Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

Climate change-induced aridity is affecting agriculture in Northeast Italy

CONTEXT: The Mediterranean basin and specifically Northeast Italy are recognised as climate change hotspots. The latter is a key socio-economic area in Europe among the most agriculturally productive. However, increasingly frequent drought periods (typical of drier climates) are threatening agriculture. An extreme event occurred in the summer of 2022. It dramatically affected northern Italy, through high temperatures, water shortages and indirect processes (such as saltwater intrusion in the Po River Delta). OBJECTIVE: The objective is to map and quantify the agricultural areas in Northeast Italy at risk of climate zone shift due to human-induced climate change, providing a comprehensive overview of the main threatened agricultural systems and supporting the use of projections through historical data analysis. METHODS: We compared the distribution of current (1980 > 2016) and future (2071 > 2100; RCP8.5 scenario) climate zones for 8 main agricultural systems in 14 key provinces in Northeast Italy. Further analyses were performed on historical data to support future climate projections and to analyse agricultural drought during extreme events: (1) a multi-temporal Aridity Index (AI) to investigate aridification dynamics; (2) a focus on the 2022 event (drought and temperature extremes, a situation that is likely to occur more often in the future), combining a Vegetation Health Index (VHI) with a zonal investigation of high Land Surface Temperature (LST); (3) a climate focus for the Po River Delta cultural landscape. RESULTS AND CONCLUSIONS: The results show that the climate in Northeast Italy is evolving towards drier conditions, posing a challenge to agriculture. The Adriatic coast could become an Arid zone, a finding in line with historical observations. Rice fields will be most at risk (76% of their surface could become Arid in the future), as well as the irrigated lands that are essential for food security (around 20% expected in the Arid zone). Worthy is what is foreseen for crops on slopes (often not irrigated), which may experience drier summers (60% of the surface). SIGNIFICANCE: We identified the areas at risk of climate change at the farm scale in Northeast Italy, mapping where the threatened fields are located, what their extent is, and which agricultural systems are currently implemented. Such information would facilitate early action, guiding large-scale planning towards more resilient agriculture. Findings could promote sustainable water management plans, open the debate on which crops are worth growing based on future climate, and inspire more localised studies in the design of mitigation measures

A new method for determination of most likely landslide initiation points and the evaluation of digital terrain model scale in terrain stability mapping

This paper introduces a new approach for determining the most likely initiation points for landslides from potential instability mapped using a terrain stability model. This approach identifies the location with critical stability index from a terrain stability model on each downslope path from ridge to valley. Any measure of terrain stability may be used with this approach, which here is illustrated using results from SINMAP, and from simply taking slope as an index of potential instability. The relative density of most likely landslide initiation points within and outside mapped landslide scars provides a way to evaluate the effectiveness of a terrain stability measure, even when mapped landslide scars include run out zones, rather than just initiation locations. This relative density was used to evaluate the utility of high resolution terrain data derived from airborne laser altimetry (LIDAR) for a small basin located in the Northeastern Region of Italy. Digital Terrain Models were derived from the LIDAR data for a range of grid cell sizes (from 2 to 50 m). We found appreciable differences between the density of most likely landslide initiation points within and outside mapped landslides with ratios as large as three or more with the highest ratios for a digital terrain model grid cell size of 10 m. This leads to two conclusions: (1) The relative density from a most likely landslide initiation point approach is useful for quantifying the effectiveness of a terrain stability map when mapped landslides do not or can not differentiate between initiation, runout, and depositional areas; and (2) in this study area, where landslides occurred in complexes that were sometimes more than 100 m wide, a digital terrain model scale of 10 m is optimal. Digital terrain model scales larger than 10 m result in loss of resolution that degrades the results, while for digital terrain model scales smaller than 10 m the physical processes responsible for triggering landslides are obscured by smaller scale terrain variability

Mapping potential surface ponding in agriculture using UAV-SfM

Among the environmental problems that could affect agriculture, one of the most critical is ponding. This may be defined as water storage on the surface in concavities and depressions due to soil saturation. Stagnant water can seriously affect crops and the management of agricultural landscapes. It is mainly caused by prolonged rainfall events, soil type, or wrong mechanization practices, which cause soil compaction. To better understand this problem and thus provide adequate solutions to reduce the related risk, high-resolution topographic information could be strategically important because it offers an accurate representation of the surface morphology. In the last decades, new remote sensing techniques provide interesting opportunities to understand the processes on the Earth's surface based on geomorphic signatures. Among these, Uncrewed Aerial Vehicles (UAVs), combined with the structure-from-motion (SfM) photogrammetry technique, represent a solid, low-cost, rapid, and flexible solution for geomorphological analysis. This study aims to present a new approach to detect the potential areas exposed to water stagnation at the farm scale. The high-resolution digital elevation model (DEM) from UAV-SfM data is used to do this. The potential water depth was calculated in the DEM using the relative elevation attribute algorithm. The detection of more pronounced concavities and convexities allowed an estimation and mapping of the potential ponding conditions. The results were assessed by observations and field measurements and are promising, showing a Cohen's k(X) accuracy of 0.683 for the planimetric extent of the ponding phenomena and a Pearson's rxy coefficient of 0.971 for the estimation of pond water depth. The proposed workflow provides a useful indication to stakeholders for better agricultural management in lowland landscapes

Geomorphometric characterisation of natural and anthropogenic land covers

The scientific community has widely discussed the role of abiotic and biotic forces in reshaping the Earth\u2019s surface. Currently, the literature is debating whether humans are leaving a topographic signature on the landscape. Apart from the influence of humans on processes, does the resulting landscape bear an unmistakable signature of anthropogenic activities? This research analyses from a statistical point of view the morphological signature of anthropogenic and natural land covers in different topographic context, as a fundamental challenge in the emerging debate of human-environment relationships and the modelling of global environmental change. It aims to explore how intrinsically small-scale processes, related to land use, can influence the form of entire landscapes and to determine whether these processes create a distinctive topography. The work focusses on four study areas in floodplains, plain to hilly, hills and mountains, for which LiDAR-derived Digital Terrain Models (DTMs) are available. Surface morphology is described with different geomorphometric parameters (slope, mean curvature and surface peak curvature) and their frequency distribution. The results show that the distribution of geomorphometric indices can reveal anthropogenic land covers and landscapes. In most cases, different land covers show statistically significant differences (p < 0.05) in their morphology. Finally, this study demonstrates the possibility to use a geomorphic analysis to quantify anthropogenic impact based on land covers in different landscape contexts. This provides useful insight into understanding the impact of human activities on the present morphology and offers a comprehensive understanding of coupling human-land interaction from a geomorphological point of view. [Figure not available: see fulltext.]

Modelling shallow landslide susceptibility by means of a subsurface flow path connectivity index and estimates of soil depth spatial distribution

Topographic index-based hydrological models have gained wide use to describe the hydrological control on the triggering of rainfall-induced shallow landslides at the catchment scale. A common assumption in these models is that a spatially continuous water table occurs simultaneously across the catchment. However, during a rainfall event isolated patches of subsurface saturation form above an impeding layer and their hydrological connectivity is a necessary condition for lateral flow initiation at a point on the hillslope. &lt;br&gt;&lt;br&gt; Here, a new hydrological model is presented, which allows us to account for the concept of hydrological connectivity while keeping the simplicity of the topographic index approach. A dynamic topographic index is used to describe the transient lateral flow that is established at a hillslope element when the rainfall amount exceeds a threshold value allowing for (a) development of a perched water table above an impeding layer, and (b) hydrological connectivity between the hillslope element and its own upslope contributing area. A spatially variable soil depth is the main control of hydrological connectivity in the model. The hydrological model is coupled with the infinite slope stability model and with a scaling model for the rainfall frequency–duration relationship to determine the return period of the critical rainfall needed to cause instability on three catchments located in the Italian Alps, where a survey of soil depth spatial distribution is available. The model is compared with a quasi-dynamic model in which the dynamic nature of the hydrological connectivity is neglected. The results show a better performance of the new model in predicting observed shallow landslides, implying that soil depth spatial variability and connectivity bear a significant control on shallow landsliding

CORRIGENDUM

n/

Geomorphometric assessment of the impacts of dam construction on river disconnectivity and flow regulation in the Yangtze basin

open4Rivers are under increasing pressure from anthropogenic impacts with incremental dam construction, experiencing global and regional alteration due to river disconnectivity, flow regulation, and sediment reduction. Assessing the cumulative impacts of dams on river disconnectivity in large river basins can help us better understand how humans disintegrate river systems and change the natural flow regimes. Using the Yangtze basin as the study area, this study employed three modified metrics (river connectivity index, RCI; basin disconnectivity index, BDI; and the degree of regulation for each river section, DOR) to evaluate the cumulative impacts on river disconnectivity over the past 50 years. The results indicated that the Yangtze had experienced strong alterations, despite varying degrees and spatial patterns. Among the major tributaries, the greatest impact (lowest RCI value) happened in the Wu tributary basin due to the construction of cascade dams on the main stem of the tributary, while the lowest impact (highest RCI value) happened in the Fu tributary basin, which still has no dams on its main stem. Collectively, rivers in the upper Yangtze reaches experienced more serious disturbances than their counterparts in the middle and lower reaches. The BDI results displayed that a substantial part of the Yangtze River, especially the Wu, Min, Jialing, and Yuan tributaries, only maintain connectivity among one to three representative river systems. No part of the Yangtze connects all the 12 representative river systems. This study also revealed that small dams can also exert significant impacts in flow regulation on regional river systems through their sheer number and density. The study results can help promote more environmentally sustainable river management policies in the Yangtze basin.openYang X.; Lu X.; Ran L.; Tarolli P.Yang, X.; Lu, X.; Ran, L.; Tarolli, P