Interpretation Of Aerial Photographs Research Articles

Mapping subsurface tile lines on a research farm using aerial photography, paper maps, and expert knowledge

AbstractAccurate maps of subsurface tile drainage lines are needed for agronomic and environmental research studies and the maintenance of current tile drainage systems. In this study, tile lines at the Purdue University Agronomy Center for Research and Education near West Lafayette, Indiana were located using a combination of visual aerial photo interpretation, expert knowledge, and paper construction drawings. The mapping accuracy was assessed at 27 locations where tile lines were located physically using a tile probe. Tile lines were correctly predicted 89% of the time with an average spatial accuracy of ±1.23 m of the true tile locations. This approach was better than a previous tile line location map prepared using an automated remote sensing method, which had an average spatial accuracy of ±2.12 m.

Detecting trends in seagrass cover through aerial imagery interpretation: Historical dynamics of a Posidonia oceanica meadow subjected to anthropogenic disturbance

Over the last century, P. oceanica meadows have undergone significant regression along Mediterranean coasts due to anthropogenic disturbances. Using an integrated approach based on historical aerial photography interpretation in GIS environment and Bayesian modelling, we mapped the upper limits of a P. oceanica meadow and identified the most influential disturbance factors acting along the coast of Giglio Island, where several local human-mediated impacts have historically co-occurred from 1968 to 2013. Model selection based on the DIC criterion suggested that the presence of the impacts is suitable for describing the seagrass coverage variation. Similarities in P. oceanica cover within 13 investigated zones were highlighted when considering the most relevant impacts, such as harbour expansion, mining, and anchoring. The detected adverse effects indicate the need for implementing management actions focusing on the present and past sources of impact to reduce their effect on P. oceanica beds actively.

Mapping invasive alien plant species with very high spatial resolution and multi-date satellite imagery using object-based and machine learning techniques: A comparative study

ABSTRACT Invasive alien plant species (IAPS) have negative impacts on ecosystems, including the loss of biodiversity and the alteration of ecosystem functions. The strategy for mitigating these impacts requires knowledge of these species’ spatial distribution and level of infestation. In situ inventories or aerial photo interpretation can be used to collect these data but they are labor-intensive, time-consuming, and incomplete, especially when dealing with large or inaccessible areas. Remote sensing may be an effective method of mapping IAPS for a better management strategy. Several studies using remote sensing to map IAPS have focused on single species detection and were conducted in relatively homogeneous natural environments, while other common, more heterogeneous environments, such as urban areas, are often invaded by multiple IAPS, posing management challenges. The main objective of this study was to develop a mapping method for three major IAPS observed in the urban agglomeration of Quebec City (Canada), namely Japanese knotweed (Fallopia japonica); giant hogweed (Heracleum mantegazzianum); and phragmites (Phragmites australis). Mono-date and multi-date classification approaches were used with WorldView-3 and SPOT-7 satellite imagery, acquired in the summer of 2020 and in the autumn of 2019, respectively. To estimate presence probability, object-based image analysis (OBIA) and nonparametric classifiers such as Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were used. Overall, multi-date classification using WorldView-3 and SPOT-7 images produced the best results, with a Kappa coefficient of 0.85 and an overall accuracy of 91% using RF. For XGBoost, the Kappa coefficient was 0.81 with an overall accuracy of 89%, whereas the Kappa coefficient and overall accuracy were 0.80 and 88% for SVM classifier, respectively. Individual class performances based on F1-score revealed that Japanese knotweed had the highest maximum value (0.95), followed by giant hogweed (0.91), and phragmites (0.87). These results confirmed the potential of remote sensing to accurately map and simultaneously monitor the main IAPS in a heterogeneous urban environment using a multi-date approach. Although the approach is limited by image and reference data availability, it provides new tools to managers for IAPS invasion control.

Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae

AbstractBrown macroalgae habitats provide a range of ecosystem services, offering coastal protection, supporting and increasing biodiversity, and more recently have been recognized for their potential role as blue carbon habitats. Consequently, accurate areal estimates of these habitats are vitally important. Satellite imagery is often utilized for areal estimates of vegetated habitats due to their ability to capture vast areas but are disadvantaged by their lower resolution. In contrast, imagery collected by unmanned aerial vehicles (UAV) provide high‐resolution datasets but are unable to cover the necessary spatial scale required for calculating areal estimates at regional, national or international scales. This study successfully and accurately corrects the outputs from low‐resolution Sentinel 2 imagery to the standard of high‐resolution UAV imagery by using a novel brown algae index and a simple regression model to provide accurate spatial estimates. This model was applied to rocky shores across Wales, UK to predict a spatial extent of 6.2 km2 for three fucoid macroalgae species; Ascophyllum nodosum, Fucus vesiculosus and F. serratus. The regression model was validated in two ways. First, the data used to create the regression model was split to train and test (50:50) the model, with a root mean square error of ~8%–14%. Secondly, spatial estimates of fucoids in independent aerial imagery were assessed using aerial photography interpretation and compared to that of the regression model (7% difference). The carbon standing stock of fucoids calculated from the spatial estimate (6.2 km2) was found to be significantly lower than that of other marine carbon stores, indicating that fucoids do not significantly contribute as a blue carbon habitat based on biomass alone. This study produces a robust and accurate remote sensing technique to estimate spatial extent of macroalgae at large spatial scales, with possible worldwide applicability.

An Unsupervised Saliency-Guided Deep Convolutional Neural Network for Accurate Burn Mapping from Sentinel-1 SAR Data

SAR data provide sufficient information for burned area detection in any weather condition, making it superior to optical data. In this study, we assess the potential of Sentinel-1 SAR images for precise forest-burned area mapping using deep convolutional neural networks (DCNN). Accurate mapping with DCNN techniques requires high quantity and quality training data. However, labeled ground truth might not be available in many cases or requires professional expertise to generate them via visual interpretation of aerial photography or field visits. To overcome this problem, we proposed an unsupervised method that derives DCNN training data from fuzzy c-means (FCM) clusters with the highest and lowest probability of being burned. Furthermore, a saliency-guided (SG) approach was deployed to reduce false detections and SAR image speckles. This method defines salient regions with a high probability of being burned. These regions are not affected by noise and can improve the model performance. The developed approach based on the SG-FCM-DCNN model was investigated to map the burned area of Rossomanno-Grottascura-Bellia, Italy. This method significantly improved the burn detection ability of non-saliency-guided models. Moreover, the proposed model achieved superior accuracy of 87.67% (i.e., more than 2% improvement) compared to other saliency-guided techniques, including SVM and DNN.

Deep-Seated Gravitational Slope Deformations in Molise region (Italy): novel inventory and main geomorphological features

ABSTRACT This paper focuses on Deep-Seated Gravitational Slope Deformations (DSGSDs) identified in the Molise region (Italy), along the central-southern sector of the Apennines chain. These phenomena were detected and mapped through a combination of aerial-photo interpretation, morphological analyses, and field surveys, integrated through satellite SAR interferometry (InSAR). The obtained general map is organized in different sections: inventory map with identification of DSGSDs and main geological and orographic features, geostructural scheme, representative cross sections, list and characteristics of DSGSDs, geomorphometric maps, landslides map, and seismotectonic setting. This study allowed us to realize a complete inventory of DSGSDs in the Molise region and define the main characteristics of each phenomenon. Through this research, it was possible to establish a general correlation between the analyzed deformations and the geological and geomorphological setting of the region as well as with its geodynamic context.

Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia

Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbour (KNN) are the four algorithms used in this study for flood zoning in Jeddah City, Saudi Arabia. The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. For this purpose, 14 continuous factors and different categorical are identified to examine their effect on flooding in the study area. The dependency analysis (DA) was used to analyse the strength of the predictors. The study comprises two different input variables combination (C1 and C2) based on the features sensitivity selection. The under-the-receiver operating characteristic curve (AUC) and root mean square error (RMSE) were utilised to determine the accuracy of a good forecast. The validation findings showed that BE-C1 performed best in terms of precision, accuracy, AUC, and specificity, as well as the lowest error (RMSE). The performance skills of the overall models proved reliable with a range of AUC (89–97%). The study can also be beneficial in flash flood forecasts and warning activity developed by the Jeddah flood disaster in Saudi Arabia.

Outgassing of hydrogen from the Earth and geological signs for prospecting and production

Hydrogen is the most wide-spread element in the Universe and is an ideal source of energy. Natural hydrogen continuously releases from the Earth as a result of various chemical reactions. Free hydrogen is detected in gas and water in oil and gas reservoirs, in gas released from magmatic and metamorphic rocks, in mine gases, as well as in gas and water in sedimentary strata. The package of geological exploration aimed to find accumulations of gaseous hydrogen in the Earth should include:– zoning by the signs of hydrogen content, as well as by the scale and intensity of hydrogen outgassing from the Earth on the basis of geological–geochemical and geophysical analyses;– assessment of natural hydrogen outgassing scale in different structural and tectonic conditions from the analysis and monitoring of hydrogen outgassing, in-situ geochemical research in the hydrogen-promising areas, as well as from the studies of the scale and rate of natural hydrogen outflow;– 2D and 3D geological–geochemical–geophysical modeling of deep hydrogen reservoirs and flows in different structural and tectonic conditions. Thermodynamics-based physicochemical modeling of interaction between deep fluids and natural hydrogen flows, reservoir water, rocks and gases in the lithosphere;– development of criteria for deep hydrogen prospecting and diagnostics using a set of geophysical and geochemical methods and aerial photointerpretation data. Geological–geochemical forecast mapping of hydrogen content and its distribution in the land and water area of Russia;– prospecting and exploration drilling within a hydrogen-bearing object, with trial production of natural hydrogen;– monitoring of hydrogen outgassing from the Earth on land and sea, and during hydrogen exploration.

Development of multiclass alternating decision trees based models for landslide susceptibility mapping

Worldwide landslide occurrences imply the need for intelligence tools to identify the most susceptible areas toward adopting efficient mitigation strategies and reaction plans. In this study, we developed three spatially explicit ensemble predictive models for the prediction of landslide susceptibility in the Muong Nhe district of the Dien Bien Province, Vietnam. The Multiclass Alternating Decision Trees (MADT) method was used as the base classifier with the Dagging, MultiboostAB, and Random Subspace (RSS) as the ensemble learners. The location of past landslides was identified through field surveys and the interpretation of Google Earth images, aerial photographs, and historical archives of the Muong Nhe district. The landslide locations were liked to twelve landslide conditioning factors (slope, aspect, elevation, curvature, topographic wetness index (TWI), stream power index (SPI), geology, flow accumulation, normalized difference vegetation index (NDVI), and distance to rivers, roads, and faults) to investigate the spatial patterns of landslide susceptibility across the study area. The results showed that the RSS-MADT model achieved the highest performance in terms of predicting future landslides (AUC = 0.878), followed by DG-MADT (AUC = 0.857), MAB-MADT (AUC = 0.854), and the single MADT model (AUC = 0.828), respectively. Approximately 13% and 10% of the Muong Nhe district were identified as having moderate and severe (high/very high) susceptibility to landslide occurrences. These areas that extend along the rivers, primarily in the central parts of the Muong Nhe district, should be treated with the highest priority to mitigate the negative impacts of future landslides.

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

Landslide susceptibility prediction (LSP) is the key technology in landslide monitoring, warning, and evaluation. In recent years, a lot of research on LSP has focused on machine learning algorithms, and the ensemble learning algorithm is a new direction to build the optimal prediction. Logistic model tree (LMT) combines the advantages of decision tree and logistic regression, which is smaller and more robust than ordinary algorithms. The main aim of this study is to construct and test LMT-based random forest (RF) and selected ensemble learning algorithms including bagging and boosting algorithms to compare their performance. Firstly, taking the county of Ziyang, China, as the study area, through historical reports, aerial-photo interpretations, and field investigations, 690 inventory maps of landslide locations were constructed and randomly divided into the 70/30 ratio for a training and validation dataset. Secondly, considering geological conditions, and landslide-induced disease and its characteristics, 14 landslide-conditioning factors was selected. Thirdly, the variance-inflation factor (VIF) and tolerance (TOL) were used to analyze the 14 factors, and the prediction ability was calculated with information-gain technology. Ultimately, the receiver-operating-characteristic (ROC) curve was applied to verify and compare model performance. Results showed that the LMT-RF model (0.897) was superior to other models, and the performance of LMT single model (0.791) was the worst. Therefore, it can be inferred that the LMT-RF model is a promising model, and the outcome of this study will be useful to planners and scientists in landslide sensitivity studies in similar situations.

Sediment mobilization by rain-driven landslides in a subtropical moist, mix-use, and predominantly metamorphic setting

Sediment mobilization by rain-driven landslides tends to be responsible for the relatively high sediment yields of high-standing islands within tropical cyclone corridors where sediments represent a threat to water reservoirs and marine environments such as coral reefs. An opportunity to quantify sediment releases and delivery to streams by shallow landslides emerged as a consequence of the precipitation associated with Hurricane María in 2017 within the predominantly metamorphic Río Loco watershed in western Puerto Rico. Aerial photo interpretation and high-resolution digital elevation models representing conditions before and after the hurricane were used to map and quantify the volume of individual landslide scars. The 642 landslides mapped were estimated to have mobilized 40,640–78,530 Megagrams (Mg) of sediment for an overall denudation of 1.6–3.0 mm, and with ~30 % of the sediment being delivered to the stream network. Susceptibility to landslides was controlled by slope, lithological-soil substrate, land cover, and distance to roads. Landslide mobilization during this single rainstorm equaled 10–35 years of sediment production by surface erosion on croplands and 0.8 to 2.3 years of watershed-scale sediment delivery. However, area-normalized mobilization for Río Loco (1870–3620 Mg km−2) was lower than that on the adjacent Río Yauco-Lago Lucchetti watershed (5260–9960 Mg km−2), even though the two watersheds share similar topographic relief and had similar precipitation amounts during Hurricane María. The difference between the two areas is related to the dominance of serpentinite rocks within Río Loco (69 % of the watershed), a rock type that weathers into soils that are not apt for the type of coffee and mixed-crop cultivation that is typical of the central-western highlands of Puerto Rico. As a consequence, portions of the Río Loco watershed underlain by serpentinite are predominantly forested and have a relatively low road density (~7 km km−2) in comparison to actively-cultivated areas that are underlain by intrusive rocks within Río Loco (22 km km−2) and Río Yauco-Lago Lucchetti (~21 km km−2). Given the importance of proximity to roads in inducing landsliding, the lower road densities in Río Loco led to relatively lower watershed-scale sediment mobilization rates than in Río Yauco-Lago Lucchetti during Hurricane María. The results highlight the diversity of geomorphic response to the same rainstorm, in this case driven by lithological substrate controls on past and present land use and road building.

Obituary: R.H.Sawkar (1935 –2022)

An eminent Geologist, Shri R.H.Sawkar breathed his last peacefully on the morning of June 1,2022 at the age of 87 in his home at Bengaluru. Shri R.H. Sawkar is survived by his Wife, Son, Daughter and Grand-Children and their families are settled in Bengaluru. Born on March 29, 1935 to Hem Reddy Sawkar and Lingamma Hiregoudar, he is from Magala Village, Huvinhadagalli taluk, Vijayanagar dist, Karnataka. He had four brothers and two sisters. He graduated with B.Sc. (Hons) in Geology in 1959 and M.Sc. in Geology in 1961 from Mysore University followed by Diploma in Aerial Photo Interpretation and training in I.T.C (Australia)-Mineral Exploration and Mine Development Programme under Colombo Plan. He was sportsman, swimmer and wrestler. Started his career in Dept of Mines and Geology, Govt of Karnataka as Assistant Geologist in1959. During 1959 -1964, he was associated for the exploration of Iron ore of West Coast, Clay & Feldspar deposits in Goribidanur taluk, mineral resources of South Canara, Limestone deposits in Bijapur and Belgaum, Gold deposits of Gadag Gold Fields, Karnataka. From 1964-1966 as Geologist (Jr.) he was deputed to National Mineral Development Corporation Ltd (NMDC), here he was associated with the exploration of Iron ore deposits of Kudremukh area, for the preparation of the detailed project report for Visvesvaraya Iron and Steel Ltd for mining iron ore. During 1966 -1970, he was associated the Dharwad division inspection of mining leases, exploration of Iron, Manganese ores of North Canara,Bauxite deposits of coastal area, site selection of Vijayanagar steel Plant, Karnataka. From 1970 - 1976, on deputation, he was associated with the Mysore Minerals Ltd (Karnataka State Minerals Corporation Ltd) as Project Manager and Agent for ore production, sales, shipment, transportation, mine planning, exploration of various minerals, etc. During 1976-1985, on deputation, he worked as Project Manager of Karnataka Copper Consortium Ltd. for the exploration and mine development of Kalyadi Copper Project, Ingaldhal Copper Mine, assisted the Karnataka State Govt in the merger proposal of these copper companies with the Hutti Gold Mines Co Ltd (HGML), merger took place with HGML on 12.07.1985. From1985 -1995, he worked in different capacities in HGML for the Chitrdurga Copper Unit, Gadag Gold Project and Corporate Planning etc. After Retiring from HGML on 31.3.1995 as Executive Director, he worked in HGML as Technical Advisor up to 1.6.1997. He was associated with all the leading earth science associations in India and abroad. Currently, he was Secretary General of the Geological Society of India and actively associated with the MEAI, Rashtriya Jal Biradari, Water Development Rural and Urban societies, Dam Safety etc. He was a member of various State and Central Govt committees. He has presented and published several papers in the national and international seminars and also chaired the technical sessions. He was actively involved as advisor/consultant to the exploration, mining and metallurgy industries. He travelled widely, a simple man with tons of curiosity to discover new things. He guided and inspired the many. Shri R.H. Sawkar will remain in the hearts of the many. He will be missed deeply by his family and colleagues.

The use of geoinformation technologies in applied tasks of agricultural lands monitoring

The article discusses software technologies for processing Earth remote sensing (ERS) data used in applied tasks of agricultural lands monitoring. Some results of interpretation of aerial photographs taken by an unmanned aerial vehicle (UAV) are given. The potential for use of Landsat-8 images is presented, including modern methods for digital improvement of their quality. An analysis of satellite images from open sources was carried out, which makes it possible to identify problem areas by indirect and direct signs.

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Understanding the retreat of the Jurassic Cantabrian coast (N. Spain): Comprehensive monitoring and 4D evolution model of the Tazones Lighthouse landslide

Forecasting coastal dynamics and sea cliff retreat under different sea level rise scenarios requires a good understanding of the conditioning factors and their relative contribution to cliff stability. The so-called Jurassic Cantabrian Coast extends along 76 km of the coastline of the Asturias region (N Spain) and is well-known worldwide due to its paleontological heritage, in particular the presence of dinosaur remains and footprints. The abundance of stratigraphic, paleontological and tectonic studies contrasts with the scarcity of studies focused on the stability of this rocky coastline where cliffs predominate, sometimes exceeding 120 m in height. In fact, evidence of current and recent instability processes can be observed along the entire coastline. In this regard, continuous monitoring is crucial to understand ongoing instabilities in rocky coastlines, as in these settings some instabilities might initiate as slow movements that induce subtle topographic changes whose detection from either satellite or aerial imagery is problematic due to the spatial and temporal resolutions. This contribution presents a 4D evolution model of a key site, the Tazones Lighthouse landslide, located on the Cantabrian Coast of Asturias (N Spain), which affects subvertical rocky cliffs sculpted in the Jurassic bedrock made of alternating sandstone and marl. A high resolution multiapproach methodology was developed in order to understand its structure and kinematic characteristics, including: i) interpretation of aerial photographs and unmanned aerial photogrammetric surveys (UAV); ii) 22 monthly monitoring campaigns by total station; iii) 5 manual boreholes; iv) geomechanical characterization of the cliff bedrock; v) geomorphological evidence mapping; vi) analysis of landscape deformations obtained from UAV; and vii) precipitation, soil moisture and significant wave height (Hs) data analysis. The results show that the slope evolves by means of a complex-type mass movement, which combines translational and sliding mechanisms, and occupies tens of thousands of square meters. DTM and fieldwork analysis indicate that mass movement is mainly controlled by bedrock discontinuities (S0, 360/15-17; J1, 262/85; J2 166/75). The most important accelerations of slope movement correlate very well with rainfall, soil moisture and waves. Thus, the largest displacements occurring in January and October–November 2019, coincide with 2 periods of storms (maximum 24-h rainfall of 64.5 mm and 82.1 mm and maximum Hs of 6.54 and 9.09, respectively) and soil moisture values above 90%. Half of the markers moved more than 1 m and one of them exceeded 15 m. The 4D model obtained after the interpretation of the Tazones Lighthouse slope whole dataset, allows an understanding of how the surrounding cliffs have evolved in the past, fundamental to predicting their future behaviour.

AN INTERDISCIPLINARY APPROACH FOR UNVEILING AND ENHANCING THE FIRST WORLD WAR HERITAGE IN THE LANDSCAPE

Abstract. More than one hundred years ago, the European landscape was profoundly transformed by multiple factors related to the Great War, which altered its territorial and environmental ecosystems, giving the landscape the value of a cultural asset with a civilizational value. After more than a century, the "signs of history" remain in the contemporary world at different levels of recognition, very often in a state of abandonment and at risk of loss. This contribution presents the elaboration of a non-invasive and "from remote" operative method, based on the analytical interpretation of historical cartography and historical aerial photographs to reconstruct the evolutionary dynamics of the landscape and recognize the areas where the impact of the conflict has been stronger. The georeferencing process of the sources in the QuantumGIS environment made it possible to elaborate interesting overlaps between cartographies and photographs belonging to different temporal frames. In this way, the dynamics of landscape transformation and land cover/use have been analyzed in relation to the destructive impact of the war to identify the areas in which it is more likely to find material remains of the vestiges, whose recognition becomes a prerequisite for the future practices of protection, transformation, and enhancement.

Torrential Hazard Prevention in Alpine Small Basin through Historical, Empirical and Geomorphological Cross Analysis in NW Italy

Debris flow is one of the most dangerous natural processes in mountain regions and it occur in a wide variety of environments throughout the world. In the Italian Alps, some tens of thousands of damaging debris flow and, in general, torrential floods associated to intense sediment transport in secondary catchments have been documented in the last 300 years. These have caused socio-economic damage, damage to anthropogenic structures or infrastructures and in many cases casualties. Often, in the same basins, the occurrence of debris-flow processes recurs many years later. Prediction can often be spatial and based on the magnitude of the largest known process, while the temporal forecast is the most uncertain. It is also possible to increase the resilience of the population and of the territory. The present study aims at investigating different levels of debris-flow hazard in urban areas on Alpine alluvial fans and proposes a strategy for debris-flow prevention based on historical research and on a simplified analytical approach, methods that also involve relatively low costs. For such analysis, Ischiator stream catchment (ca. 20 km2) and its alluvial fan (NW Italy) were selected. This area was partly affected by historical torrential flood associated to intense sediment transport and debris-flow processes. Present-day instability conditions along the slope and the stream network were detected and synthesized through surveys and aerial photo interpretation integrated by satellite images (period 1954–2021). An estimation of the potential amount of moving detritus, referred to as debris flow, was carried out regarding the June 1957 debris-flow event, based on the predictive models. The individual hazard index value was estimated based on different methods. The results indicate that 56% of the area is exposed to flood associated to intense sediment transport hazard, which fluctuates from high to very high levels; such results are supported by debris-flow historical records. Since today almost half of the settlement (Bagni di Vinadio) is located on potentially risk-exposed areas, the urban evolution policy adopted after the 1957 event failed to manage the risk connection to debris-flow activity.

Morphological adjustments of the lower Orba River (NW Italy) since the mid-nineteenth century

A multi-temporal and multi-scale analysis based on historical data collection and review, maps and aerial photograph interpretation, field observations, topographic surveys, and GIS investigations was performed to (i) examine the morphological evolution of lower Orba River since the mid-nineteenth century, (ii) identify its controlling factors, and (iii) correctly interpret ongoing hydrogeomorphic conditions under a river management perspective. The riverbed experienced overall slight and non-homogeneous morphological adjustments, along with some horizontal displacements, up to the early twentieth century (Phase 0). Subsequently, two phases of channel degradation were identified. The first, from the 1920s to the 1950s (Phase 1), is characterized by slight narrowing (12.3%), the beginning of incision, and rare and little lateral migration. The second, between the 1950s and the years around 2010 (Phase 2), displays significant channel incision (mostly from the 1950s to the 1970s, from 0–1 m to 4–5 m), weak narrowing (21.0%), and only minor horizontal displacements. Over the last decade (Phase 3), widespread slight channel widening (3.4%, up to 25.7% at the reach scale) associated with the absence of a generalized bed-level variation trend was observed. The study reach has exhibited a single-thread or transitional channel since the mid-nineteenth century. Channel incision was mainly caused by extensive in-channel sediment extractions primarily carried out between the 1950s and the early 1980s at the reach and catchment scales. The long-term presence of widespread channelization works was recognized as the main reason why overall little riverbed planform adjustments occurred during the whole period investigated. The most recent dynamics were triggered by floods and were interpreted as the river response to human-induced alterations. The riverbed changes that occurred during the twentieth century match the channel adjustments commonly experienced by Italian single-thread and transitional channels as a response to various types of human disturbances. However, the detected morpho-evolutionary phases of the Orba River are only partially in agreement with those reported in the literature for other Italian rivers affected by similar anthropogenic pressures, as some differences exist in timing and magnitude of prevalent channel adjustments. An overall slight potential for channel recovery, priorities for rehabilitation, and the main objectives for the next-decades management were identified. This research provides insights into the medium- and short-term morphological evolution of Italian rivers, and its outcomes represent an essential piece to drive river management towards sustainable and effective river restoration and risk mitigation measures framed within a larger, multidisciplinary, and objective-based context. A wide spectrum of both consolidated and newly developed GIS-based planform metrics was considered to assess the channel morphological evolution in detail, providing an operative reference framework for future investigations.

Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System

High spatial and thematic resolution of Land Use/Cover (LU/LC) maps are central for accurate watershed analyses, improved species, and habitat distribution modeling as well as ecosystem services assessment, robust assessments of LU/LC changes, and calculation of indices. Downscaled LU/LC maps for Switzerland were obtained for three time periods by blending two inputs: the Swiss topographic base map at a 1:25,000 scale and the national LU/LC statistics obtained from aerial photointerpretation on a 100 m regular lattice of points. The spatial resolution of the resulting LU/LC map was improved by a factor of 16 to reach a resolution of 25 m, while the thematic resolution was increased from 29 (in the base map) to 62 land use categories. The method combines a simple inverse distance spatial weighting of 36 nearest neighbors’ information and an expert system of correspondence between input base map categories and possible output LU/LC types. The developed algorithm, written in Python, reads and writes gridded layers of more than 64 million pixels. Given the size of the analyzed area, a High-Performance Computing (HPC) cluster was used to parallelize the data and the analysis and to obtain results more efficiently. The method presented in this study is a generalizable approach that can be used to downscale different types of geographic information.

Investigation of the Luco dei Marsi DSGSD revealing the first evidence of a basal shear zone in the central Apennine belt (Italy)

Deep-seated gravitational slope deformations (DSGSDs) show a wide range of geomorphological characteristics and kinematic behaviours. In many cases, deforming rock masses move on a continuous surface or a thick basal shear zone (BSZ) overlying the stable bedrock. The nature of this boundary is a significant issue in scientific debates since examples of BSZs have been observed or inferred in some DSGSDs worldwide. In the central Apennines, although several cases of DSGSDs have been described in recent decades, no evidence of BSZs has been documented thus far.This work presents the first case of a BSZ found in the region at the bottom of a large-scale gravitational deformation that affects the Mesozoic-Cenozoic carbonate ridge overhanging the Luco dei Marsi village (Abruzzi region). The BSZ consists of several metres-thick, cataclastic breccia developed within middle-Upper Cretaceous biodetritic limestone. The breccia level is exposed for approximately 200 m with a subhorizontal geometry and shows severe rock damage and weathering.The DSGSD hosting the BSZ affects an NNW-SSE-oriented and wide Miocene anticline whose eastern limb is dismembered by Pliocene-Quaternary normal faults delimiting the edge of a large Quaternary intermontane basin (the Fucino Basin). Field survey, aerial photointerpretation, and remote sensing (DInSAR technique) analyses outline an active gravity-driven process. This is characterized by several kinds of geomorphological features, including downhill- and uphill-facing scarps, ridge-top depressions, gravitational grabens and trenches in the upper and middle parts of the ridge, and bulging at the toe of the slope. These features, which can be distinguished from tectonic elements due to their shape and extension, are an indication of a high degree of internal deformation and a compound sagging geometry for the Luco dei Marsi DSGSD.The short-term activity of the process was revealed by DInSAR time series covering almost thirty years of satellite datasets, including ERS1/2, ENVISAT, COSMO-SkyMed, and SENTINEL 1 constellations. Strain rates on the order of a few mm/yr were inferred, with a marked difference between different sectors of the DSGSD area. The long-term (y > 102) lifespan of the DSGSD was framed into a multiple-step conceptual model summarizing the Early Pleistocene-Holocene geological evolution of the area. The model results outline the control exercised by extensional tectonics on DSGSD development, as progressive displacements along normal faults in the latest Pleistocene were the cause of lateral unconfinement at the toe of the slope.This work further contributes to the increasing knowledge on DSGSDs in the central Apennines and the understanding of the relationship between deformation features induced by slope morphogenesis, such as the BSZ, and Quaternary tectonics within the mountain belt.