GIS-based impact assessment of land-use changes on groundwater quality: study from a rapidly urbanizing region of South India

This study attempts to assess the influence of changing land-use patterns on the groundwater quality of the hard rock aquifer system in the Maheshwaram watershed, near Hyderabad, India. The study area is a rapidly urbanizing region with land development progressing at a fast pace. To study the impact of this rapid urbanization and overall land-use transition, a groundwater quality index (GQI) was prepared within a geographical information system (GIS). The GQI integrates the different water quality parameters to give a final index value that can be used for spatio-temporal comparisons. The land-use transitions were closely monitored from 2003 to 2008 using multispectral satellite images. The land-use pattern has changed drastically with an increase in the built-up area at the expense of other land uses. The analysis reveals a rapid deterioration of groundwater quality related mainly to the increase in built-up land with unsewered sanitation and poultry farms. Seasonal variability of the groundwater quality was also assessed. Mean GQI decreased from 84.16 to 83.26 over a period of 5 years from 2003 to 2008, while seasonal variability of water quality increased. GQI and Seasonal Variability of water quality were integrated in GIS to yield a groundwater sustainability map, in terms of water quality. Zones of sustainable and unsustainable groundwater use were demarcated for better decision making related to municipal land allotment in this rapidly urbanizing region.     

电阻率图像重建的佐迪(Zohdy)反演及其应用效果

本文讨论了利用佐迪反演方法对地表高密度电阻率法的数据采集结果进行电阻率图像重建。通过对多种复杂地电模型的数值模拟和野外实例的反演分析,表明该方法简单易行,计算成本较低,成像的分辨率高,效果较理想,将其用于地表测量的电阻率层析成像完全可行。作者用Fortran语言编制了实现上述过程的程序,对所有数学模型的正演计算均采用2.5维有限元法。为突出成像效果,本文在结果图示时均采用了伪彩色图像处理技术。     

Mapping wetland cover in the greater Himalayan region: a hybrid method combining multispectral and ecological characteristics

In the greater Himalayan region, wetland ecosystems such as lakes, marshes, and peat lands play an important role in regulating the flow of major rivers. These ecosystems are often overlooked and not well represented on land-use planning and conservation maps. Wetland complexes are partly ephemeral and difficult to map accurately either with digital image processing or visual interpretation. This study developed a hybrid method of extracting spatial patterns of wetland areas which combines month-on-month multispectral classifications of Moderate Resolution Imaging Spectroradiometer data with a sample wetland extraction method based on knowledge of the spectral characteristics of satellite data and wetland ecological systems. The results were compared with the best available sources for lakes and wetlands on global and national scales. It was demonstrated that the method could extract wetlands automatically to a reasonable degree of accuracy and, therefore, reduce the need for extensive ground knowledge. The model was transferred by adjusting parameters through application of high-resolution satellite data (Landsat) in some sub-basin areas. These findings are cognizant with field interviews conducted by wetland experts. The hybrid method and high-altitude wetlands maps will provide decision makers with valuable information about wetland distribution and change in response to global warming and human activities.     

Land degradation analysis of mine-impacted zone of Kolubara in Serbia

This paper presents the effect of land-use changes on land degradation by utilization of remote sensing methods and the Gavrilovi? erosion potential method, due to surface mining activities in the period 2001–2011. Studied area includes the zone of influence of the Rudarski basen Kolubara, which covers the exploitation area of the Kolubara coal basin in the Republic of Serbia. The analysis is based on the processing of Landsat 5 satellite images from 2001 and 2011. In the analysis of satellite images, the supervised classification method was used. The results suggest that the observed spatial–temporal area is exposed to erosion as a result of land-use changes. Preliminary conclusion is that the dominant anthropogenic factor, responsible for changes in land use, is pronounced through the mining activity, industrialization, construction as well as agricultural activities. Very small changes in land use that occurred in the period 2001–2010 have led to relatively small amount of change from the aspect of land degradation. Also, due to the planned exploitation of the mine which included into account the sustainability of ecosystems, primarily through appropriate conservation measures, RB Kolubara is not in danger of a possible erosion processes that could undermine the stability of the basin.     

Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island,Malaysia

Land surface temperature (LST) plays an important role in local, regional and global climate studies. LST controls the distribution of the budget for radiation heat between the atmosphere and the earth’s surface. Therefore, it is important to evaluate abrupt changes in land use/land cover (LULC). Penang Island, Malaysia has been experiencing a rapid and drastic change in urban expansion over the past two decades due to growth in industrial and residential areas. The aim of this study was to investigate and evaluate the impact of LST with respect to land use changes in Penang Island, Malaysia. Three supervised classification techniques known as maximum likelihood, minimum distance-to-mean and parallelepiped were applied to the images to extract thematic information from the acquired scene by using PCI Geomatica 10.1 image processing software. These remote sensing classification techniques help to examine land-use changes in Penang Island using multi-temporal Landsat data for the period of 1999–2007. Training sites were selected within each scene and seven land cover classes were assigned to each classifier. The relative performance of each technique was evaluated. The accuracy of each classification map was assessed using a reference data set consisting of a large number of samples collected per category. Two Landsat satellite images captured in 1999 and 2007 were chosen to classify the LULC types using the maximum likelihood classification method, determined from visible and near-infrared bands. The study revealed that the maximum likelihood classifier produced superior results and achieved a high degree of accuracy. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC. The results showed that the urban (highly built-up) area increased dramatically, and grassland area increased moderately. Inversely, barren land decreased obviously, and forest area decreased moderately. While urban (minimally built-up) area decreased slightly. These changes in LULC caused at significant difference in LST between urban and rural areas. Strong correlation values were observed between LST and NDVI for all LULC classes. The remote sensing technique used in this study was found to be efficient; it reduced the time for the analysis of the urban expansion, and it was found to be a useful tool to evaluate the impact of urbanisation with LST.     

Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images

The mountainous region represents the most important agricultural and biodiversity haven in Jordan. The objective of this study is to characterize the seasonal pattern of land use and vegetation using multi-temporal SPOT images. Multi-temporal SPOT images were analyzed to characterize the land use and cropping pattern in the mountain regions of Jordan. The images were radiometrically corrected using invariant objects located on the image, and a linear inter-calibration method was used to calibrate the other images. A hybrid classification approach was used in the classification; the spectral signatures of the land-use classes were derived in an iterative procedure using the ISODATA and field survey data. Then, the maximum likelihood classification was applied on all images to classify the class signatures into thematic land-use types. The hybrid classification approach gives more accurate classification accuracy especially for the multi-seasonal image classification. The overall accuracy of the multi-temporal data set was achieved with 87.9%, while classification accuracy for single-date classifications were 61.3, 76.8, 72.2, and 65.5 for months of October, February, April, and June, respectively. In addition, the scene combinations that were derived from February and April were classified the land-use types almost as well as those combinations including more scenes. Regarding the classification details, the multi-temporal images enable higher level of classification for land-use types such as Anderson level 2, and produce accurate boundaries for the different cropping and farming systems.     

Correlation between land-use change and greenhouse gas emissions in urban areas

Urban areas are the main sources of greenhouse gas (GHG) emissions. Previous studies have identified the effectiveness of better urban design on mitigating climate change and land-use patterns in cities as important factors in reducing GHG by local governments. However, studies documenting the link between land-use and GHG emissions are scant. Therefore, this study explores the driving forces of land-use change and GHG emission increments in urban areas and investigates their correlations. The study area, Xinzhuang, is a satellite city of Taipei that has rapidly urbanized in the past few decades. Twenty-one potential variables were selected to determine the driving forces of land-use change and GHG emission increments by binomial logistic regression based on the investigation data of national land use in 1996 and 2007. The correlation of land-use change and GHG increments was examined by Spearman rank-order analysis. Results of logistic regression analysis identified that population and its increasing density rate are main driving forces on both land-use change and GHG increments. The Spearman rank correlation matrix indicates that fluctuating urbanization level is significantly correlated with the increase of total GHG emissions, the emissions of residence, commerce, and transportation sectors in neighborhoods; and the emissions of residence and transportation sectors seem closely connected to current urbanization level. The findings suggest that relationships among land-use, urbanization, and GHG emissions in urban areas vary greatly according to residence and transportation characteristics. Land-based mitigation may provide the most viable mechanism for reducing GHG emissions through residence and transportation sectors.     

A comparison of different land-use classification techniques for accurate monitoring of degraded coal-mining areas

Classification of different land features with similar spectral response is an enigmatical task for pixel-based classifiers, as most of these algorithms rely only on the spectral information of the satellite data. This study evaluated the performance of six major pixel-based land-use classification techniques (both common and advanced) for accurate classification of the heterogeneous land-use pattern of Jharia coalfield, India. WorldView-2 satellite data was used in the present study. The land-use classification results revealed that Maximum Likelihood classifier algorithm performed best out of the four common algorithms with an overall accuracy of about 84%. The advanced classifiers used in the study were Neural-Net and Support Vector Machine both of which gave excellent results with an overall accuracy of 91% and 95%, respectively. It was observed that use of very high-resolution data is not sufficient for obtaining high classification accuracy, selection of an appropriate classification algorithm is equally important to get better classification results. Advanced classifiers gave higher accuracy with minimal errors, hence, for critical planning and monitoring tasks these classifiers should be preferred.     

TM影像在1∶25万国土资源综合大调查中的应用

本文简述了作者在对新疆博斯坦、西藏聂拉木地区遥感初步解释过程中体会：用专业遥感图像处理系统，对TM数据进行相关的处理，专题信息提取；并根据色调、影纹结构、阴影等各种直接或间接解译标志，从地貌、地质构造、生态等方面着手，论述了其在TM影像上的特征。为该地区进行1：25万国土资源大调查做了较为充分的技术准备，以及为下一步更好地开发大调查打下了良好的基础。     

TM影像在1∶25万国土资源综合大调查中的应用——新疆博斯坦、西藏聂拉木国土资源调查遥感初译简述

本文简述了作者在对新疆博斯坦、西藏聂拉木地区遥感初步解译过程中的体会 :用专业遥感图像处理系统 ,对TM数据进行相关的处理 ,专题信息提取 ;并根据色调、影纹结构、阴影等各种直接或间接解译标志 ,从地貌、地质构造、生态等方面着手 ,论述了其在TM影像上的特征。为该地区进行 1 :2 5万国土资源大调查做了较为充分的技术准备 ,以及为下一步更好地开展大调查打下了良好的基础     

基于“3S”技术的物探测量系统

对勘探精度及分辨率要求越来越高的地球物理勘探方法对测量技术提出了更严格的标准,而“3S”（GPS、RS、GIS）技术在物探测量中的应用,为物探测量工作带来更好的解决方案。通过分析”3S”技术在测量中存在的若干技术和概念问题．指出GPS技术在物探测量中的应用,可有效提高测量精度,保证测量数据的可靠性,提高测量效率,降低工程费用：RS技术在物探测量中的应用．为在沼泽地、高山区等不易到达的地区施测提供了参照资料,其小于1m的影像图分辨率,对于物探施工设计、变观放样及精确绘制测线草图,提供了较大方便;基于位置多属性处理和分析的GIS技术,在数据处理、图形处理、空间查询、可视化表达等方面,为提高物探测量技术水平提供了强有力的技术支持．     

利用卫星数据建立排山楼热液石英脉型金矿模型

航天矿床标志模型(SDS)是遥感技术在矿床勘探中应用的结果.在遥感数据(陆地卫星MSS、TM和ETM+图像)解译、地质特征及金矿化类型研究的基础上,总结了不同控矿因素及其鉴别特征.通过如下步骤建立模型:1)选择适当的图像处理技术;2)研究主要控矿因素(构造、地层、蚀变);3)确定鉴别标志,建立矿床图像模型,优化找矿靶区.应用该模型优化靶区已获得成功.卫星影像处理显示,在3个区域(南带、中带、北带)具有反映金成矿的强烈蚀变特征.实地调查证明,这3个预测区具有金矿化,它们与常规勘探方法(地球物理、地球化学)发现的其他矿化相似.该模型在排山楼地区的应用结果表明,卫星数据在金矿普查中可以发挥重要作用.     

基于遥感图像的海岸线提取方法研究

海岸线是划分海洋与陆地管理区域的基准线,不同地貌的海岸线划分依据不尽相同,在卫星图像上的解译标志与提取方法也存在差异。根据我国东南沿海海岸地貌特征,研究其海岸线在ETM+、ALOS及SAR卫星图像中的解译特征标志,借助数字卫星图像处理技术,对试验区范围内不同类型海岸线图像进行提取。     

Scenarios of land-use change in Sudano-sahelian countries of Africa to better understand driving forces

Dynamic simulation models allow an integrated representation of human and biophysical driving forces, to test their influence on land use. Dynamic models emphasise the interactions among the components of the system and take into account feedback loops and threshold effects. In this paper, the SALU model was used to generate “what-if” scenarios to explore hypotheses on the relative role of driving forces of land-use change in the Sudano-sahelian countries of Africa. The model simulations provided useful insights to better understand the processes of land-use change. Rural population growth represents a larger stimuli for land-use change than urban population growth. Demographic variables have a greater impact on land use than recurring droughts. The demographic driving forces are slow variables while rainfall is a fast variable. Recurring droughts could be viewed as trigger events, and urban population growth and consumption as mediating factors, while rural population growth defines long-term trends. Technological change defines thresholds in land use. Land-use change results from interactions between driving forces. The timing of occurrence of drought with respect to transitions in land use has a major impact on land-use change. Polices aimed at protecting pastoral land and supporting agricultural intensification both contribute to maintain pastoral activities. Simulating a conservative carrying capacity has a major impact on land use predictions. By examining environmental, social and economic implications of various land-use scenarios, the modelling approach adopted in SALU can provide support for decision-making.     

The Role of non-timber forest products as safety-nets: A review of evidence with a focus on South Africa

Poor, rural communities are vulnerable to adversity. To secure their livelihoods, people adopt multiple livelihood strategies, including using non-timber forest products (NTFPs). NTFPs have been identified as important to rural livelihoods, as an alternative land-use option as well as in fulfilling an important safety-net function although empirical evidence on the latter’s strength is limited. Whilst NTFPs may contribute towards alleviating poverty, this safety-net function needs more critical and quantitative investigation. This includes the establishment of an applicable definition so this function can be communicated to policy makers and taken into account in national poverty alleviation strategies and, in attempts to promote resource-conserving behaviour by highlighting the value of natural resources (including NTFPs) compared to alternative land-use options. Poverty in rural households is complex and households are vulnerable to a range of shocks. During adversity households can turn to a range of possible safety-nets. What determines the use of NTFPs as a safety-net, how this safety-net function manifests and the strength of this function is poorly understood and there is need for further investigation.     

利用方式对东北黑土粘土矿物组成的影响

土地利用方式是影响土壤可持续利用的重要因素。粘土矿物是土壤重要的活泼物质,会受利用方式的影响而发生改变。为了揭示利用方式对土壤粘土矿物的影响,对不同利用方式下的东北黑土进行土壤理化特性及粘粒XRD分析。结果表明:荒地、旱田(大豆)、旱田(玉米)、水田利用的土壤矿物颗粒的风化依次增强,伊利石的含量和结晶度也依次降低。经水田利用后的黑土,粘土矿物的演变过程为:云母→伊利石→蛭石和绿泥石。定量结果表明黑土水田利用会出现蛭石绿泥石化现象。综合说明旱田利用会使土壤结构破坏以及水土流失加剧,而水田利用则能较好的协调土壤理化性质,可较大程度的改良土壤。     

Mapping of Neoproterozoic source rocks of the Huqf Supergroup in the Sultanate of Oman using remote sensing

In recent years, the Neoproterozoic Huqf Supergroup formations of the Oman Salt Basins have been the target for oil exploration. The present study maps the surficial exposure of the Huqf Supergroup in and around Khufai Dome of the Huqf area in the Sultanate of Oman using low-cost multi-spectral remotely sensed satellite data and image processing methods such as decorrelation stretching, principal component analysis (PCA) and spectral angle mapper (SAM), as alternative to expensive and time-consuming tools, which have the capability and potential to be used by geoscientists for oil exploration. In this research, the study of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral bands 8, 3 and 1 by decorrelation stretching well discriminated the Masirah Bay, the Khufai, the Shuram and the Buah Formations of the Nafun Group, the source rocks of Huqf Supergroup with the Quaternary deposits. The analysis of visible and near infrared–shortwave infrared spectral bands of ASTER by PCA clearly showed the occurrence and spatial distribution of such formations in the RGB principal component images (R:PC1, G:PC2, B:PC3). The spatial distributions of such formations are assessed by confusion matrix after using maximum likelihood (ML), spectral angle mapper (SAM) and spectral information divergence (SID) algorithms. The matrix of ML algorithm has provided the best overall accuracy of 92.93% and kappa coefficient of 0.92. The minerals of the formations were detected by SAM. Further, the detection of such mineral groups was confirmed through the ASTER thermal infrared (TIR) spectral indices image developed using the carbonate index (CI), quartz index (QI), and mafic index (MI). All results of image analyses are evaluated in the field and laboratory studies. The study also evaluates the satellite data and image processing methods for the formations of Jabal Akhdar, the equivalent formations of the Khufai Dome, to show the sensor capability and the use of the image processing methods to study the source rocks. The results of the study provided similar discriminations comparable to the Khufai Dome. Therefore, the data and the techniques are recommended to the exploration geologists for use in similar regions of the world.     

面向基元的高空间分辨率矿区遥感影像土地利用分类

为了合理开发矿产资源和有效监测矿区生态环境,采用面向基元的分类方法,对广西横县某矿区的高分辨率航空遥感影像进行了土地利用分类.通过优化分形网络演化多尺度分割方法,高效提取了矿区两个尺度上的影像基元层;基于基元信息,详细分析了各地表地物光谱特征、空间特征以及类相关特征,建立了研究区土地利用的分类知识库;采用决策支持的模糊逻辑推理法进行分类,使分类的精度从53%提高到了90%.表明面向基元的方法能较好地利于高空间分辨率矿区影像的各种特征进行高精度的土地利用分类.      

吉林西部多时相遥感数据分类方案的构建及应用

为了深化遥感监测方法在生态环境调查中的应用,本文以吉林西部为试验区,设计了一种多时相遥感数据分类方案。该方案以物候信息为主,结合地物特征变量(植被、水体和土地信息)构建的多维特征空间数据集用于土地覆被分类。该遥感分类方案提取了9种地表覆被类型,结果表明:地表植被季节变化信息和土地利用信息的引入能明显改善土地覆被的分类精度;与基于原始波段的分类方案相比,多时相遥感数据分类方案的分类精度最好,总体分类精度为95.50%,Kappa系数为95.04%。     

Slope failure susceptibility zonation using integrated remote sensing and GIS techniques: A case study over Jhingurdah open pit coal mine,Singrauli coalfield,India

The susceptibility of slopes in open pit coal mines to various modes of failure (i.e., plane, wedge, circular and toppling failure) could be envisaged by virtue of processing and analysis of pertinent satellite data. The aim of the present study was to integrate thematic maps generated using remote sensing image processing techniques, in order to finally produce slope failure hazard zonation maps in and around Singrauli coalfield, India. The various failure-inducing factors, variables and parameters can be extracted from different satellite data and imageries. The data acquired by different sensors such as TM, ETM+, etc., of LANDSAT series and CARTOSAT of ISRO Bhuvan was used in this study. All these data were subsequently used to create different thematic maps such as slope map, lithological map, land use/land cover map, principal component analysis map, digital elevation model (DEM), etc. An advanced analysis for extraction of lineament attributes was also undertaken.