Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data

It is important to identify and locate glacial lakes for assessing any potential hazard. This study presents a combination of semi-automatic method Double-Window Flexible Pace Search (DFPS) and edge detection technique to identify glacial lakes using Sentinel 2A satellite data. Initially, Normalized Difference Water Index (NDWI) has been used to identify water and non-water areas, while DFPS and Edge detection technique has been used to identify an optimum threshold value to distinguish between water and shadow areas. The optimal threshold from DFPS process is 0.21, while threshold value of gradient magnitude using edge detection process is 0.318. The number of glacial lakes identified using the above algorithm is in close agreement with previously published results on glacial lakes in Gangotri glacier using different techniques. Thus, a combination of DFPS and edge detection process has successfully segregated glacial lakes from other features present in Gangotri glacier.     

Evaluation of multisource data for glacier terrain mapping: a neural net approach

Spectrally similar nature of land covers in a glacierized terrain hampers their automated mapping from multispectral satellite data, which may be overcome by using multisource data. In the present study, an artificial neural network (ANN)-based information extraction approach was applied for mapping the Kolahoi glacier and adjoining areas, using Landsat TM (Thematic Mapper) data and several ancillary layers such as image transformations and topographic attributes. Results reveal that ANN (highest overall accuracy (OA): 83.74%) outperforms maximum likelihood classifier (highest OA: 66.90%) and the incorporation of ancillary data into the classification process significantly enhances the mapping accuracy (>9%), particularly the addition of Near Infrared Red/Short Wave Infrared (NIR/SWIR) data to the spectral data. A nine-band combination dataset (spectral data, slope, Red/NIR and decorrelation stretch) was found to be the best multisource dataset. Results of the Z-tests (at 95% confidence level) also corroborate and statistically validate the above findings.     

遥感和GIS在冰湖溃决洪水中的应用研究

近年来随着全球气候变暖和冰川退缩,以及人类在高海拔地区活动的增多,冰湖溃决洪水灾害呈增加趋势。由于遥感和GIS技术的众多优势,使其在冰湖溃决洪水研究方面得到广泛应用。首先对冰湖溃决洪水及其研究做简单介绍,然后从冰川、冰湖空间数据获取,冰湖溃决评价指标获取,冰湖溃决洪水模拟和DEM的建立及应用4个方面对遥感和GIS在冰湖溃决洪水研究中的应用进行综述,最后指出,目前遥感和GIS在冰湖溃决洪水应用中的不足,为进一步研究和应用指明了方向。     

河北省太行山南段东坡第四纪冰川遗迹的遥感解译

利用大比例尺黑白航空像片首次在太行山南段东坡发现了大面积保存较完好的冰川遗迹如角峰、刃脊、冰斗、悬谷、冰蚀盆地及冰蚀槽谷等,经系统组合、配套后认为,第四纪时期,该区曾发育过三一四期冰川活动,第I、II冰期冰川属于山谷一山麓冰川类型,而第IⅡ、IV(?)期冰川则为冰斗冰川。本文详述了各类冰蚀地貌的期次,分布、类型及发育特征,为该区第四纪古冰川活动的研究提供了新的证据。     

High Resolution TerraSAR-X Image Speckle Suppression and its Fusion with Multispectral IRS LISS –III Data for Himalayan Glacier Feature Extraction

In this study an attempt is made for studying the Himalayan glacier features using TerraSAR-X and Indian Remote Sensing Satellite, Linear Imaging Self Scanning System III (IRS LISS –III) images. New generation, synthetic aperture radar (SAR) data from TerraSAR-X (TS-X) sensor provide opportunity for glacier feature studies in Himalayan rugged terrain. Spot Light High resolution mode TS-X data give idea about glacial features which remained untraceable from other existing SAR system. However, presence of speckle noise in SAR images degrades the interpretability of the glacier features. Speckle suppression filters (Lee, Frost, Enhanced Lee, Gamma-Map) are applied on SAR intensity images. On the basis of field sight seeing and insitu observations it is observed that still features are not clear. Hence attempt has been made for fusing multitemporal multispatial speckle reduced TS-X SAR data and multispectral IRS LISS-III data for extracting the glacial features such as crevasses, exposed ice and superaglacier lakes. Principal component analysis (PCA) represents the high spectral resolution data in a linear subspace with minimum information loss. Herein, PCA based image fusion technique is adopted for this study and comparison is made between IHS fusion technique and PCA based technique for glacier studies in the Himalayan region.     

苏州运河水质的TM分析

本文利用苏州地区陆地卫星TM数据和同期的地面水质监测资料建立了TM图像遥感水质模型,并将该模型应用于TM可见光彩色合成图像的分割处理,得到苏州地区水质空间分布图像。结合苏州运河水网的水文及水污染特征对遥感水质空间分布图像作了分析。结果表明TM数据真实地反映了苏州地区水质的空间分布规律,并得到当地环境监测部门的验证。     

Review of Alternative Methods for Estimating Terrestrial Emittance and Geothermal Heat Flux for Yellowstone National Park Using Landsat Imagery

Yellowstone National Park (YNP) is legally mandated to monitor geothermal features for their future preservation, and remote sensing is a component of the current monitoring plan. Landsat imagery was explored as a tool for mapping terrestrial emittance and geothermal heat flux for this purpose. Several methods were compared to estimate terrestrial emittance and geothermal heat flux (GHF) using images from 2007 (Landsat Thematic Mapper) and 2002 (Landsat Thematic Mapper Plus). Accurate estimations were reasonable when compared to previously established values and known patterns but were likely limited due to inherent properties of Landsat data, the effects of solar radiation, and variation among geothermal areas. Landsat data can be valuable for calculation of GHF in YNP. The method suggested in this paper is not highly parameterized. Landsat data provide the means to calculate GHF for all of YNP and have the potential to enable scientists to identify locations for in-depth study.     

Rule‐based Integration of Remotely‐sensed Data and GIS for Land Cover Mapping in NE Costa Rica

Abstract

A classification method was developed for mapping land cover in NE Costa Rica at a regional scale for spatial input to a biogeochemical model (CENTURY). To distinguish heterogeneous cover types, unsupervised classifications of Landsat Thematic Mapper data were combined with ancillary and derived data in an iterative process. Spectral classes corresponding to ground control types were segregated into a storage raster while ambiguous pixels were passed through a set of rules to the next stage of processing. Feature sets were used at each step to help sort spectral classes into land cover classes. The process enabled different feature sets to be used for different types while recognizing that spectral classification alone was not sufficient for separating cover types that were defined by heterogeneity. Spectral data included the TM reflective bands, principal components and the NDVI. Ancillary data included GIS coverages of swamp extents, banana plantation boundaries and river courses. Derived data included neighborhood variety and majority measures that captured texture. The final map depicts 18 land cover types and captures the general patterns found in the region. Some confusion still exists between closely related types such as pasture with different amounts of tree cover.     

Integrated remote sensing and GIS study for hydrogeomorphological mapping and delineation of groundwater potential zones in Khallikote block,Ganjam district,Orissa

The present study attempts to delineate different groundwater potential units using remote sensing and geographic information system (GIS) in Khallikote block of Ganjam disrict, Orissa. Thematic maps of geology, geomorphology, land use and land cover, drainage density, lineament density, slope and DEM (digital elevation model) were prepared using the Landsat Thematic Mapper data in 3 spectral bands, band 7 (mid-infrared light), band 4 (near-infrared light), Band 2 (visible green light). Relationship of each layer to the groundwater regime has been evaluated through detailed analysis of the individual hydrological parameters. The SMCE (Spatial Multi-Criteria Evaluation) module in ILWIS (Integrated Land and Water Information System) supports the decision-making process for evaluating the ground water potential zones in the area. The study shows that more than 70% of the block is covered by medium to excellent category having good ground water potential.     

Discrimination of low-grade magnetite ores using remote sensing techniques

The remote sensing community in geology is widely using the Multispectral Landsat Thematic Mapper (TM) data which has a wider choice of spectral bands (six between 0.45 and 2.35 μm, plus a thermal infrared channel 10.4-12.5 urn). These were evaluated for low-grade magnetite ores mapping over the high-grade granulite region of Kanjamalai area of Tamil Nadu state, India. The Fourier Transform Infrared (FTIR) spectroscopy data (0.4-4.0 μm) for powders of the magnetite ores exposed with granulite rock and published spectral reflectance data were used as guides in selecting TM band reflectance ratios, which maximize discrimination of magnetite ores on the basis of their respective mineralogies. The study shows that the weathering mineralogy of magnetite ores causes absorption features in their reflectance spectra which are particularly characteristic of the near infrared. Comparison of TM data with field and petrographic observations shows the presence of magnetite and aluminosilicate minerals & show strong absorption at 0.7-1 μ.m wavelength spectral region & increase in the product of two TM band ratios: band 5 (1.55-1.75 μm) to band 4 (0.76-0.9 μm) and band 3 (0.63-0.69 μm) to band 4 (0.76-0.9 μm). Various computer image enhancement and data extraction techniques such as interactive digital image classification techniques using color compositing stretched ratio, maximum likelihood and thresholding statistical approaches using Landsat TM data are used to map the low-grade magnetite ores of the granulite region. The field traverses and local verification enhanced to map the other rock types namely granulites and gneisses of the study area.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

Problems of bias in mapping linear landforms from satellite imagery

Remotely sensed images are an important data source for the mapping of glacial landforms and the reconstruction of past glacial environments. However the results produced can differ depending on a wide range of factors related to the type of sensors used and the characteristics of the landforms being mapped. This paper uses a range of satellite imagery to explore the three main sources of variation in the mapped results: relative size, azimuth biasing and landform signal strength. Recommendations include the use of imagery illuminated with low solar elevation, although an awareness of the selective bias introduced by solar azimuth is necessary. Landsat ETM+ imagery meets the requirements for glacial landform mapping and is the recommended data source. However users may well have to consider alternative data in the form of SPOT, Landsat TM or Landsat MSS images. Digital elevation models should also be considered a valuable data source.     

SPOT与TM图像的数字复合试验

陆地卫星TM图像含有丰富的光谱信息,SPOT全色波段图像数据分辨率较高,因此,如何将这两种图像数据有效地结合起来,在遥感应用领域中显得越来越重要。本文研究了SPOT和TM图像数据的数字复合方法。结果表明,复合后的图像提高了分辨率,增加了光谱信息。     

Lightning flash mensuration using video from the space shuttle Columbia (STS‐32)

Abstract

Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.     

TM classification using local spectral variability

A classification method which takes into account not only spectral but also spatial features for LANDSAT‐4 and 5 Thematic Mapper (TM) data is proposed. In accordance with improvement of Instantaneous Field of View (IFOV), spatial information such as textural, contextual, etc. is also increased so that some treatments of such information is highly required. One of the simplest spatial features is local spectral variability such as standard deviation, variability constant, variance, etc. in small cells such as 2x2,3x3 pixels. Such information can be used together with conventional spectral features in an unified way, for the traditional classifier such as a pixel‐wise Maximum Likelihood Decision Rule (MLDR). From the experiments, there was a substantial improvement in overall classification accuracy for TM forestry data. The probability of correct classification (PCC) for the new clearcut and the alpine meadow classes increased by 7% to 97% correct. The confusion between alpine meadow and new clearcut was reduced from 9% to 3%.     

太湖梅梁湖湾蓝藻生物量遥感估算

本文利用陆地卫星TM数据和图像处理方法对太湖富营养化严重的梅梁湖区的浮游蓝藻生物量作了遥感估算。1992年夏季在海梁湖开展了星地同步浮游藻类监测,获得了湖中16个采样点的现场叶绿素a生物量(Q_a)和蓝藻生物量(Q_B)数据,并利用这两组数据分别建立了与差异植被指数DVI的遥感回归模型,从而得到水体中叶绿素a以及蓝藻生物量的空间分布信息。从遥感定量模型出发,运用逐个像元积分统计技术,估算出梅梁湖叶绿素a总量为2133kg、蓝藻总量为178.2t.与地面方法相比,遥感估算方法充分利用了TM数据中的浮游藻类含量分布与变化信息,具有较高的估算精度。     

归一化光谱分解模型在城市化信息提取中的应用

以2001年7月11日LandsatETM7影像和2009年7月16日TM影像为数据源,基于V-I-S理论模型,采用归一化光谱分解模型提取了乌鲁木齐市区范围内2个时段的植被、土壤、不透水层3个连续地表参数分量。通过对不透水层不同阈值的划分,提取了2时段的乌鲁木齐市城市发展的空间信息,结果较为满意;通过空间叠加计算方式获取了8年来乌鲁木齐市城市化发展的空间信息和主要拓展方向。结果表明,乌鲁木齐城市化发展速度较快,特别是北扩趋势显著。     

An effective ensemble classification framework using random forests and a correlation based feature selection technique

Accurate classification of heterogeneous land surfaces with homogeneous land cover classes is a challenging task as satellite images are characterized by a large number of features in the spectral and spatial domains. The identifying relevance of a feature or feature set is an important task for designing an effective classification scheme. Here, an ensemble of random forests (RF) classifiers is realized on the basis of relevance of features. Correlation‐based Feature Selection (CFS) was utilized to assess the relevance of a subset of features by studying the individual predictive ability of each feature along with the degree of redundancy between them. Predictability of RF was greatly improved by random selection of the relevant features in each of the splits. An investigation was carried out on different types of images from the Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and QuickBird sensors. It has been observed that the performance of the RF classifier was significantly improved while using the optimal set of relevant features compared with a few of the most advanced supervised classifiers such as maximum likelihood classifier (MLC), Navie Bayes, multi‐layer perception (MLP), support vector machine (SVM) and bagging.     

Estimation of Snow Cover Area,Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data

Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R² value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.     

Estimating landscape imperviousness index from satellite imagery

This letter presents a practical method for landscape imperviousness estimation through the synergistic use of Landsat Enhanced Thematic Mapper Plus (ETM+) and high-resolution imagery. A 1-m resolution color-infrared digital orthophoto was used to calibrate a stepwise multivariate statistical model for continuous landscape imperviousness estimation from medium-resolution ETM+ data. A variety of predictive variables were initially considered, but only brightness and greenness images were retained because they were account for most of the imperviousness variation measured from the calibration data. The performance of this method was assessed, both visually and statistically. Operationally, this method is promising because it does not involve any more sophisticated algorithms, such as classification tree or neural networks, but offers comparable mapping accuracy. Further improvements are also discussed.