基于水体指数的密云水库面积提取及变化监测

密云水库作为北京市惟一的地表饮用水源地,监测其水面的变化可服务于政府的管理和决策。本文在分析地物光谱特征的基础上,利用TM影像的短波红外波段(TM5)和红光波段(TM3),构造了修订型归一化水体指数(RNDWI)来提取水库水面。RNDWI法能够削弱混合像元因素和山体阴影的影响,精确地提取出水陆边界,甚至可以提取出狭窄条状水体。比较RNDWI、改进归一化差异水体指数(MNDWI)及单波段法的水库水面面积提取精度,发现单波段法精度低,MNDWI法精度高,而RNDWI法精度最高。并基于RNDWI法利用TM影像监测了密云水库近二十年的水面面积动态变化,1996年时面积最大(152.306km2),近十年水库面积逐渐减少,2004年面积最小(56.632km2)。     

水库建设对下游LUCC及景观格局的影响

以陈村水库下游泾县为研究区,Landsat TM/MSS影像为数据源,在FRAGSTATS、RS、GIS技术的支持下,研究1985~2013年间陈村水库下游的土地利用/覆被变化幅度、速度和变化方向,以及水库建设前后的景观格局变化,以期为陈村水库下游流域土地利用格局优化和生态文明建设提供科学依据。结果表明,1985~2013年间泾县的土地利用从面积上看转出为植被和农业用地的最多,建设用地次之;从变化趋势上看建设用地面积增加最明显,农业用地和水域面积都呈现出下降趋势;泾县的景观基质无明显变化,1985~2013年间均以植被为主,但其景观多样性在水库建设后呈现下降趋势,且其生境破碎化程度加深,人为干扰强度增加。     

基于遥感数据水体指数的水体面积变化监测

以Landsat TM/OLI卫星影像作为数据源,采用水体指数提取方法,提取辽宁汤河水库水域面积信息,分析水域面积变化情况及驱动力因素。结果表明,近二十年汤河水库水域面积呈现“先增加后减少再增加”的变化趋势,水库最南端面积变化最突出;气象因素中的气温是影响水库面积变化的主导因素。     

基于光谱混合分解的流域不透水面提取及其动态分析——以于桥水库为例

不透水面不仅是城市非点源污染的主要来源,还是流域生态环境变化的主要因素之一。不透水面的数量、位置、几何形状、分布格局以及透水率与不透水率的比值,均影响着流域的水文环境,因此成为研究热点。本文以天津于桥水库流域为例,综合遥感(RS)与地理信息系统(GIS)技术,从流域尺度上研究1984~2013年间不透水面覆盖度的变化。在ENVI 5.1软件支持下,利用遥感影像获取1984,1994,2004和2013年4个时相的不透水面信息。采用修正后的归一化水体指数剔除水体信息,排除水体对不透水面提取精度的影响。运用线性光谱混合分析法(Linear Spectral Mixture Analysis,LSMA),提取流域不透水面覆盖度。结果表明:流域内不透水面覆盖度大多集中在1~5级,植被覆盖程度较高。近30年间不透水面比例逐年增加,2013年比1984年增加了2.802%,呈线性增长。中等分辨率的遥感影像适合流域尺度的不透水面提取的结果可作为流域水文及规划管理的重要基础性数据。     

基于地理国情数据的哈尔滨市不透水面变化监测及驱动力分析

选取哈尔滨市为研究区,以2015年地理国情普查成果和2016年基础性地理国情监测成果为数据源,根据国情数据中不同地表覆盖分类对应的透水特性提取不透水面,对哈尔滨市不透水面的空间分布格局和变化特征进行分析,选取平均工资、距城市道路距离、距医院的距离、距铁路的距离、距学校的距离、平均气温、全年降水量、土壤等8个因子,利用地理探测器模型,探测不透水面扩张率的空间分异驱动因子,研究结果表明:2015年至2016年哈尔滨市不透水面面积呈扩张趋势,居民收入水平和距城市道路距离等人为因素是造成不透水面扩张空间分异性的主要原因。     

淮河流域中游湿地景观动态变化研究

采用空间测算模型,首先对基于土地利用一级分类的淮河流域中游湿地景观动态变化进行总体分析,然后对各二级湿地类型进行了详细的动态变化分析。结果表明,从各湿地景观类型变化速率来看,1950¹980年期间,淮河中游湿地景观格局变化明显,各湿地景观之间的转换量较大,天然湿地湖泊、河流、滩地、沼泽地萎缩,人工湿地水田和水库坑塘面积大幅度增长;19801980年期间,淮河中游湿地景观格局变化明显,各湿地景观之间的转换量较大,天然湿地湖泊、河流、滩地、沼泽地萎缩,人工湿地水田和水库坑塘面积大幅度增长;1980²000年期间,土地利用的基本格局变化很小。     

泸水市增强型不透水面指数的时空演变分析

针对边境地区的贫困县在我国扶贫攻坚政策的推进和“一带一路”合作倡议的实施下快速发展这一现象,该文以毗临边境的怒江傈僳族州泸水市为研究区,利用增强型不透水面指数(ENDISI)对泸水市2013—2021年不透水面进行了提取并对其时空演变进行了分析。结果表明：(1)ENDISI指数可以较好地实现对高山峡谷地区的不透水面提取;(2)在2013—2021年,泸水市不透水面共增长了60.18%,实现了较大幅度增长,其下辖各乡镇不透水面均有显著增长;(3)通过标准差椭圆分析,泸水市不透水面扩张主要沿怒江沿线扩张,且具有明显的方向性。     

广州市建成区空间扩张与驱动因子研究

利用Landsat与DMSP/OLS相结合的建成区提取方法分析了1993-2010年广州市建成区扩张的时空特点及其驱动因子。首先采用突变检测法提取DMSP/OLS夜间灯光数据的建成区范围,并对利用SVM监督分类方法提取的Landsat影像建成区进行过滤,得到广州市城市建成区空间分布;再通过统计数据的相关性分析验证该方法的有效性;然后采用紧凑度、扩张速度、重心迁移等指标分析了广州市建成区扩张的时空特点;最后通过建成区面积与统计数据中经济参量的回归分析探讨了建成区扩张的驱动因子。     

2009—2017年北京市土地利用变化时空特征

对北京市土地利用类型、城市扩张程度和植被覆盖度进行时空特征分析,为合理制定城市空间规划和土地利用管理提供科学依据。以北京市全域为研究对象,基于2009、2013和2017年Landsat遥感影像基础,利用非监督分类方法得到土地类型图,计算城市扩张指数和植被覆盖度指数,并进行环路地类动态变化分析。结果表明：1)2009—2017年北京市绿地/耕地面积减少746.22 km²,建筑用地面积增加601.36 km²,两者面积变化表现出此消彼长的趋势;2)绿地/耕地及建筑用地的空间消长特点存在明显的环路差异;3)高植被覆盖度区域面积剧烈减少;4)土地利用类型和植被覆盖度变化在2009—2013年和2009—2017年两阶段内差异明显。可见北京市城市扩张速度有所减缓,绿地/耕地面积相对减少率高,城市土地利用变化存在明显的时空差异。     

基于遥感影像对济南城区不透水面的变化监测与分析

不透水面面积变化反映了社会的城市化进程和经济发展水平,济南市作为山东省的省会城市,代表着山东省的发展水平,不透水面的信息是准确地了解山东省经济发展必不可少的要素.为准确地了解济南不透水面变化和经济发展水平,本文对济南市城区训练样本进行选择,训练分类器,使用支持向量机(SVM)的分类算法,在Matlab中将2008年和2018年的Landsat遥感影像进行不透水面与非不透水面的分类,提取10年间的不透水面信息,分析不透水面变化.其中,总体分类精度分别为98.8％和91.45％,不透水面的面积从446.47 km2到584.15 km2,总体增长了137.68 km2.城区格局开始向东北—西南方向演变,而后对不透水面的增长进行分析,得出不透水面变化扩张的驱动力是来自经济、人口、交通等各个方面的结论.     

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.     

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。     

干旱—半干旱地区地表特征探测的成像雷达干涉测量方法与模型

从估测干涉测量雷达数据相关性出发，通过利用欧洲资源卫星1号和2号获取的重轨干涉测量雷达数据，开展新疆喀什试验区地表土地类型的识别与分类，区分和识别出裸土，盐碱地，灌丛，裸岩/戈壁，沼泽和水体6类土地类型。并对不同土地类型的后向散射特性和相关性进行定量分析。探讨干涉测量数据相关性干旱-半干旱地区地表特征的关系，从而建立了干旱-半干旱地区地表变化的干涉测量时相关模型。为干旱-半干旱地区利用干涉测量数据进行地表变化监测提供理论依据。     

Improved Understanding of Soil Surface Roughness Parameterization for L-Band Passive Microwave Soil Moisture Retrieval

Surface roughness parameterization plays an important role in soil moisture retrieval from passive microwave observations. This letter investigates the parameterization of surface roughness in the retrieval algorithm adopted by the Soil Moisture and Ocean Salinity mission, making use of experimental airborne and ground data from the National Airborne Field Experiment held in Australia in 2005. The surface roughness parameter is retrieved from high-resolution (60 m) airborne data in different soil moisture conditions, using the ground soil moisture as input of the model. The effect of surface roughness on the emitted signal is found to change with the soil moisture conditions with a law different from that proposed in previous studies. The magnitude of this change is found to be related to soil textural properties: in clay soils, the effect of surface roughness is higher in intermediate wetness conditions (0.2–0.3 v/v) and decreases on both the dry and wet ends. Consequently, this letter calls for a rethink of surface roughness parameterization in microwave emission modeling.      

Attenuation effect on seasonal basin-scale water storage changes from GRACE time-variable gravity

In order to effectively recover surface mass or geoid height changes from the gravity recovery and climate experiment (GRACE) time-variable gravity models, spatial smoothing is required to minimize errors from noise. Spatial smoothing, such as Gaussian smoothing, not only reduces the noise but also attenuates the real signals. Here we investigate possible amplitude attenuations and phase changes of seasonal water storage variations in four drainage basins (Amazon, Mississippi, Ganges and Zambezi) using an advanced global land data assimilation system. It appears that Gaussian smoothing significantly affects GRACE-estimated basin-scale seasonal water storage changes, e.g., in the case of 800 km smoothing, annual amplitudes are reduced by about 25–40%, while annual phases are shifted by up to 10°. With these effects restored, GRACE-estimated water storage changes are consistently larger than model estimates, indicating that the land surface model appears to underestimate terrestrial water storage change. Our analysis based on simulation suggests that normalized attenuation effects (from Gaussian smoothing) on seasonal water storage change are relatively insensitive to the magnitude of the true signal. This study provides a numerical approach that can be used to restore seasonal water storage change in the basins from spatially smoothed GRACE data.     

Geospatial analysis of land-use change processes in a densely populated coastal city: the case of Port Harcourt,south-east Nigeria

This study examines the pattern and processes of land-use change in a densely populated coastal city in the oil-rich Niger Delta region of Nigeria for 27?years using remote sensing and Geographic Information Systems. The results indicate that between 1986 and 2013, settlement/bare surface, mangrove forest/wetland and water body increased at 0.84% per year, 0.05% per year and 0.10% per year, respectively, while arable land/secondary vegetation and natural forest loss at 0.47% per year and 0.52% per year, respectively. This implies that intensity of change varies temporally. The implications of these changes include loss of valuable environmental goods and services, alteration of the food chain and increased impacts of climate change–related disasters. It is recommended that steps be taken to slow down the rate of land-use change in the study area to reduce its impact on the environment and social well-being.     

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.     

An Assessment of QuikSCAT Ku-Band Scatterometer Data for Soil Moisture Sensitivity

The QuikSCAT enhanced (2.225-km) backscattering product is investigated for sensitivity to changes in soil moisture and its potential for spatial disaggregation of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture. Specifically, an active–passive methodology based on temporal change detection is tested using data from the 2006 National Airborne Field Experiment data set. This campaign was carried out from October 29 to November 20, 2006 in a 60 km $times$ 40 km area of the Murrumbidgee catchment, southeast Australia. Temporal change detection analysis and accuracy in terms of spatial pattern distribution throughout the domain were assessed using a passive microwave airborne product derived from the Polarimetric L-band Multibeam Radiometer at 1-km spatial resolution. QuikSCAT–AMSR-E intercomparisons indicated higher correlations when using C-band observations. The greatest sensitivity to soil moisture was observed when using V-polarized backscatter measurement. While backscattering data showed adequate temporal sensitivity to changes in soil moisture due to precipitation events, the spatial agreement was complicated by the presence of irrigation and standing water (rice fields). This resulted in low Cramer's Phi values (less than 0.06), which were used as a measure of spatial correspondence in terms of change in soil moisture and backscatter. In addition, the high QuikSCAT sensor frequency and existence of noise in the observed data contributed to the observed discrepancies.      

Dual-tree complex wavelet transform-based image enhancement for accurate long-term change assessment in coal mining areas

The main objective of this study was to improve the long-term land use change detection by improving classification accuracy of previous generation satellite image using a recent super-resolution technique. The study also analysed the change in land cover over a period of 41 years in a coal mining area. A dual-tree complex wavelet transform-based image super-resolution technique was used to enhance Landsat images of 1975 and 2016. Separating pixels with similar spectral response is an enigmatical task, especially when those pixel represent different ground features. Therefore, an advanced neural net supervised classifier was used to minimize classification errors. Accuracy of the classified images (both super-resolved and original) were measured using confusion matrices and kappa coefficients. A significant improvement of more than 10% was observed in the overall classification accuracy for the image of 1975, highlighting that the classification accuracy of earlier generation satellite data can be improved substantially.