Assimilation of satellite data to 3D hydrodynamic model of Lake Säkylän Pyhäjärvi

2015 ◽  
Vol 71 (7) ◽  
pp. 1033-1039 ◽  
Author(s):  
Akiko Mano ◽  
Olli Malve ◽  
Sampsa Koponen ◽  
Kari Kallio ◽  
Antti Taskinen ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Time Series ◽  
Satellite Data ◽  
Suspended Matter ◽  
Concentration Field ◽  
Total Suspended Matter ◽  
Observation Data ◽  
Lake Area ◽  
Lake Surface ◽  
Spatial Differences

To analyze the applicability of direct insertion of total suspended matter (TSM) concentration field based on turbidity derived from satellite data to numerical simulation, dispersion studies of suspended matter in Lake Säkylän Pyhäjärvi (lake area 154 km2; mean depth 5.4 m) were conducted using the 3D COHERENS simulation model. To evaluate the practicality of direct insertion, five cases with different initialization frequencies were conducted: (1) every time, when satellite data were available; (2) every 10 days; (3) 20 days; (4) 30 days; and (5) control run without repeated initialization. To determine the effectiveness of initialization frequency, three methods of comparison were used: simple spatial differences of TSM concentration without biomass in the lake surface layer; averaged spatial differences between initialization data and the forecasts; and time series of TSM concentration and observation data at 1 m depth at the deepest point of the lake. Results showed that direct insertion improves the forecast significantly, even if it is applied less often.

scholarly journals Validation of Daily Rainfall Based on Global Satellite Mapping of Precipitation (GSMAP) Data of Bali and Nusa Tenggara Region

2018 ◽  
Vol 19 (1) ◽  
pp. 12
Author(s):  
Sanjaya Natadiredja ◽  
I Ketut Sukarasa ◽  
Gusti Ngurah Sutapa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Satellite Data ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Observation Data ◽  
Similar Data ◽  
Strong Positive Correlation ◽  
Cause Analysis ◽  
Satellite Rainfall

Limitations of observation data cause analysis and prediction of precipitation is difficult. One way to overcome such limitations is the use of satellite data such as GSMaP, but satellite data needs to be validated before use. This study aims to validate GSMaP rainfall data on observation data in Bali and Nusa Tenggara. Through monthly time series analysis, GSMaP rainfall data tend to have smaller value than observation data, but it has similar data pattern in each region with rain pattern that occurs in November to March (NDJFM). While validation between GSMaP satellite rainfall data and observation using Pearson and RMSE correlation and MBE at each location showed strong positive correlation value (> 0.5), correlation value obtained from each location from 0.82 to 0.93 with RMSE value from 2.08 to 5.51 and MBE values ??from 0.23 to 0.89, this indicates that GSMaP satellite data is valid and can be used to fill in empty data especially in 5 observation areas ie Denpasar, Ampenan, Sumbawa Besar, Bima and Kupang.

scholarly journals Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data

2011 ◽  
Vol 61 (8) ◽  
pp. 1205-1214 ◽  
Author(s):  
Bouchra Nechad ◽  
Aida Alvera-Azcaràte ◽  
Kevin Ruddick ◽  
Naomi Greenwood
Keyword(s):  
Time Series ◽  
Suspended Matter ◽  
Total Suspended Matter

Potential of Sentinel and high resolution EO data for monitoring nature protection in cities – the SeMoNa22 project

2021 ◽  
Author(s):  
Anna Iglseder ◽  
Markus Immitzer ◽  
Christoph Bauerhansl ◽  
Hannes Hoffert-Hösl ◽  
Klaus Kramer ◽  
...  
Keyword(s):  
Time Series ◽  
Nature Conservation ◽  
High Resolution ◽  
Satellite Data ◽  
Laser Scanning ◽  
Object Oriented ◽  
Earth Observation ◽  
Observation Data ◽  
Earth Observation Data ◽  
Sentinel 2

<p><span><span>At the end of the 1980s the Municipal Department for Environmental Protection of Vienna - MA 22 initiated a detailed biotope mapping on the basis of the Viennese nature conservation law. Approximately 40 % of Vienna’s city area were covered, however only 2 % of the densely populated areas. This biotope mapping was the basis for the biotope types mapping (2005-2011) and of </span></span><span><span>the</span></span><span><span> green areas monitoring (2005). An update of these surveys has been planned in order to meet the various requirements of urban nature conservation and the national and international, respectively, legal monitoring and reporting obligations.</span></span></p><p><span><span>Since the 1970s the municipality of Vienna has built up a comprehensive database and uses state-of-the-art methods for collecting geodata carrying out services for surveying, airborne imaging and laser-scanning. Currently systems for mobile mapping, oblique aerial photos and a surveying flight with a single photon LiDAR system are being implemented or prepared. Because of the numerous high-resolution data available within the municipality and limitations mainly in spatial resolution of satellite data, the City of Vienna saw no need or benefit in integrating satellite images until now.</span></span></p><p><span><span>However, satellite data are now available within the European Copernicus program, which have considerable potential for monitoring green spaces and biotope types due to their high temporal resolution and the large number of spectral channels and SAR data. For the first time, the Sentinel-1 mission offers a combination of high spatial resolution in Interferometric Wide Swath (IW) recording mode and high temporal coverage of up to four shots every 12 days in cross-polarization in the C-band. The Sentinel-2 satellites deliver multispectral data in 10 channels every 5 days with spatial resolutions of 10 or 20 m.</span></span></p><p><span><span>Within the SeMoNa22 project, various indicators are derived for the Vienna urban area (2015-2020) and used for object-oriented mapping and classification of biotope types and characterization of the green space:</span></span></p><ul><li> <p><span><span>Sentinel-1 data (→ time series on the annual cycles in the backscattering properties of the vegetation, phenology),</span></span></p> </li> <li> <p><span><span>Sentinel-2 data (→ multispectral time series via parameters for habitat classification / vegetation indices),</span></span></p> </li> <li> <p><span><span>High-resolution earth observation data (airborne laser scanning (ALS), image matching, orthophoto → various parameter describing the horizontal and vertical vegetation structure).</span></span></p> </li> </ul><p><span><span>The main goals of SeMoNa22 is to explore efficient and effective ways of knowing if, how and to what extent the data collected can form the basis and become an integrative part of urban conservation monitoring. For this purpose, combinations of different earth observation data (satellite- and aircraft- supported or terrestrial sensors) and existing structured fieldwork data collections (species mapping, soil parameters, meteorology) are examined by means of pixel- and object-oriented methods of remote sensing and image processing. The study is done for several test sites in Vienna covering different ecosystems. In this contribution the ongoing SeMoNa22 project will be presented and first results will be shown and discussed.</span></span></p>

scholarly journals Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

2020 ◽  
Vol 12 (18) ◽  
pp. 2953 ◽  
Author(s):  
Eliakim Hamunyela ◽  
Sabina Rosca ◽  
Andrei Mirt ◽  
Eric Engle ◽  
Martin Herold ◽  
...  
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Google Earth ◽  
Series Data ◽  
Observation Data ◽  
Large Area ◽  
Change Monitoring

Monitoring of abnormal changes on the earth’s surface (e.g., forest disturbance) has improved greatly in recent years because of satellite remote sensing. However, high computational costs inherently associated with processing and analysis of satellite data often inhibit large-area and sub-annual monitoring. Normal seasonal variations also complicate the detection of abnormal changes at sub-annual scale in the time series of satellite data. Recently, however, computationally powerful platforms, such as the Google Earth Engine (GEE), have been launched to support large-area analysis of satellite data. Change detection methods with the capability to detect abnormal changes in time series data while accounting for normal seasonal variations have also been developed but are computationally intensive. Here, we report an implementation of BFASTmonitor (Breaks For Additive Season and Trend monitor) on GEE to support large-area and sub-annual change monitoring using satellite data available in GEE. BFASTmonitor is a data-driven unsupervised change monitoring approach that detects abnormal changes in time series data, with near real-time monitoring capabilities. Although BFASTmonitor has been widely used in forest cover loss monitoring, it is a generic change monitoring approach that can be used to monitor changes in a various time series data. Using Landsat time series for normalised difference moisture index (NDMI), we evaluated the performance of our GEE BFASTmonitor implementation (GEE BFASTmonitor) by detecting forest disturbance at three forest areas (humid tropical forest, dry tropical forest, and miombo woodland) while comparing it to the original R-based BFASTmonitor implementation (original BFASTmonitor). A map-to-map comparison showed that the spatial and temporal agreements on forest disturbance between the original and our GEE BFASTmonitor implementations were high. At each site, the spatial agreement was more than 97%, whereas the temporal agreement was over 94%. The high spatial and temporal agreement show that we have properly translated and implemented the BFASTmonitor algorithm on GEE. Naturally, due to different numerical solvers being used for regression model fitting in R and GEE, small differences could be observed in the outputs. These differences were most noticeable at the dry tropical forest and miombo woodland sites, where the forest exhibits strong seasonality. To make GEE BFASTmonitor accessible to non-technical users, we developed a web application with simplified user interface. We also created a JavaScript-based GEE BFASTmonitor package that can be imported as a module. Overall, our GEE BFASTmonitor implementation fills an important gap in large-area environmental change monitoring using earth observation data.

scholarly journals Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?

2019 ◽  
Author(s):  
Xiaolong Liu ◽  
Zhengtao Shi ◽  
Guangcai Huang ◽  
Yanchen Bo ◽  
Guangjie Chen
Keyword(s):  
Time Series ◽  
Surface Area ◽  
Dominant Factor ◽  
Natural Disturbances ◽  
Lake Area ◽  
Lake Surface ◽  
Natural Event ◽  
Inland Lake ◽  
Lake Surface Area ◽  
Surface Area Change

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural event is playing as the dominant factor in inland lake surface area change. In this study, we proposed a Douglas-Peucker simplification algorithm and bend simplification algorithm combined method to locate major lake surface area disturbances; these disturbances were then characterized to extract the time series change features according to documented records; and the disturbances were finally classified into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31 years long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records was used as references to aid the feature extraction and disturbance identification accuracy. Results of our method for both disturbance location and the disturbance identification could be concluded as follows: 1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the lake area time series curves in our study area was 95.24%. 2) Our proposed method achieved an overall accuracy of 91.67%, with F-score of 94.67 for anthropogenic disturbances and F-score of 85.71 for natural disturbances. 3) According to our results, lakes in Yunnan Provence, China, have undergone extensive disturbances, and the human-induced disturbances occurred almost twice as often as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or natural event.

scholarly journals Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?

2020 ◽  
Vol 12 (4) ◽  
pp. 612 ◽  
Author(s):  
Xiaolong Liu ◽  
Zhengtao Shi ◽  
Guangcai Huang ◽  
Yanchen Bo ◽  
Guangjie Chen
Keyword(s):  
Time Series ◽  
Surface Area ◽  
Yunnan Province ◽  
Dominant Factor ◽  
Natural Disturbances ◽  
Lake Area ◽  
Lake Surface ◽  
Inland Lake ◽  
Lake Surface Area ◽  
Surface Area Change

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural events is the dominant factor in inland lake surface area change. In this study, we proposed a method that combines the Douglas-Peucker simplification algorithm and the bend simplification algorithm to locate major lake surface area disturbances. These disturbances were used to extract the features that been used to classify disturbances into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31-year long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records were used as references to aid the feature extraction and disturbance identification accuracy assessment. Results of our method for disturbance location and disturbance identification could be concluded as follows: (1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the time series of lake surface area in our study area was 94.73%. (2) Our proposed method achieved an overall accuracy of 87.75%, with an F-score of 85.71 for anthropogenic disturbances and an F-score of 88.89 for natural disturbances. (3) According to our results, lakes in Yunnan Province of China have undergone intensive disturbances. Human-induced disturbances occurred almost twice as much as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or a natural event, and to monitor whether disturbances of lake surface area are intensified for a region.

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