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 LONG-TERM REMOTE MONITORING OF THREE TYPICAL LAKE AREA VARIATIONS IN THE NORTHWEST CHINA OVER THE PAST 40 YEARS

2018 ◽  
Vol XLII-3 ◽  
pp. 1165-1168
Author(s):  
Y. Liu ◽  
Y. Lu ◽  
Y. Li ◽  
H. Yue
Keyword(s):  
Development Strategy ◽  
Meteorological Data ◽  
Northwest China ◽  
Qinghai Lake ◽  
Lake Area ◽  
Bosten Lake ◽  
Water Index ◽  
The Past ◽  
Normalized Difference Water Index ◽  
The Impact

water resources management and sustainable development strategy, but also provide reference for assessing the impact of climate change and human activities. This paper selects three inland lakes in Northwest China, using Landsat MSS/TM/ETM+/OLI data from 1970 to 2015, Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) were used to extract lake area and analysed the dynamic trends. Meteorological station rainfall, evaporation and other meteorological data of the lakes were used to analyse reasons for the area change. The results showed that area of Hongjiannao Lake in the past 40&amp;thinsp;a was reduced, the groundwater impoundment and underground coal mining are the main cause of area reduction; the area of Bosten Lake in recent 40&amp;thinsp;a showed a decreasing trend after the first increase, the area was mainly affected by the surface runoff and snowmelt; the area of Qinghai Lake in the past 40&amp;thinsp;a shows a trend of decreasing first and then increasing, the change of its area is mainly affected by regional precipitation and the inflow.

scholarly journals CLOUD-DESKTOP REMOTE SENSING DATA MANAGEMENT TO ENSURE TIME SERIES ANALYSIS, INTEGRATION OF QGIS AND GOOGLE EARTH ENGINE

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 553-557
Author(s):  
E. Panidi ◽  
I. Rykin ◽  
P. Kikin ◽  
A. Kolesnikov
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Data Storage ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Data Representation ◽  
Google Earth ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine

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.

scholarly journals The Dynamic Change of Bosten Lake Area in Response to Climate in the Past 30 Years

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 4 ◽  
Author(s):  
Xiaoai Dai ◽  
Xingping Yang ◽  
Meilian Wang ◽  
Yu Gao ◽  
Senhao Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Area ◽  
Water Body ◽  
Meteorological Factors ◽  
Lake Basin ◽  
Bosten Lake ◽  
Water Index ◽  
The Past ◽  
Normalized Difference Water Index

The widely distributed lakes, as one of the major components of the inland water system, are the primary available freshwater resources on the earth and are sensitive to accelerated climate change and extensive human activities. Lakes play an important role in the terrestrial water cycle and biogeochemical cycle and substantially influence the health of humans living in the surrounding areas. Given the importance of lakes in the ecosystem, long-term monitoring of dynamic changes has important theoretical and practical significance. Here, we extracted water body information and monitored the long-term dynamics of Bosten Lake, which is the largest inland lake in China. We quantified the meteorological factors of the study area from the observation data of meteorological stations between 1988 and 2018. The characteristics of climate change and its correlation with the change of area in the Bosten Lake Basin in the past 30 years were analyzed. The major contributions of this study are as follows: (1) The initial water body was segmented based on the water index model Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) with a pre-assigned threshold value. The results were evaluated with the area extracted through artificial visual interpretation. Then we conducted mathematical morphology operators, opening and closing operations, and median filter to eliminate noise to ensure the accuracy of water body information extraction from the Bosten Lake. A long-term water surface area database of the Bosten Lake was established from high-resolution remote sensing images during 1988–2018. (2) Due to the seasonal difference of snow, ice content, and other objects on images, the areadynamics of Bosten Lake in the recent 30 years were analyzed separately in dry season and rainy season. The water surface area of Bosten Lake showed large inter-annual variations between 1988–2018. (3) Based on the assumption that climatic change has more direct effects on lake than human activities, six meteorological factors were selected to analyze the impacts of climate change on the annual mean lake surface area. The result indicated that in the past 30 years, climate conditions in the Bosten Lake Basin fluctuated greatly. We conducted correlations analysis between the areal dynamics of the Bosten Lake and the meteorological factors. Here, the annual average evaporation had the highest correlation with the areal dynamics of Bosten Lake followed by air temperature, precipitation, sunshine hours, and relative humidity, while the annual average wind speed had the weakest correlation.

scholarly journals Assessment of Terengganu Coastal Change

2020 ◽  
Vol 8 (5) ◽  
pp. 3333-3337
Keyword(s):  
Landsat 8 ◽  
Intensity Increase ◽  
Coastal Change ◽  
Water Index ◽  
The Past ◽  
The North ◽  
Normalized Difference Water Index ◽  
North Part ◽  
Dominant Process ◽  
Reclamation Project

Coastal erosion, accretion, and reclamation along Terengganu coastal line occur irregularly but it is noticed that their intensity increase during the past decade. The main objective of this study is to investigate the change in the coastal area for the period of 1989 to 2018. Dataset acquired from Landsat 5 TM and Landsat 8 OLI have been used in this study. Normalized Difference Water Index (NDWI) is used to differentiate land and water body. The result of this study shows that erosion is the dominant process over Tok Jembal before the reclamation work for the extension of the airport runaway. The erosion occurs from 2006 through 2014 have eroded 0.0299 km2 of land especially at the north of the runaway. However, the reclamation project has injected 0.972 km2 new land. Overall the reclamation and accretion activities have contributed 1.337 km2 of land to this area for the 1989-2018 period. Meanwhile the result Teluk Lipat shows that the worst erosion event occurred between the 2004-2008 periods. In this period 0.092 km2 of land was eroded. Meanwhile, the highest accretion event occurs between 1988-1992. During this period, 0.299 km2 of accretion take place especially in the north part of the study area.

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 Pequenas barragens de água: definição do grau de hierarquização para fins de fiscalização.

2021 ◽  
Author(s):  
◽  
Carla Isoneide Araújo da Silva ◽  
Keyword(s):  
Google Earth ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

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.

A waterline method to derive intertidal bathymetry from multispectral satellite images and its application to hydrodynamic modelling

2021 ◽  
Author(s):  
Wagner Costa ◽  
Karin Bryan ◽  
Giovanni Coco
Keyword(s):  
Satellite Images ◽  
Google Earth ◽  
Hydrodynamic Modelling ◽  
Water Index ◽  
High Data ◽  
Bathymetric Data ◽  
Tidal Propagation ◽  
Normalized Difference Water Index ◽  
Multispectral Satellite Images ◽  
Sentinel 2A

&lt;p&gt;Bathymetric data are a key parameter to assess shallow-water hydrodynamic processes. In-situ surveys provide high data quality; however, surveys are expensive and cover a limited spatial extent. To fill this gap, over recent years, the Satellite Derived Bathymetry (SDB) techniques have been developed. The present work aims to elaborate a technique to estimate bathymetric data from satellite images for intertidal zones. The method applied in this work is composed of 6 steps: (1) image querying and pre-processing is done through Google Earth Engine application (API) using Copernicus Sentinel 2A and B, product type 2A. (2) Identification of the intertidal zone for the study area by temporal variability of the Normalized Difference Water Index (NDWI). (3) Recognition of the waterline in each image by the use of an adaptive threshold technique; and assignment of the elevation for each detected waterline based on local observed tide heights. (4) Validation of the estimated bathymetry by comparison with LiDAR measurements. (5) Implementation of a SDB correction: numerical and/or statistical and, (6) assessment of the validity of SDB for hydrodynamic modelling. The SDB technique was applied to 4 different estuaries in New Zealand: Maketu, Ohiwa, Whitianga and Tauranga Harbour showing similar or better estimations in comparison to previous works using optical or synthetic aperture radar (SAR). For Tauranga Harbour, results from the statistical and dynamical corrections showed that the major error source is due to the image optical properties and environmental conditions when the image was acquired (35%). However, the tidal propagation can significantly decrease the SDB accuracy (13%). Finally, the use of the SDB in numerical simulations does not present huge differences in the predicted waterlevels in comparison to the use of survey bathymetry, showing that SDB could be potentially used for coastal flooding simulations. &amp;#160;&lt;/p&gt;

scholarly journals Fishpond Mapping by Spectral and Spatial-Based Filtering on Google Earth Engine: A Case Study in Singra Upazila of Bangladesh

2020 ◽  
Vol 12 (17) ◽  
pp. 2692
Author(s):  
Zhiqi Yu ◽  
Liping Di ◽  
Md. Shahinoor Rahman ◽  
Junmei Tang
Keyword(s):  
Google Earth ◽  
Water Bodies ◽  
Lulc Change ◽  
Important Indicator ◽  
Selection Technique ◽  
Water Index ◽  
Small Water ◽  
Normalized Difference Water Index ◽  
Google Earth Engine

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.

scholarly journals Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 866
Author(s):  
Hamid Mehmood ◽  
Crystal Conway ◽  
Duminda Perera
Keyword(s):  
Confusion Matrix ◽  
Google Earth ◽  
Data Cube ◽  
Flood Inundation ◽  
Mapping Algorithm ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Information Products ◽  
Google Earth Engine ◽  
The Cost

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.

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