Assessment of Land use/land cover Change in the North-West District of Delhi Using Remote Sensing and GIS Techniques

Land is one of the prime natural resources. A city grows not only by population but also by changes in spatial dimensions. Urban population growth and urban sprawl induced land use changes and land transformation. The land transformation is a natural process and cannot be stopped but it can be regulated. Many geographical changes at the urban periphery are associated with the transfer of land from rural to urban purpose. There is an urgent need for fast growing areas like Delhi, which can be easily done by high-resolution remote sensing data. Land use/land cover of North West of Delhi has been analyzed for the time period of 1972?C2003. The remote sensing data used in study is Aster image of 2003 with a spatial resolution of 15?m and other data of 1972 Survey of India (SOI) toposheet at the scale of 1:50,000. Supervised digital classification using maximum likelihood classifier was applied for preparing land use/land cover. A change detection model was applied in ERDAS Imagine to find out the land use/land cover during 1972 to 2003. Eight land use classes was identified but main dominated classes were built up and agricultural land. A drastic change has been recorded during 30 years of time i. e. (1972-2003). In 1972, 92.06% of the land was under agricultural practice, which reduced to 64.71% in 2003. This shows 27.35% decrease in agricultural land in three decades. On the other hand built up area was 6.31% in 1972, which increased to 34% in 2003. One of the main cause of this land use change is the population growth due to the migration in the district from small cities and rural areas of Delhi.     

Spatial analysis of human-induced vegetation productivity decline over eastern Africa using a decade (2001–2011) of medium resolution MODIS time-series data

Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250-m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25-km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not related to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68% was found between the rainfall-corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ‘severe’ vegetation productivity losses under high land-use intensities.     

近40年老哈河流域土地利用变化监测与分析

利用决策树和支持向量机分类方法,基于多期Landsat MSS,TM and ETM+遥感图像和其他辅助数据,对1970s以来近40年半干旱的老哈河流域土地利用变化(land use and land cover change,LUCC)进行动态监测,并利用GIS方法对LUCC进行了定量分析和空间分布制图.结果显示,利用支持向量机分类方法对该地区1976年、1989年、1999年和2007年土地覆盖类型分类可达到较满意的效果;近40年老哈河流域土地利用变化显著,水体和草地减少,城乡用地持续扩张,耕地大幅增加,林地和未利用地大幅度波动、总体减少.LUCC主要发生在林地、草地和耕地之间,表明农、林、牧用地之间转换显著,且在各个时期的空间分布差别较大.从变化强度来看,土地利用的年综合变化率最大值渐趋增大,年均土地动态度在空间分布上差异很大,另外在各研究期赤峰市区周边动态度都很大,反映了赤峰市持续性的城市化进程.     

黑河下游土地利用与景观格局时空特征分析

基于研究区1986年LandSat TM和2003年LandSat ETM 遥感影像资料,综合运用遥感与GIS技术手段,结合FRAGSTATS景观格局分析软件,就近17 a来黑河下游额济纳流域土地利用与景观格局变化进行了分析,并探讨了引起变化的驱动因素。结果表明:研究区草地、林地、水域及耕地面积减少,居民工矿建设用地和未利用土地面积显著增加;景观破碎化程度降低,多样性及异质性程度减小,稳定性降低;气候的暖干化趋势、黑河下泄水量的持续减少以及人类不合理的经济活动是黑河下游土地利用和景观格局变化的主要原因。     

Satellite Earth observation data to identify anthropogenic pressures in selected protected areas

Protected areas are experiencing increased levels of human pressure. To enable appropriate conservation action, it is critical to map and monitor changes in the type and extent of land cover/use and habitat classes, which can be related to human pressures over time. Satellite Earth observation (EO) data and techniques offer the opportunity to detect such changes. Yet association with field information and expert interpretation by ecologists is required to interpret, qualify and link these changes to human pressure. There is thus an urgent need to harmonize the technical background of experts in the field of EO data analysis with the terminology of ecologists, protected area management authorities and policy makers in order to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIR framework, providing a terminology to relate the concepts of state, pressures, and drivers with the application of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e. changes in land cover and/or habitat type and/or condition). Four broad categories of changes in state are identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentation and changes in landscape connectivity, and changes in plant community structure. These categories of change in state can be mapped through EO analyses, with the goal of using expert judgement to relate changes in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of protected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysed to (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pressure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) the most appropriate spatial and temporal EO data sources as well as interpretations from ecologists and field data useful in connection with EO data analysis. We provide detailed examples for two protected areas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expert interpretation to relate such change to specific anthropogenic pressures. We conclude with a discussion of the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures, suggesting additional research efforts required in this direction.     

Analysis of different methods for monitoring the urbanization process

The monitoring of urban sprawl in agricultural and natural areas requires the frequent acquisition of information relative to land cover changes. The loss of high capability agricultural lands is a major problem. The sound management of resources requires the knowledge of the nature and orientation of the urban dynamics.

Remote sensing is a useful tool for highlighting areas where changes have occured,for determining the type of change and for quantifying these changes. A spatial‐temporal analysis of the urban processes is carried out for the urban area of Montreal, Canada. Different sources of information are used: three Landsat MSS satellite images acquired in 1972, 1979 and 1982, planimetric data from the Department of Municipal Affairs of Quebec and statistics compiled by Environment Canada.

The satellite data shows a sharp increase, in the order of 65%, in urban areas during the period under consideration. These results are compared with governmental data derived from classical photo‐interpretation techniques.

On one hand, we observe that the results obtained by automatic classification of the satellite data are superior in the order of between 5% to 30%, depending on the year and the different governmental sources. On the other hand, we discuss problems of homogeneity in the use of terms related to land cover between the various governmental organizations.     

Monitoring Spatial and Temporal Land Use/Cover Changes; a Case Study in Western Black Sea Region of Turkey

Rapid land use/land cover changes have taken place in many cities of Turkey. Land use and land cover changes are essential for wide range of applications. In this study, Landsat TM satellite imageries date from 1987, 1993, 2000 and 2010 were used to analyse temporal and spatial changes in the Western Black Sea Region of Turkey. Zonguldak and Eregli two largest and economic important cities which have been active coal mining and iron fabric areas. Maximum Likelihood Classification technique was implemented and the results were represented in classes of open area, forest, agricultural, water, mining, urban and pollution in the sea. Urban areas on both cities increased from 1987 to 2010. The agricultural and open areas from 1987 to 2010 decreased in parallel to land use and land cover change in both cities. Meanwhile, forest areas increased continuously with about 20 % from 1987 to 2010 in both cities. As industrial activity, the coal fields doubled from 1987 to 2010.     

Monitoring land-use change by combining participatory land-use maps with standard remote sensing techniques: Showcase from a remote forest catchment on Mindanao,Philippines

This paper combines participatory activities (PA) with remote sensing analysis into an integrated methodology to describe and explain land-cover changes. A remote watershed on Mindanao (Philippines) is used to showcase the approach, which hypothesizes that the accuracy of expert knowledge gained from remote sensing techniques can be further enhanced by inputs from vernacular knowledge when attempting to understand complex land mosaics and past land-use changes. Six participatory sessions based on focus-group discussions were conducted. These were enhanced by community-based land-use mapping, resulting in a final total of 21 participatory land-use maps (PLUMs) co-produced by a sample of stakeholders with different sociocultural and ecological perspectives. In parallel, seven satellite images (Landsat MSS, Landsat TM, Landsat ETM+, and SPOT4) were classified following standard techniques and provided snapshots for the years 1976, 1996, and 2010. Local knowledge and collective memory contributed to define and qualify relevant land-use classes. This also provided information about what had caused the land-use changes in the past. Results show that combining PA with remote-sensing analysis provides a unique understanding of land-cover change because the two methods complement and validate one another. Substantive qualitative information regarding the chronology of land-cover change was obtained in a short amount of time across an area poorly covered by scientific literature. The remote sensing techniques contributed to test and to quantify verbal reports of land-use and land-cover change by stakeholders. We conclude that the method is particularly relevant to data-poor areas or conflict zones where rapid reconnaissance work is the only available option. It provides a preliminary but accurate baseline for capturing land changes and for reporting their causes and consequences. A discussion of the main challenges encountered (i.e. how to combine different systems of knowledge), and options for further methodological improvements, are also provided.     

A new approach for vegetation change detection in urban areas

IntroductionVegetation distribution and change is regardedas ani mportant sign of urban environment . Withcity expanding and population increasing, herecomes a series of problems on environment ,andmoreover ,greening ratio is regarded as a stand-ard of ci…     

A new approach for vegetation change detection in urban areas

Because of complex change in urban areas, modified CVA application based on mask techniques can minify the effect of non-vegetation changes and improve upon efficiency to a great extent. Moreover, drawing from methods in polar plots, the technique measures changes with absolute angular and total magnitude of PVI calculated on the basis of linear fit with least-square estimation and GVI calculated using 3D G-S transformation. Finally, this application is performed with Landsat ETM+ imageries of Wuhan in 2002 and 2005, and assessed by error matrix, in the way it could detect change pixels 94.91% correct, and the total consistent coefficient Kappa and could reach to 0.85. The evaluation result demonstrates this new application trends as an efficient and effective alternative to urban vegetation change extraction.     

地表覆盖遥感产品更新完善的研究动向

近年来,多尺度地表覆盖遥感产品的不断涌现,为环境变化研究、地球系统模拟、地理国(世)情监测和可持续发展规划等提供了重要科学数据。为更好地满足广大用户日益增长的应用需求,应对地表覆盖遥感产品进行持续更新完善,保持其时效性、增强时序性、丰富多样性。针对大面积地表覆盖遥感产品更新完善所面临的主要问题,介绍和评述了国内外有关研究动向,包括影像与众源信息相结合的更新、数据类型细化与完善、地表覆盖真实性验证,并作了简要展望。     

江苏沿海滩涂利用模式遥感影像解译和时空变化分析

为了弄清江苏沿海滩涂利用模式分布情况,定量分析其时空变化趋势,本文基于Google Earth Engine云计算平台和Landsat卫星遥感影像数据,结合随机森林分类方法,通过机器学习建立了滩涂利用模式自动识别分类方法,样本训练后识别准确率达93％以上.利用该方法提取了2002年、2014年和2019年江苏沿海的土地...     

A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques

Normally, to detect surface water changes, water features are extracted individually using multi-temporal satellite data, and then analyzed and compared to detect their changes. This study introduced a new approach for surface water change detection, which is based on integration of pixel level image fusion and image classification techniques. The proposed approach has the advantages of producing a pansharpened multispectral image, simultaneously highlighting the changed areas, as well as providing a high accuracy result. In doing so, various fusion techniques including Modified IHS, High Pass Filter, Gram Schmidt, and Wavelet-PC were investigated to merge the multi-temporal Landsat ETM+ 2000 and TM 2010 images to highlight the changes. The suitability of the resulting fused images for change detection was evaluated using edge detection, visual interpretation, and quantitative analysis methods. Subsequently, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML) classification techniques were applied to extract and map the highlighted changes. Furthermore, the applicability of the proposed approach for surface water change detection was evaluated in comparison with some common change detection methods including image differencing, principal components analysis, and post classification comparison. The results indicate that Lake Urmia lost about one third of its surface area in the period 2000–2010. The results illustrate the effectiveness of the proposed approach, especially Gram Schmidt-ANN and Gram Schmidt-SVM for surface water change detection.     

Estimation of Wheat/Rice Residue Burning Areas in Major Districts of Haryana,India, Using Remote Sensing Data

Haryana has emerged as an important state for Rice & Wheat production in India contributing significantly in the central pool. Mechanized combine harvesting technologies, which have become common in Rice Wheat System (RWS) in India, leave behind large quantities of straw in the field for open burning of residue. Besides causing pollution, the burning kills the useful micro flora of the soil causing soil degradation. There is no field survey (Girdawari) data available with the Government for the areas where stubble burning is taking place. The present paper describes the methodology and results of wheat and rice residue burning areas for three districts of Haryana namely Kaithal, Kurukshetra and Karnal for the year 2010 using complete enumeration approach of multi-date IRS-P6 AWiFS and LISS-III data. In season ground truth was collected using hand held GPS and used to identify area of burnt wheat/rice residues, associated crops and land features. After geo-referencing the satellite images, district images were masked-out and multi-date image data stacks were created. Normalized Difference Vegetation Index (NDVI) of each date was generated and used at the time of classification along with other spectral bands. The non-agricultural classes in the image included: forest, wasteland, water bodies, urban/settlement and permanent vegetation etc. The vector of these non-agriculture classes were extracted from the land use, imported and mask was generated. During the classification non-agriculture area was excluded by using mask of these classes. From this the agricultural area could be separated out. The area was estimated by computing pixels under the classified image mask. In season multi-date AWiFS data along with available single-date LISS-III data between third week of April to last week of May are found to be useful for estimation of wheat residue burning areas estimation. The data between second week of October to last week of November is useful for estimation of rice residue burning areas estimation at district level.     

1983—1992年中国陆地植被NDVI演变特征的变化矢量分析

以NDVI时序资料为基本数据源，综合应用变化矢量分析和主成分分析方法对1983年至1992年中国陆地植被NDVI的变化强度、变化类型及空间结构变化特征进行了分析。研究结果表明在此期间中国陆地植被NDVI变化有以下特点：（1）十年间NDVI变化东西分异明显，东部变化幅度远大于西部。NDVI变化整体表现为稳中略增，增加区主要分布在台湾、福建、四川、河南等地；减少区主要分布在云南省和新疆北部等地。（2）空间结构信息表现了景观异质性，其变化主要发生在南方，反映了植被的生长和衰老过程及地形（山脉走向）变化。     

Probabilistic mapping of flood-induced backscatter changes in SAR time series

The information content of flood extent maps can be increased considerably by including information on the uncertainty of the flood area delineation. This additional information can be of benefit in flood forecasting and monitoring. Furthermore, flood probability maps can be converted to binary maps showing flooded and non-flooded areas by applying a threshold probability value p_F = 0.5. In this study, a probabilistic change detection approach for flood mapping based on synthetic aperture radar (SAR) time series is proposed. For this purpose, conditional probability density functions (PDFs) for land and open water surfaces were estimated from ENVISAT ASAR Wide Swath (WS) time series containing >600 images using a reference mask of permanent water bodies. A pixel-wise harmonic model was used to account for seasonality in backscatter from land areas caused by soil moisture and vegetation dynamics. The approach was evaluated for a large-scale flood event along the River Severn, United Kingdom. The retrieved flood probability maps were compared to a reference flood mask derived from high-resolution aerial imagery by means of reliability diagrams. The obtained performance measures indicate both high reliability and confidence although there was a slight under-estimation of the flood extent, which may in part be attributed to topographically induced radar shadows along the edges of the floodplain. Furthermore, the results highlight the importance of local incidence angle for the separability between flooded and non-flooded areas as specular reflection properties of open water surfaces increase with a more oblique viewing geometry.     

Snow and ice mask for the MODIS aerosol products

Atmospheric products have been derived operationally from multichannel imaging data collected with the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua spacecraft. Preliminary validations of the products were previously reported. Through analysis of more extensive time-series of MODIS aerosol products (Collection 4), we have found that the aerosol products over land areas are slightly contaminated by snow and ice during the springtime snow-melting season. We have developed an empirical technique using MODIS near-infrared channels centered near 0.86 and 1.24 /spl mu/m and a thermal emission channel near 11 /spl mu/m to mask out these snow-contaminated pixels over land. Improved aerosol retrievals over land have been obtained. Sample results from application of the technique to MODIS data acquired over North America, northern Europe, and northeastern Asia are presented. The technique has been implemented into the MODIS Collection 5 operational algorithm for retrieving aerosols over land from MODIS data.     

土地利用变化的卫星遥感监测——河北省南皮县试验报告

本文报道了运用图像处理技术,分别计算河北省南皮县试区两个不同时相TM与SPOT图像的亮度指数和垂直植被指数,进而求算变化向量、自动输出变化分类图的试验研究结果。经实地对22块变化图斑进行检验,都准确无误,表明从不同时相的卫星图像提取土地利用变化信息,分析耕地消长及大宗作物种植面积波动是完全可能的,有广阔的应用前景。     

Measures of spatio-temporal accuracy for time series land cover data

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.     

基于TM影像的北京城乡结合部土地利用变化分析

利用遥感分类技术能够快速获取土地利用变化信息。基于1996年和2006年两时相的北京城乡结合部地区TM卫星影像数据,采用监督分类和分类后处理方法,对研究区10年间的土地利用变化情况进行了详细分析,得到如下结论:10年间北京城南地区城乡结合部的各种土地利用类型之间相互转化,并以耕地,林地和建设用地相互转化最为显著;耕地和大范围水域面积较大幅度减少,城市居民点及工矿用地和未利用土地面积大幅度地增加,城乡结合部的范围在10年间从北向南进行了大范围地移动。