LANDSAT Sub-pixel Analysis in Mapping Impact of Climatic Variability on Prairie Pothole Changes

The Prairie Pothole Region in the United States contains millions of seasonal, semi-permanent, or permanent lakes and wetlands that typically range in size from 0.1 to 10 ha. These lakes and wetlands are vulnerable to climate change, especially in our study area in South Dakota, in which a period of deluge following a sharp drought considerably expanded the areal extent of prairie pothole lakes during the last decade of the twentieth century. Preliminary estimates of lake areas, determined using LANDSAT 5 and 7 images, had appreciable errors especially for the smallest of these lakes. This article describes a new sub-pixel approach integrated with a CART (Classification and Regression Tree) model using a GIS (Geographical Information System) to quantify mixed water pixels along lake boundaries to improve area estimations for pothole lakes. Errors in estimated area were typically 10% or less for lakes greater than 1 ha in size. An analysis of lakes in our study area demonstrates how lake area changed with the transition from drought to deluge. Small lakes exhibited a distinct seasonal variation in contrast to large lakes that tended to follow precipitation trends more broadly. The total water area of lakes is consistent with broad variation in rainfall.     

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.     

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.     

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.     

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.     

Landscape dynamics in Hokersar Wetland, Jammu & Kashmir—An application of geospatial approach

Geospatial presentation of habitat has become a key issue for planning conservation and management of any ecosystem. Hokersar wetland, one of the best resorts of migratory waterfowl in Kashmir, is under anthropogenic pressure and siltation due to floods. This has resulted in the degradation and change in the habitat quality of varied aquatic flora and fauna. Moreover, the seasonal changes affect the water level and land cover characteristics of the landscape. In the present study temporal mapping of the wetland has been carried out using the data sets for the autumn and spring seasons to assess the land cove/land use dynamics. The temporal change analysis, in the urban sprawl and the wetland, has been carried out to assess the rate of changes in the wetland and its environs. The wetland initially comprised of patch of marshy waterfowl habitat with some open water bodies. It has been fragmented into a large number of land uses because of anthropogenic activities. The increase in the settlement has been observed proportionate to the rate of fragmentation in the wetland. This study has created an information base, which will help to design conservation schemes for long term maintenance of the wetland.     

Monitoring dual-season hydrological dynamics of seasonally flooded wetlands in the lower reach of Mayurakshi River,Eastern India

The seasonally flooded wetlands are often neglected due to their ephemeral and erratic appearance and small size, however, their hydro-ecological importance, socio-economic values are equivalent to the permanent wetlands. Using dual-season Landsat multi-spectral imagery, this study highlights a comprehensive monitoring of the hydrological dynamics of wetlands in the lower reach of the Mayurakshi River at a 14-year temporal resolution with the seasonal mode of 1987–2014 time frames. Our results demonstrate the seasonal and periodic hydrological variability of water presence frequency (WPF) in six wetland complexes defined for this study. The Hijal, Ghambira and Maldah wetland complexes are highly affected by the change of WPF, while Dwarka complex is relatively stable. The assessment of WPF change analysis showed that the method is proficient in identifying stable and physically vulnerable wetland patches and absolute loss of wetland inundation frequency resulting from various anthropogenic causes like regulation of river, dry farming practices and other integrated developmental works. The outcome of our study provides a robust basis for the fundamental hydrological and ecological studies and helpful for the conservation and management of seasonally flooded wetland resources in the tropical monsoon climate.     

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。     

Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland

Monitoring wetland as one of the important parts of the global ecosystem is necessary for conservational programs. But, usually, collecting in situ data is restricted in these areas because of their remote locations, vast area and dynamic conditions. Remote sensing provides a cost effective tool to investigate hydrological patterns and the seasonal trend of changes in wetlands. In this paper, Land-use/land-cover change during water inundation period of Hamun wetland was investigated in order to determine change trend during this period. Hamun wetland is an unsustainable ecosystem, and monitoring this wetland is essential for conservation goals. This trend is critical for decision makers in order to plan the conservational scheme in all unsustainable ecosystems. To reach this objective, the land-use/land-cover maps during inundation period of Hamun were produced using Landsat 8 time series images. The results of accuracy assessment showed the classification of water and vegetation have the highest accuracy (94% and 93%, respectively). And the accuracy of plants in the water classes was the lowest (water–veg?=?89.9%, veg–water 1?=?88.8%, veg–water 2?=?87.6%). This means the higher misclassification is in determining the vegetation in the water. Then, the changes in the land-cover classes in relation to wetland inundation were investigated. Results of land-use/land-cover change illustrate the regions that were suitable for water birds but lost their suitability when the wetland dried out. These areas are crucial for water bird’s conservation. Satellite data determined these areas with acceptable accuracy.     

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。     

Wetland Monitoring,Serving as an Index of Land Use Change-A Study in Samaspur Wetlands,Uttar Pradesh,India

Wetlands are among the most productive ecosystems in the world and any alterations might lead to changes in their bio-physical, socio-economic and climatic conditions. Wetland dynamics as an index of land use change were studied. Satellite remote sensing was utilized to understand the periodic and seasonal dynamics of Samaspur wetlands using Landsat and RESOURCESAT-1 temporal data. Index-based (i.e., Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI)) classification resulted in meaningful discrimination of wetland classes. Results indicate (i) effective water spread areas have increased to optimum capacity at 1990 due to the influence of Sharda canal, (ii) expansion of the agricultural area has led to reduction of the wetland buffer area, and (iii) increase in vegetation biomass due to pesticide-fertilizer runoff and sedimentation load. We also reiterate (i) free availability of the Landsat satellite data in public domain facilitating such monitoring studies and (ii) availability and utility of SWIR band information in wetland classification exercise. The study concludes that policy-driven measures have both long and short term impacts on land use and its natural wetland ecosystems; and the characterizing the later serves as indictor of the former and perhaps vice versa.     

Methane emission modelling using MODIS thermal and optical data: A case study on Gujarat

Wetlands are one of the most important sources of atmospheric methane (CH₄) contributing about 22% to the global methane budget. But to improve estimates of CH₄ emission at regional and global scales there is a need to observe the sources such as wetlands frequently and develop process-based models. In this regard, wetland inventory using satellite remote sensing data has conventionally been carried out by analysis of optical data. Due to thermal inertia differences emittive thermal channels data has shown promise to provide highly critical information about wetlands such as water spread, aquatic vegetation and mud flats etc. Thermal channels data of MODIS (Moderate Resolution Imaging Spectroradiometer) sensor with a spatial resolution of 1km and swath of 2330 km is emerging as the key source of remote sensing data for global/ regional wetland estimation and assessment of green house gas emission. In the present study MODIS thermal channels (31 and 32) and optical channels (1,2, and 3) data have been used for evaluating methane emission from wetlands in Gujarat. An empirical model based on temperature and productivity has been used to investigate the response of methane emission from different sources. Model has the potential to estimate country level methane emission based on satellite remote sensing in conjunction with collateral data/information. In this study. MODIS data of two dates pertaining to Gujarat have been analyzed and results compared with respect to methane emission.     

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.     

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。     

The Lost Natural Wetlands of Punjab (India): An Inventory

Wetlands play a vital role in maintaining groundwater levels in an area. This is true for Punjab that was bestowed with several natural wetlands in the flood plains of its rivers. These natural wetlands have never been mapped and their existence has not been acknowledged. The large scale agricultural development in the state has made it India's leading food producing state. This development was done at the cost of certain ecologically sensitive parts, mainly the flood plains thus leading to the demise of wetlands. This paper is an attempt to retrace the lost wetlands that were existed in the flood plains of the three major rivers: Satluj, Beas and Ravi in the present day Punjab at the beginning of 20th century. A majority of these wetlands have not been documented so far and do not even have names. The purpose is to emphasize their elimination in addition to establishing a baseline dataset that can be a tool for wetland planning and management.     

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,...     

Characterizing wetland dynamics: a post-classification change detection analysis of the East Kolkata Wetlands using open source satellite data

The East Kolkata Wetlands is a unique resource recovery system. The Ramsar Convention recognized it as a ‘Wetland of International Importance’ in August 2002. However, the long-term resource exploitation and land use changes in the dynamic ecosystem have resulted in non-linear environmental responses. This is an attempt using open source remote sensing datasets to capture the spatio-temporal transformation of the wetland resulting from various anthropogenic activities. Landsat MSS and TM imageries of 1973, 1980, 1989, 2001 and 2010 were classified using Maximum Likelihood Classifier to monitor the wetland change; however, to study wetland dynamics, the post-classification wetland change detection maps have been generated for two temporal phases, i.e. 1973–1989 and 1989–2010. This study finds that the area under wetlands has reduced comprehensively in the past 40 years due to the conversion of wetlands into various other uses such as urban expansion of the Kolkata metropolitan city.     

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.     

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。     

Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images

Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between Rice_Landsat and Rice_NLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region.