System For Automated Geoscientific Analyses Research Articles

Soil Erosion in Taiwan

Soil erosion is a complex process involving material detachment, transportation (mainly by water, occasionally by wind), and eventual deposition when energy wanes. Human activities like tillage and construction can exacerbate soil erosion. Various forms of water erosion, such as sheet erosion, pedestal erosion, rills, piping, and gullying, are recognized. This pioneering study aims to comprehensively model water erosion across Taiwan at a 20 m spatial resolution, a departure from previous research focusing on smaller scales. Using the Revised Universal Soil Loss Equation (RUSLE) model, it seeks to examine the significant issue of soil erosion in Taiwan beyond agricultural areas and enable cross-regional comparisons. A large number of stations and the most recent data were used to establish the distributions of the rainfall runoff erosivity factor and the soil erodibility factor. In addition, we used the Google Earth Engine (GEE) to calculate the Normalized Difference Vegetation Index (NDVI) and a locally derived empirical equation to compute the cover-management factor. The topographic factor was determined using the System for Automated Geoscientific Analyses (SAGA). The support practice factor was analyzed using two different methods using datasets on World Urban Areas and Global Urban Boundaries from the literature. The analysis showed that despite the difference in the support practice factor, Taiwan’s resulting average yearly soil erosion rates are very similar (200.7 Mg ha−1 year−1 and 207.4 Mg ha−1 year−1). The amounts were validated against five watersheds that were instrumented with erosion pins. With prediction ratios ranging from 1.04 to 1.82 across four of the five watersheds, our findings provide empirical support for the alignment of our model with soil erosion pin measurements, especially within the Tsengwen reservoir watershed. However, it is noteworthy that these results also exhibit a tendency towards conservative estimations in the remaining watersheds. Our calculated estimate, falling within the range of 201–207 Mg ha−1 year−1, plausibly represents the upper limit of mean soil erosion in Taiwan. This assertion is predicated on the deliberate omission of local soil conservation measures from our study, a decision necessitated by the absence of comprehensive and detailed island-wide data. Despite this limitation, our results instill confidence in the robustness of our methodological approach, thereby suggesting that our estimation of soil erosion in Taiwan provides a reliable approximation.

Human-Induced Landscape Alteration in the Coastal Regulation Zone of Goa, India, from 2000 to 2017

In Goa, the tourism industry is the major cornerstone of the economy. With the increasing number of tourists along the coastal areas, more tourism-related infrastructure is emerging within the Coastal Regulation Zone. The sensitive and fragile coastal zones are being covered by concrete structures and the coastal environment becomes vulnerable to degradation. The objective of the paper was to study the changes in landuse and landcover in the Coastal Regulation Zone of Salcete taluka using remote sensing data and geospatial techniques. To fulfill the objective, both primary and secondary data were used. Primary data was based on personal observations and field visits while secondary data consisted of topographic maps, LANDSAT 7 ETM, and LANDSAT 8 satellite images, which were processed and analyzed using ArcGIS 10.3, ERDAS IMAGINE 2014, SAGA (System for Automated Geoscientific Analyses) and MS Excel. From the landuse and landcover analysis for a period of 17 years (2000-2017), it was found that the land cover within the CRZ underwent a tremendous change with the increase in tourism activity and related infrastructure. The analysis revealed that the agricultural area has decreased, whereas built-up areas, barren land, and vegetation area increased. The change detection analysis using SAGA software allowed to understand the conversion between different classes. The study revealed that the increasing number of tourists and tourism activities along the Salcete Coast is deteriorating the environmental setup and disturbing the inherent coastal landscape.

Mapping the susceptibility of persons with disabilities to landslides in a highland landscape of Bushika Sub County, Mount Elgon, Eastern Uganda.

Terrain parameters such as slope aspect, angle, curvature, stream power and altitude have been noted to spur landslide occurrence as well as, acting as a hindrance to evacuation efforts. Yet, persons with disabilities (PWDs) are seldom given priority during rescue and recovery programmes during pre- and post-disaster evacuation. The study was guided by two objectives, namely, (1) to map the landslide risk for households of PWDs and (2) to investigate the disability type that is perceived to be most affected by landslides. A cross-sectional household survey was adopted employing snowball sampling, Key Informant Interviews (KII), and Focus Group Discussions (FGDs) for primary data collection. A 30-m Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) was used for terrain spatial landslide risk analysis in ArcGis 10.8 and System for Automated Geoscientific Analyses (SAGA) tools. A one-sample t-test in Statistical Package for Social Sciences (SPSS) version 23 was used to analyse the score values on a five-point Likert scale to ascertain the perceived landslide effect on the different disability categories. Qualitative data was subjected to content analysis. We found out that majority of PWDs live in high-risk landslide zones with 1400 m – 1700 m, S-E, 10–80, > 10, and –0.8–0.13 of altitude, aspect, slope angle, Stream Power Index (SPI), and slope curvature, respectively. T-test results revealed that blind and deaf-blind were perceived as most affected by landslides with t(31) = 58.42, mean = 4.7, p < 0.0001, and t(31) = 34.8, mean 4.6, p < 0.0001. The deaf people were perceived to also be highly affected by landslides with t(31) = 34.4, mean = 3.9, p < 0.0001. In conclusion, PWDs in Bushika were highly susceptible to landslide hazards and yet considered as a minority for rescue and recovery during landslide occurrences. We recommend for prioritisation of inclusive disaster programmes such as disaster training, relocation, and resettlement to reduce vulnerability and enhance landslides disaster resilience of PWDs especially in high-risk areas.

Using topographic attributes to predict the density of vegetation layers in a wet eucalypt forest

ABSTRACT Mapping the structure of forest vegetation with field surveys or high-resolution light detection and ranging (LiDAR) data is costly. We tested whether landscape topography and underlying geology could predict the vegetation density of a 19 km2 area of wet eucalypt forest at the Warra Long-Term Ecological Research Supersite, Tasmania, Australia. Using spatial layers for 12 topographic attributes derived from digital terrain models (DTMs) and a geology layer, we predicted the vegetation density of three strata with a high degree of accuracy (validation root mean square error ranged from 9.0% to 13.7%). The DTMs with 30 m resolution provided greater predictive accuracy than DTMs with higher resolution. The importance of different variables depended on spatial resolution and strata. Among the predictor variables, geology generally had the highest predictive importance, followed by solar radiation. Topographic Position Index, aspect, and System for Automated Geoscientific Analyses (SAGA) Wetness Index had moderate importance. This study demonstrates that geological and topographic attributes can provide useful predictions for the density of vegetation layers in a tall wet sclerophyll primary forest. Given the good performance of the model based on 30 m DTM resolution, the predictive power of the models could be tested on a larger geographical area using lower-density LiDAR point clouds combined with medium-resolution satellite data.

Моделирование водной эрозии почв в провинциях Сикассо и Коуликоро (Республика Мали)

Soil along water is arguably the most precious resource on the planet. In addition to its economic benefits, soil provides critical biological services [7]. Despite its pillar functions for society, soil is often overlooked and thus is subjected to degradation and erosion. Soil erosion represents a serious global threat to land, freshwater, and oceans [3]. In Western Africa, erosion is perceived as a critical threat to the livelihoods of millions of people. This study attempts to assess and map the potential annual soil loss in the provinces of Sikasso and Koulikoro (republic of Mali). Spatial modeling of soil loss by rainfall for the year 2018 was provided using rainfall data derived from the European Joint Re-search Center, the Soil Map of the World (FAO), digital elevation model (SRTM), vegetation activity (MODIS / Terra). Methods of calculation were based on the Remote Sensing and the Revised Universal Soil Loss Equation (RUSLE). The Geoinformation processing of the RUSLE subcomponents involved the use of the LS-factor algorithm of the Sys-tem for Automated Geoscientific Analyses (SAGA) and the Raster calculator of the ArcGIS tool box. The potential soil loss within the area ranged from 0.02 ton/ha/year to 98.87 tons/ha/year with a mean of 1.63 ton/ha/year. The spatial pattern of the erosion showed a rate of 0.02 to 1 ton/ha/year for 39% of the territory, 1 to 3 tons/ha/year for 47.58%, while 0.01% experienced a rate of more than 50 tons/ha/year. This study despite its match with the result of the global soil loss by water established by Borreli et al (2020) [3], needs to be verified by direct measurements.

Comparative Analysis of Pixel and Object Based Classification Approach for Rapid Landslide Delineation with the Aid of Open Source Tools in Garhwal Himalaya

Landslides are the natural geomorphic processes that are essential for the landscape development. Loss of tremendous amount of life, natural resources as well as property has made this phenomenon a natural disaster. Number of researches have been done to understand this phenomenon and how to overcome this calamity with the aid of upcoming modern information services and technological advancements. Despite all these, there are still certain undocumented slope failure events due to inaccessibility and lack of a proper database. With the advancement of satellite remote sensing and geographical information system (GIS), it has certainly become easier to monitor and prepare landslide database especially in rough and rugged terrain of the Himalaya for delineation of risk zones. Free availability of high-resolution images and an open source efficient software have certainly been proved advantageous for this purpose. This paper aims at quick and accurate landslide inventory mapping, using high resolution Sentinel 2 data along with normalized difference vegetation index (NDVI), over which unsupervised and object-based image analysis (OBIA) was done to extract landslide features in an efficient manner. Both these processes were achieved with the aid of open source SAGA (System for Automated Geoscientific Analyses) software. The performances of these classifications were analyzed for their quantitative reliability in extracting the landslide features in two different areas (test area 1 and 2). In this, the object based image classification outstands, with the overall accuracy percentage difference of 17.11% higher than pixel-based classification approach in test area 1 and about 21% higher in test area 2.

Assessing Hydrological Connectivity Mitigated by Reservoirs, Vegetation Cover, and Climate in Yan River Watershed on the Loess Plateau, China: The Network Approach

Hydrologic connectivity is related to the water-mediated transport of matter, energy, and organisms within or between elements of the hydrologic cycle. It reflects the hydrological consequences caused by topographic, land cover, and climatic factors, and is an important tool to characterize and predict the hydrological responses to climate and landscape change. In the Loess Plateau region, a large number of reservoirs have been constructed to trap sediment and storage water for drinking, irrigation, and industries. The land cover has been significantly reshaped in the past decades. These changes may alter the watershed hydrological connectivity. In this study, we mapped the spatial pattern of hydrological connectivity with consideration of reservoir impedances, mitigation of climate, and land cover in the Yan River watershed on the Loess Plateau by using the network index (NI) approach that is based on topographical wetness index. Three wetness indices were used, i.e., topographical wetness index (TWI), SAGA (System for Automated Geoscientific Analyses) wetness index (WIS), and wetness index adopted aridity index (AI) determined by precipitation and evapotranspiration (WIPE). In addition, the effective catchment area (ECA) was also employed to reveal the connectivity of reservoirs and river networks to water source areas. Results show that ECA of reservoirs and rivers account for 35% and 65%, respectively; the hydrological connectivity to the reservoir was lower than that to the river networks. The normalized hydrological connectivity revealed that the connectivity to river channels maintained the same distribution pattern but with a decreased range after construction of reservoirs. As revealed by comparing the spatial patterns of hydrological connectivity quantified by NI based on WIS and WIPE respectively, vegetation cover patterns had significantly alternated watershed hydrological connectivity. These results imply a decreased volume of flow in river channels after reservoir construction, but with same temporal period of flow dynamic. It is illustrated that the network index (NI) is suitable to quantify the hydrological connectivity and it is dynamic in the context of human intervention and climate change.

GENETIC ALGORITHM BASED FEATURE SELECTION FOR LANDSLIDE SUSCEPTIBILITY MAPPING IN NORTHERN IRAN

Abstract. Recognizing where landslides are most likely to occur is crucial for land use planning and decision-making especially in the mountainous areas. A significant portion of northern Iran (NI) is prone to landslides due to its climatology, geological and topographical characteristics. The main objective of this study is to produce landslide susceptibility maps in NI applying three machine learning algorithms such as K-nearest neighbors (KNN), Support Vector Machines (SVM) and Random Forest (RF). Out of the total number of 1334 landslides identified in the study area, 894 (≈67%) locations were used for the landslide susceptibility maps, while the remaining 440 (≈33%) cases were utilized for the model validation. 21 landslide triggering factors including topographical, hydrological, lithological and Land cover types were extracted from the spatial database using SAGA (System for Automated Geoscientific Analyses), ArcGIS software and satellite images. Furthermore, a genetic algorithm was employed to select the most important informative features. Then, landslide susceptibility was analyzed by assessing the environmental feasibility of influential factors. The obtained results indicate that the RF model with the overall accuracy (OA) of 90.01% depicted a better performance than SVM (OA = 81.06%) and KNN (OA = 83.05%) models. The produced susceptibility maps can be productively practical for upcoming land use planning in NI.

SaLEM (v1.0) – the Soil and Landscape Evolution Model (SaLEM) for simulation of regolith depth in periglacial environments

Abstract. We propose the implementation of the Soil and Landscape Evolution Model (SaLEM) for the spatiotemporal investigation of soil parent material evolution following a lithologically differentiated approach. Relevant parts of the established Geomorphic/Orogenic Landscape Evolution Model (GOLEM) have been adapted for an operational Geographical Information System (GIS) tool within the open-source software framework System for Automated Geoscientific Analyses (SAGA), thus taking advantage of SAGA's capabilities for geomorphometric analyses. The model is driven by palaeoclimatic data (temperature, precipitation) representative of periglacial areas in northern Germany over the last 50 000 years. The initial conditions have been determined for a test site by a digital terrain model and a geological model. Weathering, erosion and transport functions are calibrated using extrinsic (climatic) and intrinsic (lithologic) parameter data. First results indicate that our differentiated SaLEM approach shows some evidence for the spatiotemporal prediction of important soil parental material properties (particularly its depth). Future research will focus on the validation of the results against field data, and the influence of discrete events (mass movements, floods) on soil parent material formation has to be evaluated.

A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms

In this work the synergistic use of Sentinel-1 and 2 combined with the System for Automated Geoscientific Analyses (SAGA) Wetness Index in the content of land use/cover (LULC) mapping with emphasis in wetlands is evaluated. A further objective has been to develop a new Object-based Image Analysis (OBIA) approach for mapping wetland areas using Sentinel-1 and 2 data, where the latter is also tested against two popular machine learning algorithms (Support Vector Machines - SVMs and Random Forests - RFs). The highly vulnerable iSimangaliso Wetland Park was used as the study site. Results showed that two-part image segmentation could efficiently create object features across the study area. For both classification algorithms, an increase in overall accuracy was observed when the full synergistic combination of available datasets. A statistically significant difference in classification accuracy at all levels between SVMs and RFs was also reported, with the latter being up to 2.4% higher. SAGA wetness index showed promising ability to distinguish wetland environments, and in combination with Sentinel-1 and 2 synergies can successfully produce a land use and land cover classification in a location where both wetland and non-wetland classes exist.

Impacts of Land Cover Change on Conservation, a Linkage of Spatial Analysis and Anthropogenic Activities in Tanzania

We used spatial analysis to assess the Land Use Land Cover (LULC) changes, and studied the impacts of LC changes on conservation of buffer zone of the Selous Game Reserve (SGR) and their implication on community’s livelihood in Vikumbulu Ward of Kisarawe District, Tanzania. Socio-economic data from Kisarawe District and TNBS were linked to spatial data to offer an integrated perspetive of LULC change in the Ward. Three cloud free image dates of 1998, 2011 and 2015 were analysed using System for Automated Geoscientific Analyses (SAGA) GIS for three categories of land cover, i.e. forest, wooded grassland and bare land/settlements/cultivation. Vikumbulu demographic and socio-economic data were linked to spatial data applying distance as a function of LULC change. Spatial analysis has shown a decreasing trend of forest and woodland cover in Vikumbulu Ward between 1998 and 2015. The sharp decline indicates increasing social economic activities such as shifting agriculture and charcoal burning as an outcome of population growth and poverty. Rapid conversion of forest cover to wooded grassland occurred between 1998 and 2015 in Vikumbulu Ward. However, loss of forest cover was associated with a decreasing trend in wooded land in the ward between 2011 and 2015. As there was only 0.15% area under crop cultivation in Vikumbulu until 2015, it is highly likely that LC change is caused by charcoal burning and shifting cultivation. This study suggests developing integrated strategies that target LULC change, conservation and people’s livelihoods to effectively improve the current situation in rural areas of Tanzania.

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

AbstractSoil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R2 = 0.47 and RMSE 9.34 VWC%, and for the latter R2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m2 digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley &amp; Sons, Ltd.

The Gravitational Process Path (GPP) model (v1.0) – a GIS-based simulation framework for gravitational processes

Abstract. The Gravitational Process Path (GPP) model can be used to simulate the process path and run-out area of gravitational processes based on a digital terrain model (DTM). The conceptual model combines several components (process path, run-out length, sink filling and material deposition) to simulate the movement of a mass point from an initiation site to the deposition area. For each component several modeling approaches are provided, which makes the tool configurable for different processes such as rockfall, debris flows or snow avalanches. The tool can be applied to regional-scale studies such as natural hazard susceptibility mapping but also contains components for scenario-based modeling of single events. Both the modeling approaches and precursor implementations of the tool have proven their applicability in numerous studies, also including geomorphological research questions such as the delineation of sediment cascades or the study of process connectivity. This is the first open-source implementation, completely re-written, extended and improved in many ways. The tool has been committed to the main repository of the System for Automated Geoscientific Analyses (SAGA) and thus will be available with every SAGA release.

Studi Laju Perubahan Garis Pantai di Pesisir Tenggara Bali Menggunakan Citra Satelit Landsat (Studi Kasus Kabupaten Gianyar dan Klungkung)

Erosion and abrasion are the events that led to the beach shoreline position changes. The impact of climate change is the rise in sea level also causes changes in the coastline. South East coast of Bali, especially along the coast Gianyar and Klungkung changing coastline. This study aims to identify and calculate the rate of shoreline change in Gianyar and Klungkung from 1995 to 2015. The study was a preliminary information shoreline change and do not analyze the causes such as tides, currents, waves, and wind. The method used remote sensing analysis with the extraction of the coastline from the Landsat 5 satellite images in 1995, Landsat 7 in 2005, and Landsat 8 2015. Landsat imagery analyzed by a combination of methods approach the threshold and band ratio of wave infrared and green. Image processing using software Quantum GIS 2.8 and System for Automated Geoscientific Analyses (SAGA) GIS 2.2, extention Digintal Shoreline Analysis System (DSAS) to make calculations transect coastline. The results of the analysis of overlaying identify coastline in Gianyar and Klungkung change at a rate that varies every village. The rate of change of coastline in Gianyar due to accretion between 0.5096 - 8.6074 m / yr, while due to erosion between -3.7343 to -1.3201 m / yr. The rate of change in Klungkung regency coastline due to accretion between 0.6337 - 2.6875 m / yr, while due to erosion between -8.8795 to -0.8833 m / yr.

Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China

Estimation of carbon and nitrogen stocks is important for quantifying carbon and nitrogen sequestration as well as greenhouse gas emissions and inventorying national carbon and nitrogen balances. For Liaoning province of China, we estimated the vertical distribution of soil organic carbon (SOC), soil total nitrogen (STN), bulk density (BD), and mapped their spatial distribution at five standard soil depth intervals (0–5, 5–15, 15–30, 30–60 and 60–100cm) using nine environmental variables as predictors including precipitation, temperature, land use, elevation, system for automated geoscientific analyses (SAGA) wetness index, and Normalized Difference Vegetation Index (NDVI). The highest average contents of SOC and STN were 15.2gkg−1 and 1.6gkg−1 in the 0–5cm soil layer, and 1.5gkg−1 SOC and 0.4gkg−1 STN in the 60–100cm soil layer, respectively. The prediction precision for SOC, STN and BD all decreased with soil depth. Average SOC and STN stocks for 0–30cm were 3.1kgm−2 and 0.5kgm−2, respectively. For the top 1m, SOC and STN were 4.5kgm−2 and 0.9kgm−2, respectively. In total, the soils stored approximately 588Tg SOC and 128Tg STN within the top 1m. The soils under forest had the highest amount of carbon (356Tg) and nitrogen (58Tg) followed by agriculture and wetland that contributed 34% and 48% of the total stock, respectively. >91% of the total SOC and STN stocks were in Argosols and Cambosols. We adopted a digital soil mapping method to map the spatial distribution of SOC and STN stocks and predict their uncertainties. The estimation was validated with a 10-fold cross-validation procedure. The data and high-resolution maps from this study can be used for future soil carbon and nitrogen assessment and inventorying.

Region merging using local spectral angle thresholds: A more accurate method for hybrid segmentation of remote sensing images

Image segmentation is the decisive process in object-based image analysis, but segmenting a landscape scene into meaningful geo-objects remains a challenge. In recent years, there has been growing interest in hybrid methods that combine initial segmentation with subsequent region merging, because they exploit spectral signals across an entire geo-object, including both the boundary signals used to delineate the initial segments, and the interior signals used to merge similar segments. However, existing algorithms commonly use a single, global parameter to control the process of region merging, thereby limiting the goodness-of-fit between segments and geo-objects, since homogeneous and heterogeneous segments are treated equally. To overcome this limitation, we developed a new hybrid segmentation method that employs local spectral angle (SA) thresholds for region merging. We implemented our local SA method in three very different landscapes, then compared our region merging method to the global SA method, as well as the global elevation method used in System for Automated Geoscientific Analyses (SAGA). In all three landscapes, the results revealed that the local SA segmentation provides a better fit to reference polygons than the two global threshold methods, as measured using a modified discrepancy measure for the purpose of geo-object recognition (QRM). We also found that the local SA method produced segments with a greater variation in size, indicating the method is effective for achieving multi-scale segmentation.

Hydrological and hydraulic models for determination of flood-prone and flood inundation areas

Abstract. Geographic Information Systems (GIS) are widely used in most studies on water resources. Especially, when the topography and geomorphology of study area are considered, GIS can ease the work load. Detailed data should be used in this kind of studies. Because of, either the complication of the models or the requirement of highly detailed data, model outputs can be obtained fast only with a good optimization. The aim in this study, firstly, is to determine flood-prone areas in a watershed by using a hydrological model considering two wetness indexes; the topographical wetness index, and the SAGA (System for Automated Geoscientific Analyses) wetness index. The wetness indexes were obtained in the Quantum GIS (QGIS) software by using the Digital Elevation Model of the study area. Flood-prone areas are determined by considering the wetness index maps of the watershed. As the second stage of this study, a hydraulic model, HEC-RAS, was executed to determine flood inundation areas under different return period-flood events. River network cross-sections required for this study were derived from highly detailed digital elevation models by QGIS. Also river hydraulic parameters were used in the hydraulic model. Modelling technology used in this study is made of freely available open source softwares. Based on case studies performed on watersheds in Turkey, it is concluded that results of such studies can be used for taking precaution measures against life and monetary losses due to floods in urban areas particularly.

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

Abstract. The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.

Semi-automatic segmentation of petrographic thin section images using a “seeded-region growing algorithm” with an application to characterize wheathered subarkose sandstone

Accurate imaging of minerals in petrographic thin sections using (semi)-automatic image segmentation techniques remains a challenging task chiefly due to the optical similarity of adjacent grains or grain aggregates rendering definition of grain boundaries difficult. We present a new semi-automatic image segmentation workflow for the quantitative analysis of microscopic grain fabrics. The workflow uses an automated seeded region growing algorithm, which is based on variance analysis of five or more RGB images. The workflow is implemented in the open-source Geographic Information System (GIS) software SAGA (System for Automated Geoscientific Analyses). SAGA provides all required tools for image analysis and geographic referencing. It also features a graphical user interface that allows the user to simultaneously display and link multiple images and, thus, facilitates manual post-processing of the images. SAGA's capabilities for vector data analysis offer instant calculation and visualization of the compiled geometric database within a GIS environment. Specifically, grain contacts are automatically identified by lines of intersection and manually classified by contact type to characterize the mineral fabric of petrographic thin sections. To demonstrate the effectiveness of the workflow, 39 transmitted light images of 13 weathered sandstone samples of the Buntsandstein Formation in northwestern Germany were analyzed. Based on the segmentation results obtained from the samples, a number of parameters, including modal composition, geometry of grain contacts, porosity, and grain size distribution were determined and statistically evaluated. The results of the image analysis are utilized to assess the weathering susceptibility of the sandstone samples and point to the importance of cementation determining the geotechnical properties of a given sandstone sample.

A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water

The Universal Soil Loss Equation (USLE) model is the most frequently used model for soil erosion risk estimation. Among the six input layers, the combined slope length and slope angle (LS-factor) has the greatest influence on soil loss at the European scale. The S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length. The combined LS-factor describes the effect of topography on soil erosion. The European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil erosion assessment to achieve a better understanding of the spatial and temporal patterns of soil erosion in Europe. The LS-calculation was performed using the original equation proposed by Desmet and Govers (1996) and implemented using the System for Automated Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and contributes to a precise estimation of flow accumulation. The LS-factor dataset was calculated using a high-resolution (25 m) Digital Elevation Model (DEM) for the whole European Union, resulting in an improved delineation of areas at risk of soil erosion as compared to lower-resolution datasets. This combined approach of using GIS software tools with high-resolution DEMs has been successfully applied in regional assessments in the past, and is now being applied for first time at the European scale.