星载SAR水下地形和水深遥感的最佳雷达系统参数模拟

根据星载合成孔径雷达 (SAR)浅海水下地形和水深成像机理 ,建立了浅海水下地形和水深雷达后向散射截面仿真模型。该模型包括奈维 斯托克斯方程、谱作用量平衡方程和雷达后向散射模式。利用该模型仿真结果 ,探讨了不同波段 (P、L、C和X)、不同极化 (VV和HH)和不同入射角 (2 0°— 70°)的星载SAR测量浅海水下地形和水深的能力。研究结果表明 ,浅海水下地形和水深遥感的最佳波段为P波段 ,L波段次之 ,C波段比X波段要好一些。VV极化SAR的测量能力要强于HH极化。 2 0°— 40°是星载SAR测量浅海水下地形和水深的最佳入射角范围。     

基于多波段全极化SAR影像的湿地分类

作为湿地类型较多、分布广泛的国家,中国的城市发展进程在一定程度上对我国丰富的湿地资源形成了很大的威胁,部分湿地遭到破坏。合成孔径雷达(Synthetic Aperture Radar,SAR)作为一种主动式微波遥感,其全天时全天候的特性十分适合湿地的监测工作。然而现在利用SAR进行湿地分类的研究尚未成熟,未能充分使用多个波段的信息分析,本文提出一种利用星载C波段以及机载p波段湿地分类的方法,有效地改善了分类的效果,提高了分类的精度。     

Study of coastal wetland classification based on decision rules using ALOS AVNIR-2 images and ancillary geospatial data

Wetlands play irreplaceable key roles in ecological and environmental procedures. To make effective conservation and management, it is essential to understand the wetlands’ distribution and changes. In this study, an approach based on decision rules algorithm in conjunction with maximum likelihood classification is proposed for coastal wetland mapping using multi-temporal remotely sensed imagery and ancillary geospatial data. As a case study, Multi-temporal Advanced Visible and Near Infrared Radiometer type 2 images acquired by Japanese Advanced Land Observation Satellite are analysed to investigate the seasonal change pattern of coastal wetlands in Washington State, USA. Geospatial data, including Digital Elevation Model and spatial neighbourhood knowledge, are further integrated to characterize wetland features and discriminate classes within a certain elevation ranges. The final result is a refined coastal wetland map with 15 land cover categories. Preliminary evaluation of the final result shows that the proposed approach is effective in coastal wetland mapping.     

Comparison and assessment of NDVI time series for seasonal wetland classification

Satellite-based wetland mapping faces challenges due to the high spatial heterogeneity and dynamic characteristics of seasonal wetlands. Although normalized difference vegetation index (NDVI) time series (NTS) shows great potential in land cover mapping and crop classification, the effectiveness of various NTS with different spatial and temporal resolution has not been evaluated for seasonal wetland classification. To address this issue, we conducted comparisons of those NTS, including the moderate-resolution imaging spectroradiometer (MODIS) NTS with 500?m resolution, NTS fused with MODIS and Landsat data (MOD_LC8-NTS), and HJ-1 NDVI compositions (HJ-1-NTS) with finer resolution, for wetland classification of Poyang Lake. Results showed the following: (1) the NTS with finer resolution was more effective in the classification of seasonal wetlands than that of the MODIS-NTS with 500-m resolution and (2) generally, the HJ-1-NTS performed better than that of the fused NTS, with an overall accuracy of 88.12% for HJ-1-NTS and 83.09% for the MOD_LC8-NTS. Future work should focus on the construction of satellite image time series oriented to highly dynamic characteristics of seasonal wetlands. This study will provide useful guidance for seasonal wetland classification, and benefit the improvements of spatiotemporal fusion models.     

Evaluation of image fusion methods using PALSAR,RADARSAT-1 and SPOT images for land use/ land cover classification

This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification.     

银川平原湿地时空变化信息的可视化分析

针对在湿地动态变化监测中,经典的土地利用转移矩阵无法实现湿地类型变化信息的定位可视化表达的问题,提出了一种新的时空变化信息的可视化分析方法。利用由空间单元和时序单元集成的时空复合单元建立转移图谱单元分类体系,较好地记录每一个图谱单元湿地类型的起始和终止状态,实现了湿地时空变化信息的定位表达;基于遥感制图和地学图谱的湿地空间演变规律特征图谱,采用图表和图谱的形式展示湿地类型变化的数量及空间位置变化信息,实现了湿地时空变化的可视化表达。基于银川平原湿地的实验结果表明:该方法能够清晰、直观地显示1999—2013年期间银川平原湿地的时空变化,能够为该地区合理利用湿地资源提供科学的理论依据。     

一种基于TM影像的湿地信息提取方法及其变化检测

近年来,有着"地球之肾"之称的湿地,其对于生态环境的特殊性和重要性受到全世界的极大关注,湿地研究己成为当前国际上的热门领域。本文结合缨帽变换及决策树分类法提出了一种新的湿地提取方法。首先对原始影像进行缨帽变换,提取其湿度分量,利用湿度分量进行分级提取,区分湿地与陆地。接下来在深入分析研究区影像光谱特征规律的基础上,建立了湿地信息提取决策树模型,用于区分不同类型湿地。最后,对两个时相的湿地分布影像进行了变化检测,提取出了湿地的变化范围。     

The potential of using LiDAR and color-infrared aerial imagery for palustrine wetland typology and change

Wetlands are dynamic landscapes and their spatial extent and types can change over time. Mapping wetland locations, types, and monitoring wetland typological changes have important ecological significance. The National Wetlands Inventory data suffer from two problems: the omission error that some wetlands are not mapped, and the out-of-date wetland types in many counties of the United States. To address these two problems, we proposed an automatic wetland classification model for newly mapped (or existing) wetland polygons lacking typological information. The research goals in this study were (1) to develop a nonparametric and automatic rule-based model to assign wetland types to palustrine wetlands using high-resolution remotely sensed data and (2) to quantify wetland typological changes based on the wetland types obtained from the previous step. The model is a direct application of the Cowardin et al. (1979) wetland classification system without modification. The input information for the proposed model includes Light Detection and Ranging (LiDAR)-derived vegetation height and color infrared aerial imagery-derived vegetation spectral information. We tested the model for the palustrine wetlands in Horry County, SC, and analyzed 29,090 palustrine wetland polygons (101,427 ha). The model achieved an overall agreement of 87% for wetland-type classification and showed the dynamics of wetland typological changes. This nonparametric model can be easily applied to other areas where wetland inventory needs updating.     

GIS和RS技术在湿地资源研究中的应用——以大庆市湿地为例

以大庆市湿地为研究对象,以地理信息系统和遥感技术为技术平台,介绍了湿地环境因子研究工作程序及湿地现状类型分布研究,分析了地理信息系统和遥感技术在湿地资源研究中的地位和作用,从而为GIS与RS技术在湿地资源应用研究中提供理论支持。     

GIS和RS技术在湿地资源研究中的应用——以大庆市湿地为例

以大庆市湿地为研究对象,以地理信息系统和遥感技术为技术平台,介绍了湿地环境因子研究工作程序及湿地现状类型分布研究,分析了地理信息系统和遥感技术在湿地资源研究中的地位和作用,从而为GIS与RS技术在湿地资源应用研究中提供理论支持。     

多频率InSAR提取沼泽湿地DEM精度对比分析

选取3种波长的干涉SAR数据对提取沼泽湿地区域的DEM，并随机从1：10 000地形图中选取111个点数据进行精度验证，最后对比分析了沼泽湿地植被对于不同SAR波长的干涉相干性差异。结果表明：L-band ALOS-1 PALSAR精细模式的HH单视复数数据与1：10 000地形图数据吻合度较好，76.58%的高程值差异在3 m以内，其相干系数比C-band Sentinel-1A IW模式的VV单视复数数据和X-band TerraSAR HH单视复数数据要高；更适合利用雷达干涉测量技术提取沼泽湿地的DEM；不同湿地植被类型的相干系数有较大差异，岛状林和灌草结合的湿地植被分布区相干系数值较大，而浅水沼泽植被区和深水沼泽植被区相对较低。     

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.     

面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例

以洞庭湖流域为研究区,对大范围湿地信息遥感提取方法进行了研究。先基于时间序列MODIS EVI及物候特征参数,通过J-M(Jeffries-Matusita distance)距离分析,构建了MODIS(250 m)最佳时序组合分类数据;其次,通过Johnson指数确定了最佳分割尺度,采用面向对象的遥感分类方法(Random tree分类器)提取了洞庭湖流域的湿地信息,并验证该方法的适用性。研究结果表明,基于时序数据与面向对象的Random tree分类的总体精度和Kappa系数分别为78.84%和0.71,较之基于像元的相同算法的总体分类精度和Kappa系数分别提高了5.79%和0.04。同时,基于面向对象方法的湿地整体的用户精度与生产者精度较基于像元方法分别提高了4.56%和6.21%,可有效提高大区域湿地信息提取的精度。     

基于波段组合的高光谱数据湿地分类研究

对高光谱数据进行波段组合,可以减少信息量的冗余,提高数据的处理速度。对黄河口入海口湿地进行分类,对合理利用、开发保护该地区湿地资源具有重要意义。本文首先分析了“珠海一号”高光谱数据各个波段的信息量及波段之间的相关系数,然后利用最佳波段指数(OIF)方法选出波段组合B7-B8-B32,进一步在OIF基础上设置信息量与相关系数阈值,选出波段组合B7-B18-B32,实验结果证明分类精度提高了5.4%。最后,根据地物的光谱特征分析,选择光谱差异较大的波段进行组合B6-B13-B18,分类后精度比OIF筛选出的波段组合精度高12.6694%。经实验验证,结合地物光谱特征的波段组合可以大大提高分类精度。     

Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

A vast portion of Newfoundland and Labrador (NL) is covered by wetland areas. Notably, it is the only province in Atlantic Canada that does not have a wetland inventory system. Wetlands are important areas of research because they play a pivotal role in ecological conservation and impact human activities in the province. Therefore, classifying wetland types and monitoring their changes are crucial tasks recommended for the province. In this study, wetlands in five pilot sites, distributed across NL, were classified using the integration of aerial imagery, Synthetic Aperture Radar, and optical satellite data. First, each study area was segmented using the object-based method, and then various spectral and polarimetric features were evaluated to select the best features for identifying wetland classes using the Random Forest algorithm. The accuracies of the classifications were assessed by the parameters obtained from confusion matrices, and the overall accuracies varied between 81% and 91%. Moreover, the average producer and user accuracies for wetland classes, considering all pilot sites, were 71% and 72%, respectively. Since the proposed methodology demonstrated high accuracies for wetland classification in different study areas with various ecological characteristics, the application of future classifications in other areas of interest is promising.     

双台河口保护区湿地景观格局变化研究

在分析国内外主要湿地分类系统、总结有关湿地分类方法的基础上,结合双台河口保护区的实际情况,建立了适合研究区的湿地景观分类体系。利用1988年、2001年和2007年三个时相的Landsat TM/ETM+遥感影像数据,分析研究区内湿地景观类型的变化特点,以及影响研究区湿地景观格局变化的主要因素。分析结果表明:从1988年到2007年,研究区内以天然湿地为主,但天然湿地面积呈减少趋势,比重从72.68%降到56.64%;人工湿地面积比重逐渐上升,从2.93%上升到11.86%,增加量为1988年人工湿地面积的3.04倍;非湿地面积的比重从24.38%变化到31.50%。人为因素已成为研究区湿地景观格局变化的主导因素。     

利用多时相遥感监测与分析黄河三角洲湿地变化动态

黄河三角洲湿地地区土地利用变化信息可为有关部门制定湿地保护和经济发展提供参考。本文选取2005、2009和2019年三时相黄河三角洲入海口周边,以及东营属地内黄河沿岸的Landsat 7 ETM＋遥感影像为数据源,采用监督分类方法研究土地利用类型,通过分析编码的变化判别黄河三角洲的土地利用转移情况,从而进行湿地变化动态分析。结果表明,研究区内2005—2009年湿地总面积减少了437 km²,呈减少趋势;2009—2019年湿地总体面积由2009年的930 km²变为2019年的998 km²,10年间研究区内湿地总体趋于稳定,并呈增长趋势;废弃三角洲海岸线发生缩减,黄河入海口位置发生变化。研究区内14年间土地利用类型转换频繁,湿地土地利用类型与非湿地土地利用类型形成动态转换系统。     

青藏高原湿地遥感监测与变化分析

青藏高原是中国湿地分布较为集中的地区之一,也是全球变化的敏感区。了解青藏高原湿地分布与变化对湿地保护和全球变化研究具有重要意义。基于Landsat 8 OLI(operation land imager)数据,使用面向对象分类方法和人工解译相结合的方式得到2016年青藏高原湿地分布数据,结合2008年湿地分类数据以及高程、流域界线等辅助数据,分析了青藏高原的湿地分布现状和2008—2016年的湿地变化情况。结果表明:①2016年青藏高原研究区湿地总面积为115584 km2。其中,湖泊湿地面积为48737 km2,沼泽湿地面积为34698 km2,河流湿地面积为15927 km2,洪泛湿地面积为15035 km2,人工湿地面积为1188 km2。②2008—2016年,青藏高原湿地总面积增加3867 km2,主要表现为湖泊、河流和洪泛湿地的增加,但同时沼泽湿地减少5799 km2。③青藏高原的湿地分布与变化表现出显著的区域差异性。自然湿地的分布及面积变化集中在4~5km高程范围内,而人工湿地的变化则集中在2~4 km高程范围内;湖泊和洪泛湿地的增加集中在内流区,河流的增加及沼泽的减少集中在外流区。④青藏高原的气温和降水均呈上升趋势,与湿地总体变化呈正相关;各流域冰川面积的变化与湿地变化也具有相关性;人为因素对青藏高原的湿地变化以消极作用为主。该研究为青藏高原环境变化研究与湿地保护提供了有益支持。     

Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data

Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m² (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas.     

联合GEE与多源遥感数据的黑龙江流域沼泽湿地信息提取

湿地是地球上最重要的生态系统之一,在维持全球生态环境安全等方面发挥着举足轻重的作用.由于湿地独特的水文特征,传统的湿地监测需要耗费大量的人力和财力,对于大尺度的湿地信息提取更是困难重重.随着大数据和云计算的兴起,为大尺度和长时间序列的空间数据处理提供了契机.本文基于Google Earth Engine(GEE)云平台...