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

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.

Author(s):  
L. Bi ◽  
B. L. Fu ◽  
P. Q. Lou ◽  
T. Y. Tang

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.


Author(s):  
Victoria Passucci ◽  
Facundo Carmona ◽  
Raúl Rivas Rivas

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.


Author(s):  
◽  
Carla Isoneide Araújo da Silva ◽  

Dados precisos sobre a distribuição e características de pequenas barragens são importantes para fins de gestão de emergências e planejamento de recursos hídricos em bacia hidrográfica e para auxiliar o monitoramento de indicadores do Objetivo de Desenvolvimento Sustentável (ODS) 6, sobre o uso e disponibilidade dos recursos hídricos e a implementação da gestão integrada dos recursos hídricos em todos os níveis. É necessário, assim, um sistema simplificado que auxilie no processo de identificação e classificação dessas pequenas barragens. Nesse contexto, a proposta deste estudo é identificar a presença de pequenos reservatórios através de imagens do MSI/Sentinel-2 entre janeiro e dezembro de 2020 e elaborar um Grau de Hierarquização (GR) para ações de fiscalização dos órgãos gestores. Foram utilizados para identificação o Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index e o método de transformação de espaço de cores RGB para HSV. O software QGIS versão 3.10 e o Google Earth Engine foram utilizados para o processamento das imagens e composição dos mapas apresentados. Os resultados comprovaram que o método HSV apresentou melhor resultado na identificação dos alvos propostos. A partir da aplicação do GR a uma pequena barragem de água, foi possível avaliar o seu nível de risco potencial e propor uma escala de prioridade para ações de fiscalização. Por fim, pode-se concluir que o GR pode auxiliar na tomada de decisão, fornecendo aos órgãos públicos uma ferramenta de fácil utilização para avaliar a prioridade de ação em pequenos barramentos.


Author(s):  
E. Panidi ◽  
I. Rykin ◽  
P. Kikin ◽  
A. Kolesnikov

Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.


2020 ◽  
Vol 12 (17) ◽  
pp. 2692
Author(s):  
Zhiqi Yu ◽  
Liping Di ◽  
Md. Shahinoor Rahman ◽  
Junmei Tang

Inland aquaculture in Bangladesh has been growing fast in the last decade. The underlying land use/land cover (LULC) change is an important indicator of socioeconomic and food structure change in Bangladesh, and fishpond mapping is essential to understand such LULC change. Previous research often used water indexes (WI), such as Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), to enhance water bodies and use shape-based metrics to assist classification of individual water features, such as coastal aquaculture ponds. However, inland fishponds in Bangladesh are generally extremely small, and little research has investigated mapping of such small water objects without high-resolution images. Thus, this research aimed to bridge the knowledge gap by developing and evaluating an automatic fishpond mapping workflow with Sentinel-2 images that is implemented on Google Earth Engine (GEE) platform. The workflow mainly includes two steps: (1) the spectral filtering phase that uses a pixel selection technique and an image segmentation method to automatically identify all-year-inundated water bodies and (2) spatial filtering phase to further classify all-year-inundated water bodies into fishponds and non-fishponds using object-based features (OBF). To evaluate the performance of the workflow, we conducted a case study in the Singra Upazila of Bangladesh, and our method can efficiently map inland fishponds with a precision score of 0.788. Our results also show that the pixel selection technique is essential in identifying inland fishponds that are generally small. As the workflow is implemented on GEE, it can be conveniently applied to other regions.


Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 866
Author(s):  
Hamid Mehmood ◽  
Crystal Conway ◽  
Duminda Perera

The Earth Observation (EO) domain can provide valuable information products that can significantly reduce the cost of mapping flood extent and improve the accuracy of mapping and monitoring systems. In this study, Landsat 5, 7, and 8 were utilized to map flood inundation areas. Google Earth Engine (GEE) was used to implement Flood Mapping Algorithm (FMA) and process the Landsat data. FMA relies on developing a “data cube”, which is spatially overlapped pixels of Landsat 5, 7, and 8 imagery captured over a period of time. This data cube is used to identify temporary and permanent water bodies using the Modified Normalized Difference Water Index (MNDWI) and site-specific elevation and land use data. The results were assessed by calculating a confusion matrix for nine flood events spread over the globe. The FMA had a high true positive accuracy ranging from 71–90% and overall accuracy in the range of 74–89%. In short, observations from FMA in GEE can be used as a rapid and robust hindsight tool for mapping flood inundation areas, training AI models, and enhancing existing efforts towards flood mitigation, monitoring, and management.


Author(s):  
Fandi Dwi Julianto ◽  
Cahya Rizki Fathurohman ◽  
Salsabila Diyah Rahmawati ◽  
Taufiq Ihsanudin

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.


Author(s):  
Duong Thi Loi ◽  
Dang Vu Khac ◽  
Dao Ngoc Hung ◽  
Nguyen Thanh Dong ◽  
Dinh Xuan Vinh ◽  
...  

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.


Author(s):  
J. A. Sartori ◽  
J. B. Sbruzzi ◽  
E. L. Fonseca

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.


2020 ◽  
Author(s):  
Dan Li ◽  
Baosheng Wu ◽  
Bowei Chen ◽  
Yanjun Wang ◽  
Yi Zhang ◽  
...  

<p><strong>Abstract:</strong> Water plays a vital role in plants, animals and human survival, as well as water resources planning and protection. The spatial and temporal changes of rivers have a profound impact on climate change and the scientific protection of the regional ecological environment in Qingzang-Tibet plateau. Due to the influence of snow and cloud cover, optical remote sensing images in this region have less effective coverage. Many researches in the past mainly faced the challenge of misclassification caused by shadows from cloud and mountain. In this study, we proposed a method to improve the extraction of rivers by reducing the effect of shadows by fusing Sentinel-1 radar data and Sentinel-2 optical imagery. For the optical imagery, water indices including MNDWI (Modified Normalized Difference Water Index) and RNDWI (Revised Normalized Difference Water Index) and morphological operations were used to extract the river coverage. In addition, radar data is used to extract water in areas where there is no optical image coverage or where optical images are misclassified by using a combination of both the histogram and Otsu threshold methods. The GEE (Google Earth Engine) platform is used to implement the analysis using two classification datasets at a regional level. Relevant results from Sentinel-1 and Sentinel-2 data showed that the RNDWI has a more accurate water extraction results in this region. We further compared the final river width results with the manually measured samples from Google Earth and situ data of hydrological stations for accuracy assessment. The R<sup>2 </sup>value is 0.90, and the standard deviation is 18.663m. The river width can be estimated well by this method, which can provide basic data for the study of water in depopulated zone.</p><p><strong>Keywords: </strong>Remote sensing, shadow removal, water extraction, water index, Otsu threshold, Google Earth Engine</p>


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