辽河三角洲滨海湿地资源时牢动态变化研究

利用2000／2009年Landsat ETM／TM数据解译提取了辽河三角洲滨海湿地资源数据,建立了基于GIS的Markov预测模型,分析预测了滨海湿地资源的时空动态变化特征与演变趋势。研究结果表明,2000～2009年,辽河三角洲滨海湿地资源受到了人类活动的强烈干扰,天然湿地显著减少,以滩涂和沼泽减少为主,而人工湿地明显增加,大量的滨海天然湿地被开发为坑塘水库、养殖场、盐田以及建设用地;到2020年,天然湿地资源将急剧减少,滩涂将减少13.1％,沼泽将减少14.5％。摘要：利用2000／2009年LandsatETM／TM数据解译提取了辽河三角洲滨海湿地资源数据,建立了基于GIS的Markov预测模型,分析预测了滨海湿地资源的时空动态变化特征与演变趋势。研究结果表明,2000～2009年,辽河三角洲滨海湿地资源受到了人类活动的强烈干扰,天然湿地显著减少,以滩涂和沼泽减少为主,而人工湿地明显增加,大量的滨海天然湿地被开发为坑塘水库、养殖场、盐田以及建设用地;到2020年,天然湿地资源将急剧减少,滩涂将减少13.1％,沼泽将减少14.5％。     

辽河三角洲滨海湿地资源时空动态变化研究

利用2000/2009年Landsat ETM/TM数据解译提取了辽河三角洲滨海湿地资源数据,建立了基于GIS的Markov预测模型,分析预测了滨海湿地资源的时空动态变化特征与演变趋势。研究结果表明,2000~2009年,辽河三角洲滨海湿地资源受到了人类活动的强烈干扰,天然湿地显著减少,以滩涂和沼泽减少为主,而人工湿地明显增加,大量的滨海天然湿地被开发为坑塘水库、养殖场、盐田以及建设用地;到2020年,天然湿地资源将急剧减少,滩涂将减少13.1%,沼泽将减少14.5%。     

杭州湾滨海湿地遥感解译分析及质量评价

根据1984~2015年间4景TM、ETM遥感影像图,利用ENVI软件输出4幅不同时期杭州湾滨海湿地的分类图,分析其变化规律,建立杭州湾滨海湿地质量评价体系。结果表明,30 a间杭州湾滨海湿地中的自然湿地逐步向人工湿地转变,必须加强滨海湿地保护措施。     

大沽河口湿地变化的遥感监测

遥感影像分类是遥感影像应用研究的重点。本文以大沽河口湿地为研究对象,利用2009年到2017年的Landsat ETM+和OLI影像,使用支持向量机分类法对研究区域中的河流、盐田、养殖区、耕地、建设用地、滩涂进行分类,分类结果的总体分类精度达到98%,Kappa系数达到0.98,表明该方法在湿地分类方面具有很大应用潜力。对分类结果的面积进行统计分析并研究其变化原因,结果显示,该区域受人类活动和自然因素双重影响,这九年来建设用地、河流面积分别增加772.27hm2、75.30hm2,耕地、养殖区面积分别减少了39.54hm2、522.95hm2。     

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。     

粤港澳大湾区红树林长时间序列遥感监测

随着遥感数据量的爆发式增长,对变化过程分析的精细化要求与本地算力不足之间的矛盾日益突出。GEE（Google Earth Engine）地理云平台的出现,解决了用户算力紧张的行业痛点。本文以粤港澳大湾区为研究区,在GEE的支持下,构建1987年—2020年年度湿地分类数据集,分析大湾区红树林的时间阶段性特征和空间扩张过程,结合连续长历时分析对变化时间点的准确识别,揭示了保护区设立与滩涂造林等工程在红树林保护与修复中的积极成效。主要结论有：（1）截止到2020年,大湾区共有红树林2174 ha,81%的红树林集中在深圳湾、淇澳岛和镇海湾;（2）大湾区的红树林经历了由平稳发展（1987年—2003年）到快速增长（2003年—2020年）的变化过程,其主要增量来源于镇海湾（40%）和淇澳岛（28%）;（3）淇澳岛和镇海湾的红树林仍处于快速增长期,但淇澳岛增速最快,从2002年至今面积翻了30倍,深圳湾则在早期的快速增长（1987年—2009年）后进入平稳期（2009年—2020年）;（4）由于保护区设立时间较早,深圳湾成为大湾区唯一一个形成稳定核心区的红树林分布区,镇海湾虽然拥有最大的红树林面积,但林道狭窄,景观破碎,生态系统反而更加脆弱;（5）设立自然保护区和滩涂造林都对红树林面积增长起到了积极作用。本研究为大湾区海岸带湿地生态系统保护与修复提供科学的证据支持,对沿海生态屏障建设具有一定的指导作用。     

江苏滨海湿地土地利用/覆盖变化与地表温度响应遥感监测

以Landsat TM/ETM+图像为主要数据源,提取了江苏省盐城滨海湿地土地利用/覆盖信息;基于单窗算法反演了地表温度,综合分析了1992—2009年间湿地土地利用/覆盖变化(LUCC)及其对地表温度的影响。结果表明:在1992—2009年的17 a间,江苏省滨海湿地最主要的LUCC类型是人类主导型,且以自然类型向人工类型转化最为显著;互花米草、碱蓬和芦苇等自然景观总面积减少了385.52 km2,养殖场、盐田及建筑用地等人工景观总面积增加了376.97 km2;人为干扰是湿地LUCC最主要的驱动因素。在LUCC的影响下,湿地地表温度也有显著波动,多数湿地类型都有不同层次的升温。     

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。     

结合多源专题数据和目视解译的大区域密集湿地样本数据生产

样本数据是开展湿地制图的研究基础之一,对于数据生产和精度验证具有重要作用。针对湿地生态系统类型多样,大区域的全湿地类型样本生产困难的问题,本研究提出了一种准确、高效的大区域密集湿地样本生产框架。该框架主要包括两部分：首先,基于已有的湿地数据产品,使用规则筛选的方法直接生产稳定的湿地样本点,能够得到河流、湖泊、水库、滨海木本沼泽（红树林）、滩涂的5种湿地类型样本点;其次,基于多源专题数据进行规则筛选,生产潜在湿地样本点,并利用Google Earth Engine大数据云平台、Google Earth平台和Collect Earth平台进行目视解译,以确定潜在湿地样本点的类型属性。本文开展大洲尺度的全湿地类型样本生产,结果表明：本研究共生产了150688个湿地样本点,其中内陆湿地样本点为121412个,滨海湿地样本点为11563个,人工湿地样本点为17693个。13种湿地类型中,湖泊样本点占比最大（39.22%）,潟湖样本点占比最小（0.19%）。本文生产了稳定、高质量的湿地样本点,样本数量充足,空间分布合理,能够为湿地分类器的训练和分类结果精度验证提供可靠的数据基础。     

湿地景观格局与生态安全遥感监测分析——以江苏滨海湿地为例

基于景观生态学原理及遥感、地理信息系统技术,分析了1992—2009年江苏滨海湿地景观格局动态变化。结合"压力-状态-响应"模型、层次分析法及Delphi法,评价了湿地生态安全及其变化趋势。结果表明,1992—2009年间,研究区自然景观总面积显著减少,人工景观面积持续增加,大量湿地自然景观转为人工景观;景观破碎化程度逐渐加强,景观平均分维数呈递减趋势,受人类干扰程度不断加剧;Shannon多样性指数逐步增加,表明随着自然景观向人工景观转移,湿地景观类型丰富度在增加。湿地生态系统在3个时相均处于临界安全状态,病态和不安全等级的总比例逐年增加。     

胶州湾湿地时空演化遥感分析与质量评价

胶州湾湿地是山东半岛面积最大的河口海湾型湿地,对于完善青岛城市生态功能具有重要意义。通过对胶州湾区域2003,2007和2011年3个时相SPOT-5卫星遥感数据的解译,得到相应年份的分类后影像,在此基础上计算各个地物类型的面积,得到胶州湾湿地核心区、实验区和缓冲区的面积变化图表以及相应区域地物类型的组成架构和质量评价系数。分析结果表明:胶州湾湿地的面积变化与质量变化并不一致,其湿地面积呈下降趋势,湿地质量在2003~2007年呈现下降趋势,而在2007~2011年却有所回升,这是由人为因素和自然因素共同影响导致的,其中人为因素占主导。     

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.     

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,...     

Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery

Accurate wetland maps are a fundamental requirement for land use management and for wetland restoration planning. Several wetland map products are available today; most of them based on remote sensing images, but their different data sources and mapping methods lead to substantially different estimations of wetland location and extent. We used two very high-resolution (2 m) WorldView-2 satellite images and one (30 m) Landsat 8 Operational Land Imager (OLI) image to assess wetland coverage in two coastal areas of Tampa Bay (Florida): Fort De Soto State Park and Weedon Island Preserve. An initial unsupervised classification derived from WorldView-2 was more accurate at identifying wetlands based on ground truth data collected in the field than the classification derived from Landsat 8 OLI (82% vs. 46% accuracy). The WorldView-2 data was then used to define the parameters of a simple and efficient decision tree with four nodes for a more exacting classification. The criteria for the decision tree were derived by extracting radiance spectra at 1500 separate pixels from the WorldView-2 data within field-validated regions. Results for both study areas showed high accuracy in both wetland (82% at Fort De Soto State Park, and 94% at Weedon Island Preserve) and non-wetland vegetation classes (90% and 83%, respectively). Historical, published land-use maps overestimate wetland surface cover by factors of 2–10 in the study areas. The proposed methods improve speed and efficiency of wetland map production, allow semi-annual monitoring through repeat satellite passes, and improve the accuracy and precision with which wetlands are identified.     

若尔盖湿地近25年湿地变化及分形特征分析

文章利用若尔盖湿地国家级自然保护区1989、1995、2001、2007和2014年5期遥感影像,研究了近25年保护区湿地面积的变化趋势,探索了保护区湿地分形维数的变化规律、质心的空间变化特征以及气候与湿地变化的相关性。研究结果表明:近25年,保护区湿地面积总体呈下降趋势,总共减少了52.28 hm2;保护区受到人为因素的干扰较大;湿地分布的质心向东北方向偏移;湿地面积变化与区域年平均温度相关性最大,与日照时数相关性最小。     

From global to local: Testing the potential of cross-scaling in global data sets

The present study investigates the potential of readily available and easily accessible global data sets to understand regional/local level interactions in wetland systems. The biogeographical zones of India were used a base-frame to select three sites. The study well fits the interests of National Wetland Committee of India to investigate and document fundamental information on wetland extent/distribution. The national partnership with SACON represents this interest. SACON commenced the inland wetland inventory module at national scale using geospatial data, although the provincial scale analysis is underway. In addition, the global irrigated area mapping (GIAM-IWMI) project generated multi-scalar spatial outputs for irrigated/rain-fed areas. With the existing information base, a multi-level geospatial analysis using Arc GIS algorithmic modelling was used to derive comprehensive appraisal of wetland systems complementing the data from GIAM and SACON. It was observed that the overlap between the two layers was 58 percent for Gujarat and 10 percent in Tamil Nadu. In Krishna basin the wetland’s cover 1.04 million hectare excluding the rice agro-ecosystem. The difference in the biogeography of the case sites governs the gradient of information derived from both data layers. Additionally, the global lakes and wetlands database (GLWD) database added thematic information on coastal wetlands. In summary we describe the cross-scaling the global data layers to compliment the regional/national level monitoring assignments.     

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。     

环渤海湾-莱州湾地区湿地现状遥感调查

应用Landsat-7 遥感数据和GIS技术对环渤海湾-莱州湾地区湿地现状进行了调查，调查该区湿地类型、面积和分布现状，并对湿地现状遥感调查结果进行了分析。     

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.     

Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain,Northeast China

Increasing interest in wetlands for environmental management requires an understanding of the location, spatial extent, and configuration of the resource. The National Wetlands Inventory is the most commonly used data source for this information. However, its accuracy is limited in some contexts, such as agricultural and forested wetlands. An large number of studies have mapped wetlands worldwide from the perspective of land use and land cover change. However, information on the actual wetland planting areas annually is limited, which greatly impacts ongoing research. In this case study of the West Songnen Plain, we developed a simple algorithm for the quick mapping of wetlands by utilizing their unique physical features, such as annual display of phenological land-cover change of exposed soils, shallow flooding water, and plants from multi-temporal Landsat images. Temporal variations of the Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) derived from Landsat images in 2010 for wetlands at different growth stages were analyzed. Results show that during the ante-tillering phase, the NDVI value (above zero) is lower than the LSWI value of paddies because of flooding of shallow water; during the reproductive and ripening phases, the NDVI value is higher than the LSWI value (above zero); and during the post-harvest wetland planting phase, the NDVI value is still higher than the LSWI value, but the LSWI value is negative. Wetland areas can be detected using one or two images in the optimum time window. The algorithm based on the difference of NDVI and LSWI values derived from Landsat images was used to extract the actual wetland planting area. Validated alongside statistical data, the algorithm showed high accuracy. Therefore, this algorithm highlights the unique features of wetlands and can help in mapping the actual wetland area annually on a regional scale. Results further indicate that the new method has a classification accuracy of 92 %. In comparison, two traditional methods based on Landsat-7/ETM registered accuracy rates of only 83 % and 87 % respectively.