中国湿地变化的驱动力分析

在全球气候变化及中国社会经济迅速发展的背景下,为了解中国湿地分布的时空动态特征及演化规律,以4期(1978年、1990年、2000年、2008年)中国湿地遥感制图数据和3期(1990年、2000年、2005年)土地利用数据为基础,同时考虑到对湿地变化的影响程度和数据的可获取性,选取12个影响因子(平均温度、平均湿度、累计降水量、人口数量、地区生产总值、农林牧渔产值、耕地面积、粮食产量、有效灌溉面积、水库库容量、除涝面积、治碱面积)研究1978年—2008年这30年间中国湿地变化的驱动机制。考虑到地理现象的空间非平稳性,本文采用地理加权回归的方法分析驱动因子对湿地变化的影响作用。地理加权回归作为一种局部线性回归方法,能够直观地反映湿地驱动因子对湿地作用的地域差异。结果表明:不同类型的湿地变化的主要影响因素不同,内陆湿地与温度、降水以及农业耕作灌溉等密切相关;人工湿地与经济发展水平和水利设施兴建密切相关;滨海湿地与农林牧渔产业和人口等密切相关。同一类型湿地变化的主要影响因素随着时间推移也有所变化,并且影响程度在地域上也存在较为明显的南北和东西差异。本次研究结果基本反映了1978年—2008年中国湿地变化的特征规律。     

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.     

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.     

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.     

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.     

Total Suspended Sediments Mapping by Using ALOS Imagery Over the Coastal Waters of Langkawi Island, Malaysia

Remote sensing has been extensively used for water delineation and has played an important role in water quality evaluation and environmental management strategies. Suspended sediments are important determinants of water quality in coastal zones. Remote sensing enables the effective monitoring of total suspended sediments (TSS) and the detection of areas with critical water quality issues. This study aims to develop and implement regression models for estimating and mapping TSS concentrations from Advanced Land Observation Satellite (ALOS) images over the coastal waters of Langkawi Island, Malaysia. The algorithm was developed based on the water reflectance model, which is a function of the inherent optical properties of water. Such properties can then be linked to TSS concentration. In this study, an ALOS Advanced Visible and Near Infrared Radiometer type 2 device was used as the imaging sensor system. Concurrent complementary in-situ water samples were collected within the area coverage of the sensor, and digital numbers (DN) for each band corresponding to the sea-truth locations were determined. The extracted DN values were converted into reflectance values and then regressed with their respective sea-truth data. An algorithm was proposed to obtain the regression coefficient. This algorithm can estimate TSS concentrations with a high correlation coefficient (R²?=?0.96) and low root-mean-square error (RMSE?=?1.98 mg/l). Finally, a map of the TSS concentration was generated by using the proposed algorithm. This study found that TSS mapping can be conducted by using ALOS data over the coastal waters of Langkawi Island, Malaysia.     

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,...     

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.     

杭州湾滨海湿地时空格局变化遥感调查研究

采用TM、SPOT-5卫星遥感数据,通过建立杭州湾滨海湿地分类体系和解译标志并进行人机交互解译,完成了杭州湾1987、1995、2003和2009年滨海湿地提取和分类。研究了4期杭州湾滨海湿地的利用状况、面积,以及时空格局变化情况,研究显示:1987~2009年期间,杭州湾滨海湿地主要表现为滩涂湿地的逐年减少和库塘湿地的逐年增加;1995年之前湿地变化以自然驱动力为主,之后人类活动影响明显,对近海域、滩涂不断开发,尤以南岸为主;以杭州湾湿地公园为典型的沼泽草甸湿地得到了较好的保护。     

Mapping and characterization of inland wetlands using remote sensing and GIS

Sustainable management of wetland ecosystem is necessary as it serves the important functions such as food storage, water quality maintenance and providing habitat for different species of wildlife. Hence, an inventory of wetlands in any given area is a pre-requisite for their conservation and management. A study has been carried out to delineate the wetlands of east Champaran district of Bihar, India, using IRS ID LISS III data. The data for the pre and post monsoon seasons have been analysed and the wetlands have been qualitatively characterized based on the turbidity and aquatic vegetation status. The extent of water logging problem in the study area was inferred from the seasonal variation of waterspread during both the seasons. The three categories of wetlands (ponds/lakes, water logged areas and oxbow lakes) have been identified. From the analysis, it has been observed that the inland wetlands constitute 2.7% of the study area, of which 1.8% is subjected to water logging. Thus, this study highlights the usefulness of remotely sensed data for wetland mapping, seasonal monitoring and characterization.     

Insight to Ecosystem Based Approach (EBA) at Landscape Level Using a Geospatial Medium

Ecosystem based approach (EBA) for resource management is a concerted, environmentally tuned and an integrated framework that holistically addresses the ecological character of the natural resource, its societal benefit spectrum and its environmental functions. In this paper, the EBA concept is closely linked with the emerging concept of multiple use systems (MUS) while taking account of environmental, economic, and social factors that govern the ecosystems services and benefits. We elucidate a multi-scalar approach and multiple case studies to understand EBA particularly in context of a wetlandscape. At the global scale, Ramsar sites of international importance are geospatially analyzed with reference to their agro-ecology and biodiversity. At regional scale, the agrarian use of inland wetlands in India was re-evaluated taking account of database from a recent inland wetland inventory. At the local scale, drawing on the landscape characterization and the ecological economics for fresh water Lake Kolleru in India and the Muthurajawela Marsh-Negombo Lagoon coastal marsh in Sri Lanka, we illustrate some of the practical challenges in balancing wetland conservation, development needs and the overall well-being of local people. We also discuss how variability in the scale, geophysical characteristics of the site and the data availability confines the ability to simplify a single complete approach to address issues in complex ecosystem such as wetlands. All levels of the study are supported by a variety of earth observation data and the geographical information system (GIS) tools. The site level analysis also draws on socio-economic assessment tools.     

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.     

环渤海滨海湿地时空格局变化遥感监测与分析

基于多期Landsat TM 等遥感数据, 采用目视解译和实地样点采集相互支撑的方法, 完成了环渤海 地区2000 年、2005 年及2008 年湿地提取和分类; 运用单一类型变化率模型、区域动态度模型和动态转移矩 阵, 揭示了环渤海湿地的时空格局、变化特点和驱动机制。研究显示环渤海地区的8 类湿地中, 属于人工湿 地类型的盐场和水库坑塘面积比例大且近8 年增长速度也最快, 年均分别增加205.52 km2 和146.10 km2, 滩 地和海涂减少最明显; 环渤海地区的三大流域中, 黄河流域湿地变化最显     

Land Cover and Vegetation Change in the Yellow River Delta Nature Reserve Analyzed with Landsat Thematic Mapper Data

Abstract

Coastal wetland is a major part of wetlands in the world. Land cover and vegetation mapping in a deltaic lowland environment is complicated by the rapid and significant changes of geomorphic forms. Remote sensing provides an important tool for coastal land cover classification and landscape analysis. The study site in this paper is the Yellow River Delta Nature Reserve (YRDNR) at the Yellow River mouth in Shangdong province, China. Yellow River Delta is one of the fastest growing deltas in the world. YRDNR was listed as a national level nature reserve in 1992. The objectives of this paper are two fold: to study the land cover status of YRDNR, and to examine the land cover change since it was declared as a nature reserve. Land cover and vegetation mapping in YRDNR was developed using multi‐spectral Landsat Thematic Mapper (TM) imagery acquired in 1995. Land cover and landscape characteristics were analyzed with the help of ancillary GIS. Land use investigation data in 1991 were used for comparison with Landsat classification map. Our results show that YRDNR has experienced significant landscape change and environmental improvement after 1992.     

Mapping small wetlands of Kenya and Tanzania using remote sensing techniques

Although wetlands in Tanzania and Kenya have great potentials for agricultural production and a multitude of uses, many of them are not even documented on official maps. Lack of official recognition has done little in preventing there over utilization. As the wetlands continue to play remarkable roles in the movement of people and terrestrial species in the region, it is important that they are monitored and properly managed. This study was undertaken in Usambara highlands and the Pangani floodplain in Tanzania, the Mount Kenya highlands and Laikipia floodplain in Kenya to map the different types of wetlands in terms of their size, density, spatial distribution and use patterns. Remote sensing techniques and field surveys were adopted, and 51 wetlands were identified in flood plains within the semi-arid and sub-humid lowlands, and inland valleys in the region. The detailed maps generated showed the intensity of wetland use, inland valleys being the most intensively used, and are useful in monitoring changes in wetlands for their effective management. The use of multispatial resolution imagery, combined with field survey and GIS produced satisfactory results for the delineation and mapping of small wetlands and their uses.     

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.     

Data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades

This study examined the applicability of data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades. A framework was designed to combine these two techniques. In the framework, 20-m hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer was first merged with 1-m Digital Orthophoto Quarter Quads using a proposed pixel/feature-level fusion strategy. The fused data set was then classified with an ensemble approach based on two contemporary machine learning algorithms: Random Forest and Support Vector Machine. The framework was applied to classify nine vegetation types in a portion of the coastal Everglades. An object-based vegetation map was produced with an overall accuracy of 90% and Kappa value of 0.86. Per-class classification accuracy varied from 61% for identifying buttonwood forest to 100% for identifying red mangrove scrub. The result shows that the framework is promising for automated vegetation mapping in the Everglades.     

Sentinel-2影像多特征优选的黄河三角洲湿地信息提取

以北方典型河口湿地—黄河三角洲湿地为研究区,采用在特征选择和分类提取等方面具有明显优势的随机森林算法,对研究区内的湿地信息进行提取。首先基于多时相、光谱信息丰富的Sentinel-2数据生成4类不同的特征变量,包括光谱特征、植被指数和水体指数、红边指数、纹理特征;再根据以上特征构建6种不同的提取方案,对黄河三角洲湿地信息进行提取并验证不同方案的提取精度,旨在选择最佳方案改善湿地信息提取的效果。结果表明:(1)有效地使用多种特征变量是提高湿地信息提取的关键,就不同特征对湿地信息提取的贡献率而言,红边指数植被指数和水体指数光谱特征纹理特征;(2)基于随机森林算法优选的特征变量提取效果最佳,总体精度高达90.93%,Kappa系数为0.90,表明随机森林算法可以有效地进行特征选择,在特征变量数据挖掘的同时,仍能保证湿地信息提取的精度,提高运行效率。本研究为湿地信息提取在数据源选择、特征选择和方法选择方面提供了一种新思路、方法和技术手段。     

MAPPING VEGETATION COMMUNITIES INSIDE WETLANDS USING SENTINEL-2 IMAGERY IN IRELAND

Wetlands provide habitat for a wide variety of plant and animal species and contribute significantly to overall biodiversity in Ireland. Despite these known ecosystem services, the total wetland area in Ireland has reduced significantly over the past few decades leading to an ongoing need to protect such environments. The EU Habitats Directive (92/43/EEC) has recognised several wetlands types as “priority” habitats. This study concentrates on a subset of the priority habitats focussing on some groundwater dependent terrestrial ecosystems, (in particular calcareous fens and turloughs), as well as raised bogs. Monitoring these sites across the country by field visits is resource-intensive. Therefore, this study has evaluated remote sensing as a potentially cost-effective tool for monitoring the ecological health of the wetlands. Identification and presence of certain vegetation communities can indicate the condition of the wetland, which can be used for monitoring, for example, activities causing degradation or the progress of restoration attempts. The ecological composition of the wetlands has been analysed using open-source Sentinel-2 data. 10 bands of Sentinel-2 Level-2 data and 3 indices, Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalised Difference Water Index (NDWI) were used to create vegetation maps of each wetland using Bagged Tree (BT) ensemble classifier and graph cut segmentation also known as MAP (maximum a posteriori) estimation. The proposed methodology has been validated on five raised bogs, five turloughs, and three fens at different times during 2017 and 2018 from which three case studies are presented. An overall classification accuracy up to 87% depending on the size of the vegetation community within each wetland has been achieved which suggests that the proposed method is appropriate for wetland health monitoring.