land surface in the Beijing plain is settling at an accelerating rate. The average deformation rate in the line-of-sight (LOS) was from −124 mm/year to 7 mm/year during 2003–2014; accumulative displacement was up to 1426 mm by the end of 2014. To predict future subsidence, the evolution of deformation was used to build a prediction model based on an improved Grey-Markov model (IGMM), which adapted the conventional GM(1,1) model by utilizing rolling mechanism and integrating a k-means clustering method in Markov-chain state interval partitioning. Evaluation of the IGMM at three representative points showed good accuracy of simulated subsidence values (root-mean-square error <3 mm). Simulated deformation during 2013 and 2014 agreed well with the observed deformation during each year based on PS-InSAR (R² = 0.94 and 0.91). Finally, InSAR measurements from 2003–2014 were used to predict subsidence in 2015–2016. It was calculated that the maximum cumulative deformation will reach 1717 mm by the end of 2016 in Beijing plain. The promising results indicate that this method provides an alternative to the conventional numerical and empirical models in order to predict short-term deformation when there is lack of detailed geological or hydraulic information.

Change detection of urban extents is now a very important topic for urban remote sensing mapping at regional and global scales. Indeed, since more and more single-date and global data sets are becoming available, change detection of urban areas is feasible. However, so far it ahs been focused on binary changes, i.e. changes that affect (either positively or negatively) the urban area extents, by considering new urbanized areas or zones given back to the natural environment.

This work aims at providing a few preliminary results of a more accurate analysis, where the density of built-up areas, and the change from low-rise to high-rise buildings (and vice versa) is also considered. The rationale for this analysis is that these situation correspond to change that mostly go unnoticed when urban extents only are considered. Still, these changes are extremely important to understand phenomena related to change in population density, leading often to overcrowding effects, and subsequently to risk management issues.

To find a sound methodology and extract this more complex changes, we explore the use of SAR, and specifically polarimetric SAR, data, under the assumption that the electromagnetic field backscattered from built-up area is affected by the geometric properties of the illuminated structures, and therefore it is different according to the type of built-up elements located in a specific geographical area. Accordingly, a segmentation of the SAR image is first performed at the first date of thr temporal sequence to be analyzed, and then multiple polarimetric parameters (in addition to the backscattered intensity) are considered, to understand which are the parameters that are more useful to analyze to track not only 2D, but also 3D changes and density changes in urban areas.

The test area for this work is the city of Nanjing, and the data sets that were used are obtained from Radarsat-2 fully polarimetric images. A complete analysis of segmentation using different object size, applied to different polarimetric decomposition parameters, and validate using both positive and negative 2D and 3D changes in urban areas have been performed. They show that it is possible to extract a richer analysis for urban changes than the simple detection of changes in urban extents in a bi-dimensional sense. However, a clear and quantitative description of the 3D change is still to be obtained, and the methodology needs to be improved to reach this goal.