scholarly journals A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

2019 ◽  
Author(s):  
Feifei Pan ◽  
Xiaohuan Xi ◽  
Cheng Wang
Keyword(s):  
Image Classification ◽  
Landsat Imagery ◽  
Red Light ◽  
Computational Cost ◽  
Google Earth ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Water Index ◽  
Otsu Method ◽  
Better Than

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEIns, and AWEIs) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEIns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEIns and AWEIs are better than NDWI, MNDWI and MNDWI2. AWEIns works better than AWEIs under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEIns.

scholarly journals A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1611
Author(s):  
Feifei Pan ◽  
Xiaohuan Xi ◽  
Cheng Wang
Keyword(s):  
Image Classification ◽  
Landsat Imagery ◽  
Google Earth ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Blue Band ◽  
Ground Truth Data ◽  
Water Index ◽  
Water Indices

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

scholarly journals MODIFIED OPTIMIZATION WATER INDEX (MOWI) FOR LANDSAT-8 OLI/TIRS

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 185-190 ◽  
Author(s):  
M. Moradi ◽  
M. Sahebi ◽  
M. Shokri
Keyword(s):  
Water Resources ◽  
Spatial Scales ◽  
Landsat Imagery ◽  
Human Life ◽  
Spectral Band ◽  
Remote Sensing Data ◽  
Particle Swarm Algorithm ◽  
Landsat 8 ◽  
Water Index ◽  
Water Detection

Water is one of the most important resources that essential need for human life. Due to population growth and increasing need of human to water, proper management of water resources will be one of the serious challenges of next decades. Remote sensing data is the best way to the management of water resources due time and cost effectiveness over a greater range of temporal and spatial scales. Between many kinds of satellite data, from SAR to optic or from high resolution to low resolution, Landsat imagery is more interesting data for water detection and management of earth surface water. Landsat8 OLI/TIRS is the newest version of Landsat satellite series. In this paper, we investigated the full spectral potential of Landsat8 for water detection. It is developed many kinds of methods for this purpose that index based methods have some advantages than other methods. Pervious indices just use a limited number of spectral band. In this paper, Modified Optimization Water Index (MOWI) defined by consideration of a linear combination of bands that each coefficient of bands calculated by particle swarm algorithm. The result shows that modified optimization water index (MOWI) has a proper performance on different condition like cloud, cloud shadow and mountain shadow.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals Spotting Green Tides over Brittany from Space: Three Decades of Monitoring with Landsat Imagery

2021 ◽  
Vol 13 (8) ◽  
pp. 1408
Author(s):  
Louise Schreyers ◽  
Tim van Emmerik ◽  
Lauren Biermann ◽  
Yves-François Le Lay
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Green Tide ◽  
Green Tides ◽  
Green Macroalgae ◽  
Water Index ◽  
Nitrogen Inputs ◽  
Landsat Satellite

Green tides of macroalgae have been negatively affecting the coasts of Brittany, France, for at least five decades, caused by excessive nitrogen inputs from the farming sector. Regular areal estimates of green tide surfaces are publicly available but only from 2002 onwards. Using free and openly accessible Landsat satellite imagery archives over 35 years (1984–2019), this study explores the potential of remote sensing for detection and long-term monitoring of green macroalgae blooms. By using a Google Earth Engine (GEE) script, we were able to detect and quantify green tide surfaces using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) at four highly affected beaches in Northern Brittany. Mean green tide coverage was derived and analyzed from 1984 to 2019, at both monthly and annual scales. Our results show important interannual and seasonal fluctuations in estimated macroalgae cover. In terms of trends over time, green tide events did not show a decrease in extent at three out of four studied sites. The observed decrease in nitrogen concentrations for the rivers draining the study sites has not resulted in a reduction of green tide extents.

scholarly journals Validation of aerosol estimation in atmospheric correction algorithm ATCOR

2015 ◽  
Vol XL-7/W3 ◽  
pp. 677-683 ◽  
Author(s):  
B. Pflug ◽  
M. Main-Knorn ◽  
A. Makarau ◽  
R. Richter
Keyword(s):  
Satellite Images ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Biomass Estimation ◽  
Spatial And Temporal Variability ◽  
Landsat 8 ◽  
Vertical Column ◽  
Sun Photometer ◽  
Better Than

Atmospheric correction of satellite images is necessary for many applications of remote sensing, i.e. computation of vegetation indices and biomass estimation. The first step in atmospheric correction is estimation of the actual aerosol properties. Due to the spatial and temporal variability of aerosol amount and type, this step becomes crucial for an accurate correction of satellite data. Consequently, the validation of aerosol estimation contributes to the validation of atmospheric correction algorithms. In this study we present the validation of aerosol estimation using own sun photometer measurements in Central Europe and measurements of AERONET-stations at different locations in the world. Our ground-based sun photometer measurements of vertical column aerosoloptical thickness (AOT) spectra are performed synchronously to overpasses of the satellites RapidEye, Landsat 5, Landsat 7 and Landsat 8. Selected AERONET data are collocated to Landsat 8 overflights. The validation of the aerosol retrieval is conducted by a direct comparison of ground-measured AOT with satellite derived AOT using the ATCOR tool for the selected satellite images. The mean uncertainty found in our experiments is ΔAOT550nm ≈ 0.03±0.02 for cloudless conditions with cloud+haze fraction below 1%. This AOT uncertainty approximately corresponds to an uncertainty in surface albedo of Δρ ≈ 0.003. Inclusion of cloudy and hazy satellite images into the analysis results in mean ΔAOT550nm ≈ 0.04±0.03 for both RapidEye and Landsat imagery. About ⅓ of samples perform with the AOT uncertainty better than 0.02 and about ⅔ perform with AOT uncertainty better than 0.05.

scholarly journals DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 5-10 ◽  
Author(s):  
L. Bi ◽  
B. L. Fu ◽  
P. Q. Lou ◽  
T. Y. Tang
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Google Earth ◽  
Pearl River ◽  
Surface Model ◽  
Landsat 8 ◽  
Pearl River Basin ◽  
Water Index ◽  
Significant Difference ◽  
Google Earth Engine

Abstract. Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.

scholarly journals Identificación de zonas anegadas y no anegadas mediante técnicas de teledetección

2017 ◽  
Vol 5 (2) ◽  
pp. 51-78 ◽  
Author(s):  
Victoria Passucci ◽  
Facundo Carmona ◽  
Raúl Rivas Rivas
Keyword(s):  
Environmental Monitoring ◽  
Unsupervised Classification ◽  
Google Earth ◽  
Scientific Development ◽  
Landsat 8 ◽  
Water Index ◽  
Tasseled Cap ◽  
Normalized Difference Water Index ◽  
Del Rio ◽  
Monitoring Stations

El seguimiento de inundaciones y sequías tiene un amplio desarrollo a nivel internacional y nacional. En nuestro país, el desarrollo científico es consistente pero con limitaciones de aplicación práctica (95% de las cuencas hidrológicas de Argentina no disponen de redes de alerta). En este marco se desarrolla el proyecto FONARSEC N°19, donde se inserta el presente trabajo, el cual consiste en la utilización de técnicasde teledetección para la identificación de zonas no anegadas que puedan ser tenidas en cuenta para la instalación de las estaciones de monitoreo ambiental. Los métodos analizados fueron: Índice de Agua de Diferencia Normalizada (NDWIgao), Índice de Agua de Diferencia Normalizada Modificado (NDWIXu), análisis de la banda infrarroja media (1,566-1,651 μm), Transformación de Tasseled Cap (TTC), clasificación no supervisada (ISODATA) y supervisada (máxima verosimilitud). Como producto final de cada método, aplicado a imágenes del satélite Landsat 8, se obtuvieron imágenes binarias (zonas anegadas/zonas no anegadas) de la cuenca del Río Salado. La consistencia se analizó con información suplementaria de Google Earth, de vectores de cuerpos de agua permanente y de cursos de agua provistos por el Instituto Geográfico Nacional (IGN), de las imágenes en falso color compuesto de las bandas de reflectividad, y de las características hidrológicas de la cuenca. De este modo, se seleccionaron los dos métodos que mejores resultados brindaron y se realizó un mapafinal del estado hídrico de la cuenca y la ubicación potencial de las estaciones de monitoreo ambiental, con el fin de buscar la disminución del riesgo de que dichas estaciones se inunden y generen inconvenientes en los registros de los instrumentos. AbstractThe monitoring of floods and droughts enjoys a wide development at national and international levels. In our country, scientific development is consistent. However, it presents limitations as regards its practical application (95% of the hydrological basins in Argentina do not have available warning networks). The FONARSEC No 19 project, where the present work is conducted, is developed within this framework, and itinvolves the use of remote sensing techniques for the identification of nonflooded areas that may be taken into consideration in the establishment of the environmental monitoring stations. The analyzed methods were: Normalized Difference Water Index (NDWIgao), Modified Normalized Difference Water Index (NDWIXu), analysis of midinfrared band (1,566-1,651 μm), Tasseled Cap Transformation (TCT), unsupervised classification (ISODATA) and supervised classification (maximum likelihood). Binary images (nonflooded areas/flooded areas) of the Río Salado basin were obtained as the final product of each method applied to Landsat 8 satellite images. Consistency was performed with suplementary information from Google Earth, permanent waterbodies and watercourses vectors provided by the Instituto Geográfico Nacional [National Geographic Institute], false-color images composed of reflectance bands, and the basin's hydrological features. Thus, the two methods that provided the best results were selected and a final map was made of the basin hydric status and the potential location for the environmental monitoring stations, aiming to reduce the risk of flooding in such stations, which would cause inconveniences in the records from the instruments.

scholarly journals AUTOMATIC EXTRACTION OF TIDE-COORDINATED SHORELINE USING OPEN SOURCE SOFTWARE AND LANDSAT IMAGERY

2015 ◽  
Vol XL-7/W3 ◽  
pp. 953-957 ◽  
Author(s):  
G. Gonçalves ◽  
N. Duro ◽  
E. Sousa ◽  
I. Figueiredo
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Landsat Imagery ◽  
High Tide ◽  
Least Square Method ◽  
Least Square ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Water Index ◽  
Intertidal Surface

Due to both natural and anthropogenic causes, the coastal lines keeps changing dynamically and continuously their shape, position and extend over time. In this paper we propose an approach to derive a tide-coordinate shoreline from two extracted instantaneous shorelines corresponding to a nearly low tide and high tide events. First, all the multispectral images are panshaperned to meet the 15 meters spatial resolution of the panchromatic images. Second, by using the Modification of Normalized Difference Water Index (MNDWI) and the kmeans clustering method we extract the raster shoreline for each image acquisition time. Third, each raster shoreline is smoothed and vectorized using a penalized least square method. Fourth, a 2D constrained Delaunay triangulation is built from the two extracted instantaneous shorelines with their respective heights interpolated from a Tidal gauche station. Finally, the desired tide-coordinate shoreline is interpolated from the previous triangular intertidal surface. The results show that an automatic tide-coordinated extraction method can be efficiently implemented using free available remote sensing imagery data (Landsat 8) and open source software (QGIS and Orfeo toolbox) and python scripting for task automation and software integration.

scholarly journals Identification of land reclamation area and potential plantation area on Bagmati river-basin in the Terai region of Nepal

2016 ◽  
Vol 26 (1) ◽  
pp. 53-59
Author(s):  
A. K. Acharya ◽  
A. K. Chaudhary ◽  
S. Khanal
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
River Basin ◽  
Land Reclamation ◽  
Forest Products ◽  
Landsat 8 ◽  
Reclaimed Land ◽  
Water Index ◽  
Reclamation Area ◽  
Bagmati River

Utilization of land reclamation area offers the potentiality of increasing greenery as well as providing forest products. This study refers to the identification of the land reclamation areas and potential plantation areas on the Bagmati river-basin in the Terai region of Nepal, and recommends appropriate species for plantation in order to rehabilitate such areas. Multi-temporal Landsat Satellite Images (Landsat 7 and Landsat 8) were acquired for 2002 and 2014. Object-based Image Classification method was used to classify the land cover classes into four broad categories: i) Water, ii) Sand and gravel, iii) Plantation potential (open areas suitable for plantation) and iii) Others (forest, agriculture, built-up areas etc.). The Mean Normalized Difference Water Index (NDWI) values and Mean Brightness values were found to be helpful in identifying the water and sand & gravel areas from the other land cover classes. The overall classification accuracy was 0.97 with a kappa coefficient of 0.89 in the case of the 2014 Image classification. In this study, the land reclamation area referred to the areas occupied by water, sand & gravel on the river-beds that were converted into plantation potential and other classes between 2002 and 2014. Similarly, the potential plantation area referred to the summation of the area of reclaimed land, the area of ‘Others’ class converted into ‘Plantation potential’ class and the area that remained to be plantation potential on the bed of the Bagmati River and its tributaries between 2002 and 2014. Altogether, 4,819.10 ha land was reclaimed in the study area, and a total of 5,395.10 ha land was found to be potential for plantation within the study area.Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page:53-59, 2016

scholarly journals SHORELINE CHANGES AFTER THE SUNDA STRAIT TSUNAMI ON THE COAST OF PANDEGLANG REGENCY, BANTEN

2021 ◽  
Vol 17 (2) ◽  
pp. 201
Author(s):  
Fandi Dwi Julianto ◽  
Cahya Rizki Fathurohman ◽  
Salsabila Diyah Rahmawati ◽  
Taufiq Ihsanudin
Keyword(s):  
Low Cost ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Water Index ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Tier 1 ◽  
Normalized Difference Water Index ◽  
The Village

The Sunda Strait tsunami occurred on the coast of west Banten and South Lampung at 22nd December 2018, resulting in 437 deaths, with10 victims missing. The disaster had various impacts on the environment and ecosystem, with this area suffering the greatest effects from the disaster. The utilisation of remote sensing technology enables the monitoring of coastal areas in an effective and low-cost manner. Shoreline extraction using the Google Earth Engine, which is an open-source platform that facilitates the processing of a large number of data quickly. This study used Landsat-8 Surface Reflectance Tier 1 data that was geometrically and radiometrically corrected, with processing using the Modification of Normalized Difference Water Index (MNDWI) algorithm. The results show that 30.1% of the coastline in Pandeglang Regency occurred suffered abrasion, 20.2% suffered accretion,while 40.7% saw no change. The maximum abrasion of 130.2 meters occurred in the village of Tanjung Jaya. Moreover, the maximum shoreline accretion was 43.3 meters in the village of Panimbang Jaya. The average shorelinechange in Pandeglang Regencywas 3.9 meters.

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