基于SPOT4数据的黄土高原植被动态变化研究

以SPOT4/VEGETATION数据为基础,以NDVI变化率和年均NDVI值作为植被覆盖动态变化的指标,分析了1998~2005年黄土高原植被覆盖的时空动态变化特征。结果表明黄土高原地区植被动态变化显著增强,1998~2001年黄土高原的植被覆盖有所减少,幅度约为10.5%,2001年后,植被活动显著增强,植被覆盖面积呈增加趋势,2004年后稍有回落。植被生长季的延长和生长加速是该区域NDVI值增加的主要原因,黄土高原地区植被增加和减少的区域相互交错,这一特性是由农业生产活动、城市建设、政府决策及植被对气候变化的响应等综合因素作用的结果。     

The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data

The relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) associated with urban land‐use type and land‐use pattern is discussed in the City of Shanghai, China using data collected by the Enhanced Thematic Mapper Plus (ETM+) and aerial photographic remote sensing system. There is an apparent correlation between LST and NDVI from the visual interpretation of LST and NDVI contrasts. Mean LST and NDVI values associated with different land‐use types are significantly different. Multiple comparisons of mean LST and NDVI values associated with pairings of each land‐use type are also shown to be significantly different. The result of a regressive analysis shows an inverse correlation relationship between LST and NDVI within all land‐use polygons, the same to each land‐use type, but correlation coefficients associated with land‐use types are different. An analysis on the relationship between LST, NDVI and Shannon Diversity Index (SHDI) shows a positive correlation between LST and SHDI and a negative correlation between NDVI and SHDI. According to the above results, LST, SHDI and NDVI can be considered to be three basic indices to study the urban ecological environment and to contribute to further validation of the applicability of relatively low cost, moderate spatial resolution satellite imagery in evaluating environmental impacts of urban land function zoning, then to examine the impact of urban land‐use on the urban environment in Shanghai City. This provides an effective tool in evaluating the environmental influences of zoning in urban ecosystems with remote sensing and geographical information systems.     

Satellite-based analysis of regional evapotranspiration trends in a semi-arid area

A regional framework for a spatial and temporal distributed assessment of time series trends in the hydrological variable and its related ecological factors of an arid area was presented in this research. To achieve this, we tested the Surface Energy Balance System (SEBS) algorithm for estimating evapotranspiration (ET) in regional scale and the result was further validated by water budget. The ET assessment was applied for the Yinchuan and Weining (YW) Plains (China), the upstream areas of the Yellow River. Moreover, we analysed the recent trends (from year 2001 to 2014) in actual ET, normalized difference vegetation index (NDVI), farmland and wetland using a combination of remote sensing and ground observations. The results illustrated that the yearly ET of 78% areas has no change during the study period and the areas with decreasing ET are larger than the areas with increasing trend. The highest decreasing rate is observed in urban areas and the value is –20 mm year^–1, while the rate of increasing trend is especially higher in the wetlands reaching around 60 mm year^–1. This phenomenon can be explained by changes in NDVI, farmland and wetland. The distribution and magnitude of NDVI trends shows that the higher NDVI (NDVI > 0.4) area is occupied 56% and the farmland (NDVI > 0.55) covered about 35% of the YW Plains. The increasing trends of mean NDVI is mostly observed in farmland and shrub covers, while the decreasing NDVI areas are mainly wetlands and urban area. The emerging image showed that the greening trend of vegetation and variation of evapotranspiration in the YW Plains are related to land-cover changes and to the adjustment of crop pattern for agriculture. The increasing of wetland area is also believed as a cause related to evapotranspiration change. Such spatiotemporally distributed analysis in regional level is important for water management at this scale, which can be also applied to other similar areas.     

Aerosol optical thickness over Pearl River Delta region,China

We validated moderate resolution imaging spectroradiometer (MODIS) Level 2 aerosol products with ground-based sun photometer (CE-318) measurements over the Pearl River Delta (PRD) region. MODIS aerosol products are also used to investigate the temporal and spatial variations of aerosol optical thickness (AOT). The results show that MODIS AOT is validated quantitatively with a higher correlation coefficient (r = 0.88, 0.80 at Guangzhou and r = 0.95, 0.92 at Hong Kong) and lower root mean square errors (RMSE = 0.15, 0.16 at Guangzhou and RMSE = 0.07, 0.08 at Hong Kong), while the Ångström exponent (α) is still in doubt (r = 0.09). The MODIS AOT values are generally higher than those of the CE-318 values in Guangzhou and smaller than those in Hong Kong. The regional multi-year monthly (July 2002–December 2012) mean AOT values are 0.66 ± 0.20 and 0.64 ± 0.18 for Terra- and Aqua-MODIS, respectively. From month to month, the values of Terra-MODIS AOT are larger than those of Aqua-MODIS during most of the month. This implies that AOT in the morning is generally larger than that in the afternoon. The largest monthly AOT occurred in April at 0.85 ± 0.16 and 0.88 ± 0.17 for Terra-MODIS and Aqua-MODIS, respectively, and the smallest occurred in November for both Terra- and Aqua-MODIS at 0.47 ± 0.13 and 0.47 ± 0.10, respectively. The spatial distribution of AOT in spring and summer shows more variation than in autumn and winter. This can be partially attributed to the cleansing effect of precipitation which clears aerosol particles over the whole region in spring and summer and results in a lower AOT outside urban areas, while AOT in urban areas is higher where anthropogenic aerosols build up quickly despite the cleansing effect of the rain.     

Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data

Information about the extent of impervious surface and its rate of development is a valuable indicator of urban growth and environmental quality and thus relevant for a wide range of research related to urban ecosystems. Using SPOT-5 data from 2005 to 2009, impervious surface was estimated at a subpixel level for the area of Can Tho province in the Mekong Delta, based on a Support Vector Regression model. Training data comprised a set of SPOT-5 reflectance values each associated with an individual value of subpixel imperviousness as their respective labels. The latter were obtained on the basis of a land cover map, which in turn was derived from a pansharpened QB subset by means of an object-oriented image classification approach. In addition, by varying different sets of training data in the model building process the spectral interrelationships between the urban land cover classes (water, bare soil, vegetation, and impervious surface) and their effect on the estimation of subpixel imperviousness could be examined. In order to exclude irrelevant areas (natural/undeveloped land) from the impervious surface estimation process, single-polarised TerraSAR-X data were used to delineate settlement areas by an object-oriented image classification approach. Furthermore, a change detection method was applied for the respective time period in order to test the suitability of the approach for the automated detection of structural developments within the urban topography. Settlement areas were correctly identified with overall accuracies between 81% and 94%, whilst the comparison of the modelled impervious estimates to the training values gave an absolute mean error below 15%. The results prove the suitability of the approach for an area-wide but selective mapping and monitoring of impervious surface cover within settlement areas only.     

Controlling Corruption in Hong Kong: From Colony to Special Administrative Region

Corruption has been a perennial problem in Hong Kong. The Hong Kong of the post‐war years represented what appeared to have become an intractable case of a society in which corruption was entrenched as part of political, economic and social life. This paper seeks to delineate the experience of Hong Kong’s fight against corruption in the midst of a rapidly changing political and social environment. After describing the context in which the Hong Kong anti‐corruption programme is set, this paper identifies the critical policy decisions that account for the programme’s success and the lessons Hong Kong has learned from the campaign. It ends by highlighting some of the current issues and problems that arise from the changing circumstances of Hong Kong’s development.     

Spatial variability of air temperature and appropriate resolution for satellite‐derived air temperature estimation

This study investigates the spatial variability of air temperature over Hong Kong using in situ air temperature recorded from a mobile traverse combined with an ASTER thermal satellite image. Three different degrees of urbanization in Hong Kong, including city downtown (Kowloon), suburban areas (Yuen Long and Shatin), and rural countryside (Tai Mo Shan and Lam Tsuen) are analysed. The spatially variable relationship between air and surface temperature was evaluated using two spatial averaging techniques, namely spatial resampling and buffering around air temperature points. The strength of the correlation coefficient was tested for every decreasing resolution and the appropriate spatial scales of air temperature in urban, suburban and rural areas were found to be 200 m, 450 m and 700 m, respectively. The differences in the spatial scales of air temperature in these regions are attributed mainly to structural factors of land cover such as city block size, building density and percentage of green areas, and secondarily to the climatic conditions being operating in, and which commonly typify these individual regions. Thus small scale lengths in the urban area corresponded to heterogeneous land cover, a well developed urban boundary layer, low wind speeds and a low lapse rate, whereas longer scale lengths were observed in suburban and rural areas having more homogeneous land cover, higher wind speeds and higher lapse rate.     

Desert dust aerosols observed in a tropical humid city: a case study over Hong Kong

Observations from the AErosol RObotic NETwork (AERONET) sunphotometers, MODerate resolution Imaging Spectroradiometer (MODIS) satellite images, back-trajectory modelling and ‘in-situ’ PM10 measurements in Hong Kong confirmed that two dust storms on 16–17 April 2006 and 27–30 April 2009, with source areas in northwest China, affected the city. The impacts of the dust on the air quality of Hong Kong were quantified using aerosol optical properties from AERONET data and local PM10 (particle size less than 10 μm) concentrations. Combined analysis of back trajectories and the microphysical properties of the dust aerosols from AERONET inversion data suggest that the dust particulates are sometimes associated with industrial chemicals on arrival in Hong Kong. This is the first remote-sensing study to observe the presence and characteristics of Asian dust carried into the humid tropical region of south China.     

A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. Cane Wing-ki Leung is a PhD student in the Department of Computing, The Hong Kong Polytechnic University, where she received her BA degree in Computing in 2003. Her research interests include collaborative filtering, data mining and computer-supported collaborative work. Stephen Chi-fai Chan is an Associate Professor and Associate Head of the Department of Computing, The Hong Kong Polytechnic University. Dr. Chan received his PhD from the University of Rochester, USA, worked on computer-aided design at Neo-Visuals, Inc. in Toronto, Canada, and researched in computer-integrated manufacturing at the National Research Council of Canada before joining the Hong Kong Polytechnic University in 1993. He is currently working on the development of collaborative Web-based information systems, with applications in education, electronic commerce, and manufacturing. Fu-lai Chung received his BSc degree from the University of Manitoba, Canada, in 1987, and his MPhil and PhD degrees from the Chinese University of Hong Kong in 1991 and 1995, respectively. He joined the Department of Computing, Hong Kong Polytechnic University in 1994, where he is currently an Associate Professor. He has published widely in the areas of computational intelligence, pattern recognition and recently data mining and multimedia in international journals and conferences and his current research interests include time series data mining, Web data mining, bioinformatics data mining, multimedia content analysis,and new computational intelligence techniques.     

Evaluating environmental influences of zoning in urban ecosystems with remote sensing

The influence of zoning on Normalized Difference Vegetation Index (NDVI) and radiant surface temperature (T_s) measurements is investigated in the City of Indianapolis, IN, USA using data collected by the Enhanced Thematic Mapper Plus (ETM+) remote sensing system. Analysis of variance indicates statistically significant differences in mean T_s and NDVI values associated with different types of zoning. Multiple comparisons of mean T_s and NDVI values associated with specific pairings of individual zoning categories are also shown to be significantly different. An inverse relationship between T_s and NDVI was observed across the city as a whole and within all but one zoning category. A range of environmental influences on sensible heat flux and urban vegetation was detected both within and between individual zoning categories. Examples for implementing these findings in urban planning applications to find examples of high and low impact development are demonstrated.     

Logistic函数方法拟合多时序NDVI数据的改进研究

单Logistic函数曲线拟合法是NDVI时间序列重建及物候遥感中关键物候期划分的重要方法之一。虽然该方法不需要设定阈值或经验系数、较适应于不同环境区域的物候遥感监测,但是在山区NDVI噪音较大的情形下,其拟合精度仍会受到较大影响。选取秦岭中部山区为研究区,在分析了多年NDVI时间序列数据特征基础上,利用山区NDVI数据序列最大值相对于最小值更为稳定的特性,对传统单Logistic模型求解方法进行改进,采用更为稳定的参数构建模型以提高NDVI时间序列重建的精度。基于秦岭样区MODIS NDVI多年遥感数据,分别在保持植被生长季特征能力和保留高质量原始真值程度两方面对原方法与改进方法的计算结果进行比较。研究表明改进的方法在上述两个方面都具有更好的效果。在植被指数噪音较大的山区,改进的方法对NDVI时间序列重建表现出更好的适用性,可为复杂的山区物候遥感相关研究提供参考。     

The reconstruction of abnormal segments in HJ-1A/B NDVI time series using MODIS: a statistical method

HJ-1A/B is the first small satellite constellation built by China for environmental and disaster monitoring and forecasting. The satellite group has a 2-day repetition cycle and 30 m spatial resolution (charge coupled device camera). Thus, HJ-1A/B can provide hyper-temporal normalized difference vegetation index (NDVI) time series with a medium–high spatial resolution. However, the quality of the HJ NDVI time series can be abnormally low due to a number of factors, such as cloud cover, continuous fog, and haze. In the rainy season or in areas with serious atmospheric pollution, low-quality series often appear in succession, which is referred to as an abnormal segment. Neither the composition method nor quality flags satisfactorily solve this problem; therefore, a large amount of noise and long periods of abnormally low values often remain in HJ NDVI time series. This article presents a method to reconstruct the abnormal segments in HJ NDVI time series with the assistance of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series. The cointegration test was adopted to decide whether MODIS can be used for the reconstruction of NDVI time series for the corresponding HJ image pixels. Statistical quality control methods were used for singling out the abnormal segments in the HJ NDVI time series and establishing an error correction model that combines MODIS and HJ NDVI time series to perform the reconstruction. The study area is located in Jiangsu Province, China. Four-year (2009–2012) HJ multispectral images that cover the study area were used. The results show that abnormal segments in the HJ NDVI time series can be corrected using the proposed method. In a particular year, this method can decrease the root mean square error between the HJ NDVI time series and the reference sequence by 52.5%.     

A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data

Mapping urban areas at regional and global scales has become an urgent task because of the increasing pressures from rapid urbanization and associated environmental problems. Satellite imaging of stable anthropogenic lights from DMSP-OLS provides an accurate, economical, and straightforward way to map the global distribution of urban areas. To address problems in the thresholding methods that use empirical strategies or manual trial-and-error procedures, we proposed a support vector machine (SVM)-based region-growing algorithm to semi-automatically extract urban areas from DMSP-OLS and SPOT NDVI data. Several simple criteria were used to select SVM training sets of urban and non-urban pixels, and an iterative classification and training procedure was adopted to identify the urban pixels through region growing. The new method was validated using the extents of 25 Chinese cities, as classified by Landsat ETM+ images, and then compared with two common thresholding methods. The results showed that the SVM-based algorithm could not only achieve comparable results to the local-optimized threshold method, but also avoid its tedious trial-and-error procedure, suggesting that the new method is an easy and simple alternative for extracting urban extent from DMSP-OLS and SPOT NDVI data.     

Value engineering in Hong Kong — a powerful tool for a changing society

Hong Kong, one of the most dynamic cities in Asia if not the world, has been changing rapidly in recent years as a result of technological advancement, fierce local and international competition, and ever-increasing customer requirements. In addition, as design and production costs become more significant, different cost control techniques and the means of achieving better value for money are becoming increasingly important. Value engineering (VE) is one way to improve efficiency and seek the best balance between the cost, reliability and performance of a product or service. Though value engineering is still in its infancy in Hong Kong, there are already signs that value engineering is being used in the private and public sectors. This paper highlights the current development of value engineering in Hong Kong in terms of owners' acceptance of it. Details of VE education, research and professional institution in Hong Kong are also given.     

Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up‐to‐date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub‐pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS‐2 SAR data. Validated by reference data derived from high‐resolution colour‐infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud‐prone and rainy areas.     

Automating map production of the 1981 Census data for Brighton and Hove,England

In 1981 a full census was held in England and Wales. This paper describes how the census data was mapped for two adjacent urban areas in south east England, namely Brighton and Hove. The techniques used to transform nearly 3.2 million numbers relating to the 580 statistical areas used in the census for our area of study, into a coherent set of choropleth shaded maps and using a decile class system, are discussed and demonstrated.     

Land use/land cover changes in the coastal zone of Ban Don Bay,Thailand using Landsat 5 TM data

Land use/land cover of the Earth is changing dramatically because of human activities and natural disasters. Information about changes is useful for updating land use/land cover maps for planning and management of natural resources. Several methods for land use/land cover change detection using time series Landsat imagery data were employed and discussed. Landsat 5 TM colour composites of 1990, 1993, 1996 and 1999 were employed for locating training samples for supervised classification in the coastal areas of Ban Don Bay, Surat Thani, Thailand. This study illustrated an increasing trend of shrimp farms, forest/mangrove and urban areas with a decreasing trend of agricultural and wasteland areas. Land use changes from one category to others have been clearly represented by the NDVI composite images, which were found suitable for delineating the development of shrimp farms and land use changes in Ban Don Bay.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

We have analysed monthly composites of normalized difference vegetation index (NDVI) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991) to ascertain if the dominant vegetation types could be differentiated,and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest ( submontana and terras baixas ), open forest ( submontana and terras baixas ), transitional forest, seasonal forest ( caatinga ), and two types of savanna ( cerrado ). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identification, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and significant reduction ( p 0.95) in dry years, such as those with El Nino Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identification of forest ecosystems. Our research found NDVI images from NOAA AVHRR offer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.     

基于GIS和相关数学模型的城市环境空气质量评价系统

环境影响评价对于城市规划管理具有重要意义,它所处理的对象带有明显的空间属性。介绍了城市环境空气质量评价系统利用ArcView GIS对空间数据的编辑、管理和分析功能,通过在VB中开发的应用程序调用环境空气质量数学模型CALINE4和ISCST3,对城市区域环境空气质量进行计算预测,并在香港的市区发展规划项目中得到了成功的应用。