scholarly journals An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery

2021 ◽  
Vol 13 (23) ◽  
pp. 4899
Author(s):  
Shujie Chen ◽  
Wenli Huang ◽  
Yumin Chen ◽  
Mei Feng
Keyword(s):  
Optical Sensors ◽  
Water Distribution ◽  
Human Life ◽  
Google Earth ◽  
Current Data ◽  
Adaptive Thresholding ◽  
Landsat Data ◽  
Dynamic Surface ◽  
Current Water ◽  
Sar Imagery

Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products.

scholarly journals Construction of High Spatial-Temporal Water Body Dataset in China Based on Sentinel-1 Archives and GEE

2020 ◽  
Vol 12 (15) ◽  
pp. 2413
Author(s):  
Yang Li ◽  
Zhenguo Niu ◽  
Zeyu Xu ◽  
Xin Yan
Keyword(s):  
Surface Water ◽  
Temporal Resolution ◽  
Optical Sensors ◽  
Water Body ◽  
Large Scale ◽  
Google Earth ◽  
Water Levels ◽  
Water Bodies ◽  
Accurate Information ◽  
Dynamic Surface

Surface water is the most important resource and environmental factor in maintaining human survival and ecosystem stability; therefore, timely accurate information on dynamic surface water is urgently needed. However, the existing water datasets fall short of the current needs of the various organizations and disciplines due to the limitations of optical sensors in dynamic water mapping. The advancement of the cloud-based Google Earth Engine (GEE) platform and free-sharing Sentinel-1 imagery makes it possible to map the dynamics of a surface water body with high spatial-temporal resolution on a large scale. This study first establishes a water extraction method oriented towards Sentinel-1 Synthetic Aperture Radar (SAR) data based on the statistics of a large number of samples of land-cover types. An unprecedented high spatial-temporal water body dataset in China (HSWDC) with monthly temporal and 10-m spatial resolution using the Sentinel-1 data from 2016 to 2018 is developed in this study. The HSWDC is validated by 14,070 random samples across China. A high classification accuracy (overall accuracy = 0.93, kappa coefficient = 0.86) is achieved. The HSWDC is highly consistent with the Global Surface Water Explorer dataset and water levels from satellite altimetry. In addition to the good performance of detecting frozen water and small water bodies, the HSWDC can also classify various water cover/uses, which are obtained from its high spatial-temporal resolution. The HSWDC dataset can provide more detailed information on surface water bodies in China and has good application potential for developing high-resolution wetland maps.

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

Dynamic updating of post-earthquake damage and functional restoration forecasts of water distribution systems using Bayesian inferencing

2021 ◽  
pp. 875529302110380
Author(s):  
Agam Tomar ◽  
Henry V Burton ◽  
Ali Mosleh
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Epistemic Uncertainty ◽  
Discrete Event ◽  
Complete Recovery ◽  
Current Data ◽  
Functional Restoration ◽  
Mitigation Measures ◽  
Total Recovery

A framework for dynamically updating post-earthquake functional recovery forecasts is presented to reduce the epistemic uncertainty in the predictive model. A Bayesian Network (BN) model is used to provide estimates of the total recovery time, and a process-based discrete event simulation (PBDES) model generates forecasts of the complete recovery trajectory. Both models rely on component damage and duration-based input parameters that are dynamically updated using Bayes’ theorem, as information becomes available throughout the recovery process. The effectiveness of the proposed framework is demonstrated through an application to the pipe network of the City of Napa water distribution system. More specifically, pipe damage and repair data from the 2014 earthquake are used as a point of comparison for the dynamic forecasts. It is shown that, over time, the mean value of the total recovery duration generated by the BN-based model converges to the observed value and the dispersion is reduced. Also, despite a crude initial estimate, the median trajectory generated by the PBDES model provides a reasonable approximation of the observed recovery within 30 days following the earthquake. The proposed framework can be used by emergency managers to investigate the efficacy of post-event mitigation measures (e.g. crew allocation, resource prioritization) utilizing the most current data and knowledge.

Mapping land use on Irish raised bogs using Sentinel-2 imagery and Google Earth Engine

2021 ◽  
Author(s):  
Wahaj Habib ◽  
John Connolly ◽  
Kevin McGuiness
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Change ◽  
Optical Sensors ◽  
Ghg Emissions ◽  
Google Earth ◽  
Good Prospect ◽  
Raised Bogs ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Peatlands are one of the most space-efficient terrestrial carbon stores. They cover approximately 3 % of the terrestrial land surface and account for about one-third of the total soil organic carbon stock. Peatlands have been under severe strain for centuries all over the world due to management related activities. In Ireland, peatlands span over approximately 14600 km<sup>2</sup>, and 85 % of that has already been degraded to some extent. To achieve temperature goals agreed in the Paris agreement and fulfil the EU’s commitment to quantifying the Carbon/Green House Gases (C/GHG) emissions from land use, land use change forestry, accurate mapping and identification of management related activities (land use) on peatlands is important.</p><p>High-resolution multispectral satellite imagery by European Space Agency (ESA) i.e., Sentinel-2 provides a good prospect for mapping peatland land use in Ireland. However, due to persistent cloud cover over Ireland, and the inability of optical sensors to penetrate the clouds makes the acquisition of clear sky imagery a challenge and hence hampers the analysis of the landscape. Google Earth Engine (a cloud-based planetary-scale satellite image platform) was used to create a cloud-free image mosaic from sentinel-2 data was created for raised bogs in Ireland (images collected for the time period between 2017-2020). A preliminary analysis was conducted to identify peatland land use classes, i.e., grassland/pasture, crop/tillage, built-up, cutover, cutaway and coniferous, broadleaf forests using this mosaicked image. The land-use classification results may be used as a baseline dataset since currently, no high-resolution peatland land use dataset exists for Ireland. It can also be used for quantification of land-use change on peatlands. Moreover, since Ireland will now be voluntarily accounting the GHG emissions from managed wetlands (including bogs), this data could also be useful for such type of assessment.</p>

Global Upscaling of the MODIS Land Cover with Google Earth Engine and Landsat Data

2021 ◽  
Author(s):  
Emma Izquierdo-Verdiguier ◽  
Alvaro Moreno-Martinez ◽  
Jose E. Adsuara ◽  
Jordi Munoz-Mari ◽  
Gustau Camps-Valls ◽  
...  
Keyword(s):  
Land Cover ◽  
Google Earth ◽  
Landsat Data ◽  
Google Earth Engine

scholarly journals Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation

2020 ◽  
Vol 12 (17) ◽  
pp. 2840 ◽  
Author(s):  
Sean P. Healey ◽  
Zhiqiang Yang ◽  
Noel Gorelick ◽  
Simon Ilyushchenko
Keyword(s):  
Optical Sensors ◽  
Forest Structure ◽  
Model Calibration ◽  
Google Earth ◽  
Ecosystem Dynamics ◽  
Ecosystem Functions ◽  
Calibration Data ◽  
Local Models ◽  
Google Earth Engine ◽  
Signal Saturation

While Landsat has proved to be effective for monitoring many elements of forest condition and change, the platform has well-documented limitations in measuring forest structure, the vertical distribution of the canopy. This is important because structure determines several key ecosystem functions, including: carbon storage; habitat suitability; and timber volume. Canopy structure is directly measured by LiDAR, and it should be possible to train Landsat structure models at a highly local scale with the dense, global sample of full waveform LiDAR observations collected by NASA’s Global Ecosystem Dynamics Investigation (GEDI). Local models are expected to perform better because: (a) such models may take advantage of localized correlations between structure and canopy surface reflectance; and (b) to the extent that models revert to the mean of the calibration data due to a lack of discrimination, local models will revert to a more representative mean. We tested Landsat-based relative height predictions using a new GEDI asset on Google Earth Engine, described here. Mean prediction error declined by 23% and important prediction biases at the extremes of the range of canopy height dropped as model calibration became more local, minimizing forest structure signal saturation commonly associated with Landsat and other passive optical sensors. Our results suggest that Landsat-based maps of structural variables such as height and biomass may substantially benefit from the kind of local calibration that GEDI’s dense sample of LiDAR data supports.

scholarly journals Are There Sufficient Landsat Observations for Retrospective and Continuous Monitoring of Land Cover Changes in China?

2019 ◽  
Vol 11 (15) ◽  
pp. 1808 ◽  
Author(s):  
Zhou ◽  
Dong ◽  
Liu ◽  
Metternicht ◽  
Shen ◽  
...  
Keyword(s):  
Land Cover ◽  
Retrospective Analysis ◽  
Continuous Monitoring ◽  
Google Earth ◽  
Data Availability ◽  
Land Cover Changes ◽  
Similar Data ◽  
Landsat Data ◽  
Management Interventions ◽  
Land Cover Dynamics

Unprecedented human-induced land cover changes happened in China after the Reform and Opening-up in 1978, matching with the era of Landsat satellite series. However, it is still unknown whether Landsat data can effectively support retrospective analysis of land cover changes in China over the past four decades. Here, for the first time, we conduct a systematic investigation on the availability of Landsat data in China, targeting its application for retrospective and continuous monitoring of land cover changes. The latter is significant to assess impact of land cover changes, and consequences of past land policy and management interventions. The total and valid observations (excluding clouds, cloud shadows, and terrain shadows) from Landsat 5/7/8 from 1984 to 2017 were quantified at pixel scale, based on the cloud computing platform Google Earth Engine (GEE). The results show higher intensity of Landsat observation in the northern part of China as compared to the southern part. The study provides an overall picture of Landsat observations suitable for satellite-based annual land cover monitoring over the entire country. We uncover that two sub-regions of China (i.e., Northeast China-Inner Mongolia-Northwest China, and North China Plain) have sufficient valid observations for retrospective analysis of land cover over 30 years (1987–2017) at an annual interval; whereas the Middle-Lower Yangtze Plain (MLYP) and Xinjiang (XJ) have sufficient observations for annual analyses for the periods 1989–2017 and 2004–2017, respectively. Retrospective analysis of land cover is possible only at a two-year time interval in South China (SC) for the years 1988–2017, Xinjiang (XJ) for the period 1992–2003, and the Tibetan Plateau (TP) during 2004–2017. For the latter geographic regions, land cover dynamics can be analyzed only at a three-year interval prior to 2004. Our retrospective analysis suggest that Landsat-based analysis of land cover dynamics at an annual interval for the whole country is not feasible; instead, national monitoring at two- or three-year intervals could be achievable. This study provides a preliminary assessment of data availability, targeting future continuous land cover monitoring in China; and the code is released to the public to facilitate similar data inventory in other regions of the world.

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