Geostatistical Resampling of LiDAR-Derived DEM in Wide Resolution Range for Modelling in SWAT: A Case Study of Zgłowiączka River (Poland)

Śliwiński, D.; Konieczna, Anita; Roman, Kamil

doi:10.3390/rs14051281

Cited by 16 publications

(10 citation statements)

References 39 publications

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“…This demonstrates that the resampled data are a reliable representation of the original data and that there is a strong relationship between the two sets of data. Further more, a value of relative bias almost close to 0 implies that the resampled data are unbi ased and offer an accurate representation of the original data [108]. The 2014 FHI maps generated from the gauges and the IMERG-F product resampled using the nearest neighbor, bilinear, and cubic convolution methods are shown in Figure 5.…”

Section: Statistical Analysis On the Original And Resampled Data Of I...mentioning

confidence: 98%

Section: Statistical Analysis On the Original And Resampled Data Of I...mentioning

confidence: 99%

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Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

Syakira,

Tan,

Zulkafli

et al. 2023

Water

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The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical flood hazard mapping in the Kelantan River Basin (KRB), Malaysia, using the GIS-based analytic hierarchy process (AHP) method. In addition to the precipitation factor, another eleven factors that contribute to flooding in the KRB were included in the AHP method. The findings demonstrated that the spatial pattern and percentage area affected by floods simulated under the IMERG-Early (IMERG-E), IMERG-Late (IMERG-L), and IMERG-Final (IMERG-F) products did not differ significantly. The receiver operating characteristics curve analysis showed that all three IMERG products performed well in generating flood hazard maps, with area under the curve values greater than 0.8. Almost all the recorded historical floods were placed in the moderate-to-very-high flood hazard areas, with only 1–2% found in the low flood hazard areas. The middle and lower parts of the KRB were identified as regions of “very high” and “high” hazard levels that require particular attention from local stakeholders.

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Section: Statistical Analysis On the Original And Resampled Data Of I...mentioning

confidence: 98%

Section: Statistical Analysis On the Original And Resampled Data Of I...mentioning

confidence: 99%

Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

Syakira,

Tan,

Zulkafli

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…Different loss functions have been shown to provide different results in, for example, road segmentation with CNN from remotely sensed, and the function should be selected based on the use case and dataset [38]. Śliwi ński et al [39] found that when delineating watercourses from DEMs of different resolutions, 1.5 m and 1 m resolution DEM resulted in only a small decrease in lengths of the watercourse lines, which indicates that the resolution of 0.5 m of the DEM used in our study is sufficient for capturing most watercourses.…”

Section: Causes Of False Predictionsmentioning

confidence: 99%

Mapping Small Watercourses from DEMs with Deep Learning—Exploring the Causes of False Predictions

et al. 2023

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Vector datasets of small watercourses, such as rivulets, streams, and ditches, are important for many visualization and analysis use cases. Mapping small watercourses with traditional methods is laborious and costly. Convolutional neural networks (CNNs) are state-of-the-art computer vision methods that have been shown to be effective for extracting geospatial features, including small watercourses, from LiDAR point clouds, digital elevation models (DEMs), and aerial images. However, the cause of the false predictions by machine-learning models is often not thoroughly explored, and thus the impact of the results on the process of producing accurate datasets is not well understood. We digitized a highly accurate and complete dataset of small watercourses from a study area in Finland. We then developed a process based on a CNN that can be used to extract small watercourses from DEMs. We tested and validated the performance of the network with different input data layers, and their combinations to determine the best-performing layer. We analyzed the false predictions to gain an understanding of their nature. We also trained models where watercourses with high levels of uncertainty were removed from the training sets and compared the results to training models with all watercourses in the training set. The results show that the DEM was the best-performing layer and that combinations of layers provided worse results. Major causes of false predictions were shown to be boundary errors with an offset between the prediction and labeled data, as well as errors of omission by watercourses with high levels of uncertainty. Removing features with the highest level of uncertainty from the labeled dataset increased the overall f1-score but reduced the recall of the remaining features. Additional research is required to determine if the results remain similar to other CNN methods.

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“…Furthermore, we determined that PM is significantly correlated with metrics that are indicators of landscape structure. For this reason, looking at the relationship between PM and metric values obtained from high-resolution satellite imagery and/or detailed DEM data in future research is expected to enable more detailed interpretations [110].…”

Section: Pm 10 Concentration and Habitat Relationsmentioning

confidence: 99%

Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

et al. 2022

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With the widespread population growth in cities, anthropogenic influences inevitably lead to natural disturbances. The metropolitan area of Istanbul, with its rapid urbanization rate, has faced intense pressure regarding the sustainability of urban habitats. In this context, landscapes comprising patches affected by various disturbances and undergoing temporal changes must be analyzed, in order to assess city-related disturbances. In this study, the main objective was to understand how urbanization changed the function of the spatial distribution of the urban mosaic and, more specifically, its relationship with the size, shape, and connection among land-use classes. For this purpose, we took Besiktas, a district of Istanbul, as the study area. We evaluated the landscape pattern of the urban environment in two stages. First, we used medium-resolution satellite imagery to reveal the general interactions in the urbanization process. Landscape- and class-level landscape metrics were selected to quantify the landscape connectivity, and the distances between classes (green areas and artificial surfaces), patterns, and processes, using five satellite images representing a time span of 51 years (1963, 1984, 1997, 2005, and 2014). The general landscape structure was examined by looking at the temporal–spatial processes of artificial surface and green areas obtained from these medium-resolution satellite images. The trends in selected landscape-level metrics were specified and discussed through the use of a moving window analysis. We then used Pleiades high-resolution satellite imagery (2015) to analyze the landscape structure in more detail. This high-resolution base image allows us to recognize the possibility of classifying basic cultural landscape classes. The findings regarding the spatial arrangement of each class in the areas allocated to 14 cultural landscape classes were interpreted by associating them with the landscape functions. Finally, particulate matter (PM10) concentration data were collected and evaluated as an ecological indicator, in order to reveal the relationships between landscape structure and landscape function. In short, we first evaluated the whole landscape structure using medium-resolution data, followed by the classification of cultural landscapes using high-resolution satellite imagery, providing a time-effective—and, therefore, essential—auxiliary method for landscape evaluation. This two-stage evaluation method enables inferences to be made that can shed light on the landscape functions in an urban environment based on the landscape structure.

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Geostatistical Resampling of LiDAR-Derived DEM in Wide Resolution Range for Modelling in SWAT: A Case Study of Zgłowiączka River (Poland)

Cited by 16 publications

References 39 publications

Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

Mapping Small Watercourses from DEMs with Deep Learning—Exploring the Causes of False Predictions

Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

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