With its unrivaled and global land monitoring capability, the Sentinel-1 mission has been established as a prime provider in SAR-based flood mapping. Compared to suitable single-image flood ...algorithms, change-detection methods offer better robustness, retrieving flood extent from a classification of observed changes. This requires data-based parametrization. Moreover, in the scope of global and automatic flood services, the employed algorithms should not rely on locally optimized parameters, which cannot be automatically estimated and have spatially varying quality, impacting much on the mapping accuracy. Within the recently launched Global Flood Monitoring (GFM) service, we implemented a Bayes-Inference (BI)-based algorithm designed to meet these ends. However, whether other change detection algorithms perform similarly or better is unknown. This study examines four Sentinel-1 change detection models: The Normalized Difference Scattering Index (NDSI), Shannon’s entropy of NDSI (SNDSI), Standardized Residuals (SR), and Bayes Inference over Luzon in the Philippines, which was flood-hit by a typhoon in November 2020. After parametrization assessment against an expert-created Sentinel-1 flood map, the four models are inter-compared against an independent Sentinel-2 classification. The obtained findings indicate that the Bayes change detection profits from its scalable classification rules and shows the least sensitivity to parametrization choices while also performing best in terms of mapping accuracy. For all change detection models, a backscatter seasonality model for the no-flood reference delivered best results.
Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. ...Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel’s full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS).
Sentinel-1-based flood mapping works well but with well-known issues over rugged terrain. Applying exclusion masks to improve the results is common practice in unsupervised and global applications. ...One such mask is the height above the nearest drainage (HAND), which uses terrain information to reduce flood lookalikes in SAR images. The TU Wien flood mapping algorithm is one operational workflow using this mask. Being a Bayesian method, this algorithm can integrate auxiliary information as prior probabilities to improve classifications. This study improves the TU Wien flood mapping algorithm by introducing a HAND prior function instead of using it as a mask. We estimate the optimal function parameters and observe the performance in flooded and non-flooded scenarios in six study sites. We compare the flood maps generated with HAND and (baseline) non-informed priors with reference CEMS rapid mapping flood extents. Our results show enhanced performance by decreasing false negatives at the cost of slightly increasing false positives. In utilizing a single parametrization, the improved algorithm shows potential for global implementation.
In August and September 2022, Pakistan was hit by a severe flood, and millions of people were impacted. The Sentinel-1-based flood mapping algorithm developed by Technische Universität Wien (TU Wien) ...for the Copernicus Emergency Management Service (CEMS) global flood monitoring (GFM) component was used to document the propagation of the flood from 10 August to 23 September 2022. The results were evaluated using the flood maps from the CEMS rapid mapping component. Overall, the algorithm performs reasonably well with a critical success index of up to 80 %, while the detected differences can be primarily attributed to the time difference of the algorithm's results and the corresponding reference. Over the 6-week time span, an area of 30 492 km2 was observed to be flooded at least once, and the maximum extent was found to be present on 30 August. The study demonstrates the ability of the TU Wien flood mapping algorithm to fully automatically produce large-scale results and how key data of an event can be derived from these results.
UNMANNED AERIAL VEHICLE (UAV) SURVEY-ASSISTED 3D MANGROVE TREE MODELING Domingo, G. A.; Claridades, A. R. C.; Tupas, M. E. A.
International archives of the photogrammetry, remote sensing and spatial information sciences.,
01/2018, Letnik:
XLII-4/W9
Journal Article, Conference Proceeding
Recenzirano
Odprti dostop
3D visualization is a tool that supports geospatial analysis through the application of scientific information. It enhances the quality of standard photography and can be used in many applications. ...Through this study, a 3D mangrove tree model is generated, as assisted by a tree crown derived from UAV images. The researchers explored different platforms namely: MeshLab, SketchUp (with 3D Tree Maker extension), and Clara.io, to come up with a more realistic three-dimensional (3D) model of a mangrove tree. From an Unmanned Aerial Vehicle (UAV) derived Digital Surface Model (DSM), an isolated tree crown was selected which was then used as an assisting tool in creating the final 3D mangrove tree model. A default tree object was modified according to the characteristics as described by the DSM. Additional branches and leaves were added to the existing tree object, and its shape was modified to conform to the tree crown. The resulting model may be used to more accurately depict objects in the area to be visualized, however an automation procedure is recommended for an easier and more effective generation of multiple tree models expected in an area.
Floods continue to affect millions of the global population annually. SAR-based methods are one of the most reliable tools for mapping floods expeditiously. Among these are Bayesian flood mapping ...methods that rely on conditional and prior probability formulations to make labeling decisions. Recent work demonstrated a globally applicable Height Above the Nearest Drainage (HAND)-based prior probability function to improve Bayesian flood mapping. However, limitations were identified due to the input DEM. In this contribution, we assess the performance of three (near-)globally available HAND datasets as input to this function. Compared to the HAND dataset used in the previous study, the MERIT and Deltares HAND datasets were derived from improved SRTM DEMs and finer detailed drainage networks. We hypothesize that these finer-resolution HAND datasets can potentially improve probabilistic flood mapping further. Thus, we compare the flood mapping performance using the baseline SRTM-derived, MERIT, and Deltares HAND data as priors on (the original) six study sites for both flooded and non-flooded scenarios. Our results show similar performance in the flooded scenarios using the MERIT and Deltares HAND datasets. The MERIT dataset shows slightly better performance among the three. However, an increase in False Positive Rates was apparent in non-flooded scenarios attributed to smaller drainages in the new datasets tested. These results suggest caution in applying the HAND prior method with HAND datasets derived from drainage networks with small upstream contributing areas.
The Phl-Microsat program in the Philippines was initiated for capacity building and with the end goal of having a source of remotely-sensed data for local planning, disaster risk mitigation, and ...resource management for the country. To increase its benefits, an established process to effectively utilize these images such as image classification is needed. This study aims to determine the most appropriate supervised algorithm for image classification among a set of classifiers that will yield the best results for DIWATA-I Spaceborne Multispectral Images (SMI). SMI is an optical payload, with 80m resolution, and a multiwavelength selection at 10nm width at 1nm steps. Three study sites within the Philippines were selected to test the classifiers - Camarines Sur, Ilocos Norte, and Oriental Mindoro. Spectral reflectance values were then derived from atmospheric calibrations of the images. These images were then classified using six supervised classifiers and were post-processed using Majority Analysis. Accuracy is assessed by comparing the overall accuracy, kappa coefficient, producer's accuracy and user's accuracy extracted from the confusion matrix. From the results, Support Vector Machine and Maximum Likelihood classifiers produced the most desirable and most consistent results.
Diwata-1 is the Philippines' first Earth observation microsatellite launched to space through the International Space Station. It has an altitude of 400 km and a velocity of 7 km/s. As an observation ...satellite, it is required to have high target pointing accuracy. However, being a low Earth orbit microsatellite, it experiences stronger external disturbances compared to larger and higher altitude satellites. Such disturbances are from the sun, and the Earth's albedo and magnetic field. How these disturbances impact the pointing accuracy of the satellite must be determined to improve the setting of satellite missions, where such disturbances would be minimal. In addition, the satellite's orbit decays quickly due to these disturbances, which means accurate satellite prediction is also critical in setting correct parameters for its targeting operations. In this paper, a comparison of different satellite prediction models to the Diwata-1 telemetry in varying TLE ages were done to determine which model fits best to the satellite's orbit and what corrections are needed to minimize the difference between the actual satellite position from the predicted version, and in effect improve the satellite's pointing accuracy. In addition, cases comparing the target area set during the creation, and the upload of the satellite command with the actual location captured by the satellite, were linked to disturbances from the sun and the earth to determine if these disturbances affect the target pointing of the satellite.
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