Analysis of Urban Greening of Tai'an City in China Based on Remote Sensing Image with High Resolution

By taking urban greening of Tai'an City of Shandong Province for example,selecting remote sensing image Quickbird with high resolution,and combining visual interpretation with automatic classification of the computer,based on urban green space systematic planning map,green space information of the built-up area has been selected for the research centering on green lands in urban parks,productive green lands,green lands attached to residential areas and units,green lands attached to the road,other green lands,water surfaces and so on.Through the statistics and analysis,the distribution condition of each type of urban green land has been obtained,and some suggestions have been proposed in view of existing problems of urban greening.It should enhance the construction of green lands in urban parks,residential areas and units,improve road greening level,implement vertical greening,increase the area of productive green lands and fully make use of idle lands.

Urban Expansion of China from the 1970s to 2020 Based on Remote Sensing Technology

Based on Remote Sensing (RS) and Geographical Information System (GIS) technology,urban expansion of 75 cities in China from the 1970s to 2020 was reconstructed by visual-interpretation method,which described the growing process of urban lands and its influences on local land use structures synchronously.By employing annual expansion area per city and urban expansion density,spatial-temporal characteristics and macro patterns of urban expansion were analyzed from the aspects of regional-distributions,administrative-levels and population-sizes comprehensively.Results indicate that:1) urban expansion in China was universal,distinct,persistent,periodic and fluctuating.In the past five decades,urban lands of 75 monitored cities in China expanded dramatically from 3606.26 km2 to 30 521.13 km2.2) Though urban expansion presented significant differences from the aspects of regional distribution,administrative levels,and population sizes,it exhibited a deceleration trend in the 13th Five-Year Plan among all kinds of cities.3) Cultivated lands were the first land resource for urban expansion,and 55.17% of newly-expanded urban lands appeared by encroaching this land use type.China’s urban expansion has caused sustained pressure on cultivated land protection,especially in super megacities,and the contradiction between urban expansion and cultivated land protection will always exist.4) The compactness of urban lands in China increased before 1987 and reduced in the next three decades,which was consistent with the implementation of major policies and the deployment of national strategies,and is expected to become compact with a stopping declining or even rebounding after the 13th Five-Year Plan.

Study the Urban Expansion of Taif City Using Remote Sensing and GIS Techniques for Decision Support System

The goal of this study is to spatially portray Taif’s urban expansion and determine for last 30 years, from 1990 to 2020. It is only including the residential neighborhoods approved by the Taif Municipality, which is responsible and organized for urban planning in the city. The geographical location of the city of Taif is a vital crossroad between eastern and western parts of the Kingdom of Saudi Arabia, which made it a tourist destination, as well as commercial and agricultural preference for many years, as it was considered the summer capital of the KSA. Moreover, it serves as the entrance to Makkah city from the eastern side. The proposed study has necessitated because the lack of recent scientific studies that dealt with the spatial analysis of urban expansion and its trends in the city of Taif and follow the stages of expansion during periods of time by relying on remote sensing and geographic information systems (GIS) techniques. The many development projects in the city of Taif, such as Taif International Airport, the new Taif project, and other projects, which will cause an increase in demand for residential, commercial, industrial and service units have also prompted the proposed study. This was investigated using a multitemporal Landsat data for the years of 1990, 2002 and 2020, as well as census data from 1990 to 2020, along with Remote Sensing (RS) and Geographic Information System (GIS) techniques. The results revealed that over the last 30 years, urban land cover has increased by 20,448 (ha) whereas other land covers, such as green area, have decreased significantly by 14,554 (ha). The results also indicate that the increase in urban areas amounted to 114.8% during the period from 1990 to 2020. The locations of new developments such as Taif airport, Taif university, Ministry of Housing projects, etc. were located to the North and Northeast. This is due to the area’s topography, which played a major role in determining the direction of urban expansion. According to the study, multiple urban centers, rising low-density dispersed communities, and leapfrogging growth were all hallmarks of urban expansion in Taif. The study demonstrated that Taif is at risk of ecosystem loss as a result of continued urban expansion. To ensure environmental sustainability, the current effort asks for actions that will restrict urban sprawl and prepare the city for future growth.

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.

基于YOLOv7-RS的遥感图像目标检测研究

针对遥感图像目标检测过程中存在的背景复杂、目标特征不明显、小目标排列密集的问题,基于YOLOv7算法,提出了一种改进的遥感图像目标检测算法YOLOv7-RS(YOLOv7-Remote Sensing),提高了遥感图像的目标检测精度。首先,向特征提取网络中融合SimAM减少背景噪声的干扰;其次,提出了D-ELAN网络增强遥感目标的特征提取能力;再次,利用SIOU损失函数以提高算法模型的收敛速度;最后,优化了正负样本分配策略,改善了遥感图像中小目标密集排列时的漏检问题。实验结果表明,YOLOv7-RS在NWPU VHR-10和DOTA数据集上的mAP达到95.4%和74.1%,相较于其他主流算法有了明显提升。

Classifications of Satellite Imagery for Identifying Urban Area Structures

This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.

Fusion of Landsat 8 OLI and PlanetScope Images for Urban Forest Management in Baton Rouge, Louisiana

In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral imagery in order to assist policymakers in the effective planning and management of urban forest ecosystem in Baton Rouge. To accomplish these objectives, Landsat 8 and PlanetScope satellite images were acquired from United States Geological Survey (USGS) Earth Explorer and Planet websites with pixel resolution of 30m and 3m respectively. The reference images (observed Landsat 8 and PlanetScope imagery) were acquired on 06/08/2020 and 11/19/2020. The image processing was performed in ArcMap and used 6-5-4 band combination for Landsat 8 to visually inspect healthy vegetation and the green spaces. The near-infrared (NIR) panchromatic band for PlanetScope was merged with Landsat 8 image using the Create Pan-Sharpened raster tool in ArcMap and applied the Intensity-Hue-Saturation (IHS) method. In addition, location of urban forestry parks in the study area was picked using the handheld GPS and recorded in an excel sheet. This sheet was converted into Excel (.csv) file and imported into ESRI ArcMap to identify the spatial distribution of the green spaces in East Baton Rouge parish. Results show fused images have better contrast and improve visualization of spatial features than non-fused images. For example, roads, trees, buildings appear sharper, easily discernible, and less pixelated compared to the Landsat 8 image in the fused image. The paper concludes by outlining policy recommendations in the form of sequential measurement of urban forest over time to help track changes and allows for better informed policy and decision making with respect to urban forest management.

Analysis of spatio-temporal pattern and driving force of land cover change using multi-temporal remote sensing images

Landuse and land cover change is regarded as a good indicator that represents the impact of human activities on earth’s environment.When the large collection of multi-temporal satellite images has become available,it is possible to study a long-term historical process of land cover change.This study aims to investigate the spatio-temporal pattern and driving force of land cover change in the Pearl River Delta region in southern China,where the rapid development has been witnessed since 1980s.The fast economic growth has been associated with an accelerated expansion of urban landuse,which has been recorded by historical remote sensing images.This paper reports the method and outcome of the research that attempts to model spatio-temporal pattern of land cover change using multi-temporal satellite images.The classified satellite images were compared to detect the change from various landuse types to built-up areas.The trajectories of land cover change have then been established based on the time-series of the classified land cover classes.The correlation between the expansion of built-up areas and selected economic data has also been analysed for better understanding on the driving force of the rapid urbanisation process.The result shows that,since early 1990s,the dominant trend of land cover change has been from farmland to urban landuse.The relationship between economic growth indicator(measured by GDP)and built-up area can well fit into a linear regression model with correlation coefficients greater than 0.9.It is quite clear that cities or towns have been sprawling in general,demonstrating two growth models that were closely related to the economic development stages.

Urban Expansion and Its Influencing Factors in Natural Wetland Distribution Area in Fuzhou City,China

This paper principally focuses on land use dynamics,urban expansion and underlying driving forces in the Natural Wetland Distribution Area(NWDA)of Fuzhou City in the southeastern China.Based on time series Landsat TM/ETM+imageries and historical data,relationships between urban land expansion and its influencing factors from 1989 to 2009 were analyzed by using an integrated approach of remote sensing(RS)and geographic information system(GIS)techniques.The results showed that built-up land increased from 151.16 km2in 1989 to 383.76 km 2in 2009. Approximately 64.25%of the newly emerging built-up land was converted from cropland(29.47%),forest and shrub (25.78%),water(3.73%),wetland(4.61%),and bare land(0.66%)during 1989 and 2009.With a remarkable decrease in cropland,the proportion of non-agricultural population increased by 23.6%.Moreover,rapid development of infrastructures,facilities,industrial parks,and urban and rural settlements along the Minjiang River resulted in the eastward and southward expansion of built-up land.Additionally,the growth pattern of built-up land in the NWDA is highly correlated with socio-economic factors,including the gross domestic product(GDP),GDP per capita,and structure of industry.As a result,the observed environmental degradation such as loss of cropland and wetland due to heavy pressure of rapid urbanization have greatly impaired the carrying capacity of city.Thus,in addition to scientific and rational policies towards minimizing the adverse effects of urbanization,coordination between the administrative agencies should be urgently strengthened to balance the conflicts between urban development and ecological conservation to make sure the sustainable land use.

基于RS与GIS的城市边缘空间扩张趋势预测仿真

为明确区域扩张方向、避免城市发展中心偏移,提出一种基于RS与GIS的扩张趋势预测方法。利用RS和GIS采集城市边缘的空间数据,利用调节系数去除遥感图像中噪声,将城市边缘空间扩张,看作一个只有时间存在变动的线性变化现象,根据上述特点建立预测空间,通过历史输入值、输出值以及标准值,推导计算空间中信息响应域及数据流域分布情况,将实际待检测区域特征代入,推导响应域及数据流域拟合度最高的空间状态,最后凭借预测向量矩阵得出占地面积、预测面积以及房屋数据量等指标的预测输出。仿真结果证明,在占地面积、图像表达以及扩张方向三个指标对比中,所提方法预测准确率都是最高的,可为城市扩建提供决策基础。

基于RS、GIS的城市绿地生态效益评价与预测模型

以遥感(RS)信息作为主要的数据来源,结合其他实地监测和统计数据,采用GIS技术进行数据分析和处理,建立城市绿地生态效益评价与预测模型,为城市绿地规划、改善城市生态环境提供定量化的科学依据。

基于RS和GIS的小型城市土地利用动态分析——以杨凌示范区为例

根据1986年,1995年和2003年3期TM遥感影像,利用遥感技术和地理信息系统空间分析方法,研究了1986—2003年间杨凌示范区的土地利用动态变化情况。分析结果显示:(1)在1986—2003年间,耕地面积逐年下降,17 a间下降了20 km2多。1986年,1995年及2003年耕地分别占总土地面积的74%,59.7%和50.8%;而园地面积增加幅度最大,增加了近12 km2,年变化率近10%;林地和交通用地也有了明显的增加,增长幅度分别为7.4%,7.1%;此外,居民点用地也有明显增加。(2)杨凌在1986—1995年间,居民点用地几乎没有变化,而1995—2003年间居民点用地有了大幅度的增加。另外,2003年交通用地比1995年在总土地面积中所占的比例增加了1.6%,这充分说明了杨凌的城市化步伐不断加快。

多级对比学习下的弱监督高分遥感影像城市固废堆场提取

城市固体废物是城市化进程中的重要污染源,对城市生态环境和公共健康造成了巨大危害。高分影像固废堆场智能解译是实现自动排查,提升监测效率的核心和关键技术。基于深度学习的固废堆场自动提取方法严重依赖于获取成本高、制作难度大的高质量像素级标注。为此,本文提出使用更易获取的影像级标注,利用影像自监督学习实现像素级固废堆场提取。围绕固废堆场的影像特征,本文方法在尺度对比约束下综合像素、影像两个层次的对比学习方法,对固废堆场的类别激活图细化和完善,并基于此生成高质量的固废堆场伪像素级标注,用于训练固废堆场提取模型。试验结果表明,本文方法在固废堆场提取的F 1值和IoU分数方面分别达到了71.58%和55.74%,显著优于所有对比方法。这说明利用多级对比学习的弱监督方法能够获得更加完整且准确的类别激活图,从而取得更高的固废堆场提取精度。

基于RS与GIS技术的北京城市公园湿地景观格局研究

城市公园湿地景观的空间格局分析是城市湿地生态系统研究的重要内容。本研究尝试应用1m分辨率的IKONOS遥感图像,利用RS、GIS等先进技术,获取城市公园湿地景观数据。运用景观生态学的原理,采用多样性指数、优势度指数、破碎化指数和斑块的分数维等空间结构指标,对北京市城市公园湿地的景观格局进行了研究和分析。

基于RS、GIS重庆市都市圈生态绿地特征研究

在TM影像的基础上,应用REDAS和GIS软件,对重庆市都市区的绿地生态进行了研究。为了提取正确的生态绿地专题信息,对TM影像进行了波段组合从而完成解译、监督分类,导入GIS软件进行绿地属性信息的提取。分析了重庆绿地的总体情况,并提出了一定的建议。

城市植被遥感分类研究进展与展望

城市植被是城市环境的重要组成部分,城市植被遥感分类是对城市绿度空间监测分析的重要方式。本文通过梳理国内外城市植被遥感分类研究进展,从遥感数据源和分类方法入手,分析该领域目前面临的问题及发展趋势,以期为城市绿度空间研究提供参考。首先,概述了光学数据、激光雷达数据及地面传感数据等数据源在城市植被遥感分类领域的应用,对不同数据源的优势与不足进行了深入分析;其次,基于阈值分割、机器学习和深度学习3种分类方法的研究,总结了应用于城市植被遥感分类领域各方法的特点;最后,提出了城市植被遥感分类研究中现存问题和未来发展方向。

基于GIS与RS的城市绿化环境效益及其空间特征研究

以万分之一彩红外航空遥感图像为主要信息源,以ArcGIS和ERDAS为主要研究工具,对宁波市的城市绿化生态环境效益及其空间特征进行了研究.研究结果表明:不同道路的绿化系统具有不同的相对环境效益;宁波市城市绿化生态环境效益分布不均;相对绿化环境效益与绿化覆盖率密切相关.研究结果丰富了城市绿化评价指标,为城市绿化系统的设计,特别是为不同环境要求的功能区绿化系统设计、城市绿化群落布局的合理性评价和城市绿化规划提供有价值的技术参数.

基于CBERS的喀斯特山区土地利用专题信息提取方法研究——以贵州省都匀市为例

以“3S”技术为技术背景,以遥感数字图像处理技术为主要技术支持,以中巴地球资源卫星CBERS和美国L andsat-7 TM影像为遥感数据源,探索CBERS数据在喀斯特山区的土地利用遥感解译中最理想的图像处理方法及分类方法,对比研究CBERS和TM在喀斯特地区土地利用分类实验中的各自优势。通过多次遥感图像处理技术实验与分析,摸索出了利用数学建模解决CBERS在喀斯特山区水体与阴影的混分问题以及建设用地与旱地、水田的混分问题的新方法,并以基于最佳指数波段选择法推算出了431波段为喀斯特地区土地利用分类的最佳波段组合。实验证明,中巴资源卫星影像较好地实现喀斯特地区大比例尺土地利用分类,完全可以替代国外遥感影像TM而运用于喀斯特山区土地利用调查中。

基于RS、GIS重庆市都市区生态绿地分析

基于TM影像的基础上,应用REDAS和GIS软件,对重庆市都市区的绿地生态进行了研究.为了提取正确的生态绿地专题信息,对TM影像进行了波段组合从而进行解译,监督分类,导入GIS软件进行绿地属性信息的提取,分析了重庆绿地的总体情况,并提出了一定的建议.