Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

Debris \ufb02ows are among the most hazardous phenomena in mountain areas. To cope with debris \ufb02ow hazard, it is common to delineate the risk-prone areas through routing models. The most important input to debris \ufb02ow routing models are the topographic data, usually in the form of Digital Elevation Models (DEMs). The quality of DEMs depends on the accuracy, density, and spatial distribution of the sampled points; on the characteristics of the surface; and on the applied gridding methodology. Therefore, the choice of the interpolation method affects the realistic representation of the channel and fan morphology, and thus potentially the debris \ufb02ow routing modeling outcomes. In this paper, we initially investigate the performance of common interpolation methods (i.e., linear triangulation, natural neighbor, nearest neighbor, Inverse Distance to a Power, ANUDEM, Radial Basis Functions, and ordinary kriging) in building DEMs with the complex topography of a debris \ufb02ow channel located in the Venetian Dolomites (North-eastern Italian Alps), by using small footprint full- waveform Light Detection And Ranging (LiDAR) data. The investigation is carried out through a combination of statistical analysis of vertical accuracy, algorithm robustness, and spatial clustering of vertical errors, and multi-criteria shape reliability assessment. After that, we examine the in\ufb02uence of the tested interpolation algorithms on the performance of a Geographic Information System (GIS)-based cell model for simulating stony debris \ufb02ows routing. In detail, we investigate both the correlation between the DEMs heights uncertainty resulting from the gridding procedure and that on the corresponding simulated erosion/deposition depths, both the effect of interpolation algorithms on simulated areas, erosion and deposition volumes, solid-liquid discharges, and channel morphology after the event. The comparison among the tested interpolation methods highlights that the ANUDEM and ordinary kriging algorithms are not suitable for building DEMs with complex topography. Conversely, the linear triangulation, the natural neighbor algorithm, and the thin-plate spline plus tension and completely regularized spline functions ensure the best trade-off among accuracy and shape reliability. Anyway, the evaluation of the effects of gridding techniques on debris \ufb02ow routing modeling reveals that the choice of the interpolation algorithm does not signi\ufb01cantly affect the model outcomes

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks

Minnesota River Basin Flood Mapping and Impact Assessment

South Central Minnesota has experienced frequent flooding in the past because of the relatively flat topography, low elevation, and large amounts of snow melt in the spring. When these events happen, there is a large economic impact and potential health hazards to residents of the area. Having up-to-date flood zone maps can help residents be better prepared for emergency situations. Using Geographic Information Science (GIS), flood zone maps can be updated frequently in a more time efficient and inexpensive manner. Using a 30-meter digital elevation model (DEM) Compound Topographic Index (CTI) and Stream Power Index (SPI) layers were calculated for the all of South Central Minnesota for this study. These two indices combined were used to identify areas that would be more prone to flooding. 30-meter, 10-meter, and 3-meter resolution DEMs were used to create CTI and SPI layers for analysis in the Seven Mile Creek Watershed. They were used to not only find areas prone to flooding but also to find how the resolution of the DEM affects the outcomes of the indices. Also, flood levels were created for the city of Mankato, MN using a DEM cell selection process. These flood levels were used to identify land and buildings that could be inundated in the event of flood water breaching the levee. The costs of such damages were also calculated using parcel shapefiles and lidar-derived building footprints. The role of the DEM was also examined during the creation of these flood levels. Using 30-meter, 10-meter, and 1-meter resolution DEMs, the differences in the total extent of each flood stage and the predicted financial impacts were examined

Using Lidar Data to Analyse Sinkhole Characteristics Relevant for Understory Vegetation under Forest Cover\u2014Case Study of a High Karst Area in the Dinaric Mountains

In this article, we investigate the potential for detection and characterization of sinkholes under dense forest cover by using airborne laser scanning data. Laser pulse returns from the ground provide important data for the estimation of digital elevation model (DEM), which can be used for further processing. The main objectives of this study were to map and determine the geomorphometric characteristics of a large number of sinkholes and to investigate the correlations between geomorphology and vegetation in areas with such characteristics. The selected study area has very low anthropogenic influences and is particularly suitable for studying undisturbed karst sinkholes. The information extracted from this study regarding the shapes and depths of sinkholes show significant directionality for both orientation of sinkholes and their distribution over the area. Furthermore, significant differences in vegetation diversity and composition occur inside and outside the sinkholes, which indicates their presence has important ecological impacts

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

Scaling gridded river networks for macroscale hydrology: Development, analysis, and control of error

A simple and robust river network scaling algorithm (NSA) is presented to rescale fine‐resolution networks to any coarser resolution. The algorithm was tested over the Danube River basin and the European continent. Coarse‐resolution networks, at 2.5, 5, 10, and 30 min resolutions, were derived from higher‐resolution gridded networks using NSA and geomorphometric attributes, such as river order, shape index, and width function. These parameters were calculated and compared at each resolution. Simple scaling relationships were found to predict decreasing river lengths with coarser‐resolution data. This relationship can be used to correct river length as a function of grid resolution. The length‐corrected width functions of the major river basins in Europe were compared at different resolutions to assess river network performance. The discretization error in representing basin area and river lengths at coarser resolutions were analyzed, and simple relationships were found to calculate the minimum number of grid cells needed to maintain the catchment area and length within a desired level of accuracy. This relationship among geomorphological characteristics, such as shape index and width function (derived from gridded networks at different resolutions), suggests that a minimum of 200–300 grid cells is necessary to maintain the geomorphological characteristics of the river networks with sufficient accuracy

Defining optimal DEM resolutions and point densities for modelling hydrologically sensitive areas in agricultural catchments dominated by microtopography

AbstractDefining critical source areas (CSAs) of diffuse pollution in agricultural catchments depends upon the accurate delineation of hydrologically sensitive areas (HSAs) at highest risk of generating surface runoff pathways. In topographically complex landscapes, this delineation is constrained by digital elevation model (DEM) resolution and the influence of microtopographic features. To address this, optimal DEM resolutions and point densities for spatially modelling HSAs were investigated, for onward use in delineating CSAs. The surface runoff framework was modelled using the Topographic Wetness Index (TWI) and maps were derived from 0.25m LiDAR DEMs (40 bare-earth points m−2), resampled 1m and 2m LiDAR DEMs, and a radar generated 5m DEM. Furthermore, the resampled 1m and 2m LiDAR DEMs were regenerated with reduced bare-earth point densities (5, 2, 1, 0.5, 0.25 and 0.125 points m−2) to analyse effects on elevation accuracy and important microtopographic features. Results were compared to surface runoff field observations in two 10km2 agricultural catchments for evaluation. Analysis showed that the accuracy of modelled HSAs using different thresholds (5%, 10% and 15% of the catchment area with the highest TWI values) was much higher using LiDAR data compared to the 5m DEM (70–100% and 10–84%, respectively). This was attributed to the DEM capturing microtopographic features such as hedgerow banks, roads, tramlines and open agricultural drains, which acted as topographic barriers or channels that diverted runoff away from the hillslope scale flow direction. Furthermore, the identification of ‘breakthrough’ and ‘delivery’ points along runoff pathways where runoff and mobilised pollutants could be potentially transported between fields or delivered to the drainage channel network was much higher using LiDAR data compared to the 5m DEM (75–100% and 0–100%, respectively). Optimal DEM resolutions of 1–2m were identified for modelling HSAs, which balanced the need for microtopographic detail as well as surface generalisations required to model the natural hillslope scale movement of flow. Little loss of vertical accuracy was observed in 1–2m LiDAR DEMs with reduced bare-earth point densities of 2–5 points m−2, even at hedgerows. Further improvements in HSA models could be achieved if soil hydrological properties and the effects of flow sinks (filtered out in TWI models) on hydrological connectivity are also considered