scholarly journals Evaluation of Various Vegetation Indices for Multispectral Satellite Images

2019 ◽  
Vol 8 (10) ◽  
pp. 3494-3500
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Water Area ◽  
Multispectral Images ◽  
Predominant Role ◽  
Landsat Images ◽  
Spectral Bands ◽  
Different Types ◽  
Multispectral Satellite Images

Vegetation indices play a predominant role in the field of Remote processing systems which assimilate vital multispectral images. The digital numbers identify the spectral information in one or more spectral bands. It focuses mainly on two or more spectral regions and obtains different types of surfaces like vegetation, built-up, bare soil and water area. Different types of vegetation can be studied and analyzed using LANDSAT images. In this paper, comparison has been made on ten major vegetation indices such as RVI, DVI, NDVI, TNDVI, NDWI, MNDWI, NDBI, UI, SAVI, and NDMI using different spectral bands and different features are detected and extracted with the help of ArcGIS and MATLAB tools. This study reveals better classification accuracy.

scholarly journals Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

2020 ◽  
Vol 10 (16) ◽  
pp. 5540 ◽  
Author(s):  
Maria Casamitjana ◽  
Maria C. Torres-Madroñero ◽  
Jaime Bernal-Riobo ◽  
Diego Varga
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Surface Soil ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Multispectral Images ◽  
Surface Soil Moisture ◽  
Mountain Environments ◽  
Tropical Mountain ◽  
Moisture Analysis

Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

scholarly journals Snow and Ice Studies By Thematic Mapper and Multispectral Scanner Landsat Images

1987 ◽  
Vol 9 ◽  
pp. 109-118 ◽  
Author(s):  
Olav Orheim ◽  
Baerbel K. Lucchitta
Keyword(s):  
Satellite Images ◽  
Thematic Mapper ◽  
Landsat Images ◽  
Snow Properties ◽  
Spectral Bands ◽  
Dronning Maud Land ◽  
Visible Wavelengths ◽  
Transverse Strains ◽  
Thermal Band ◽  
Snow And Ice

Digitally enhanced Landsat Thematic Mapper (TM) images of Antarctica reveal snow and ice features to a detail never seen before in satellite images. The six TM reflective spectral bands have a nominal spatial resolution of 30 m, compared to 80 m for the Multispectral Scanner (MSS). TM bands 2–4 are similar to the MSS bands. TM infra-red bands 5 and 7 discriminate better between clouds and snow than MSS or the lower TM bands. They also reveal snow features related to grain-size and possibly other snow properties. These features are not observed in the visible wavelengths. Large features such as flow lines show best in the MSS and lower TM bands. Their visibility is due to photometric effects on slopes. TM thermal band 6 has a resolution of 120 m. It shows ground radiation temperatures and may serve to detect liquid water and to discriminate between features having similar reflectivities in the other bands, such as blue ice.Repeated Landsat images can be used for sophisticated glaciological studies. By comparing images from 1975 and 1985, flow rates averaging 0.72 km a−1, and mean longitudinal and transverse strains of respectively 1.3 × 10 −4 a −1 and 130 × 10−4 a−1 have been measured for Jutulstraumen, Dronning Maud Land.

scholarly journals EXTRACTING THE WATERLINE FROM LANDSAT IMAGES

2009 ◽  
Vol 12 (12) ◽  
pp. 52-58
Author(s):  
Thao Thi Phuong Pham ◽  
Duan Dinh Ho ◽  
To Van Dang
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Numerical Models ◽  
Scientific Research ◽  
Remote Sensing And Gis ◽  
Landsat Images ◽  
Shoreline Changes ◽  
Remote Sensing Technology ◽  
Spectral Bands ◽  
Sensing Technology

Remote sensing technology nowadays is one of the most useful tools for scientific research in general and for oceanography in particular. From satellite images, the useful information such as waterline images can be extracte for a large region simultaneously. After tidal adjustments, the waterlines can be used as the observed shorelines which are important inputs for estimating shoreline changes by either using the integration of remote sensing and GIS or using numerical models. Based on the spectral bands of various Landsat images, the paper presents the methods to detect the waterlines in Phan Thiet region in the 40 years period using the images of 1973, 1976, 1990, and 2002 respectively. The extracted results relatively agree with the information of waterline from the images.

scholarly journals Analysis of the Influence of Vegetation Index Choice on the Classification of Satellite Images for Monitoring Forest Pathology

2020 ◽  
pp. paper49-1-paper49-12
Author(s):  
Evgeniy Trubakov ◽  
Olga Trubakova
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Multispectral Images ◽  
Classification Methods ◽  
Planted Forests ◽  
The Earth ◽  
Evaluation Algorithm ◽  
And Control

Rational use of natural resources and control over their recovery, as well as over destruction due to natural and technogenic causes, is currently one of the most urgent problems of the humanity. Forests are no exception. Multispectral images from Earth’s satellites are most often used for monitoring changes in forest planting. This is due to the fact that merging images taken in certain spectra makes it possible to recognize vegetation containing chlorophyll quite well. It also allows to detect changes in the level of chlorophyll, which shows the differences between healthy and damaged plants. Large areas of planted forests create the need to process huge amounts of data, which is difficult to do manually. One of the most important stages of image processing is the classification of objects in these images. This paper deals with various classification methods used to solve the problem of classifying images of remote sensing of the Earth. As a result, it was decided to evaluate the accuracy of classification methods on various vegetation indices. In the course of the study, the evaluation algorithm was determined, as well as one of the options for analyzing the results obtained. Conclusions were made about the work of classification methods on different vegetation indices.

scholarly journals Urban Classification from Aerial and Satellite Images

2020 ◽  
Vol 10 (2) ◽  
pp. 163-172
Author(s):  
Iuliana Maria Pârvu ◽  
Iuliana Adriana Cuibac Picu ◽  
P.I. Dragomir ◽  
Daniela Poli
Keyword(s):  
Optical Sensors ◽  
Satellite Images ◽  
Near Infrared ◽  
Aerial Images ◽  
Support Vector ◽  
Multispectral Images ◽  
Spectral Bands ◽  
Urban Classification ◽  
Landsat 7 ◽  
Extract Information

AbstractWhen talking about land cover, we need to find a proper way to extract information from aerial or satellite images. In the field of photogrammetry, aerial images are generally acquired by optical sensors that deliver images in four bands (red, green, blue and near-infrared). Recent researches in this field demonstrated that for the image classification process is still place for improvement. From satellites are obtained multispectral images with more bands (e.g. Landsat 7/8 has 36 spectral bands). This paper will present the differences between these two types of images and the classification results using support-vector machine and maximum likelihood classifier. For the aerial and the satellite images we used different sets of classification classes and the two methods mentioned above to highlight the importance of choosing the classes and the classification method.

scholarly journals Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario

2015 ◽  
Vol 57 ◽  
Author(s):  
Matteo Picchiani ◽  
Marco Chini ◽  
Stefano Corradini ◽  
Luca Merucci ◽  
Alessandro Piscini ◽  
...  
Keyword(s):  
Neural Network ◽  
Volcanic Ash ◽  
Satellite Images ◽  
Test Cases ◽  
Multispectral Images ◽  
The Neural Network ◽  
Multispectral Satellite Images ◽  
Potential Use

<div class="WordSection1"><div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event. </span></p></div></div></div><p><em><br /></em></p><p><em><br /></em></p></div><em><br clear="all" /></em>

scholarly journals Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification

2021 ◽  
Vol 13 (8) ◽  
pp. 1411
Author(s):  
Yanchao Zhang ◽  
Wen Yang ◽  
Ying Sun ◽  
Christine Chang ◽  
Jiya Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Multispectral Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Spectral Bands ◽  
Vegetation Indexes

Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine learning methods. We acquired multispectral images over an almond plantation using a UAV. First, a multispectral orthoimage was generated from the acquired multispectral images using SfM (Structure from Motion) photogrammetry methods. Eleven types of vegetation indexes were proposed based on the multispectral orthoimage. Then, 593 data points that contained multispectral bands and vegetation indexes were randomly collected and prepared for this study. After comparing six machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, Linear Discrimination Analysis, Decision Tree, Random Forest, and Gradient Boosting), we selected three (SVM, KNN, and LDA) to study the fusion of multi-spectral bands information and derived vegetation index for classification. With the vegetation indexes increased, the model classification accuracy of all three selected machine learning methods gradually increased, then dropped. Our results revealed that that: (1) spectral information from multispectral images can be used for machine learning-based ground classification, and among all methods, SVM had the best performance; (2) combination of multispectral bands and vegetation indexes can improve the classification accuracy comparing to only spectral bands among all three selected methods; (3) among all VIs, NDEGE, NDVIG, and NDVGE had consistent performance in improving classification accuracies, and others may reduce the accuracy. Machine learning methods (SVM, KNN, and LDA) can be used for classifying almond plantation using multispectral orthoimages, and fusion of multispectral bands with vegetation indexes can improve machine learning-based classification accuracy if the vegetation indexes are properly selected.

scholarly journals Snow and Ice Studies By Thematic Mapper and Multispectral Scanner Landsat Images

1987 ◽  
Vol 9 ◽  
pp. 109-118 ◽  
Author(s):  
Olav Orheim ◽  
Baerbel K. Lucchitta
Keyword(s):  
Satellite Images ◽  
Thematic Mapper ◽  
Landsat Images ◽  
Snow Properties ◽  
Spectral Bands ◽  
Dronning Maud Land ◽  
Visible Wavelengths ◽  
Transverse Strains ◽  
Thermal Band ◽  
Snow And Ice

Digitally enhanced Landsat Thematic Mapper (TM) images of Antarctica reveal snow and ice features to a detail never seen before in satellite images. The six TM reflective spectral bands have a nominal spatial resolution of 30 m, compared to 80 m for the Multispectral Scanner (MSS). TM bands 2–4 are similar to the MSS bands. TM infra-red bands 5 and 7 discriminate better between clouds and snow than MSS or the lower TM bands. They also reveal snow features related to grain-size and possibly other snow properties. These features are not observed in the visible wavelengths. Large features such as flow lines show best in the MSS and lower TM bands. Their visibility is due to photometric effects on slopes. TM thermal band 6 has a resolution of 120 m. It shows ground radiation temperatures and may serve to detect liquid water and to discriminate between features having similar reflectivities in the other bands, such as blue ice. Repeated Landsat images can be used for sophisticated glaciological studies. By comparing images from 1975 and 1985, flow rates averaging 0.72 km a−1, and mean longitudinal and transverse strains of respectively 1.3 × 10 −4 a −1 and 130 × 10−4 a−1 have been measured for Jutulstraumen, Dronning Maud Land.

scholarly journals Land Use and Land Cover Classification Using Deep Belief Network for LISS-III Multispectral Satellite Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 94-98
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Multispectral Images ◽  
Deep Belief Networks ◽  
Learning Techniques ◽  
Multispectral Satellite Images ◽  
Liss Iii ◽  
High Level

Land Use and Land Cover (LULC) classification is one of the familiar applications of geographical monitoring. Deep learning techniques like deep belief networks (DBN), are used for the purpose of feature extraction and classification of multispectral images. In this proposed framework, by applying DBN, spatial and spectral features were extracted and classified with high level of classification accuracy. LISS III images of Kottayam district, Kerala were used as experimental images. This proposed framework proved that, DBN has a high ability to extract the feature and classify the multispectral images with high accuracy than traditional methods.

Sign in / Sign up

Export Citation Format

Share Document