Single-band Threshold Research Articles

Comparison of Lake Ice Extraction Methods Based on MODIS Images

As an important part of the cryosphere, lake ice is a sensitive indicator of climate change. Remote sensing technology can quickly and accurately monitor the process of its formation and decay, among which Moderate Resolution Imaging Spectroradiometer (MODIS) images are the most widely used data in the remote sensing monitoring of lake ice. The reasonable selection of monitoring methods is of great significance to grasp the dynamic process and response to climate change of lake ice. In this study, five commonly used remote sensing monitoring methods of lake ice based on MODIS MOD09GA data, including the single band threshold method (SBT), reflectance difference threshold method (RDT), normalized difference snow index method (NDSI), modified normalized difference snow index method (MNDSI) and lake ice index method (LII), were selected to compare their accuracies in extracting lake ice extent by combining them with four evaluation metrics of accuracy, precision, recall and mean intersection over union (MIoU). In addition, the ability of the high-precision LII method for extracting long time series lake ice phenology and its applicability to multiple types of lakes were verified. The results showed that compared with the NDSI method, the other four methods more easily distinguished between lake ice and lake water by setting thresholds. The SBT method and the RDT method had better extraction effects in the freezing process and the melting process, respectively. Compared with the NDSI and MNDSI methods, the LII method showed a significant improvement in lake ice extraction over the entire freeze–thaw cycle, with the smallest mean monitoring error of 1.53% for the percentage of lake ice area in different periods. Meanwhile, the LII method can be used to determine long term lake ice phenology dates and had good performance in extracting lake ice for different types of lakes on the Qinghai–Tibet Plateau with the optimal threshold interval of 0.05~0.07, which can be used for lake ice monitoring and long-term phenological studies in this region.

Identification of high-temperature targets in remote sensing based on correspondence analysis

Abstract High temperature targets (temperature above 500 K), are the special on the surface of the earth such as forest fire, prairie fire, oil well torches, heap coking, volcanic eruptions, significantly different from those of normal surfaces at lower temperatures. Identification of high-temperature targets plays an important role in environmental monitoring, disaster warning, and resource investigation. In remote sensing data, high-temperature target pixels and bands are studied. And they are deemed samples and variables, respectively, in multivariate analysis. And classification of samples for identification of high-temperature targets is necessary. To classify samples, feature analysis of spectrum needs to be done first. In feature analysis of spectrum, feature bands that can be used to distinguish samples need to be selected. Correspondence analysis is the method that can project samples and variables into the same factor space in the meantime. It can realize the classification of samples and variables synchronously, and the results can be interpreted by each other. First, the correspondence analysis is conducted on Landsat8/OLI remote sensing imagery to build the relationship between samples and variables. After that the correspondence relationship between identification results of high-temperature targets and feature bands can be built in the physical theory of remote sensing and factors which have indicative significance on fire are confirmed. Finally, the single band threshold method is adopted to realize high temperature target recognition by using factor scores. In the field confirmation, results suggest that the precision of identification of high-temperature targets reaches 92%. And we also get a consistent result with SWIR temperature inversion.

Comparative Study on Recognition Models of Black-Odorous Water in Hangzhou Based on GF-2 Satellite Data.

To improve the ability of remote sensing technology in recognizing black-odorous water bodies in Hangzhou, this study analyzed the typical spectral characteristics of black-odorous water in Hangzhou based on measured spectral data and water quality parameters, including the transparency, dissolved oxygen, oxidation reduction potential, and ammonia nitrogen. The single-band threshold method, the normalized difference black-odorous water index (NDBWI) model, the black-odorous water index (BOI) model, and the color purity on a Commission Internationale de L’Eclairage (CIE) model were compared to analyze the spatial and temporal distribution characteristics of the black-odorous water in Hangzhou. The results showed that: (1) The remote sensing reflectance of black-odorous water was lower than that of ordinary water, the spectral curve was gentle, and the wave peak shifted toward the near-infrared direction in the wavelength range of 650–850 nm; (2) Among the aforementioned models, the normalized and improved normalized black-odorous water index methods had a higher accuracy, reaching 87.5%, and the threshold values for black-odorous water identification were 0.14 and 0.1, respectively; (3) From 2015 to 2018, the quantity of black-odorous water in the main urban area of Hangzhou showed a decreasing trend, and black-odorous water was mainly distributed in the Gongshu District and tended to appear in narrow rivers, densely populated areas, and factory construction sites. This study is expected to be of great practical value for the rapid tracking and monitoring of urban black-odorous water by using remote sensing technology for future work.

Compressed High Resolution Satellite Image Processing to Detect Water Bodies with Combined Bilateral Filtering and Threshold Techniques

Water body extraction has become an increasingly important source of information for the prediction of heavy flood situations over the last few decades. Several approaches for identifying water bodies from separate High Resolution satellite data with varying spatial, spectral, and temporal characteristics have been developed. The vast amount of data available, supported by open access regulations adopted by many agencies, is transforming the remote sensing environment, underlining the necessity of data processing speed and efficiency. The current study presents an autonomous method for extracting water bodies from satellite pictures that use a single-band threshold approach with bilateral filtering. One important goal is to use low-cost software to understand raw picture data to evaluate multi-temporal and multi-sensor images. Using the proposed framework, the workflow is constructed on a free and open-source software processing framework. Water was detected properly and quickly from other land cover features.

A Water Body Extraction Methods Comparison Based on FengYun Satellite Data: A Case Study of Poyang Lake Region, China

Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability.

Comparison of remote sensing extraction methods for glacier firn line-considering Urumqi Glacier No.1 as the experimental area

In mid-latitude glaciers, the altitude of the snowline at the end of the ablating season can be used to indicate the equilibrium line, which can be used as an approximation for it. In this paper, Urumqi Glacier No.1 was selected as the experimental area while Landsat TM/ETM+/OLI images were used to analyze and compare the accuracy as well as applicability of the visual interpretation, Normalized Difference Snow Index, single-band threshold and albedo remote sensing inversion methods for the extraction of the firn lines. The results show that the visual interpretation and the albedo remote sensing inversion methods have strong adaptability, alonger with the high accuracy of the extracted firn line while it is followed by the Normalized Difference Snow Index and the single-band threshold methods. In the year with extremely negative mass balance, the altitude deviation of the firn line extracted by different methods is increased. Except for the years with extremely negative mass balance, the altitude of the firn line at the end of the ablating season has a good indication for the altitude of the balance line.

An integrated study on change detection and environment evaluation of surface water

Surface water pollution is one of the serious environment pollution problems, posing threat to human and other creatures. Extraction, change detection and environment evaluation of surface water are prerequisite for water resource management. Undoubtedly, remote sensing data play an important role in these researches because of its large geographic coverage and high temporal frequency. In this study, the Tianjin Binhai New Area was chosen as the study area and the surface water extraction method Modified Normalized Difference Water Index (MNDWI) was used by combining with adaptive dynamic threshold to extract surface water and detect its change. Comparing with single-band threshold, model of multi-band spectral relationship, Iterative Self-organizing Data Analysis Technique Algorithm and MNDWI, MNDWI-based adaptive dynamic threshold method performed better, which considered the influence of background. Analysis on dynamic change of water showed the area of lake and river had increased and the area of seawater had decreased. Meanwhile, the correlation analysis between area change of surface water and impact factors indicated both climatic and anthropogenic factors made positive contribution to the present water environment situation. Finally, an improved model of surface water environment evaluation was established to evaluate water quality by combining genetic algorithm (GA) and backpropagation (BP) neural network model. And the test results proved that the optimized GA-BP neural network is better than the single BP neural network. The environment evaluation indicated that water quality of the Haihe River section in the study area was poor. Therefore, water environment protection should be strengthened in this area. Some suggestions on practical management were given accordingly.

The threshold determination methods of water body information extraction using GF-1 satellite image

The GF-1 satellite is the first satellite of China High-resolution Earth Observation System (referred to short as CHEOS) with the advantages of high spatial resolution, high temporal resolution and 4 multi-spectral channels, which can quickly extract inland water body information. At present, the methods of water body information extraction by remote sensing mainly include single-band threshold method, spectral relation method, normalized difference vegetation index (NDVI) method, normalized difference water body index (NDWI) method and shade water body index (SWI) method. The results of water body information extraction will vary with different methods; especially, the thresholds determination of each method is an important factor in the accuracy of water body information extraction. In this paper, the water body information of Qinghai Lake, Hongze Lake, and Hulun Lake were extracted by NDWI index, and the optimal thresholds of water body information extraction were determined by using Jeffries-Matusita (J-M) distance method and density slice method was in the range of -0.061~0.093. Finally, the accuracy of the extraction results was analyzed, and the overall accuracy is more than 88%.

Residences information extraction from Landsat imagery using the multi-parameter decision tree method

The rapid and accurate grasp of changes in residences is crucial for urban planning and urbanisation. However, the traditional methods for extracting residences exists several problems, which lead to inaccurate extraction results. In this study, the Landsat image is used to establish a new method for extracting the residences quickly and accurately. The specific steps are as follows: (1) We calculate surface albedo to exclude the interference of waters and shadows; (2) Using single-band threshold method, we eliminate the interference of shadows; (3) Normalized Difference Vegetation Index is calculated to exclude the effects of vegetation; (4) Roads are removed by calculating the shape index. Verification shows that the accuracy of this extraction method is 92.81%, which is more accurate than the traditional methods and solves the problems existed in the traditional methods. This novel method is a new reference for other land cover research on the technical aspect.

Summary of water body extraction methods based on ZY-3 satellite

Extracting from remote sensing images is one of the main means of water information extraction. Affected by spectral characteristics, many methods can be not applied to the satellite image of ZY-3. To solve this problem, we summarize the extraction methods for ZY-3 and analyze the extraction results of existing methods. According to the characteristics of extraction results, the method of WI& single band threshold and the method of texture filtering based on probability statistics are explored. In addition, the advantages and disadvantages of all methods are compared, which provides some reference for the research of water extraction from images. The obtained conclusions are as follows. 1) NIR has higher water sensitivity, consequently when the surface reflectance in the study area is less similar to water, using single band threshold method or multi band operation can obtain the ideal effect. 2) Compared with the water index and HIS optimal index method, object extraction method based on rules, which takes into account not only the spectral information of the water, but also space and texture feature constraints, can obtain better extraction effect, yet the image segmentation process is time consuming and the definition of the rules requires a certain knowledge. 3) The combination of the spectral relationship and water index can eliminate the interference of the shadow to a certain extent. When there is less small water or small water is not considered in further study, texture filtering based on probability statistics can effectively reduce the noises in result and avoid mixing shadows or paddy field with water in a certain extent.

INFORMATION EXTRACTION AND CHANGE ANALYSIS OF MAJOR LAKES IN TIBETAN PLATEAU BASED ON LANDSAT REMOTE SENSING IMAGES

Abstract. The water resources of Tibetan plateau, particularly the lakes, has been influenced by global climate change and also reacted to global change. It is important to study the lake changes in the Tibetan plateau. This paper aimed to analyse the changes detected from remote sensing images for the typical lakes in Tibetan Plateau, including Qinghai Lake, Nam Co and Selin Co, using different information extraction methods, such as the single-band threshold method, NDWI, MNDWI, MSCT, MLC, SVM and object-oriented classification method. Results show that object-oriented classification method is better at removing ice, snow, cloud and mountain shadow, while the NDWI and MNDWI generated the worst results for lake extraction. After that, the lake's long temporal changes for Qinghai Lake, Nam Co and Selin Co derived from 12 images in different periods have been analysed using GIS technology. Results show that between 1985 and 2015, Qinghai Lake has expanded after a steady expansion; Nam Co was in the state about stable to expansion to stable. The area increased by 70.03 km2, a growth rate of 2.33 km2 a-1 and had a small change in area; Selin Co expanded after a stable state, and the area is always growing, and Selin Co's growth rate is larger than Qinghai Lake and Nam Co at the same time.

Automated Water Classification in the Tibetan Plateau Using Chinese GF-1 WFV Data

The unique climate and topography of the Tibetan Plateau produce an abundant distribution of lakes. These lakes are important indicators of climate change, and changes in lake area have critical implications for water resources and ecological conditions. Lake area change can be monitored using the huge sets of high-resolution remote sensing data available, but this demands an automatic water classification system. This study develops an algorithm for automatic water classification using Chinese <small>GF</small>-1 (or Gaofen-1) wide-field-of-view (<small>WFV</small>) satellite data. The original <small>GF</small>-1 <small>WFV</small> data were automatically preprocessed with radiometric correction and orthorectification. The single-band threshold and two global-local segmentation methods were employed to distinguish water from non-water features. Three methods of determining the optimal thresholds for normalized difference water index (<small>NDWI</small>) images were compared: Iterative Self Organizing Data Analysis Technique (<small>ISODATA</small>); global-local segmentation with thresholds specified by stepwise iteration; and the Otsu method. The water classification from two steps of global-local segmentations showed better performance than the single-band threshold and <small>ISODATA</small> methods. The <small>GF</small>-1 <small>WFV</small>-based lake mapping across the entire Tibetan Plateau in 2015 using the global-local segmentations with thresholds from the Otsu method showed high quality and efficiency in automatic water classification. This method can be extended to other satellite datasets, and makes the high-resolution global monitoring and mapping of lakes possible.

The information extraction of Gannan citrus orchard based on the GF-1 remote sensing image

The production of Gannan oranges is the largest in China, which occupied an important part in the world. The extraction of citrus orchard quickly and effectively has important significance for fruit pathogen defense, fruit production and industrial planning. The traditional spectra extraction method of citrus orchard based on pixel has a lower classification accuracy, difficult to avoid the “pepper phenomenon”. In the influence of noise, the phenomenon that different spectrums of objects have the same spectrum is graveness. Taking Xunwu County citrus fruit planting area of Ganzhou as the research object, aiming at the disadvantage of the lower accuracy of the traditional method based on image element classification method, a decision tree classification method based on object-oriented rule set is proposed. Firstly, multi-scale segmentation is performed on the GF-1 remote sensing image data of the study area. Subsequently the sample objects are selected for statistical analysis of spectral features and geometric features. Finally, combined with the concept of decision tree classification, a variety of empirical values of single band threshold, NDVI, band combination and object geometry characteristics are used hierarchically to execute the information extraction of the research area, and multi-scale segmentation and hierarchical decision tree classification is implemented. The classification results are verified with the confusion matrix, and the overall Kappa index is 87.91%.

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013–2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.

浒苔遥感监测研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 浒苔遥感监测研究进展 DOI: 10.5846/stxb201309232339 作者: 作者单位: 杭州师范大学,杭州师范大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技支撑计划专题资助项目(2012BAC13B02) Advances in the monitoring of Enteromorpha prolifera using remote sensing Author: Affiliation: College of Life and Environmental Sciences, Hangzhou Normal University,College of Life and Environmental Sciences, Hangzhou Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:浒苔大规模集聚形成的绿潮灾害是海洋生态系统主要生态环境问题之一,基于卫星遥感影像监测浒苔及其扩展动态已成为一种及时有效的手段。对国内外浒苔遥感监测方面文献进行归纳整理,认为光学遥感数据、多波段比值法是最常用的遥感数据和监测方法。对遥感监测浒苔机理进行了阐述,并对分类方法进行评价认为监督分类法解译精度不高。目前单波段阈值法和多波段比值法应用广泛,但在监测漂浮浒苔和混合象元解译存在不足。辐射传输模型法能有效提高信息解译的精度,但还处于起步阶段。遥感监测浒苔灾害的未来发展需要提高影像空间分辨率,深入研究监测方法,进行多种平台和多源遥感数据相结合,并由定性走向定量,从而建立健全遥感监测预警系统。 Abstract:Eutrophication of sea water has been regard as one of the major problems that are harmful to our environment and society. Green tide, which is caused by the proliferation of Enteromorpha prolifera, has a major impact on marine ecosystems. Proliferation of Enteromorpha prolifera is the main cause of the green tide. Monitoring the populations of E. prolifera and their dynamic are crucial in the prevention of green tide. Satellite remote sensing image is widely used in the monitoring of E. prolifera because of its timeliness and effectiveness. In this review, we discussed the current research on monitoring of E. prolifera using remote sensing. We considered the optical remote sensing data to be the most popular data used by recent rearchs, and the multi-band ratio method as the most commonly adopted monitoring method. We also discussed the mechanism of monitoring of E. prolifera using remote sensing found that the precision in the interpretation of remote sensing data was relatively low when using the supervision classification method. The single-band threshold method and multi-band ratio method have been widely used despite their shortage in monitoring of the floats of E. prolifera and interpretation of mixed pixels. The radiative transfer model method can improve the accuracy of information interpretation; however, being a newly developed method, it needs further improvements. For better monitoring of E. prolifera, the spatial resolution of images need to be improved in future research. Combining multiple platforms and multi-source remote sensing data are important aspects that could provide more accurate data from E. prolifera monitoring. Further studies on monitoring methods are required to achieve a timely and effective monitoring. Most importantly, we should replace the current qualitative analysis with a quantitative one in order to perfect the remote sensing monitoring and finally establish the early warning system. 参考文献 相似文献 引证文献

Decision tree model of extracting blue-green algal blooms information based on Landsat TM/ETM+imagery in Lake Taihu

以9期Landsat TM/ETM+影像为数据源,基于K-T变换和归一化植被指数(NDVI),建立了湖泊蓝藻水华信息提取的决策树模型.基于大气顶面反射率图像,选用2005年10月17日太湖图像进行了对比验证,表明决策树模型比单波段阈值法、多波段阈值法(RVI、DVI、NDVI)能够更有效地提取蓝藻水华信息,区分陆生植被、水生植物和水华,省去了水体掩膜的过程.使用太湖2002年10月25日和2011年7月22日图像、巢湖2005年8月12日的图像,验证决策树模型方法和工作流程的有效性.使用多期TM图像确定了阈值的取值范围,其中,亮度、绿度、NDVI的下限值依次为0.191、-0.007、-0.054,湿度下限范围为0.07~0.15;亮度阈值上限范围为0.3~0.7、绿度为0.2~0.5、湿度为0.1~0.3,这些结果可作为湖泊蓝藻水华遥感监测的参考.;A decision tree model (KTNI) based on the K-T transformation and Normalized Difference Vegetation Index (NDVI) was developed to extract the blue-green algal blooms information in the lakes using 9 Landsat TM/ETM+images. This paper compared the KTNI with the single-band threshold value method and the multi-band threshold value methods (RVI, DVI, NDVI) in accuracy of extracting the blue-green algal blooms information, and then validated the KTNI using two observed images of the Lake Taihu and one image of the Lake Chaohu. The results showed that the KTNI can mask the water body automatically and separate the blue-green algal blooms from the land vegetation and aquatic plants effectively. Model thresholds and their ranges were determined based on the TM images;the lower limits of the Brightness, Greenness and NDVI were 0.191, -0.007 and -0.054, respectively, and Wetness ranged from 0.07 to 0.15. For the upper limits, the Brightness was from 0.3 to 0.7, Greenness was from 0.2 to 0.5, and Wetness was from 0.1 to 0.3. The decision tree model and threshold values can be used as the reference for remote sensing monitoring on the blue-green algal blooms in lakes.

Advances in the study of Ulvapolifera monitoring with remote sensing

The massive bloom of the green macroalgae Ulvaprolifera is called "green tide".The "green tide" event frequently happens in the world and is known as an oceanic disaster due to its bad effects on the marine ecological environment,coastal scene,seashore tourism and water sports.Remote sensing has the advantages over the conventional methods in oceanic monitoring because of its great capabilities of large-area,multi-resolution,multi-spectrum,quick and dynamic observations.Remote sensing has become a necessary method in the study of the origin,spatial pattern,evolution,size and movement of "green tide".At present,3 main approaches detecting Ulvaprolifera have been developed,which are single band threshold image segmentation method,multi-band ratio method and radiation transmission model method.The multi-band ratio method is most widely used,which includes two-band ratio algorithm,normalized differential vegetation index,floating algae index and normalized difference algae index.The method of radiation transmission model is mainly used to monitor Ulvaprolifera suspended in water,and its study is just in the initial stage.Further researches on distinguishing the difference between the spectrum of Ulvaprolifera and other type of floating algae,monitoring Ulvaprolifera suspended in water,solving the contradiction between the deficiency of monitoring capability and the needs of users are the directions of Ulvaprolifera monitoring in the future.

Water Body Extraction Methods Study Based on RS and GIS

Water body extraction by using remote sensing has been the most important method in the investigation of water resources, flood hazard prediction assessment and water planning with fast and accurate effectiveness. Multiple methods including unsupervised classification, supervised classification, single-band threshold, inter spectrum relation method and water index method (normalized difference water index, modified normalized difference water index, and new water index) are analyzed. Miyun reservoir and Miyun cit zone are selected as study area to find the most effective water extraction method with different usage in different time.

Comparative Analysis of Automatic Water Identification Method Based on Multispectral Remote Sensing

In this paper, from the water spectral reflectance characteristics, I select five multi-spectral remote sensing water information extraction methods, issue comparative analysis and applicability assessment in five typical experiment areas based on Landsat data. I have made a comprehensive comparison and algorithm applicability analysis on the five kinds of methods. The main conclusions are the following: MNDWI and The spectral relationship method between multi-band have the most widely applicability, MNDWI always use in the building shadow and bare land. The spectral relationship method between multi-band has the special effects on the mountain shadow areas. NDVI has an apparent superiority in the dense forest and grassland, the low-cost of single-band threshold make it have more advantages in the great plains areas, compromise and low noise of NDWI can be used in complicated areas.