An assessment of water reserve changes in Salt Lake,Turkey, through multi‐temporal Landsat imagery and real‐time ground surveys

This paper focuses mainly on the investigation of water reserve changes in Salt Lake, Turkey, using remote‐sensing data. The study is performed in two stages: (1) correlation analysis for real‐time ground and satellite data and (2) assessment of water reserve changes using multi‐temporal Landsat imagery. First, correlation analysis is conducted to investigate the relationship between digital data from Landsat‐5 TM and spectral (in situ) measurements collected using a field spectroradiometer on the same day and time. A radiometric correction procedure, including conversions from digital numbers to radiance and from radiance to at‐satellite reflectance, is executed to make satellite data comparable to in situ measurements. This procedure show that simultaneous ground and satellite remote‐sensing data are highly correlated (0·84 > R² > 97) and the near‐infrared region (for this study TM4‐Landsat‐5 TM, band 4) is the best spectral range to distinguish salt and water on the satellite data for the multi‐temporal analysis of the water reserve in Salt Lake. It also shows that the use of shortwave infrared band(s) will result in confusion for the determination of the water reserve in this water‐covered study area. In a second and last phase, the water reserve change in the lake is examined using multi‐temporal Landsat imagery collected in 1990, 2001 and 2005. The remotely sensed, sampled and treated data show that the water reserve in the lake has decreased markedly between 1990 and 2005 due to drought and uncontrolled water usage. It is suggested that the use of water supplies around Salt Lake should be controlled and that the lake should regularly be monitored by up‐to‐date remote‐sensing data (at least annually) for better management of water resources. Copyright © 2007 John Wiley & Sons, Ltd.     

Role of satellite remote sensing for monitoring of surface water resources in an arid environment

Abstract

Water bodies up to 0.9 ha surface area, which are indistinguishable by Landsat Multispectral Scanner (MSS) due to the latter's poor spatial resolution of 80 m, can be identified and mapped reliably and reasonably within ±10% accuracy by Landsat Thematic Mapper (TM) false colour composite because of its higher spectral and spatial resolution of 30 m. Comparative study of Landsat TM and the Survey of India topographical maps revealed reductions in the water surface and drainage basin areas up to 1.8 to 2.4 and 6.0 to 8.0 times, respectively, over a period of 28 years (1958–1986) due to the biotic interference resulting in desertification in the large adjoining areas.     

基于BP神经网络的在役管线焊缝故障缺陷的分类识别

本文利用计算机辅助进行在役管线焊故障缝缺陷检测,在缺陷特征提取中提出圆形度、长宽比、填充度、尖部尖锐度、对称度、灰度比以及缺陷的重心坐标相对焊缝中心的位置等7个参数作为缺陷的特征值,可有效地分类识别不同故障缺陷。在缺陷分类的解决方案上,采用具有自组织、自适应的3层前馈式神经网络,运用改进的BP算法,以焊缝缺陷的特征参数作为神经网络的训练样本。本文还通过实验的方法,分析了初始权值、隐含层的神经元数量、动量系数、误差水平及学习速率对网络训练的影响。     

Landsat TM image segmentation for delineating geological zone correlated vegetation stratification in the Kruger National Park,South Africa

Image classification approaches are widely used in mapping vegetation on remotely sensed images. Vegetation assemblages are equivalent to habitats. Whereas sub-pixel classification approaches potentially can produce more realistic, homogenous habitat maps, pixel-based hard classifier approaches often result in non-homogenous habitat zones. This salt-and-pepper habitat mapping is particularly a challenge on images of savannas, given the characteristic patchy texture of scattered trees and grass. Image segmentation techniques offer possibilities for homogenous habitat classification. This study aimed at establishing the extent to which established, field surveyed and geology-related vegetation types in South Africa’s Kruger National Park (KNP) can be reproduced using image segmentation. Rain season Landsat TM images were used, selected to coincide with the peak in vegetation productivity, which was deemed the time of year when discrimination between key habitats in KNP is most likely to be successful. The multiresolution segmentation mode in eCognition 5.0 was employed, object classification accomplished using the nearest neighbour (NN) classifier, using object texture and training area mean values in the NN feature space.Compared to delineations of the vegetation types of KNP on a digital map of the vegetation zones that was tested, image segmentation successfully mapped the zones (overall accuracy 85.3%, K^ = 82.7%) despite slight shifts in the location of vegetation zone boundaries. Maximum likelihood classification (MLC) of the same images was only 37% accurate (K^ = 24.2%). Whereas the vegetation zones resulting from MLC were non-homogenous, with considerable spectral confusion among the vegetation zones, image segmentation produced more homogenous vegetation zones, comparably more useful for conservation management, because realistic and meaningful habitat maps are important in biodiversity conservation as input data upon which to base management decisions. Image segmentation appears to be a useful approach in mapping savanna vegetation.     

Application of a neural network model in establishing a stage–discharge relationship for a tidal river

This paper presents the applicability of neural network (NN) modelling for forecasting and filtering problems. The multilayer feedforward (MLFF) network was first constructed to forecast the tidal‐level variations at the mouth of the River Chao Phraya in Thailand. Unlike the well‐known conventional harmonic analysis, the NN model uses a set of previous data for learning and then forecasting directly the time‐series of tidal levels. It was found that lead time of 1 to 24 hourly tidal levels can be predicted successfully using only a short‐time hourly learning data. The MLFF network was further used to establish a stage–discharge relationship for the tidal river. The results show a considerably better performance of the NN model over the conventional models. In addition, the stage–discharge relationship obtained by the NN model can indicate reasonably well the important behaviour of the tidal influences. Copyright © 2003 John Wiley & Sons, Ltd.     

Application of tank,NAM, ARMA and neural network models to flood forecasting

Two lumped conceptual hydrological models, namely tank and NAM and a neural network model are applied to flood forecasting in two river basins in Thailand, the Wichianburi on the Pasak River and the Tha Wang Pha on the Nan River using the flood forecasting procedure developed in this study. The tank and NAM models were calibrated and verified and found to give similar results. The results were found to improve significantly by coupling stochastic and deterministic models (tank and NAM) for updating forecast output. The neural network (NN) model was compared with the tank and NAM models. The NN model does not require knowledge of catchment characteristics and internal hydrological processes. The training process or calibration is relatively simple and less time consuming compared with the extensive calibration effort required by the tank and NAM models. The NN model gives good forecasts based on available rainfall, evaporation and runoff data. The black‐box nature of the NN model and the need for selecting parameters based on trial and error or rule‐of‐thumb, however, characterizes its inherent weakness. The performance of the three models was evaluated statistically. Copyright © 2000 John Wiley & Sons, Ltd.     

结合Landsat ETM与实测光谱估测太湖叶绿素及悬浮物含量

实地测试太湖水体的反射光谱,实验室分析水样,运用相关分析法探求叶绿素、悬浮物的光谱特征波段,估测叶绿素和悬浮物含量;对比LandsatETM波段,运用不同的函数曲线对相应的波段组合进行回归拟合,建立相应的估测模型,选取精度最好的两个分别对太湖的叶绿素和悬浮物含量进行估测.结果表明:(1)对多光谱遥感而言,LandsatTM/ETM是定量获取叶绿素和悬浮物的较好的数据源,但不是最适合的数据源;(2)通过ETM3与叶绿素建立一定的函数关系来估测叶绿素含量具有较高的精度,其中利用算术组合ETM3/ETM1估测叶绿素的精度最高;(3)ETM4与悬浮物具有较高的相关度,其中利用算术组合ETM4/ETM1估测悬浮物含量的精度最高;(4)LandsatETM卫星影像中,不同尺寸的像元窗口影响水质参数的估测精度;对叶绿素估测而青,7×7或者5×5窗口比较适合,对悬浮物估测而言,一般不超过3×3.     

新型Landsat8卫星影像的反射率和地表温度反演

Landsat 8卫星自2013年2月发射以来,其影像的定标参数经过了不断调整和完善,针对Landsat 8开发的各种算法也相继问世.本文采用最新的参数、算法和引入COST算法建立的大气校正模型,对Landsat 8多光谱和热红外波段进行了处理,反演出它们的反射率和地表温度,并与同日的Landsat 7数据和实测地表温度数据进行了对比.结果表明,现有Landsat 8多光谱数据的定标参数和大气顶部反射率反演算法已有很高的精度,本文引入COST算法建立的Landsat 8大气校正模型也与Landsat 7的COST模型所获得的结果几乎相同,相关系数可高达0.99.但是现有针对Landsat 8提出的地表温度反演算法仍不理想,已提出的劈窗算法误差都较大.鉴于TIRS 11热红外波段的定标参数仍不理想,因此在现阶段建议采用单通道算法单独反演TIRS 10波段来求算地表温度,但要注意根据大气水汽含量的情况选用正确的大气参数计算公式.     

电阻率二维神经网络反演

由于非线性特性地球物理反演一直以来都是一个比较困难的问题. 近十年来,非线性反演方法如人工神经网络、遗传算法在地球物理数据解释中得到越来越多的应用,但目前基本仍限于一维反演问题. 对于二维反问题,反演参数较多,神经网络反演运用较少. 本文利用BP神经网络优化方法,实现了电阻率二维非线性反演. 与传统线性化的迭代反演比较,神经网络反演能够克服传统方法的不足、获得更好的反演结果.     

Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion

Remote sensing classification methods can be classified as supervised and unsupervised catalogs. The maximum likelihood method (MLH) is a super-vised classification method,which is widely used in the remote sensing data classification and produces good results[1]. In the MLH, the parameters are esti-mated, assuming that the samples are normally dis-tributed in spectral space, to determine the mean vec-tor and covariance matrix of all classes. In most cases, however, the samples are not norma…     

Extracting water-related features using reflectance data and principal component analysis of Landsat images

This study aimed to map water features using a Landsat image rather than traditional land cover. We involved the original bands, spectral indices and principal components (PCs) of a principal component analysis (PCA) as input data, and performed random forest (RF) and support vector machine (SVM) classification with water, saturated soil and non-water categories. The aim was to compare the efficiency of the results based on various input data. Original bands provided 93% overall accuracy (OA) and bands 4–5–7 were the most informative in this analysis. Except for MNDWI (modified normalized differenced water index, with 98% OA), the performance of all water indices was between 60 and 70% (OA). The PCA-based approach conducted on the original bands resulted in the most accurate identification of all classes (with only 1% error in the case of water bodies). We therefore show that both water bodies and saturated soils can be identified successfully using this approach.     

Shared near neighbours neural network model: a debris flow warning system

The main purpose of this study is to develop a new type of artificial neural network based model for constructing a debris flow warning system. The Chen‐Eu‐Lan river basin, which is located in Central Taiwan, is assigned as the study area. The creek is one of the most well‐known debris flow areas where several damaging debris flows have been reported in the last two decades. The hydrological and geological data, which might have great influence on the occurrence of debris flows, are first collected and analysed, then, the shared near neighbours neural network (SNN + NN) is presented to construct the debris flow warning system for the watershed. SNN is an unsupervised learning method that has the advantage of dealing with non‐globular clusters, besides presenting computational efficiency. By using SNN, the compiled hydro‐geological data set can easily and meaningfully be clustered into several categories. These categories can then be identified as ‘occurrence’ or ‘no‐occurrence’ of debris flows. To improve the effectiveness of the debris flow warning system, a neural network framework is designed to connect all the clusters produced by the SNN method, whereas the connected weights of the network are adjusted through a supervised learning method. This framework is used and its applicability and practicability for debris flow warning are investigated. The results demonstrate that the proposed SNN + NN model is an efficient and accurate tool for the development of a debris flow warning system. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimizing statistical classification accuracy of satellite remotely sensed imagery for supporting fast flood hydrological analysis

The aim of this study is to improve classification results of multispectral satellite imagery for supporting flood risk assessment analysis in a catchment area in Cyprus. For this purpose, precipitation and ground spectroradiometric data have been collected and analyzed with innovative statistical analysis methods. Samples of regolith and construction material were in situ collected and examined in the spectroscopy laboratory for their spectral response under consecutive different conditions of humidity. Moreover, reflectance values were extracted from the same targets using Landsat TM/ETM+ images, for drought and humid time periods, using archived meteorological data. The comparison of the results showed that spectral responses for all the specimens were less correlated in cases of substantial humidity, both in laboratory and satellite images. These results were validated with the application of different classification algorithms (ISODATA, maximum likelihood, object based, maximum entropy) to satellite images acquired during time period when precipitation phenomena had been recorded.     

MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+

Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd.     

Semi‐automated GIS techniques for detecting floodplain earthworks

Levees, channels and water storages built on the world's floodplain wetlands control flows for irrigation, flood mitigation and erosion management. Assessing their distribution and hydrological impacts through time and across broad extents is limited by significant costs and technical challenges. We tested the effectiveness of three new semi‐automated geographic information systems and traditional visual interpretation techniques for detecting earthworks. We used commercially or freely available two‐dimensional and three‐dimensional spatial imagery within 19 quadrats in an agricultural floodplain of the Murray–Darling Basin, southeastern Australia. Semi‐automated digital elevation model (DEM) analysis performed best for spatial accuracy (78% of earthworks correctly predicted within 25 m), overall classification accuracy (97.7%) and kappa (0.64), compared with traditional visual interpretation techniques using Landsat TM (52%, 96.3%, 0.39), SPOT (53%, 95.8%, 0.27) and aerial photography (72%, 97.2%, 0.31). DEM analysis also outperformed semi‐automated image segmentation (16%, 93%, 0.29) and integrated analysis (75%, 96.0%, 0.43) that used spectral information. Semi‐automated techniques were slow (DEM analysis: 27 418 s/km²; integrated analysis: 27 737 s/km²; and image segmentation: 1439 s/km²) compared with visual interpretation (Landsat TM: 109 s/km²; SPOT: 166 s/km²; and aerial photography: 276 s/km²); however, processing speed of semi‐automated techniques can be further increased without compromising accuracy. Semi‐automated techniques also offered operational autonomy following model calibration. High quality, cost‐effective earthwork mapping techniques, particularly the semi‐automated techniques in this study, are critical for understanding and managing ecosystem health, flood risk and water security in developed floodplains worldwide and should be implemented by governing institutions. Copyright © 2012 John Wiley & Sons, Ltd.     

Contribution of landsat imagery to the study of volcanic structures

The principal uses of Landsat imagery in volcanological studies are for regional reconnaisance, for the interpretation of large volcanic structures and to facilitate the comparison of structures in different parts of the world. Standard black and white single band prints and standard false colour composites are the cheapest and most readily available forms of Landsat imagery. However, standard Landsat images have a poorer resolution and lower information content than enhanced images. The most generally useful enhancement techniques for volcanic studies have proved to be destriping. contrast stretching and edge enhancement. Enhancement techniques are illustrated with examples of young volcanic structures from the Central Andes. The next few years should see significant advances in satellite remote sensing technology with higher resolution imagery (down to 10–30m) and imagery in a wider range of spectral bands becoming available.Paper presented at the Symposium Volcanoes of the Earth and Planets, held at the University of Lancaster, March 17, 1981.     

Soil erosion assessment using geomorphological remote sensing techniques: an example from southern Italy

The aim of this study is to assess of the distribution and map the geomorphological effects of soil erosion at the basin scale identifying newly‐formed erosional landsurfaces (NeFELs), by means of an integration of Landsat ETM 7+ remotely sensed data and field‐surveyed geomorphological data. The study was performed on a 228·6 km²‐wide area, located in southern Italy. The study area was first characterized from a lithological, pedological, land‐use and morpho‐topographic point of view and thematic maps were created. Then, the georeferenced Landsat ETM 7+ satellite imagery was processed using the RSI ENVI 4.0 software. The processing consisted of contrast stretching, principal component analysis (PCA), decorrelation stretching and RGB false colour compositing. A field survey was conducted to characterize the features detected on the imagery. Particular attention was given to the NeFELs, which were located using a global positioning system (GPS). We then delimited the Regions of Interest (ROI) on the Landsat ETM 7+ imagery, i.e. polygons representing the ‘ground‐truth’, discriminating the NeFELs from the other features occurring in the imagery. A simple statistical analysis was conducted on the digital number (DN) values of the pixels enclosed in the ROI of the NeFELs, with the aim to determine the spectral response pattern of such landsurfaces. The NeFELs were then classified in the entire image using a maximum likelihood classification algorithm. The results of the classification process were checked in the field. Finally, a spatial analysis was performed by converting the detected landsurfaces into vectorial format and importing them into the ESRI ArcViewGIS 9.0 software. Application of these procedures, together with the results of the field survey, highlighted that some ‘objects’ in the classified imagery, even if displaying the same spectral response of NeFELs, were not landsurfaces subject to intense soil erosion, thus confirming the strategic importance of the field‐checking for the automatically produced data. During the production of the map of the NeFELs, which is the final result of the study, these ‘objects’ were eliminated by means of simple, geomorphologically‐coherent intersection procedures in a geographic information system (GIS) environment. The overall surface of the NeFELs had an area of 22·9 km², which was 10% of the total. The spatial analysis showed that the highest frequency of the NeFELs occurred on both south‐facing and southwest‐facing slopes, cut on clayey‐marly deposits, on which fine‐textured and carbonate‐rich Inceptisols were present and displaying slope angle values ranging from 12° to 20°. The comparison of two satellite imageries of different periods highlighted that the NeFELs were most clearly evident immediately after summer tillage operations and not so evident before them, suggesting that these practices could have played an important role in inducing the erosional processes. Copyright © 2009 John Wiley & Sons, Ltd.     

Evaluation of snow products over the Tibetan Plateau

Four satellite‐based snow products are evaluated over the Tibetan Plateau for the 2007–2010 snow seasons. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover daily L3 Global 500‐m grid products (MOD10A1 and MYD10A1), the National Oceanic and Atmospheric Administration Interactive Multisensor Snow and Ice Mapping System (IMS) daily Northern Hemisphere snow cover product and the Advanced Microwave Scanning Radiometer – Earth Observing System Daily Snow Water Equivalent were validated against Thematic Mapper (TM) snow cover maps of Landsat‐5 and meteorological station snow depth observations. The overall accuracy of MOD10A1, MYD10A1 and IMS is higher than 91% against stations observations and than 79% against Landsat TM images. In general, the daily MODIS snow cover products show better performance than the multisensor IMS product. However, the IMS snow cover product is suitable for larger scale (~4km) analysis and applications, with the advantage over MODIS to allow for mitigation for cloud cover. The accuracy of the three products decreases with decreasing snow depth. Overestimation errors are most common over forested regions; the IMS and Advanced Microwave Scanning Radiometer – Earth Observing System Snow Water Equivalent products also show poorer performance that the MODIS products over grassland. By identifying weaknesses in the satellite products, this study provides a focus for the improvement of snow products over the Tibetan plateau. The quantitative evaluation of the products proposed here can also be used to assess their relative weight in data assimilation, against other data sources, such as modelling and in situ measurement networks. Copyright © 2015 John Wiley & Sons, Ltd.     

Dynamics of a fringe mangrove forest detected by Landsat images in the Mekong River Delta,Vietnam

Mangrove forests dominate many tropical coastlines and are one of the most bio‐diverse and productive environments on Earth. However, little is known of the large‐scale dynamics of mangrove canopies and how they colonize intertidal areas. Here we focus on a fringe mangrove forest located in the Mekong River Delta, Vietnam; a fast prograding shoreline where mangroves are encroaching tidal flats. The spatial and temporal evolution of the mangrove canopy is studied using a time series of Landsat images spanning two decades as well as Shuttle Radar Topography Mission (SRTM) elevation data. Our results show that fast mangrove expansion is followed by an increase in Normalized Difference Vegetation Index (NDVI) in the newly established canopy. We observe three different dynamics of the mangrove fringe: in the southwest part of the fringe, near a deltaic distributary where the fringe boundary is linear, the canopy expands uniformly on the tidal flats with a high colonization rate and high NDVI values. In the northeast part of the fringe, near another distributary, the canopy expands at a much lower rate with low NDVI values. In the fringe center, far from the river mouths, the fringe boundary is highly irregular and mangroves expansion in characterized by sparse vegetated patches displaying low NDVI values. We ascribe these different dynamics to wave action and southwest longshore transport triggered by energetic northeasterly monsoons during winter. We further link the large‐scale dynamics of the fringe to small‐scale physical disturbances (waves, erosion and deposition) that might prevent the establishment of mangrove seedlings. Based on these results, we include mangrove encroachment in an already published conceptual model of progradation of the Mekong River Delta. We conclude that high NDVI values and a constantly linear vegetation–water interface are indicative of stable mangrove canopies undergoing fast expansion, probably triggered by sediment availability at the shore. Our results can be applied more generally to mangrove forests growing in minerogenic and high tidal range environments with high sediment inputs. Copyright © 2016 John Wiley & Sons, Ltd.