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 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.

scholarly journals Evaluando el desempeño de índices espectrales para identificar humedales alto andinos

2019 ◽  
pp. 59
Author(s):  
J. Aponte-Saravia ◽  
J. E. Ospina-Noreña
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Life Forms ◽  
High Mountain ◽  
Landsat 8 ◽  
Water Index ◽  
Tasseled Cap ◽  
Normalized Difference Water Index ◽  
Humidity Index ◽  
Index Index

<p>High Andean wetlands are habitats critical to life forms that have adapted to these extreme high mountain ecosystems, and for living beings that inhabit the lower parts of the basin; they are spaces that contain high diversity of flora and fauna characteristic of these places and are strongly associated with the water component. There lies the importance of identifying and monitoring ecosystems, using easy applicable methods and allowing results every two weeks approximately, they are inexpensive and highly reliable. Methods of monitoring in short periods, they are economically profitable and provide reliable information, they correspond to the evaluations by satellite images, specifically applying the methods of spectral indices. Thereby, the objective of the research was to evaluate the performance of six indices, considered to be the most used to identify high Andean wetlands (humidity index at surface level, normalized difference water index, normalized difference vegetation index, enhanced vegetation index, index of vegetation to the surface and tasseled CAP vegetation), in periods of low precipitation, using imagery Landsat 8 OLI. Comparing the performance of those indexes in the identification of wetlands through cross-validation and bootstrap statistical learning, the index that showed better performance was tasseled CAP vegetation, revealing the lowest value of the average of the mean square error of iterations between the test failure rate and training. The index tasseled CAP vegetation, shows greater reliability to identify and evaluate high Andean wetlands.</p>

scholarly journals Monitoring of coastline change using Sentinel-2A and Landsat 8 data, a case study of Cam Pha city - Quang Ninh province

2021 ◽  
Author(s):  
Duong Thi Loi ◽  
Dang Vu Khac ◽  
Dao Ngoc Hung ◽  
Nguyen Thanh Dong ◽  
Dinh Xuan Vinh ◽  
...  
Keyword(s):  
Google Earth ◽  
Landsat 8 ◽  
Small Areas ◽  
Water Index ◽  
Coastline Change ◽  
Combined Use ◽  
Normalized Difference Water Index ◽  
Sentinel 2A ◽  
Quang Ninh

The main purpose of this study is to evaluate the performance of Sentinel - 2A and Landsat 8 data in monitoring coastline change from 1999 to 2018 at Cam Pha city, Quang Ninh province. Both data were collected under similar conditions of time and weather features to minimize the differences in interpretation results caused by these factors. The coastline was extracted from Sentinel-2A and Landsat 8 in 2018 by using the Normalized Difference Water Index (NDWI). Coastline map from Quang Ninh Department of Natural Resources and Environment with a scale of 1: 50.000 in 1999 was used as a reference of the same mask and overlaid on coastline maps in 2018 to identify the changes in the study area. The data from fieldwork and Google Earth was used to evaluate the accuracy and make comparative comments. The results presented that changes dramatically occurred between 1999 and 2018 with the accretion process prevailing. This process took place quite strongly on the East and Southeast coast while the erosion process only occurred with small areas at scattered points in the study area. The results also showed that the overall classification accuracy of Sentinel-2A imagery (95.0%) was slightly higher than that of Landsat-8 (87.5%). The combined use of Landsat-Sentinel-2 imagery is expected to generate reliable data records for continuous detecting of coastline changes.

scholarly journals PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 249-253
Author(s):  
J. A. Sartori ◽  
J. B. Sbruzzi ◽  
E. L. Fonseca
Keyword(s):  
Color Image ◽  
A Priori ◽  
Unsupervised Classification ◽  
Training Sample ◽  
Google Earth ◽  
Landsat 8 ◽  
Water Index ◽  
Computing Platform ◽  
Automatic Mapping ◽  
Google Earth Engine

Abstract. This work aims to define the basic parameters for the automatic mapping of the channel between the Lagoa do Peixe and the Atlantic Ocean, which is located in the municipalities of Tavares and Mostardas, Rio Grande do Sul state, Brazil. The automatic mapping is based on an unsupervised classification of Landsat 8 satellite images at the Google Earth Engine cloud computing platform. The images used were selected to present both channel situations (opened and closed). Three images were selected with acquisition dates that presented the open channel and three that presented the closed channel. Each image was classified using the K-means clustering method, using separately band 6, band 7 (both located at shortwave infrared - SWIR) and the Normalized Difference Water Index (NDWI). Once the number of clusters must be defined a priori by the analyst, as well as the training sample area, these parameters were tested over the dataset and clustering results were compared. All of the generated clusters maps were analyzed over 10 random points, identifying the clustering hits and errors. Due to the absence of reference maps, all the final clustering maps for each date were compared with the composite true color image from the same acquisition date. The NDWI cluster maps showed the best results in separating water and non-water pixels.

scholarly journals Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine

2020 ◽  
Vol 12 (10) ◽  
pp. 1614
Author(s):  
András Gulácsi ◽  
Ferenc Kovács
Keyword(s):  
Clustering Algorithm ◽  
Correlation Coefficients ◽  
Google Earth ◽  
Landsat 8 ◽  
Nature Protection ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Satellite Sensors ◽  
Google Earth Engine ◽  
Water Cover

Saline wetlands experience large temporal fluctuations in water supply during the year and are recharged only or mainly through precipitation, meaning they are vulnerable to climate-change-induced aridification. Most passive satellite sensors are unsuitable for continuous wetland monitoring due to cloud cover and their relatively low temporal resolution. However, active satellite sensors such as the C-band synthetic aperture radar of Sentinel-1 satellites offer free, cloud-independent data. We examined surface water cover changes from October 2014 to November 2018 in the strictly protected area (13,000 ha) of the Upper-Kiskunság Alkaline Lakes region in the Danube–Tisza Interfluve in Hungary, with the aim of helping with nature protection planning. Changes and sensitivity can be defined based on the knowledge of variability. We developed a method for water cover detection based on automatic classification, applying the so-called WEKA K-Means clustering algorithm. For satellite data processing and analysis, we used the Google Earth Engine cloud processing platform. In terms of validation, we compared our results with the multispectral Modified Normalized Difference Water Index (MNDWI) derived from Landsat 8 and Sentinel-2 top-of-atmosphere reflectance images using a threshold-based binary classifier (receiver operator characteristics) for the MNDWI data. Using two completely distinct methods operating in distinct wavelength ranges, we obtained adequately matching results, with Spearman’s correlation coefficients (ρ) ranging from 0.54 to 0.80.

scholarly journals Improving flood monitoring in rural areas using remote sensing

2018 ◽  
Vol 14 (1) ◽  
pp. 160-171
Author(s):  
Zahra Ghofrani ◽  
Victor Sposito ◽  
Robert Faggian
Keyword(s):  
Rural Areas ◽  
Flood Control ◽  
Control Measures ◽  
Landsat 8 ◽  
Protective Measures ◽  
Rainfall Events ◽  
Water Index ◽  
Flood Monitoring ◽  
Normalized Difference Water Index ◽  
Heavy Rainfall Events

Abstract Precise information on the extent of inundated land is required for flood monitoring, relief, and protective measures. In this paper, two spectral indices, Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), were used to identify inundated areas during heavy rainfall events in the Tarwin catchment, Victoria, Australia, using Landsat-8 OLI imagery. By integrating the assessed condition of levees, this research also explains the inefficiency of the flood control measures of this region of Australia. NDWI and MNDWI indices performed well, but water features were enhanced better in the NDWI-derived image, with an accuracy of 96.04% and Kappa coefficient of 0.83.

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 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 &plusmn;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 AUTOMATED GIS-BASED TECHNIQUE FOR EVALUATION OF INDIRECT GROWING SEASON ESTIMATIONS

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 209-211
Author(s):  
I. Rykin ◽  
E. Panidi ◽  
V. Tsepelev
Keyword(s):  
Meteorological Data ◽  
Growing Season ◽  
Google Earth ◽  
Water Quantity ◽  
Special Significance ◽  
Water Index ◽  
Atmospheric Scattering ◽  
The Past ◽  
Normalized Difference Water Index ◽  
Automated Technique

<p><strong>Abstract.</strong> This article is based on NDWI (Normalized Difference Water Index) which is automatically computed from the daily MODIS data. The main purpose of the article is to tell how the evaluation of NDWI-based growing season estimations can be automated. The NDWI is used as an indicator of liquid water quantity in vegetation, which is less sensitive to atmospheric scattering effect then the famous growing index (NDVI). The NDWI is computed using cloud-based platform (Google Earth Engine was applied) and compared with the daily meteorological data. The available meteorological data is collected for the past 130 years and NDWI data is collecting for the past 20 years. An automated technique has been probated on the example of republic of Komi, as it has a different climate forming factors. This approach can be used to evaluate growing season estimations for other territories that contain vegetation. Due to the accumulated amount of data, the study is relevant and has a special significance for areas with sparse hydrometeorological network.</p>

scholarly journals Analysis of riverbank changes in Ho Chi Minh city in the period 1989 – 2015

2019 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Thu Trang Hoang ◽  
Khoi Nguyen Dao ◽  
Loi Thi Pham ◽  
Hong Van Nguyen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Ho Chi Minh City ◽  
Landsat 8 ◽  
River Bank ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Multi Temporal ◽  
Erosion And Accretion ◽  
Landsat Satellite

The objective of this study was to analyze the changes of riverbanks in Ho Chi Minh City for the period 1989-2015 using remote sensing and GIS. Combination of Modified Normalized Difference Water Index (MNDWI) and thresholding method was used to extract the river bank based on the multi-temporal Landsat satellite images, including 12 Landsat 4-5 (TM) images and 2 Landsat 8 images in the period 1989-2015. Then, DSAS tool was used to calculate the change rates of river bank. The results showed that, the processes of erosion and accretion intertwined but most of the main riverbanks had erosion trend in the period 1989-2015. Specifically, the Long Tau River, Sai Gon River, Soai Rap River had erosion trends with a rate of about 10.44 m/year. The accretion process mainly occurred in Can Gio area, such as Dong Tranh river and Soai Rap river with a rate of 8.34 m/year. Evaluating the riverbank changes using multi-temporal remote sensing data may contribute an important reference to managing and protecting the riverbanks.

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