scholarly journals A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

2021 ◽  
Vol 13 (3) ◽  
pp. 491
Author(s):  
Niangang Jiao ◽  
Feng Wang ◽  
Hongjian You
Keyword(s):  
Remote Sensing Data ◽  
Observation Data ◽  
Multiple Sources ◽  
Accuracy Improvement ◽  
Geometric Calibration ◽  
Proposed Model ◽  
Calibration Methods ◽  
Earth Observation Data ◽  
Sar Imagery ◽  
Geolocation Accuracy

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.

scholarly journals Predicting Flood Property Insurance Claims over CONUS, Fusing Big Earth Observation Data

2021 ◽  
Keyword(s):  
Observation Data ◽  
Flood Hazards ◽  
Flood Events ◽  
Insurance Claims ◽  
Multiple Sources ◽  
Property Insurance ◽  
Damage Level ◽  
Basin Morphometry ◽  
Earth Observation Data ◽  
Water Level Measurements

Abstract Each year throughout the contiguous United States (CONUS), flood hazards cause damage amounting to billions of dollars in homeowner insurance claims. As climate change threatens to raise the frequency and severity of flooding in vulnerable areas, the ability to predict the number of property insurance claims resulting from flood events becomes increasingly important to flood resilience. Based on random forest, we develop a flood property Insurance Claims model (iClaim) by fusing records from the National Flood Insurance Program (NFIP), including building locations, topography, basin morphometry, and land cover, with data from multiple sources of hydrometeorological variables, including flood extent, precipitation, and operational river-stage and oceanic water-level measurements. The model utilizes two steps—damage level classification and claim number regression—and subsampling strategies designed accordingly to reduce overfitting and underfitting caused by the flood claim samples, which are unevenly distributed and widely ranged. We evaluate the model using 446,446 grid samples identified from 589 flood events occurring from 2016 to 2019 over CONUS, overlapping 258,159 claims out of a total of 287,439 NFIP records of the same period. Our rigorous validation yields acceptable performance at the grid/event, county/event, and event accumulative level, with R2 over 0.5, 0.9, and 0.95, respectively. We conclude that the iClaim model can be used in many application scenarios, including assessing flood impact and improving flood resilience.

Geolocation Accuracy Improvement of Multiobserved GF-3 Spaceborne SAR Imagery

2020 ◽  
Vol 17 (10) ◽  
pp. 1747-1751
Author(s):  
Niangang Jiao ◽  
Feng Wang ◽  
Hongjian You ◽  
Xiaolan Qiu
Keyword(s):  
Accuracy Improvement ◽  
Sar Imagery ◽  
Geolocation Accuracy

scholarly journals Misperceptions of Predominant Slum Locations? Spatial Analysis of Slum Locations in Terms of Topography Based on Earth Observation Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2474 ◽  
Author(s):  
Inken Müller ◽  
Hannes Taubenböck ◽  
Monika Kuffer ◽  
Michael Wurm
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Observation Data ◽  
Population Densities ◽  
Poverty And Inequality ◽  
Test Of Homogeneity ◽  
Chi Squared ◽  
Earth Observation Data ◽  
Do So

Slums are a physical expression of poverty and inequality in cities. According to the UN definition, this inequality is, e.g., reflected in the fact that slums are much more often located in hazardous zones. However, this has not yet been empirically investigated. In this study, we derive proxies from multi-sensoral high resolution remote sensing data to investigate both the location of slums and the location of slopes. We do so for seven cities on three continents. Using a chi-squared test of homogeneity, we compare the locations of formal areas with that of slums. Contrary to the perception indirectly stated in the literature, we find that slums are in none of the sample cities predominantly located in these exposed areas. In five out of seven cities, the spatial share of slums on hills steeper than 10° is even less than 5% of all slums. However, we also find a higher likelihood of slums occurring in these exposed areas than of formal settlements. In six out of seven sample cities, the probability that a slum is located in steep areas is higher than for a formal settlement. As slums mostly feature higher population densities, these findings reveal a clear tendency that slum residents are more likely to settle in exposed areas.

scholarly journals Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks

2019 ◽  
Vol 11 (6) ◽  
pp. 711 ◽  
Author(s):  
Alireza Taravat ◽  
Matthias Wagner ◽  
Natascha Oppelt
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Animal Feed ◽  
Remote Sensing Data ◽  
Biomass Estimation ◽  
Feed Production ◽  
Time Series Dataset ◽  
Proposed Model ◽  
Sar Imagery ◽  
Validation Set

Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches for automatic grassland cutting status detection. Due to the occurrence of multiple cuttings in a year, it is crucial to effectively monitor grassland cutting events in order to achieve accurate biomass estimations of a whole season. In this study, we examined the capabilities of multilayer perceptron neural networks for automatic grassland cutting status detection using SAR imagery. The proposed model inputs are a time series dataset of VV and VH Sentinel-1 C-band SAR and second-order texture metrics (homogeneity, entropy, contrast and dissimilarity). The proposed approach has been successfully tested on a dataset collected from several fields in Germany in 2016, with an overall accuracy of 85.71% for the validation set.

Thematic services for analysis of forests using earth observation data

2020 ◽  
Vol 5 ◽  
pp. 175-181
Author(s):  
V.A. Zelentsov ◽  
◽  
M.R. Ponomarenko ◽  
I.Y. Pimanov
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Forest Vegetation ◽  
Observation Data ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
Earth Observation Data

The paper presents an overview of existing thematic services based on Earth remote sensing data from space and aimed at monitoring and analysis of forest vegetation and dynamics of its changes.

scholarly journals Digital Mapping of Soil Drainage Classes Using Multitemporal RADARSAT-1 and ASTER Images and Soil Survey Data

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Mohamed Abou Niang ◽  
Michel Nolin ◽  
Monique Bernier ◽  
Isabelle Perron
Keyword(s):  
Land Use ◽  
Remote Sensing Data ◽  
Soil Survey ◽  
Observation Data ◽  
Optical Remote Sensing ◽  
Land Use Type ◽  
Soil Drainage ◽  
Digital Mapping ◽  
Optimal Subset ◽  
Earth Observation Data

Discriminant analysis classification (DAC) and decision tree classifiers (DTC) were used for digital mapping of soil drainage in the Bras-d’Henri watershed (QC, Canada) using earth observation data (RADARSAT-1 and ASTER) and soil survey dataset. Firstly, a forward stepwise selection was applied to each land use type identified by ASTER image in order to derive an optimal subset of soil drainage class predictors. The classification models were then applied to these subsets for each land use and merged to obtain a digital soil drainage map for the whole watershed. The DTC method provided better classification accuracies (29 to 92%) than the DAC method (33 to 79%) according to the land use type. A similarity measure (S) was used to compare the best digital soil drainage map (DTC) to the conventional soil drainage map. Medium to high similarities (0.6≤S<0.9) were observed for 83% (187 km2) of the study area while 3% of the study area showed very good agreement (S≥0.9). Few soil polygons showed very weak similarities (S<0.3). This study demonstrates the efficiency of combining radar and optical remote sensing data with a representative soil dataset for producing digital maps of soil drainage.

scholarly journals Machine Learning Improvement of Streamflow Simulation by Utilizing Remote Sensing Data and Potential Application in Guiding Reservoir Operation

2021 ◽  
Vol 13 (7) ◽  
pp. 3645
Author(s):  
Shaokun He ◽  
Lei Gu ◽  
Jing Tian ◽  
Lele Deng ◽  
Jiabo Yin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Reservoir Operation ◽  
Potential Application ◽  
Short Term Memory ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Sensing Data ◽  
Earth Observation Data ◽  
Streamflow Simulation

Hydro-meteorological datasets are key components for understanding physical hydrological processes, but the scarcity of observational data hinders their potential application in poorly gauged regions. Satellite-retrieved and atmospheric reanalysis products exhibit considerable advantages in filling the spatial gaps in in-situ gauging networks and are thus forced to drive the physically lumped hydrological models for long-term streamflow simulation in data-sparse regions. As machine learning (ML)-based techniques can capture the relationship between different elements, they may have potential in further exploring meteorological predictors and hydrological responses. To examine the application prospects of a physically constrained ML algorithm using earth observation data, we used a short-series hydrological observation of the Hanjiang River basin in China as a case study. In this study, the prevalent modèle du Génie Rural à 9 paramètres Journalier (GR4J-9) hydrological model was used to initially simulate streamflow, and then, the simulated series and remote sensing data were used to train the long short-term memory (LSTM) method. The results demonstrated that the advanced GR4J9–LSTM model chain effectively improves the performance of the streamflow simulation by using more remote sensing data related to the hydrological response variables. Additionally, we derived a reservoir operation model by feeding the LSTM-based simulation outputs, which further revealed the potential application of our proposed technique.

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 12-14
Author(s):  
Eicher, A
Keyword(s):  
Earth Observation ◽  
Observation Data ◽  
Business World ◽  
The Earth ◽  
Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

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