结合地形因子与分层策略的喀斯特地区无人机影像分类

西南喀斯特山区地形起伏较大,地物分布较为破碎,致使传统的光谱特征一次分类方法的精度较低。本文基于高分辨率无人机正射影像和地形指标,充分利用无人机遥感影像空间特征、光谱特征、纹理特征及地形特征,采取面向对象CART决策树算法与分层策略提取了研究区土地覆盖类型。研究表明,结合空间地形因子和分层策略的方法减少了破碎区地物间的相干扰,故具有较高的分类精度,总体分类精度达91.2%,Kappa系数为0.87,较传统一次分类精度提高了9.8%,Kappa系数提高了0.13。该方法对西南喀斯特地区土地覆盖解译精度较好,可为土地利用监测提供参考。     

水体指数构建及其在复杂环境下有效性研究

针对复杂环境下水体提取精度易受到低反射率地表影响的问题,本文以秦淮河流域为实验区,选用2015年10月12日ETM+影像,在水体、低反射率地表和其他地表纯净像元平均反射率基础上构建Multi-Band Water Index(MBWI)。搜集1985年—2016年已有的12种水体指数,选取南京、南宁和烟台地区3景影像中不同地表环境的6个测试点,采用基于K均值聚类的水体指数法提取水体后分析水体指数在复杂环境下的有效性。结果表明,MBWI以平均总体精度、Kappa系数、错分和漏提误差分别为98.62%、0.95、3.46%和3.74%,总体上较其他水体指数具有一定的优势;实验发现TCW(Tasseled Cap Wetness index)不能有效地消除山体阴影,TCW和AWEInsh(Automated Water Extraction Index with no shadow)误将白色高反射率建筑噪声分为水体,水体提取结果中均有低反射率非水体信息存在;水体在可见光而非水体在红外反射率较高,基于二者的差异及从绿到红外波段水体似呈递减现象构建的MBWI能有效的抑制低反射率噪声,对水文水资源的研究与应用具有一定的实际价值。     

Assessment of surface and subsurface waterlogging,water level fluctuations,and lithological variations for evaluating groundwater resources in Ganga Plains

Abstract

In the present study, the multi-temporal satellite images of IRS P6 LISS III were used to map waterlogging dynamics over different seasons. An area of 594.36 km² (6.75%) and 4.17 km² (0.04%) was affected by surface waterlogging during pre and postmonsoon season, respectively. The average annual groundwater level fluctuations were calculated using 18 years (1990–2007) pre and postmonsoon groundwater level data to identify the areas which are under groundwater induced waterlogging conditions. The soil map clearly indicates that salinity and sodicity exhibit the highest severity and occur in areas with shallow groundwater levels. The hydrogeomorphical units mapped using IRS P6 LISS III satellite images are flood plain, alluvial plain, paleochannels, and oxbow lakes. The study revealed that 44.65% areas have very good to excellent groundwater resources. The litholog data clearly indicate an alternating sequence of clay and sand in which deep aquifers made up of coarse sand would be best suited for adequate water supply and good groundwater quality. The integrated study utilizing digital spatial data pertaining to waterlogging, soil salinity, water level fluctuation, and lithological variation proved that planning of any surface and subsurface water resources development activity should be taken up after assessments of said parameters.     

Spatio-temporal Assessment of Urban Heat Island Effects in Kuala Lumpur Metropolitan City Using Landsat Images

Alteration in climatic pattern has resulted to a steady decline in quality of life and the environment, especially in and around urbanized areas. These areas are faced with increasing surface temperature arising mostly from human activities and other natural sources; hence land surface temperature has become an important variable in global climate change studies. In this paper, Landsat TM/ETM imagery acquired between 1997 and 2013 were used to extract ground brightness temperature and land use/land cover change in Kuala Lumpur metropolis. The main objective of this paper is to examine the effectiveness of quantifying UHI effects, in space and time, using remote sensing data and, also, to find the relationship between UHI and land use change. Four land use types (forest, farmland, built-up area and water) were classified from the Landsat images using maximum likelihood classification technique. The result reveals that Greater KL experienced an increase in average temperature from 312.641°K to 321.112°K which was quite eminent with an average gain in surface temperature of 8.4717°K. During the period of investigation (1997–2013), generally high temperature is been experienced mostly in concentrated built-up areas, the less concentrated have a moderate to intermediate temperature. Again, the study also shows that low and intermediate temperature classes loss more spatial extent from 2,246.89 Km² to 1,164.53 Km² and 6,102.42 Km² to 3,013.63 Km² and a gain of 4,165.963 Km² and 307.098 Km² in moderate and high temperature respectively from 1997 to 2013. The results of this study may assist planners, scientists, engineers, demographers and other social scientists concerned about urban heat island to make decisions that will enhance sustainable environmental practices.     

热红外遥感浙江地表热环境分布研究

全球气候变暖与人类戚戚相关,研究发现不仅是城市的热岛效应引起了局部区域的地表热环境分布差异,很多地质构造、岩性、土壤、植被等地质及自然地理因素也有影响。本研究选取浙江省作为研究区,利用Landsat 8 OLI/TIRS遥感影像反演地表温度,分析冬季地表热环境分布及其影响因子。结果表明,断裂带附近地表热环境受到断裂带分布影响;岩石和土壤通过不同的地表覆被类型影响地表温度。地表热环境分布与断裂带等自然因素存在一定相关性。     

Delineation of surface waterlogged areas in parts of bihar using IRS-1C LISS-III data

Pre-monsoon and post-monsoon surface waterlogged areas were delineated using satellite remote sensing data for Muzaffarpur, Vaishali and Saran districts of North Bihar. Digital data of IRS-1C LISS-III sensor acquired on December 7, 1998 and April 6, 1999 were analyzed using digital image processing software-ERDAS Imagine 8.3.1. The surface waterlogged areas were delineated using modeling technique which is the most advanced and accurate method. Using the modeling technique, a pixel is classified as water if the digital number (DN) value of its Near Infra Red (NIR) band is less than the DN value of the Red band and the Green band, and the Normalized Difference Water Index (NDWI) is greater than or equal to 0.32. The pre-monsoon surface waterlogged areas are found to be 14.02, 23.61 and 9.61 km² while the post-monsoon surface waterlogged areas are found to be 231.83, 118.19 and 176.06 km² for Muzaffarpur, Vaishali and Saran districts, respectively. Also, land use/land cover maps were prepared.     

基于TM遥感影像的淮安市土地利用变化分析

随着社会的快速发展,人类活动的日益频繁,地球表面发生了翻天覆地的变化。遥感技术以其独特的优势成为土地利用调查和变化研究的重要技术手段。本文以淮安市1995年和2006年的TM遥感影像为主要数据,依据土地利用类型的光谱特征,采用NDVI,NDWI,NDBI和非监督分类方法分别提取耕地、林地、草地、水体和建筑用地类型,并对提取结果从结构和变化角度进行分析。结果证明:1)依据地物光谱特征,采用不同模型分别提取土地利用类型的方法切实可行;2)淮安市是一个以农用地为主、水资源丰富、城镇化水平较低的地区;在1995~2006年间,耕地、水域和草地分布呈减少的趋势,林地和建筑用地呈不断增加的趋势。     

Oasis dynamics change and its influence on landscape pattern on Jinta oasis in arid China from 1963a to 2010a: Integration of multi-source satellite images

As one of the vital research highlights of global land use and cover change, oasis change and its interaction with landscape pattern have been regarded as an important content of regional environmental change research in arid areas. Jinta oasis, a typical agricultural oasis characterized by its dramatic exploitation and use of water and land resources in Hexi corridor, northwest arid region in China, was selected as a case to study the spatiotemporal oasis change and its effects on oasis landscape pattern. Based on integration of Keyhole satellite photographs, KATE-200 photographs, Landsat MSS, TM and ETM+ images, we evaluated and analyzed the status, trend and spatial pattern change of Jinta oasis and the characteristics of landscape pattern change by a set of mathematical models and combined this information with landscape metrics and community surveys. During the period of 1963a–2010a, Jinta oasis expanded gradually with an area increase of 219.15 km², and the conversion between oasis and desert was frequent with a state of “imbalance–balance–extreme imbalance conditions”. Moreover, most of the changes took place in the ecotone between oasis and desert and the interior of oasis due to the reclamation of abandoned land, such as Yangjingziwan and Xiba townships. Furthermore, the area, size and spatial distribution of oasis were influenced by human activities and resulted in fundamental changes of oasis landscape pattern. The fractal characteristics, dispersion degree and fragmentation of Jinta oasis decreased and the oasis landscape tended to be simple and uniform. Oasis change trajectories and its landscape pattern were mainly influenced by water resource utilization, policies (especially land policies), demographic factors, technological advancements, as well as regional economic development. We found that time series analysis of multi-source remote sensing images and the application of an oasis change model provided a useful approach to monitor oasis change over a long-term period in arid area. It is recommended that the government and farmers should pay more attention to the fragility of the natural system and the government should enhance the leading role of environmental considerations in the development process of oasis change, particularly with respect to the utilization of the limited water and land resources in arid China.     

秦皇岛地区柳江盆地地表塌陷区的遥感研究

柳江盆地是秦皇岛市的重要水源地,由于大量开采地下水,在隐伏岩溶发育区将引起地表塌陷。笔者通过对多波段、多时相的卫星图像、计算机数学处理图像及不同比例尺的黑白、彩色航空像片的解译研究,结合地面地质调查及钻探、物探资料进行综合分析,对该地区隐伏断裂构造、第四系松散堆积物的分布及其含水性、地表岩溶及隐伏岩溶区的分布作了研究分析,并分析了引起地表塌陷的各种地质因素,进而预测了几个开采地下水后将可能引起地表塌陷的危险区,为制定预防塌陷灾害的措施提供了必要的地质资料。     

Urban heat island monitoring and analysis using a non-parametric model: A case study of Indianapolis

A procedure for the monitoring an urban heat island (UHI) was developed and tested over a selected location in the Midwestern United States. Nine counties in central Indiana were selected and their UHI patterns were modeled. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) images taken in 2005 were used for the research. The images were sorted based on cloud cover over the study area. The resulting 94 day and night images were used for the modeling. The technique of process convolution was then applied to the images in order to characterize the UHIs. This process helped to characterize the LST data into a continuous surface and the UHI data into a series of Gaussian functions. The diurnal temperature profiles and UHI intensity attributes (minimum, maximum and magnitude) of the characterized images were analyzed for variations. Skin temperatures within any given image varied between 2–15 ^°C and 2–8 ^°C for the day and night images, respectively. The magnitude of the UHI varied from 1–5 ^°C and 1–3 ^°C over the daytime and nighttime images, respectively. Three dimensional (3-D) models of the day and night images were generated and visually explored for patterns through animation. A strong and clearly evident UHI was identified extending north of Marion County well into Hamilton County. This information coincides with the development and expansion of northern Marion County during the past few years in contrast to the southern part. To further explore these results, an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 2004 land use land cover (LULC) dataset was analyzed with respect to the characterized UHI. The areas with maximum heat signatures were found to have a strong correlation with impervious surfaces. The entire process of information extraction was automated in order to facilitate the mining of UHI patterns at a global scale. This research has proved to be promising approach for the modeling and mining of UHIs from large amount of remote sensing images. Furthermore, this research also aids in 3-D diachronic analysis.     

Extracting urban water bodies from high-resolution radar images: Measuring the urban surface morphology to control for radar’s double-bounce effect

Satellite remote sensing is an effective method for extracting water bodies on a large scale. Radar imagery, such as synthetic aperture radar (SAR) imagery, can penetrate clouds and provide opportunities for water body identification when in situ observations are difficult to obtain because of severe weather conditions. However, when using SAR images in urban areas to extract water bodies, the radar’s double-bounce effect results in complicated backscatter patterns of water near urban features such as buildings due to the side-looking properties of SAR sensors and the vertical urban structures. Therefore, the objective of this study is to propose a reliable urban water extraction framework for SAR images that integrates urban surface morphological features for controlling radar’s multiple bounces. Statistical (logistic regression) and machine-learning (random forest) models were used to explore how radar’s double-bounce effect influences the prediction performance of urban water extraction. Our findings indicate that when extracting urban water bodies, urban water’s backscatter values could be significantly interfered by the neighboring building density above a threshold height that contributes to radar’s multiple bounces. Without model calibration, our framework incorporating urban surface morphology demonstrates high prediction ability with an Area Under the Curve (AUC) of 0.914 and with 97.0% of urban water cells correctly identified by testing in another city sharing similar urban forms. In summary, our study provides a better understanding of the role of the urban surface morphology in the double-bounce effect in SAR images, specifically for differentiating urban water and land, thereby improving the accuracy of urban water extraction and enhancing the feasibility of further applications of SAR imagery under complex urban landscapes.     

一种加入空间关系的海岸带遥感图像分类方法

 海岸带作为海洋、陆地和大气共同作用的地带，其地物混杂度大，变化频繁，单纯利用光谱特征分类难以取得理想的精度。 但海岸带地物滨临水体，而水体与地物存在巨大光谱差异，易于识别。据此，本文尝试提出一种简单的加入空间关系的遥感图像分类 方法，即，先准确识别出海水类，然后统计非海水类的每一个像元到最近海水的空间距离。由于不同的海岸带地类距离海水的空间距 离有其自身的特点，因此，利用这一距离信息辅助分类能提高分类精度，尤其是针对那些光谱特征相近而距离相差较大的地类。     

基于大气温度和水汽含量改进HJ-1B地表温度反演

为了提高地面气象站稀少地区地表温度遥感反演的精度,本文基于多源遥感数据的优势,首先利用MODIS影像获取研究区像元尺度上平均大气水汽含量;然后利用同时相的HJ-1B影像估算区域地表比辐射率,再采用温度-植被指数法获取近地表大气温度;最后将以上3个参数输入单窗体算法,改进其地表温度反演的精度。研究结果表明,改进单窗体算法反演地表温度与地面实测温度的偏差小于1 K,为地面气象站点稀少的植被覆盖区域提供了一种可行的精确遥感反演地表温度方法。     

城市地表温度影像时空融合方法研究

地表温度（land surface temperature,LST）是反映地表能量和水平衡物理过程的一个重要参数,受限于载荷量的限制以及传感器的技术瓶颈,当前的卫星平台均难以获取同时具有较高空间和时间分辨率的遥感地表温度影像,客观上影响了遥感地表温度影像的应用。针对地表异质性较高的城市区域,选取覆盖武汉城区的中分辨率成像光谱仪（Moderate-Resolution Imaging Spectroradiometer,MODIS）和增强型专题绘图仪（Enhanced Thematic Mapper Plus,ETM+）数据,结合时空反射率融合模型（enhanced spatial and temporal adaptive reflectance fusion model,ESTARFM）和非线性辐射温度分解算法（non-linear disaggregation procedure for radiometric surface temperature,NL-DisTrad）对地表温度影像进行时空融合研究,最终生成60 m空间分辨率的逐日地表温度融合影像。将融合影像与2002-07-09和2002-10-13的ETM+实际地表温度影像进行融合精度验证分析,其决定系数R2分别为0.80和0.86,均方根误差（root mean square error,RMSE）分别为2.65 K和1.78 K。实验结果表明,所提出的地表温度时空融合模型在城市区域的地表温度时空融合应用中具有潜在的应用前景。     

潮汐和植被物候影响下的潮间带湿地遥感提取

潮间带湿地具有重要的生态和经济价值,但受到全球变化影响,发生大面积退化甚至丧失.掌握潮间带湿地的时空分布特征,对海岸带资源的科学管理具有重要意义.由于受到多云多雨天气和潮汐动态淹没的影响,单时相遥感数据难以获取完整的潮间带湿地信息.因此,本研究开发了一种基于时序遥感指数的潮间带湿地分类算法,并以福建省亚热带海岸带为例,...     

中国岩溶洞穴数据库建设研究

为了有效地保护和利用洞穴这一具有多方面价值的、不可再生的国土资源而建立了中 国岩溶洞穴数据库,首期完成900个洞穴数据建库工作。数据库中含有图、文、声、 像等方面的数字化信息,数据容量2500MB,作为非商业数据,可通过授权共享,为 科研机构、旅游行业和公众提供服务。     

Spatiotemporal changes of urban impervious surface area and land surface temperature in Beijing from 1990 to 2014

This study examined changes in urban expansion and land surface temperature in Beijing between 1990 and 2014 using multitemporal TM, ETM+, and OLI images, and evaluated the relationship between percent impervious surface area (%ISA) and relative mean annual surface temperature (RMAST). From 1990 to 2001, both internal land transformation and outward expansion were observed. In the central urban area, the high-density urban areas decreased by almost 7 km², while the moderate- and high-density urban land areas increased by 250 and 90 km², respectively, outside of the third ring road. From 2001 to 2014, high-density urban areas between the fifth and sixth ring roads experienced the greatest increase by more than 210 km², and RMAST generally increased with %ISA. During 1990–2001 and 2001–2014, RMAST increased by more than 1.5 K between the south third and fifth ring roads, and %ISA increased by more than 50% outside of the fifth ring road. These trends in urban expansion and RMAST over the last two decades in Beijing can provide useful information for urban planning decisions.     

1975年—2018年白洋淀湿地变化分析

白洋淀湿地生态功能重要、战略地位特殊,研究其时空变化规律具有重要意义。本文基于1975年—2018年间10期Landsat卫星遥感影像,辅以2017年—2018年高分二号（GF-2）卫星遥感影像,在野外考察湿地类型及其覆被特征基础上,人机交互解译获取各期土地利用/覆被分类图,从面积变化、类型转化、景观格局变化方面分析了近43年白洋淀湿地变化时空特征,讨论了影响分析结果的不确定性因素以及湿地变化成因。结果表明：1975年—2018年白洋淀湿地面积总体呈减少趋势,净变化-68.20 km²（-24.83%）。其中,1975年—1990年湿地面积波动性小、基本稳定,1990年—2011年湿地面积持续性减少,2011年—2018年湿地面积呈现增加趋势。湿地与非湿地类型相互转换区域主要分布于淀区南部、西部、北部的水体—水生植物—耕地—建设用地过渡区域。近43年白洋淀湿地景观趋于破碎、复杂和异质。遥感影像选取月份、年份,以及土地利用/覆被分类体系、分类方法,是影响分析结果的主要不确定性因素。气候、水文等自然因素变化,叠加工农业及城镇生活用水、上游水利工程蓄水、地下水开采等人为因素变化,是白洋淀湿地面积减少、趋于干化的成因。     

Classification of Satellite Images in Assessing Urban Land Use Change Using Scale Optimization in Object-Oriented Processes (A Case Study: Ardabil City,Iran)

By using satellite imagery, the recognition and evaluation of various phenomena and extraction of information necessary for the planning of land resources or other purposes are easily accomplished. The purpose of this study is to compare the efficiency of seven commonly used methods of monitored classification of satellite data to evaluate land use changes using TM and OLI Landsat, IRS, Spot5 and Quick Bird bands as well as different color combinations of these images to detect agricultural land, residential areas and aquatic areas using object-oriented processing. Digital processing of satellite images was carried out in 1998 and 2016 using advanced methods. Training samples were extracted in five user classes by eCognition software using segmentation scale optimization, different color combinations and coefficients of shape and compression. The appropriate segmentation scale for arable land, human complications and the blue areas were, respectively, 50, 8 and 10. Then each image was classified separately using seven methods and extracted samples, and efficiency of each classification method was obtained by calculating two general health and Kappa coefficients. The results show that the accuracy of each classification method and the neural network with a total accuracy of 94.475 and Kappa coefficient of 92.095 were selected as the most accurate classification method. These results show that the sampling of educational samples with proper precision of the classes in the images and dependency probability of each satellite images pixel can be useful in classifying group available in helpful area.