Contributions of climate change and urbanization to urban flood hazard changes in China's 293 major cities since 1980.

The growing incidence of urban flood disasters poses a major challenge to urban sustainability in China. Previous studies have reported that climate change and urbanization exacerbate urban flood risk in some major cities of China. However, few assessments have quantified the contributions of these two factors to urban flood changes in recent decades at the nationwide scale. Here, surface runoff caused by precipitation extremes was used as the urban flood hazard to evaluate the impacts of climate change and urbanization in China's 293 major cities. This study assessed the contributions of these drivers to urban flood hazard changes and identified the hotspot cities with increased trends under both factors during the past four decades (1980-2019). The results showed that approximately 70% of the cities analyzed have seen an increase of urban flood hazard in the latest decade. Urbanization made a positive contribution to increased urban flood hazards in more than 90% of the cities. The contribution direction of climate change showed significant variations across China. Overall, the absolute contribution rate of climate change far outweighed that of urbanization. In half of the cities (mainly distributed in eastern China), both climate change and urbanization led to increased urban flood hazard over the past decade. Among them, 33 cities have suffered a consecutive increase in urban flood hazard driven by both factors.

Management of the designed risk level of urban drainage system in the future: Evidence from haining city, China.

The design of urban drainage infrastructure is mainly based on historical conditions. Under global warming, more intense precipitation extremes will pose severe risk to current infrastructure. The evaluation of where and by how much design standards need to change, is urgently needed to help maintain well-functioning drainage systems. In this study, we used climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and InfoWorks Integrated Catchment Modeling (ICM) to simulate urban flooding. According to the latest design standard of urban drainage infrastructure, we assess the risk of future urban flooding, and evaluate the effect and benefit of drainage infrastructure adaptation measures. The results showed that, under the shared socioeconomic pathway (SSP) 5-8.5 scenario, a 35% increase in extreme rainfall would be expected. Under a 1-in-30-year precipitation event, the maximum depth would increase by 5.59%, and the withdrawal time would rise by 2.94% in the future period, relative to the baseline level. After the enlargement of drainage infrastructure in local areas, 10% pipe enlargement has a better effect to reduce risk and higher benefits than 5% pipe enlargement. These findings provide valuable insights for policymakers in enhancing the drainage system and adapting to climate change.

Investigating relationships between landscape patterns and surface runoff from a spatial distribution and intensity perspective.

Rapid urbanization changes landscape patterns and results in frequent urban waterlogging issues, which affect citizens' daily lives and cause economic loss. Understanding the spatial patterns and impact factors associated with urban waterlogging under different rainfall intensities has significant implications for mitigating this hazard. In this study, the runoff depth calculated according to the Storm Water Management Model (SWMM) simulation results was used to investigate the spatial characteristics of urban waterlogging. Multiple scenario-based designs, a correlation analysis, and a stepwise regression model were employed to detect the relationship between surface runoff depth and landscape patterns under different rainfall intensities. The results show that when the rainfall intensity reached 12.5 mm/12 h, the conversion rate of rainfall to runoff increased significantly, indicating an increased waterlogging risk. Areas with impervious surface proportions of 25-50% and 75-100% were shown to require more attention due to the strong sensitivity of the surface runoff depth to an increase in the impervious surface. It is most cost-effective to maintain the original high-density vegetation or increase the vegetation density from 0-25% to 25-50% for urban green space. Additionally, the landscape configuration also affects the surface runoff depth. The fragmented, scattered, or regular shape of impervious surface patches can reduce surface runoff effectively; larger and less fragmented green space was also shown to have a surface runoff controlling. The adjusted R2 values were greater than 0.6 for all stepwise regression models, indicating that the landscape variables selected in the study can effectively predict the surface runoff depth. These models also showed that the landscape composition had a more profound contribution than the landscape configuration on runoff depth. These findings provide meaningful insights and perspectives for urban waterlogging hazard mitigation, quantitative landscape planning, and risk management. The method proposed by this study provides a referable framework for future studies on urban waterlogging and its response to the landscape in the context of global climate change.

Urban Inundation under Different Rainstorm Scenarios in Lin'an City, China.

Under the circumstances of global warming and rapid urbanization, damage caused by urban inundation are becoming increasingly severe, attracting the attention of both researchers and governors. The accurate simulation of urban inundation is essential for the prevention of inundation hazards. In this study, a 1D pipe network and a 2D urban inundation coupling model constructed by InfoWorks ICM was used to simulate the inundation conditions in the typical urbanized area in the north of Lin'an. Two historical rainfall events in 2020 were utilized to verify the modeling results. The spatial-temporal variation and the causes of urban inundation under different designed rainfalls were studied. The results were as follows: (1) The constructed model had a good simulation accuracy, the Nash-Sutcliffe efficiency coefficient was higher than 0.82, R2 was higher than 0.87, and the relative error was ±20%. (2) The simulation results of different designed rainfall scenarios indicated that the maximum inundation depth and inundation extent increased with the increase in the return period, rainfall peak position coefficient, and rainfall duration. According to the analysis results, the urban inundation in Lin'an is mainly affected by topography, drainage network (spatial distribution and pipe diameter), and rainfall patterns. The results are supposed to provide technical support and a decision-making reference for the urban management department of Lin'an to design inundation prevention measures.

Correlation analysis of land surface temperature and topographic elements in Hangzhou, China.

In addition to human activities, this study found that topography is also an important factor affecting land surface temperature (LST). In this paper, based on Landsat 8 OLI/TIRS remote sensing images, a radiative transfer model was adopted to retrieve the LST, and a maximum likelihood method was used to remove artificial environmental interference factors, such as water bodies and built-up lands. This paper aims to analyze the influence of topographic factors, such as elevation, slope, aspect and shaded relief, on the LST of Hangzhou. By means of a statistical analysis, we obtained the quantitative relationship between these factors and constructed a multiple linear regression model of terrain factors and LST. The research revealed the following findings: (1) in the study area, elevation and slope are negatively correlated with LST, and all the factors have linear relationships with LST. (2) The relationship between aspect and LST is not significant, and high values of LST are found on the southern, southeastern and southwestern slopes; the lowest values are found on the northern slopes. (3) There is a significant linear relationship between the values of the shaded relief map and LST, and the more shadows there are, the lower the LST value will be. (4) After comprehensive analysis of the influence of the abovementioned topographic factors on the LST, it is found that shaded relief has the greatest contribution and is positively correlated with LST. The influence of shaded relief on surface thermal environment should be paid more attention in the process of surface thermal environment work. The assessment of the influence degree of shaded relief and surface thermal environment should be the premise and basis for many other studies.

Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013.

Accurate and timely information describing urban wetland resources and their changes over time, especially in rapidly urbanizing areas, is becoming more important. We applied an object-based image analysis and nearest neighbour classifier to map and monitor changes in land use/cover using multi-temporal high spatial resolution satellite imagery in an urban wetland area (Hangzhou Xixi Wetland) from 2000, 2005, 2007, 2009 and 2013. The overall eight-class classification accuracies averaged 84.47% for the five years. The maps showed that between 2000 and 2013 the amount of non-wetland (urban) area increased by approximately 100%. Herbaceous (32.22%), forest (29.57%) and pond (23.85%) are the main land-cover types that changed to non-wetland, followed by cropland (6.97%), marsh (4.04%) and river (3.35%). In addition, the maps of change patterns showed that urban wetland loss is mainly distributed west and southeast of the study area due to real estate development, and the greatest loss of urban wetlands occurred from 2007 to 2013. The results demonstrate the advantages of using multi-temporal high spatial resolution satellite imagery to provide an accurate, economical means to map and analyse changes in land use/cover over time and the ability to use the results as inputs to urban wetland management and policy decisions.

Analytic Study on the Characteristics of Infrared Spectrum of Oil Pool Flame.

In virtue of the severity and scale of the pollution caused by oil pool flame, space remote sensing can provide us a new way of monitoring in real time the oil pool flame pollution. Space remote sensing monitoring is based on the analysis of target spectrum characteristics. Due to lack of adequate researches on the characteristics of infrared spectrum of oil pool flame, this paper carries out the analytical study on flame spectrums of several types of oil, mixed oil and other combustible objects in outdoor space by establishing all-flame infrared testing system with the spectrum range of 1～14 µm. The results show that the spectrum curves of oil pool flame of 92# gasoline, 95# gasoline, 0# diesel, aviation kerosene and lube have similar features, that there exist characteristics emission peaks at the area of certain wave lengths­H2O characteristics emission peak for 1.1, 2.4, 2.8 and 6.3 µm, CO2 characteristics emission peak for 4.2 and 4.5 µm, C­H stretching vibration emission peak for 3.4 µm, and no obvious characteristics peak for spectrum curves of 6.3 µm and above; that there is no obvious difference in the spectrum of oil pool flame among the mixtures of 92# gasoline and 0# diesel at different proportions, that the comparison of the flame spectrum of 92# gasoline with that of wood and paper shows that there appears a characteristics emission peak at 3.4 µm; that though the flame spectrum of alcohol has similar radiated emission near 3.4 µm, the proportion of its radiation intensity to that of CO2 at 4.5 µm is far less than that for the flame spectrum of 92# gasoline; that the flame spectrum of honeycomb briquette is similar to that of gray body radiation. The differences in flame spectrum among all kinds of combustible materials are closely linked to their chemical compositions and burning reaction mechanisms. Comparative analysis on the spectrum characteristics at continuous area, intermission area and flue gas area shows that C­H stretching vibration peak only exists in continuous area, which proves that the emission peak is caused by the combustible reaction of oil and gas. This result is in line with the mechanism of oil pool combustion reaction. The experimental conclusion is of great significance in the remote-sensing recognition of oil pool flame based on the analysis of spectrum characteristics.

[Hard and soft classification method of multi-spectral remote sensing image based on adaptive thresholds].

Hard and soft classification techniques are the conventional methods of image classification for satellite data, but they have their own advantages and drawbacks. In order to obtain accurate classification results, we took advantages of both traditional hard classification methods (HCM) and soft classification models (SCM), and developed a new method called the hard and soft classification model (HSCM) based on adaptive threshold calculation. The authors tested the new method in land cover mapping applications. According to the results of confusion matrix, the overall accuracy of HCM, SCM, and HSCM is 71.06%, 67.86%, and 71.10%, respectively. And the kappa coefficient is 60.03%, 56.12%, and 60.07%, respectively. Therefore, the HSCM is better than HCM and SCM. Experimental results proved that the new method can obviously improve the land cover and land use classification accuracy.

[Division of winter wheat yield estimation by remote sensing based on MODIS EVI time series data and spectral angle clustering].

Crop yield estimation division is the basis of crop yield estimation; it provides an important scientific basis for estimation research and practice. In the paper, MODIS EVI time-series data during winter wheat growth period is selected as the division data; JiangSu province is study area; A division method combined of advanced spectral angle mapping(SVM) and K-means clustering is presented, and tested in winter wheat yield estimation by remote sensing. The results show that: division method of spectral angle clustering can take full advantage of crop growth process that is reflected by MODIS time series data, and can fully reflect region differences of winter wheat that is brought by climate difference. Compared with the traditional division method, yield estimation result based on division result of spectral angle clustering has higher R2 (0.702 6 than 0.624 8) and lower RMSE (343.34 than 381.34 kg x hm(-2)), reflecting the advantages of the new division method in the winter wheat yield estimation. The division method in the paper only use convenient obtaining time-series remote sensing data of low-resolution as division data, can divide winter wheat into similar and well characterized region, accuracy and stability of yield estimation model is also very good, which provides an efficient way for winter wheat estimation by remote sensing, and is conducive to winter wheat yield estimation.

[Winter wheat area estimation with MODIS-NDVI time series based on parcel].

Several attributes of MODIS (moderate resolution imaging spectrometer) data, especially the short temporal intervals and the global coverage, provide an extremely efficient way to map cropland and monitor its seasonal change. However, the reliability of their measurement results is challenged because of the limited spatial resolution. The parcel data has clear geo-location and obvious boundary information of cropland. Also, the spectral differences and the complexity of mixed pixels are weak in parcels. All of these make that area estimation based on parcels presents more advantage than on pixels. In the present study, winter wheat area estimation based on MODIS-NDVI time series has been performed with the support of cultivated land parcel in Tongzhou, Beijing. In order to extract the regional winter wheat acreage, multiple regression methods were used to simulate the stable regression relationship between MODIS-NDVI time series data and TM samples in parcels. Through this way, the consistency of the extraction results from MODIS and TM can stably reach up to 96% when the amount of samples accounts for 15% of the whole area. The results shows that the use of parcel data can effectively improve the error in recognition results in MODIS-NDVI based multi-series data caused by the low spatial resolution. Therefore, with combination of moderate and low resolution data, the winter wheat area estimation became available in large-scale region which lacks completed medium resolution images or has images covered with clouds. Meanwhile, it carried out the preliminary experiments for other crop area estimation.

[Integration of soft and hard classifications using linear spectral mixture model and support vector machines].

This paper presents a new soft and hard classification. By analyzing the target objects in the image distribution, and calculating the adaptive threshold automatically, the image is divided into three regions: pure regions, non-target objects regions and mixed regions. For pure regions and non-target objects regions, hard classification method (support vector machine) is used to quickly extract classified results; For mixed regions, soft classification method (selective endmember for linear spectral mixture model) is used to extract the abundance of target objects. Finally, it generates an integrated soft and hard classification map. In order to evaluate the accuracy of this new method, it is compared with SVM and LSMM using ALOS image. The RMSE value of new method is 0.203, and total accuracy is 95.48%. Both overall accuracies and RMSE show that integration of hard and soft classification has a higher accuracy than single hard or soft classification. Experimental results prove that the new method can effectively solve the problem of mixed pixels, and can obviously improve image classification accuracy.

[Farmland parcel extraction based on high resolution remote sensing image].

A new method of farmland parcel extraction from high resolution remote sensing image based on wavelet and watershed segmentation was proposed in the present paper. First, classification results were used to enhance the contrast of gray-scale value of typical pixels in the original image using the high resolution remote sensing image's spectral information. Second, wavelet transform and watershed segmentation were applied to the enhanced image, then improved region merger algorithm was used to solve the problem of over-segmentation. Finally, inverse wavelet transform was taken to get the reconstructed image, then Canny operator was introduced to add the edge information, and the result of farmland parcel segmentation was obtained. To validate the proposed approach, experiments on Quickbird images were performed, we rapidly extracted the farmland parcel from the test image, and the results had a high accuracy. Despite it had a lot to do in extracting the small size parcels, on the whole the method this paper proposed had a very good robustness. Compared with the traditional methods, it had the following advantages: (1) it was an automatic extraction method, did not need too much manual intervention, and could extract the large area of farmland parcels accurately and effectively. (2) It was a very good solution to the problem of over-segmentation by using improved region merger algorithm, and improved the accuracy of the extraction. All these indicated that the proposed approach was an effective farmland parcel extraction method based on high resolution remote sensing image.