Flood hazard mapping in an urban area using combined hydrologic-hydraulic models and geospatial technologies

Flooding is one of the most occurring natural hazards every year risking the lives and properties of the affected communities, especially in Philippine context. To visualize the extent and mitigate the impacts of flood hazard in Malingon River in Valencia City, Bukidnon, this paper presents the combination of Geographic Information System, high-resolution Digital Elevation Model, land cover, soil, observed hydro-meteorological data; and the combined Hydrologic Engineering Center-Hydrologic Modeling System and River Analysis System models. The hydrologic model determines the precipitation-runoff relationships of the watershed and the hydraulic model calculates the flood depth and flow pattern in the floodplain area. The overall performance of hydrologic model during calibration was “very good fit” based on the criterion of Nash-Sutcliffe Coefficient of Model Efficiency, Percentage Bias and Root Mean Square Error – Observations Standard Deviation Ratio with the values of 0.87, -8.62 and 0.46, respectively. On the other hand, the performance of hydraulic model during error computation was “intermediate fit” using F measure analysis with a value of 0.56, using confusion matrix with 80.5% accuracy and the Root Mean Square Error of 0.47 meters. Flood hazard maps in 2, 5, 10, 25, 50 and 100-year return periods were generated as well as the number of flooded buildings in each flood hazard level and in different return periods were determined. The output of the study served as an important basis for a more informed decision and science-based recommendations in formulating local and regional policies for more effective and cost-efficient strategies relative to flood hazards

Introducing GEOBIA to landscape imageability assessment: a multi-temporal case study of the nature reserve “Kózki”, Poland

Geographic object-based image analysis (GEOBIA) is a primary remote sensing tool utilized in land-cover mapping and change detection. Land-cover patches are the primary data source for landscape metrics and ecological indicator calculations; however, their application to visual landscape character (VLC) indicators was little investigated to date. To bridge the knowledge gap between GEOBIA and VLC, this paper puts forward the theoretical concept of using viewpoint as a landscape imageability indicator into the practice of a multi-temporal land-cover case study and explains how to interpret the indicator. The study extends the application of GEOBIA to visual landscape indicator calculations. In doing so, eight different remote sensing imageries are the object of GEOBIA, starting from a historical aerial photograph (1957) and CORONA declassified scene (1965) to contemporary (2018) UAV-delivered imagery. The multi-temporal GEOBIA-delivered land-cover patches are utilized to find the minimal isovist set of viewpoints and to calculate three imageability indicators: the number, density, and spacing of viewpoints. The calculated indicator values, viewpoint rank, and spatial arrangements allow us to describe the scale, direction, rate, and reasons for VLC changes over the analyzed 60 years of landscape evolution. We found that the case study nature reserve (“Kózki”, Poland) landscape imageability transformed from visually impressive openness to imageability due to the impression of several landscape rooms enclosed by forest walls. Our results provide proof that the number, rank, and spatial arrangement of viewpoints constitute landscape imageability measured with the proposed indicators. Discussing the method’s technical limitations, we believe that our findings contribute to a better understanding of land-cover change impact on visual landscape structure dynamics and further VLC indicator development

Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data

Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the—Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1⁻3%

Regional mapping of crops under agricultural nets using Sentinel-2

Geography and Environmental Studie