Compound Topographic Index Research Articles

Can eXplainable AI Offer a New Perspective for Groundwater Recharge Estimation?—Global‐Scale Modeling Using Neural Network

AbstractDue to the difficulties in estimating groundwater recharge and cross‐boundary nature of many aquifers, estimating groundwater recharge at large scale has been called upon. Process‐based models as well as data‐driven models have been established to meet this need. Meanwhile, with the advent of explainable artificial intelligence (XAI) methods, data‐driven machine learning models can take advantage of enhanced explainability while keeping the strength of high flexibility. In this study, an ensemble neural network model was built to check the suitability of the model to predict groundwater recharge and the possibility to gain new insights from large data set. Recent large inputs of groundwater recharge data and additional input for the Arabian Peninsula collated in this study were fed to the model with multiple predictors related to climatology considering seasonality, soil and plant characteristics, topography, and hydrogeology. The model showed higher performance (adjusted R2: 0.702, RMSE: 193.35 mm yr−1) than a recent global process‐based model in predicting groundwater recharge. Using XAI methods as individual conditional expectations and Shapley Additive Explanation interaction values, the model behavior was analyzed and possible linear and non‐linear relationships between the predictors and the groundwater recharge rate were found. Long‐term averaged precipitation and enhanced vegetation index showed non‐linear relationships with groundwater recharge rate, while slope, compound topographic index, and water table depth showed low importance to the model results. Most model behaviors followed the domain knowledge, while multi‐correlation between predictors and data skewness hindered the model from learning.

Geographically induced and the spatially differentiated dimension of flood vulnerability in Greater Kumasi Metropolis, Ghana

ABSTRACT Over the past several years, the constant flooding events and their lethal consequences have reignited the debate on the need for vulnerability assessment of flood-prone communities in urban areas as a flood risk mitigation and adaptation measure. This article focuses on Ghana and uses remotely sensed high-resolution data, and community mapping to assess the flood vulnerability of 442 urban communities of the Greater Kumasi Metropolitan Area (GKMA). The Compound Topographic Index and the Shuttle Radar Topographic Mission were used systematically. The results showed that 43% of communities were found in low-risk areas, 30% were in medium-risk areas, and 27% were in high-risk areas. Flood intensity and frequencies were found to be dependent on location relative to slopes and undulating terrain. We argue that the identified flood hazard communities should guide policymakers in proffering specific and targeted interventions toward flood risk reduction and community resilience strategies

Geospatial liquefaction probability models based on sand boils occurred during the 2017 M5.5 Pohang, South Korea, earthquake

The 2017 Pohang earthquake, triggered by an enhanced geothermal system, had a moment magnitude (M) of 5.5 and caused liquefaction in Pohang City, South Korea. Notably, approximately 600 sand boils were observed only in the soft Quaternary alluvial sediments. This contrasts with most historic liquefaction cases that occurred in earthquakes with M ≥ 6.0. In addition, ground and building settlements were reported in Pohang City. To assess the liquefaction hazard for the Pohang earthquake within the city, we define a 14.4 km2 area where the majority of liquefaction was observed. Using the liquefaction data, we develop eight geospatial liquefaction probability models using logistic regression (LR) and random forest (RF) algorithms (five LR- and three RF-based models). These models consider geospatial data (peak ground acceleration, peak ground velocity, slope-derived average shear wave velocity of the upper 30 m, compound topographic index, distance to the nearest water, and roughness) and geotechnical data (averaged standard penetration test N values up to 20 m depth, depth to rock). We evaluate the performances of our eight models and existing global and regional geospatial models using four indicators: accuracy, balanced accuracy, F1 score, and area under the receiver operating characteristic curve. The three RF-based models outperform the other models, followed by the best LR-based model and the global geospatial model from Zhu et al. (2015). Finally, we apply the best RF-based model to the entire Pohang City and evaluate its applicability to other regions and earthquakes. The estimated liquefaction probabilities agree well with the observed distributions of the sand boils and settlements. These geospatial liquefaction models provide a valuable tool for continuously mapping liquefaction hazards in areas with similar geological and geotechnical conditions to Pohang.

The use of global versus region-specific data for the prediction of co-seismic landslides

This study compares the impact of global and region-specific variables on the prediction of co-seismic landslides, investigating whether the incorporation of higher-resolution data into a global framework can improve the prediction outcome for regional landslide assessments. Based on a statistical landslide model, which was developed through logistic regression involving various global landslide inventories, the likelihood of a landslide occurring during an earthquake is estimated for a regional event. Using the recently updated landslide inventory of the 2016 MW7.8 Kaikōura earthquake (centroid of landslide source area), the prediction performance of the model is evaluated by individually replacing the global variables for slope, compound topographic index, lithology and land cover with New Zealand specific datasets that offer higher resolution and/or more accurate information. Statistical metrics are used to compare the changes in the prediction performance of each modification, while probability maps and binary prediction maps are evaluated to identify spatial differences. The results suggest that calculating the slope variable from a higher-resolution DEM (e.g., 25 m) improves the model's prediction performance without changing the model equation or replacing the other global variables. Other region-specific variables such as land cover show stronger correlation but require an adjustment of the model coefficients (e.g., regression analysis). As the region-specific datasets increase the landslide estimates, a sensible interpretation of the model results is important, including the identification of a probability threshold which allows for a binary prediction (landslide occurs versus landslide does not occur). Future research should investigate the role of global and region-specific datasets for other earthquake events and consider incorporating other variables (e.g., local slope relief). The findings demonstrate the limitations of global variables for regional landslide predictions and illustrate the impact as well as implications of region-specific datasets on the model outcome, providing a better understanding on how incorporating region-specific datasets into a global approach can improve the landslide prediction in areas where regional models are not available or where inventory data is not sufficient to develop a robust model.

The effect of DEM resolution on topographic wetness index calculation and visualization: An insight to the hidden danger unraveled in Bozkurt in August, 2021

Topographic Wetness Index, also known as the compound topographic index, (TWI) is a topographic indicator that calculates the potential of where water is likely to accumulate during excessive precipitation cycles resulting from abrupt atmospheric anomalies. High index values represent serious potential of water accumulation due to low slope, and the opposite for high slope. As expected from the term, slope, Digital Elevation Model (DEM) datasets play an important role in the calculation of TWI. DEMs are produced utilizing tachometry, GPS benchmarking, UAV, aerial or satellite image capture and LIDAR capabilities. However, no matter how it is generated, a DEM is as good as the actual ground sampling algorithm, on which the final resolution is based. Using six different DEM resolutions coming from three global and one national source presented in three different setting coverages, upper feeder basin of Bozkurt sub-province, Kastamonu, was analyzed emphasizing the urbanized part of the sub-province, which was devastated during the August 11th, 2021 flood. Coarser resolution missed the overall precision while the finer resolution captured it nicely. On the flip side, finer resolution excessively fragmented the questioned area while the coarser resolution formed a unity coinciding with the destructed area recorded during the event.

Planning of best management practices for watershed-scale pollution control: A simulation-based inexact fractional programming approach

The implementation of best management practices (BMPs) is crucial for protecting and restoring watersheds from nonpoint source (NPS) pollution. BMP placement plans with a high pollution reduction to cost ratio are highly desired by decision makers. Nevertheless, few studies tackled such a ratio optimization problem in their efforts to identify BMP placement plans. BMP planning also faces challenges in handling inherent uncertainties and estimating NPS pollution loads. To address these gaps, a simulation-based inexact credibility-constrained mixed-integer fractional programming (SICMFP) model was proposed for supporting the placement of BMPs. This model was developed by integrating the export coefficient model (ECM), compound topographic index (CTI), inexact programming, and mixed-integer programming within a fractional programming framework. SICMFP extends the capability of previous BMP placement models by optimizing the ratio of conflicting objectives and handling uncertain parameters based on interval theory and credibility measure. It can also conveniently simulate the spatiotemporal distribution of pollution loads through the ECM, and efficiently identify potential wetland sites using CTI. SICMFP was applied to plan BMP placement in the Luan River Basin, located in northern China. The results showed that SICMFP could provide BMP placement plans with the optimal system efficiency between pollution reductions and cost savings under different credibility levels. These plans could help decision makers gain in-depth insights into the tradeoffs between expected system efficiency and the risk of constraint violation. As the credibility level rises from 0.6 to 0.9, the optimal ratio between pollution reduction and cost declined from [0.88, 1.66] kg/103 yuan to [0.70, 1.15] kg/103yuan, whereas the costs and pollution reductions increased. Compared with the least-cost single-objective model, SICMFP could lead to more pollution reductions and higher system efficiency. The developed SICMFP approach can also be extended to address the ratio optimization problems of water pollution management in other watersheds.

Application of Scenario Earthquakes for Analysis of Seismically Triggered Landslide Hazard: A Case Study in Costa Rica

In this study, we demonstrate the capabilities of hypothetical scenario earthquakes as a new tool for assessment of hazards associated with earthquake-triggered landslides. Costa Rica offers an ideal environment for demonstrating the utility of scenario earthquakes due to its diverse tectonic environments and associated widespread seismic hazard, rugged topography, and high landslide susceptibility. We investigate the relative influence of landslide proxies such as topographic slope, peak ground velocity (PGV), and compound topographic index (CTI), and earthquake source parameters such as magnitude and depth, on predicted landslide probability and fatality. We examine five distinct tectonic environments, including subduction events beneath the (1) Nicoya and (2) Osa peninsulas respectively, (3) intraplate earthquakes beneath the Central Volcanic Range (CVR) and (4) the Central Costa Rica Deformed Belt (CCRDB), and (5) back-arc thrust events on the eastern Caribbean coast. Our results demonstrate that the slope, PGV, and CTI thresholds necessary to produce landslide probabilities greater than 10% vary by tectonic environment. In all cases, we observe magnitude to be the primary control on the predicted maximum landslide probability and overall areal landslide coverage. We validate model predictions with observed landslide inventories from the 2009 Cinchona and 1991 Limon earthquakes, demonstrating a good fit, where over 70% of landslides occurring in zones of greater than 20% probability. We also use a global model of landslide impact to predict exposure and fatality ranges for each scenario earthquake of this study, revealing that moderate-sized earthquakes in the CCRDB and CVR and large subduction megathrust earthquakes each pose a significant hazard to Costa Rica’s population.

Application of geospatial technology coupled with fuzzy logic operations in mapping soil-erosion critical zones: the case of guder sub-basin, upper blue nile basin, ethiopia

The study attempted to map soil-erosion critical zones in the Guder sub-basin in Ethiopia. To map soil erosion sensitive areas, a digital elevation model (12 m × 12 m spatial resolution), precipitation data covering 30 years, soil type, and land use were utilized as inputs. Fuzzy logic techniques based on Geographic Information Systems (GIS) were integrated and analyzed on the ArcGIS 10.5 platform. Five contributing variables were considered as potential causes associated to soil-erosion in the study. Slope, land use, soil type, rainfall, and compound topographic index are the variables. Fuzzy membership values were constructed to generate the rankings of each parameter and their subclasses. Researcher and expert judgment with a survey of the previous studies were used to determine the membership value for each thematic layer and their classes. As a result, the soil-erosion zone map revealed very high, high, moderate, low, and very low erosion susceptibility with areal percentage distribution of 4.96%, 67.48%, 25.41%, 1.88%, and 0.27%, respectively. The study's findings were validated using cross-relationship of the contributing elements and the final map, which revealed strong relationships. The study's findings would help decision-makers and policymakers plan and implement effective watershed management strategies in highly vulnerable locations to soil erosion. Fuzzy logic approaches, when combined with GIS, have been proven to be a basic tool for determining erosion important locations. The final soil erosion map revealed that the majority of the studied areas were prone to soil erosion as a result of agricultural practices, necessitating integrated soil and water conservation practices.

A new approach to evaluate the risk of bark beetle outbreaks using multi-step machine learning methods

Bark beetles (Coleoptera: Curculionidae) alter forest ecosystem functioning through tree mortality, thus causing billions of dollars in economic and ecological damages worldwide. Dendroctonus frontalis Zimmermann is considered one of the most significant insect pest species of pine (Pinus spp.) trees in the United States (U.S.), Central America, and Mexico. To manage this threat, research has sought to predict and forecast outbreaks and identify high risk areas to focus on preventative management and reduce economic losses. Prior work has focused on using environmental predictors, but limitations have arisen regarding data availability and structure. Our research objective was to improve on current D. frontalis outbreak prediction models using contemporary modeling techniques. Beetle outbreak data were obtained from the United States Department of Agriculture Forest Service (USDA-FS) and were paired with three spatial–temporal beetle outbreak dynamics from the prior year, fifteen climate variables (DAYMET and WORLDCLIM) (temperature, radiation, wind, and water resources), two terrain attributes (NASA) (elevation and compound topographic index), and four vegetation indices [Moderate Resolution Imaging Spectroradiometer (MODIS)] [maximum and mean normalized difference vegetation index (NDVI)] as predictive features. Extreme gradient boosting was used to create two separate models that predicted the probability and magnitude of beetle outbreaks, which were used to create interpolated prediction maps for the southeastern U.S. The interpolated maps were combined to estimate outbreak risk (i.e., risk = probability of outbreak × magnitude of outbreak). Overall model accuracy was 87.7% when tested on an independent dataset. Distance to prior year outbreak and mean NDVI were the most important features when predicting the probability of outbreak, while summer maximum temperature, distance to prior year outbreak, and winter minimum temperature were the highest weighted features when predicting the magnitude. Results indicated that most of the southeastern U.S. was at low risk (<0.0001% damage per hectare) for the years 2008–2020. Risk was highest for 2012, 2016, and 2017. A few areas in Alabama, Georgia, northern Florida, and South Carolina contained stands at higher risk for damage (>0.01% per hectare) and some locations were at risk for >90% damage per hectare. Extreme gradient boosting paired with outbreak probability and magnitude performed well and is proposed as a solution for future bark beetle prediction and forecasting for more timely management strategies. The inclusion of climatic variables in outbreak models allows for forecasting the effects of future climate change on pine pest populations globally.

Mapping soil liquefaction susceptibility across Europe using the analytic hierarchy process

Mapping the susceptibility of earthquake-induced soil liquefaction at the continental scale is a challenge. Susceptibility of soils to liquefaction is the tendency of certain geomaterials to undergo a severe stiffness degradation and loss of shear strength. The latter could be induced by cyclic loading induced by seismic events. The liquefaction surface evidence is a local phenomenon, and detailed geotechnical field investigations are not available for regional studies, not to mention at continental scales. The literature review shows earthquake-induced soil liquefaction evidence in several European countries, yet, a comprehensive picture of the susceptibility at the European scale is not available. This work aims to develop a methodology to assess the earthquake-induced soil liquefaction susceptibility in Europe using geospatial parameters weighted via the Analytic Hierarchy Process (AHP). The major outcome of the study is a novel Liquefaction Susceptibility map of Europe (LSE), based on the building of ten different European macro-units for earthquake-induced soil liquefaction. These European macro-units have been delineated in this work in order to be homogenous areas from the geological, physiographical, and geomorphological points of view. The adopted input parameters are the depositional environment of the sediments, the distance from water bodies (coast and rivers), and the compound topographic index (as a proxy of the soil saturation). The resolution of the LSE map is 900 × 900 m. The results have been cross-compared with soil liquefaction susceptibility maps available for a region of Greece (i.e. Thrace), Portugal, Bulgaria, and sites where liquefaction manifestations occurred across Europe. The LSE can be adopted to identify at large scale the areas susceptible to liquefaction including also the territories across Europe characterized by low seismicity and potentially affected by anthropogenic seismicity.

Terrain Variables Based Spatial Mapping of Soil Properties in a Watershed of Himalayan Landscape Using Random Forest Model

Soil information, including their spatial variability is vital for devising various soil and land management policies and strategies as well as assessing the environmental impact of different land use changes. Among the various soil forming factors, terrain plays a vital role in determining the spatial distribution of soil properties especially in hilly terrains due to its impact on soil processes as well as the redistribution of soil materials. The study was carried out for assessing the applicability and efficiency of various terrain parameters for prediction as well as spatial mapping of different soil properties using Random Forest (RF) modeling approach, in a mid-Himalayan watershed located in Tehri Garhwal district, Uttarakhand. Field data collection was done by collecting soil samples from 0-15cm depth (surface layer) at 68 different locations in the study area, following a transect survey method. Collected samples were pre-processed and analyzed in the laboratory for estimating various soil properties such as sand, silt and clay contents as well as soil nutrients such as Soil Organic Carbon (SOC) and nitrogen (N). Fifteen terrain parameters derived by digital terrain analysis of CartoDEM of 10m spatial resolution were used as predictor variables during RF modeling. Pearson correlation analysis revealed poor linear relationship of soil properties with various terrain parameters. Variable importance ranking revealed that compound topographic index, elevation, heat load index, integrated moisture index and mean curvature are the most important variables, explaining maximum variability in different properties. The performance of RF models for predicting various soil properties were evaluated based on the coefficient of determination (R2), Root Mean Square Error (RMSE) as well as Lin’s Concordance Correlation Coefficient (CCC). The models were found to perform well with R2 and CCC values of 0.47 and 0.50 for sand, 0.52 and 0.55 for silt, 0.44 and 0.57 for clay, 0.41 and 0.36 for organic carbon and 0.55 and 0.34 for nitrogen, respectively. The validated models were further employed for generating the spatial distribution maps of various soil properties. Such attempts combining various environmental variables with machine learning based models may aid us in mapping the spatial distribution of various soil properties in the hilly mountainous terrains with considerable accuracies.

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon.

This study aimed to produce a soil organic carbon (SOC) content map with high accuracy and spatial resolution using the most effective factors in the model. The spatial SOC estimation success of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Perception Network (MLP) and MLP-OK Hybrid models were compared to obtain the most reliable model in estimating the SOC content. The study area was located in Besni district in the Southeastern Anatolia Region of Turkey. Total of 132 surface (0–30 cm) soil samples were collected from the covers 1330 km2 land and analyzed for SOC, lime, clay and sand content and soil reaction included in the estimation models. Mean annual precipitation and temperature, elevation, compound topographic index, enhanced vegetation and normalized difference vegetation index, were also used as the inputs in the modelling. The spatial distribution of SOC was determined using a MLP and a two-stage ensemble model (MLP-OK) combining the estimation of OK residuals. Soil surveys and covariates were used to train and validate the MLP-OK hybrid model. The MLP-OK model provided a more accurate estimation of SOC content with minimal estimation errors (ME: -0.028, 45 MAE: 0.042, RMSE: 0.066) for validation points compared to the other models. The MLP-OK model outperformed other models by 75.09 to 77.92%. The MLP-OK model estimated the lower and upper limits of the estimated and the measured values in a consistent manner compared to the other models. The spatial distribution map of SOC content obtained by ANN-kriging approach was significantly affected by ancillary variables, and revealed more detail than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area. The results revealed that the assembling of MLP with OK model can contribute to obtain more reliable regional, national and global spatial soil information.

Multi‐scale species density model for conserving an endangered songbird

AbstractHabitat modeling across a landscape that has gradients of habitat conditions requires potential predictor data that can be quantified at biologically relevant scales. We used remotely sensed data to develop a multi‐scale density model in 2018 for the golden‐cheeked warbler (Setophaga chrysoparia; warbler), a species that breeds in Ashe juniper (Juniperus ashei)‐oak (Quercus spp.) woodlands in central Texas, USA. We first classified Ashe juniper and broadleaf tree cover at a 1‐m resolution and used this to map potential habitat across the warbler's &gt;67,000‐km2 breeding range. We then designed a survey for estimating warbler density based on hierarchical distance sampling. We used stratified random sampling to survey for male warblers at 1,804 points across the continuum of tree canopy cover and composition and detected 810 warblers during our surveys. We developed a suite of potential predictor variables for modeling warbler density that reflected vegetation, topography, climate, and anthropogenic land use conditions across the breeding range and developed these at 3 scales representing the territory, site, and landscape. We modeled warbler density and used the best fit model to produce a spatially explicit estimate. Predicted warbler density was influenced by tree canopy cover and canopy height at the territory scale (100‐m radius); tree canopy cover, percent of the canopy comprised of juniper, and an interaction between canopy cover and compound topographic index at the site scale (1‐km radius); and annual temperature range at the landscape scale (5‐km radius). We estimated a population size of 217,444 male warblers (95% CI = 153,917–311,965) and &gt;3,000 males in each recovery unit. After controlling for the duration of point count surveys, our estimate of population size was similar to that reported from the only previous breeding range survey conducted in 2008–2009. Our model results indicated that management activities to increase warbler density should promote woodlands with high tree canopy cover, approximately 60–80% Ashe juniper composition, and tree heights &gt;3 m. In contrast to a patch‐based approach, our treatment of habitat variables as continuous helped to credibly map the warbler distribution across areas with broad transitions from woodlands to shrublands. By measuring these predictor variables at biologically relevant scales, we allowed the warbler survey data to define habitat relationships instead of using anthropogenically defined habitat patches. Outcomes from our study show the benefits of developing spatial products tailored to individual species of interest for conservation and management decisions.

Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories

Abstract. Landslides are a key hazard in high-relief areas around the world and pose a risk to populations and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall-induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that the average upstream angle and compound topographic index are strong predictors of landslide scar location, while the local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modeling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.

The use of remote sensing to characterise geomorphometry and soil properties at watershed scale

This study aims to investigate the complex ecological interactions between geomorphometry, spectral indices, and soil properties to develop a watershed management plan that can mitigate the effects of global warming. In this study, Sentinel-2 multi-spectral instrument data was used to map some spectral indices. The digital elevation model was used to map transportation capacity index (TCI), stream power index (SPI), compound topographic index (CTI), and curvature. Some soil properties were analysed using 128 top-soils. According to the results, a strong correlation (p < 0.05) was found between CTI and NDVI (0.358), NDWI (-0.336), NDMI (0.372), SBI (-0.298), pH (-0.165), and phosphorus (0.164). The highest correlation (p < 0.01) was found between SPI and phosphorus (0.301). The CTI model of the watershed, which was developed using geomorphometric data, can increase the success of forestry activities. Spectral indices have the potential to be used in the evaluation of soil properties and geomorphometric characteristics.

Determination of the relationship among compound Topographic Index (CTI), soil properties and land-use in karst ecosystems

ABSTRACT Topography is a key factor that affects edaphic, climatic, and biotic factors directly or indirectly. Relationships between Compound Topographic Index (CTI) model and soil properties were investigated in order to determine the ecological potential of the area in order to plan the sustainability of the fertile lands and the rehabilitation of the degraded areas in the study area. Totally 84 soil samples (0–20 cm) collected from different land use types (forest, cropland, rangeland) in Karst Mountain, Andırın-Kahramanmaras, Turkey. The CTI distributions are generated from digital elevation data of 12 m resolution obtained from AlosPalsar Satellite images. Although CTI values changed to Forest> Cropland> Rangeland, the highest correlation was found between CTI and soil moisture content in rangeland (R2 = 0.81). Lower correlations were found with other land uses (R2 = 0.63 in Cropland and R2 = 0.31 in Forest). CEC and Clay content were found to have a significant (p < 0.01) positive correlation with Curvature. Despite a distinctive topographic structure of karst ecosystems, these correlations verified the influence of topography on soil properties. CTI, which has strong correlations with soil moisture content can be used in planning reclamation processes of degraded karst ecosystems.

LiDAR-aided hydrogeologic modeling and object-based wetland mapping approach for Pennsylvania

Wetlands for the entire state of Pennsylvania (119 , 282 km2) were mapped using object-based image analysis (OBIA). The OBIA combined vector output from logistic statistical models informed by hydrogeologic variables (e.g., hydrology facets, soils, and climate) with LiDAR-derived terrain variables, high-resolution aerial orthoimagery, and land-cover data through a data-fusion approach. Wetland presence data and hydrogeologic features in a geographic information system were used to develop moderate-resolution (10 m2) maximum-entropy models (Maxent) both for wetlands dominated by low-lying emergent vegetation and woody vegetation, respectively. Terrain variables describing slope, landscape wetness, surface elevation, climate, precipitation, and poorly drained soils were most strongly associated with occurrences of wetlands in this modeling procedure. Maxent derived models accurately predicted validation wetlands: Area under curve of 0.91 for woody wetlands and 0.93 for emergent wetlands. High-resolution (1 m2) mapping was then performed in the OBIA by creating objects based on a compound topographic index (CTI) and leaf-off true-color orthoimagery and then classifying them using a combination of the CTI, imagery, and 10-m2 statistical models. An accuracy assessment found that the OBIA correctly classified 81.5% of validation locations compared to only 59.3% for National Wetlands Inventory (NWI), although commission rates were higher for OBIA (15.6%) versus NWI (3.4%). The user’s accuracy for OBIA was 82.8% compared to 78.1% for NWI. Our work suggests advances in remote sensing techniques and a data-fusion approach may facilitate improved wetland mapping accuracy while reducing processing time relative to traditional approaches.

Evaluation of geomorphometric characteristics and soil properties after a wildfire using Sentinel-2 MSI imagery for future fire-safe forest

Understanding spatiotemporal geomorphological and pedological changes as a consequence of wildfires can allow stakeholders, land planners, and policymakers to design efficient fire safety-based afforestation and restoration programs of forest lands. The use of remote sensing techniques is a key tool to achieve this goal. The suitable combination of Sentinel-2 MSI data for mapping of different spectral indices related to burn severity and their relationship with other morphometric and soil properties can contribute to a better understanding of the impact of fire, and this is relevant in regions where is still scarce fire-related research such as Turkey. In this investigation, the use of NDVI (Normalized Difference Vegetation Index), dNDVI (Difference Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NBR (Normalized Burn Ratio), dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio), SBI (Soil Bare Index), As (Upslope area), CTI (Compound Topographic Index), TCI (Terrain Characterization Index), SPI (Stream Power Index) and Curvature (Standard Curvature) were combined. As a study case, 47.43 ha in a burned area of Çınarpınar forest unit, Andırın, Kahramanmaraş in Turkey was selected. The results showed that dNDVI, dNBR, RBR, SBI contribute to relevant information about the effect of the wildfire. According to the dNBR fire severity classification, 75% of the total area has been exposed to high-severity fire. The relationship of Sentinel MSI satellite images with some soil and morphometric features have been found meaningful to understand the impact of forest fire in Mediterranean ecosystems. The information collected in the Turkish forest areas affected by wildfires should be relevant for planning and represent a key contribution to the selection of restoration programs and afforestation techniques for a future fire-safe forest.

Grid order prediction of ephemeral gully head cut position: Regional scale application

Predicting ephemeral gully (EG) location is essential for many land management decisions and for accurate application of widely used soil erosion models. Topographic indexes are widely used for identifying EG head locations, even though the calibration of the topographic index models limits their use for broad scales. The objective of this research was to test the accuracy and usability of grid order in predicting EG head locations at the regional scale compared to other topographic indexes commonly used for this purpose. Three hundred fifty-three EGs in eight watersheds located within different landforms (Major Land Resource Areas) in Iowa, USA, were visually digitized, georeferenced, and analyzed using 3-meter LiDAR-derived elevation datasets and orthoimages. The location prediction accuracy, spatial location prediction error stability, and the variation of grid order critical threshold (CT) values were compared to predictions from four commonly used topographic indexes, specific contributing drainage area (As), Compound Topographic Index (CTI), Stream Power Index (SPI), and a variation of Stream Power Index (AS2). Results indicate that using As, CTI, SPI, and AS2 to identify flow length upstream from predicted EG head locations would require careful calibration for each watershed or substantial errors in prediction accuracy would occur. In contrast, the accuracy of using grid order was acceptable at the regional scale even without model calibration with Nash Sutcliff Efficiency estimates ranging from 0.29 to 0.59; the distance between surveyed and predicted EG head locations was within 20 m for all studied EGs. The grid order value of 4 was the best CT value when the slope gradient was less than 10%, and 3 for steeper slopes. These results could be easily applied for different landforms across wider areas. The result of this research supports the prediction of EGs using automated systems without calibration at a broad scale where quality DEMs are available.

Megazonation of earthquake-induced soil liquefaction hazard in continental Europe

Mapping large territories for earthquake-induced soil liquefaction hazard may sound like an oxymoron since soil liquefaction is by itself a spatially highly localized phenomenon. Thus, the zonation of liquefaction hazard at the continental scale (i.e. megazonation) is a truly hard facing challenge even if accepting a low level accuracy. Yet, the availability of a megazonation chart of liquefaction hazard could be useful to identify territories that in case of an earthquake may display this phenomenon of soil instability distinguishing them from the regions where soil liquefaction is not expected even in case of strong ground shaking. A representation of the spatial variability of liquefaction hazard potential within a single country is within reach considering the resolution and accuracy of geological and geotechnical information that is currently available in the most developed nations. The LIQUEFACT project fully addressed in a specific work package the problem of constructing georeferenced, earthquake-induced soil liquefaction hazard maps in continental Europe for various return periods. They were built using homogeneous datasets in Europe on the expected seismic hazard and on the geological, geomorphological, hydrogeological, shallow lithology and digital terrain information. A probabilistic prediction model based on a logistic regression for liquefaction occurrence was purposely developed using a set of optimal geospatial predictors (explanatory variables) which include the weighted-magnitude peak ground acceleration, the weighted-mean shear-wave velocity in the top 30 m (VS30) and the compound topographic index. The optimal geospatial descriptors were selected based on the Luco and Cornell methodology, namely on the criteria of efficiency, practicality and proficiency. To calibrate and successively validate the logistic regression, a database of liquefaction manifestations occurred in continental Europe was used. Although the level of accuracy provided by these models at a local scale is low, still the availability of continental charts of liquefaction hazard may help policy makers and administrators to prioritize which urbanized territories should be investigated further to assess the seismic risk of structures and infrastructures associated to the occurrence of soil liquefaction.