检索结果-内蒙古大学图书馆

A dynamic normalized difference index for estimating soil organic matter concentration using visible and near-infrared spectroscopy

引用

ECOLOGICAL INDICATORS 2023年 147卷

作者： Cao, Jianfei Yang, Han Shandong Normal Univ Coll Geog & Environm Jinan 250014 Peoples R China Zhongke Shandong Dongying Inst Geog Sci Dongying 257000 Peoples R China

Accurate, rapid, and non-destructive estimation of soil organic matter (SOM) is crucial for soil fertility diagnosis and precision farming. Due to the complicated and unstable spectral characteristics of SOM, few SOM spectral indexes have been proposed and widely used. In this paper, a new dynamic normalized difference index (DNDI) was proposed and constructed to estimate SOM using visible and near-infrared spectroscopy. A dynamic correction factor alpha was used to adjust the optimal wavelength range to obtain more robust features of SOM. Different spectral pre-processing methods were applied and compared. The support vector machine (SVM) model and Partial least square regression (PLSR) model were calibrated based on DNDI and applied to estimate SOM. To this end, a total of 111 soil samples were collected in the southern coastal plain of Laizhou Bay. The results showed that the DNDI index constructed by wavelength optimization could have a higher correlation with SOM than the two-dimensional normalized difference index (NDI). DNDI had the maximum correlation of 0.88 from the first derivative of reflectance, and the NDI correlations were most improved by standard normal variate transform (SNV), with the maximum correlation reaching 0.81. For SOM estimation models, DNDI exhibited better model performance, yielding a validation R2, RMSE, and RPD of 0.78, 0.17 g center dot kg(-1), and 2.01, respectively. Our algorithm has strong application potential for estimating other soil properties and enhancing the application of ground-and satellite-based sensing.

关键词： Three dimensional index normalized difference index Soil organic matter Visible and near -infrared spectroscopy Spectral pre-processing

来源：评论

学校读者我要写书评

暂无评论

New normalized difference index for built-up land enhancement using airborne visible infrared imaging spectrometer imagery

引用

JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： Liu, Xiaoyi Meng, Yu Yue, Anzhi Chen, Jingbo Huang, Qingqing Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100101 Peoples R China

In remote sensing imagery, various normalized difference indices are widely used for land cover mapping. Each index has its targeting cover type with a specialized data source. However, these indices are generally only studied in multispectral data. Hyperspectral images have become increasingly attractive due to their richness of spectrum information. A new index, i.e., normalized difference Built-up index for Hyperspectral data (NDBIh), oriented to built-up land enhancement in hyperspectral remote sensing data is proposed. Spectral response curves of different cover types and possible calculation equations for NDBIh are obtained first. The equation having the best ability to differentiate built-up land from other areas is referred to as NDBIh. To evaluate the ability of our NDBIh, two other built-up indices, the conventional normalized difference Built-Up index (NDBI) and the index-based Built-Up index (IBI), are compared with NDBIh both qualitatively and quantitatively. Experiments on airborne visible infrared imaging spectrometer data indicate that the NDBIh outperforms NDBI and IBI in identifying built- up land. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： airborne visible infrared imaging spectrometer normalized difference index built-up feature enhancement hyperspectral

来源：评论

学校读者我要写书评

暂无评论

Surface primary producer phenology in Dease Strait, NU Canada, examined using submersed oceanographic sensors and satellite remote sensing

引用

ARCTIC SCIENCE 2024年第4期10卷 673页

作者： Yendamuri, Kiran Stroeve, Julienne Ehn, Jens K. Williams, William J. Nandan, Vishnu Else, Brent G. T. Komarov, Alexander S. Braybrook, Christina A. Dempsey, Mike Mundy, C. J. Univ Manitoba Ctr Earth Observat Sci Dept Environm & Geog Winnipeg MB Canada UCL Dept Earth Sci London England Univ Colorado Natl Snow & Ice Data Ctr Boulder CO USA Fisheries & Oceans Canada Inst Ocean Sci Sidney BC Canada Amrita Vishwa Vidyapeetham Dept Elect & Commun Amritapuri Kerala India Univ Calgary Dept Geog Calgary AB Canada Environm & Climate Change Canada Data Assimilat & Satell Meteorol Res Sect Ottawa ON Canada

Thinning sea ice cover and earlier melt in the Arctic impact primary producer (PP) phenology, causing earlier ice algal bloom termination and phytoplankton bloom commencement. However, logistic constraints limit capturing the complete seasonal evolution of PPs and their physical drivers. Here, we combine spectral irradiance data from subsurface oceanographic moorings with synthetic aperture radar backscatter and meteorological variables to study light in Dease Strait, investigating its relation to timing and magnitude of surface PPs for 2017 and 2019. Ice algal blooms in 2017 and 2019 lasted 66 and 84 days, respectively, peaking within 2 days of snow melt onset. In 2019, lower temperatures and a deeper snowpack before snow melt extended the ice algal bloom. Melt pond formation increased light transmission, enabling a short, 6-7-day under-ice phytoplankton bloom in both years that was likely nutrient-limited. The 2019 phytoplankton bloom was less productive, possibly due to the longer ice algal bloom depleting surface nutrients. After ice break-up in 2019, a 31-day late-summer bloom occurred via wind-driven mixing. Our findings suggest that the combined remote sensing technique has novel applicability in other settings, providing insights into the changing state of PP phenology, and the need for long-term Arctic observations to discern regional climate change effects.

关键词： Canadian Arctic primary production remote sensing synthetic aperture radar normalized difference index

来源：评论

学校读者我要写书评

暂无评论

Trophic Status Mapping of Inland Water Bodies Using normalized difference Indices Derived from Sentinel 2 MSI Imagery

引用

JOURNAL OF HYDROLOGIC ENGINEERING 2023年第1期28卷

作者： Sherjah, P. Y. Sajikumar, N. Nowshaja, P. T. Govt Engn Coll Dept Civil Engn Trichur 680010 Kerala India APJ Abdul Kalam Technol Univ Thiruvananthapuram 695016 Kerala India WRPM Consultants Trichur 680010 Kerala India

The trophic state index (TSI) is generally accepted as an indicator of the trophic status of inland water bodies. Regular monitoring of the trophic status of a water body is vital for understanding the health and functioning of its ecosystem. The band ratios (BR) and normalized difference (ND) indices are customarily used for classifying the land uses, but their capacity for quality monitoring of a water body has not been fully explored. The present study explores the possibility of using ND and BR of the bands of Sentinel 2 multispectral imagery (MSI) (S2) imagery for mapping the TSI of an inland water body. The data of Milford Lake, Kansas, were used to derive TSI. Three atmospherically corrected data sets obtained from Case 2 regional coast color processor (C2RCC), Acolite, and Sentinel 2 Correction (Sen2Cor) were used to derive ND and BR indices. The results of the correlation analyses of these indices with TSI reinforced the importance of atmospheric correction on any algorithm. TSI derived from turbidity measurements was observed to be well correlated to ND and BR indices of green (B3) and red bands (B4, B5, and B6) derived from the C2RCC data set (correlation coefficient > 0.8). Thresholds of BR and ND indices of green and red bands for delineating the hypereutrophic areas from the total area of interest were also identified. The regression relations and threshold criteria were validated with the data from five other lakes in the US and one lake in India to verify the compliance of these relations to other areas. TSI could be estimated with a mean absolute percentage error (MAPE) of 9% from these lakes by using ND34, ND35, and ND45. The threshold criteria for BR34, BR46, and BR56 for delineating hypereutrophic areas were also validated for these lakes. The validations confirm that these computationally simple indices could be utilized for quality monitoring in areas of different geographic and climatic characteristics. (C) 2022 American Society of Civil Engin

关键词： Water quality monitoring Sentinel 2 Trophic state index (TSI) normalized difference index Band ratio index Turbidity

来源：评论

学校读者我要写书评

暂无评论

Research on the Transmission Performance of Multi-layer Simulated Mural Surface by Imaging Spectrum

Research on the Transmission Performance of Multi-layer Simu...

引用

International Conference on Environmental Remote Sensing and Big Data (ERSBD)

作者： Yang, W. Lyu, S. Hou, M. Huang, C. Beijing Univ Civil Engn & Architecture Sch Geomat & Urban Spatial Informat 15 Yongyuan Rd Beijing 102616 Peoples R China Tsinghua Unigrp 7 Zhichun Rd Beijing Peoples R China

ISBN: (数字)9781510651357

ISBN: (纸本)9781510651357;9781510651340

Some ancient murals were found to be repainted on the surface of the original murals to form multi-layer murals. The patterns on the original layer are of great significance for studying the social and cultural behavior in that time. The hyperspectral imaging covers the visible and near-infrared bands, which has advantages for the information extraction of multi-layer murals. Therefore, a method to study the transmission performance of hyperspectral imaging on multi-layer simulated mural samples is proposed. By making mural samples, the mineral pigment painted on the surface is covered with 0-11 different layers of lime water. Then the samples were collected with hyperspectral images, and the method of principal component analysis and band calculation were used to analyze the enhancement effect of the mural patterns covered by different layers of lime water. The results show that hyperspectral imaging has certain transmittance to the interior of the mural and can enhance internal pigment information. The research results can support the information extraction of multi-layer murals to some extent.

关键词： Hyperspectral Imaging Information Enhancement normalized difference index Principal Component Analysis

来源：评论

学校读者我要写书评

暂无评论

A New normalized difference index for Estimating Leaf Area index of Wheat Under Yellow Rust Stress

引用

SENSOR LETTERS 2012年第1-2期10卷 324-329页

作者： Jiang, Jinbao Chen, Yunhao Huang, Wenjiang Li, Yifan China Univ Mine & Technol Coll Geosci & Surveying Engn Beijing 100083 Peoples R China Beijing Normal Univ Coll Resources Sci & Technol Beijing 100875 Peoples R China Natl Engn Res Ctr Informat Technol Agr Beijing 100097 Peoples R China

The aim of this paper is to design a new normalized difference vegetation index, which is insensitive to pigment contents and relative water contents, but is sensitive to leaf area index (LAI). The canopy reflectance of winter wheat which was infected with yellow rust of different severities, and the LAI, canopy chlorophyll density (CCD) and relative water contents (RWC) of the whole wheat were measured respectively. Through regression analysis and test, the result indicated NDVI, (SDr - SDb)/(SDr + SDb) and (R-900 - R-1450)/(R-900 + R-1450) had high precision for estimating LAI. The sensitive relationship had been analyzed between above three indices and CCD and RWC, respectively, the result showed the near-infrared (NIR) and short-wave-infrared (SWIR) normalized difference index (R-900 - R-1450)/(R-900 + R-1450) was most insensitive to CCD and RWC. Moreover, the index was not easiest to get to the saturation level than the other two indices. Therefore, the index (R-900 - R-1450)/(R-900 + R-1450) is a relatively good index for the estimation of wheat LAI, and the model determination coefficient (R-2) is 0.7924, and RMSE is 0.59, and relative error is 23.6%. This study not only provides a new method for the estimation of LAI by using hyperspectral remote sensing, but also offers technology and information support for precision agriculture.

关键词： Hypersprectral Remote Sensing LAI normalized difference index Sensitivity and Saturation Analysis Inversion Model

来源：评论

学校读者我要写书评

暂无评论

Diffuse reflectance spectroscopy for rapid estimation of soil Atterberg limits

引用

GEODERMA 2020年第0期361卷 114083-000页

作者： Mousavi, Fatemeh Abdi, Ehsan Ghalandarzadeh, Abbas Bahrami, Hossein Ali Majnounian, Bads Ziadi, Noura Univ Tehran Fac Nat Resources Dept Forestry & Forest Econ Karaj Iran Univ Tehran Dept Civil Engn Tehran Iran Tarbiat Modares Univ Fac Agr Dept Soil Sci Tehran Iran Agr & Agri Food Canada Soils & Crops Res & Dev Ctr 2560 Hochelaga Blvd Quebec City PQ Canada

Conventional methods for the determination of Atterberg limits based on laboratory tests are time-consuming. Reflectance spectroscopy which is fast and cost-effective has been reported as an alternative method for estimating Atterberg limits. To assess the efficiency of the method, 60 forest soil samples were measured by visible-near infrared reflectance spectroscopy (VNIR) within the range of 350-2500 nm. The Savitzky-Golay (SG) smoothing method for spectral data was used to reduce signal noise and the first derivative reflectance (DR) was applied to accentuate the features and assemble the data for use in the modeling process. In this study, raw reflectance (R) and first derivative reflectance (DR) data were used to estimate Atterberg limits. All two-band combinations of three types of spectra (ratio index, normalized difference index and difference index) as well as PLS and PLS-BPNN were calculated to extract optimum waveband combination and calibrations between Atterberg limits and reflectance spectra. The correlation coefficients between Atterberg limits and reflectance spectra indicated both positive and negative correlation at various wavelengths across the spectrum. The highest correlation for liquid limit (r = - 0.86), plastic limit (r = - 0.76) and plasticity index (r = - 0.7) was found at 1921, 1938 and 1913 nm, respectively. In addition, the results showed the spectral indices technique as a good method for estimating soil plasticity and improving the prediction accuracy of Atterberg limits. In the current study, PLS and PLS-BPNN were also used to develop the calibration models for estimating Atterberg limits using R and DR. The results demonstrated that the prediction accuracy of PLS-BPNN for both R and DR was higher than that of PLS. Correlation coefficients between shrinkage limit (SL) and reflectance spectra showed weak correlation, and overall reflectance had the same correlation. The results represented that this method could be used to estimate so

关键词： Ratio index normalized difference index difference index Wavelengths

来源：评论

学校读者我要写书评

暂无评论

Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 176卷 105665-105665页

作者： Bhandari, Mahendra Ibrahim, Amir M. H. Xue, Qingwu Jung, Jinha Chang, Anjin Rudd, Jackie C. Maeda, Murilo Rajan, Nithya Neely, Haly Landivar, Juan Texas A&M Univ Dept Soil & Crop Sci College Stn TX 77843 USA Texas A&M AgriLife Res & Extens Ctr Amarillo TX USA Texas A&M AgriLife Res & Extens Ctr Corpus Christi TX USA Texas A&M Univ Corpus Christi Sch Engn & Comp Sci Corpus Christi TX USA Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA Texas A&M AgriLife Res & Extens Ctr Lubbock TX USA Washington State Univ Dept Soil & Crop Sci Pullman WA 99164 USA

One of the major goals in all wheat (Triticum aestivum L.) breeding programs is to develop disease-resistant varieties. Unmanned Aerial Vehicles (UAVs) equipped with remote sensors can provide spectral measurements that can be used to assess foliage disease severity. Measurement of disease severity trait during the genotype selection process has always been challenging as it takes a substantial amount of time, cost, and labor to phenotype many breeding lines. This study investigates the potential use of low-cost UAV, equipped with digital cameras as a field phenotyping tool for foliage disease severity in a wheat breeding program. A field experiment was conducted in 2017 and 2018 at Castroville, Texas. The experiment site has favorable weather conditions for developing wheat leaf rust (Puccinia triticina f. sp. tritici). Red, Green, and Blue band (RGB) images were acquired by flying rotary-wing UAV. Images were then processed to develop orthomosaics and three vegetation indices were calculated. The obtained image dataset was further processed to generate plot-level data. Visual notes on field response and leaf rust severity were taken to calculate the coefficient of infection (CI). A significant variation in vegetation indices was found among the wheat genotypes in both years. normalized difference index (NDI), Green index (GI), and Green Leaf index (GLI) were linearly related to CI with R-2 values ranging from 0.72 to 0.79 (p < 0.05) in 2017 and 0.63 to 0.68 (p < 0.05) in 2018. Ground-based normalized difference Vegetation Indices (NDVI) also showed a significant relationship with CI in both years (R-2 = 0.86, p < 0.05 in 2017 and R-2 = 0.83, p < 0.05 in 2018). The results showed that UAVs imaging and automated data extraction can facilitate the acquisition of high throughput phenotyping data for disease severity ratings.

关键词： CI Coefficient of infection DN Digital Number GCP Ground Control Point GI Green index GLI Green Leaf index MSREN Multi-state Wheat Rust Evaluation Nursery NDI normalized difference index NDVI normalized difference Vegetation index RGB Red Green Blue UAV Unmanned Aerial Vehicle

来源：评论

学校读者我要写书评

暂无评论

A Land-cover Driven Approach for Fitting Satellite Image Time Series in a Change Detection Context 26

A Land-cover Driven Approach for Fitting Satellite Image Tim...

引用

Conference on Image and Signal Processing for Remote Sensing XXVI

作者： Solano-Correa, Yady Tatiana Meshkini, Khatereh Bovolo, Francesca Bruzzone, Lorenzo Fdn Bruno Kessler Ctr Informat & Commun Technol Via Sommar 18 I-38123 Povo Trento Italy Univ Trento Dept Engn & Comp Sci Via Sommar 5 I-38123 Povo Trento Italy

ISBN: (纸本)9781510638808

Thanks to the freely availability of several Satellite Image Time Series (SITS) covering the Earth, it is now possible to monitor and analyse Land Covers (LC) and Land Cover Changes ( LCC) on a yearly or even longer time span. Such applications are relevant in the context of Climate Change (CC), where consequences of the changes can only be seen on long term. Nevertheless, SITS suffer from atmospheric condition related problems (when talking about passive sensors) that reduce the temporal resolution of images in SITS. Several methods have been proposed in literature to mitigate these problems, and are placed under gap filling or SITS fitting methods. Such methods generally work with a single feature, being it a radiometric index or a spectral band. The use of multiple features is limited to specific single LC class or satellite sensor, limiting its usage in LCC and CC. Thus, in this paper, we propose an approach that is automatic, and both LC and feature independent. Here we propose the use of normalized difference Indices (NDI), with combination of all available spectral bands. The proposed approach uses a dropout upper-envelope strategy to reconstruct SITS trends, based on a set of rules, and guarantees a smoother closer trend to that of the original data. The proposed approach has been applied over two regions (Amazonia and Saudi Arabia) in the period 2013-2017, and has been compared to other fitting methods: Cubic Splines and Univariate Splines. It has been further evaluated by detecting LCC with long SITS methods such as Breaks For Additive Seasonal and Trend (BFAST). The preliminary results are promising demonstrating the robustness of the approach across different LCs and across different features.

关键词： Land Cover Change Fitting methods Satellite Image Time Series normalized difference index Land Cover

来源：评论

学校读者我要写书评

暂无评论

EXTRACTION OF BUILT-UP AREA USING HIGH RESOLUTION SENTINEL-2A AND GOOGLE SATELLITE IMAGERY

EXTRACTION OF BUILT-UP AREA USING HIGH RESOLUTION SENTINEL-2...

引用

International Conference on Geomatic and Geospatial Technology (GGT) - Geospatial and Disaster Risk Managemen

作者： Vigneshwaran, S. Kumar, S. Vasantha VIT Sch Civil & Chem Engn Vellore Tamil Nadu India

Accurate information about the built-up area in a city or town is essential for urban planners for proper planning of urban infrastructure facilities and other basic amenities. The normalized difference indices available in literature for built-up area extraction are mostly based on moderate resolution images such as Landsat Thematic Mapper (TM) and enhanced TM (ETM+) and may not be applicable for high resolution images such as Sentinel-2A. In the present study, an attempt has been made to extract the built-up area from Sentinel-2A satellite data of Chennai, India using normalized difference index (NDI) with different band combinations. It was found that the built-up area was clearly distinguishable when the index value ranges between -0.29 and -0.09 in blue and near-infrared (NIR) band combination. Post extraction editing using Google satellite imagery was also attempted to improve the extraction results. The results showed an overall accuracy of 90% and Kappa value of 0.785. Same approach when applied for another area also yields good results with overall accuracy of 92% and Kappa value of 0.83. As the proposed approach is simple to understand, yields accurate results and requires only open source data, the same can be used for extracting the built-up area using Sentinel-2A and Google satellite imagery.

关键词： Built-up area Extraction normalized difference index High Resolution Satellite Imagery Sentinel-2A Google Satellite Imagery

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：