Erosion processes in karst landscapes of the Russian plain northern taiga,based on digital elevation modeling

This paper considers the problems of the potential development of erosion processes in the natural landscapes of northern taiga in the Russian plain. It is considered that in forest ecosystems, erosion processes are slow and are weakly reflected in the terrain. However, the situation changes radically if the vegetation cover integrity is violated, which is inevitable with the modern methods of developing northern territories. Furthermore, global changes in average annual temperatures and the occurrence of karst processes may be the reason behind the development of erosion processes. The authors suggest a method for determining territories with a varying occurrence probability of erosional processes, based on digital elevation modelling. The territory of the Pinezhsky Nature Reserve(Arkhangelsk region) was chosen as the test plot. Direct field studies were previously used to detect exogenous geological processes in this territory. The authors were the first to suggest digital elevation modelling as a method that allows determining the potential danger of erosion in karst landscapes of the northern taiga. The geomorphometric studies resulted in the determination of areas with the greatest and lowest occurrence probability of erosion processes in the Pinezhsky Nature Reserve. It was established that the most significant erosion type was linear erosion, represented by incised river valleys and karst ravines. Sheet erosion is less significant and occurs as sinkholes, local declines, and chasms over the valleys of subterranean rivers.

An evaluation of digital elevation modeling in GIS and Cartography

This study considers seven commonly used surface fitting methods within Golden Software and ArcGIS^(TM) environments.Using grid sizes of 68 rows by 100 columns(6800 grids)and 680 rows by 1000 columns(680,000 grids)and 294,208 elevation points covering the entire landmass of Nigeria,the study evaluates the performance of these methods in terms of execution time and faithfulness in the representation of the spatial elements.Results show marked differences in time taken to execute the fitting and that Inverse Distance,the Natural Neighbor,the Nearest Neighbor,and Triangulation with Linear Interpolation seem to give the highest level of correspondence or faithfulness.

Effect of digital elevation models on monitoring slope displacements in open-pit mine by differential interferometry synthetic aperture radar

Displacement monitoring in open-pit mines is one of the important tasks for safe management of mining processes.Differential interferometric synthetic aperture radar(DInSAR),mounted on an artificial satellite,has the potential to be a cost-effective method for monitoring surface displacements over extensive areas,such as open-pit mines.DInSAR requires the ground surface elevation data in the process of its analysis as a digital elevation model(DEM).However,since the topography of the ground surface in open-pit mines changes largely due to excavations,measurement errors can occur due to insufficient information on the elevation of mining areas.In this paper,effect of different elevation models on the accuracy of the displacement monitoring results by DInSAR is investigated at a limestone quarry.In addition,validity of the DInSAR results using an appropriate DEM is examined by comparing them with the results obtained by global positioning system(GPS)monitoring conducted for three years at the same limestone quarry.It is found that the uncertainty of DEMs induces large errors in the displacement monitoring results if the baseline length of the satellites between the master and the slave data is longer than a few hundred meters.Comparing the monitoring results of DInSAR and GPS,the root mean square error(RMSE)of the discrepancy between the two sets of results is less than 10 mm if an appropriate DEM,considering the excavation processes,is used.It is proven that DInSAR can be applied for monitoring the displacements of mine slopes with centimeter-level accuracy.

Re-delineating mountainous areas with three topographic parameters in Mainland Southeast Asia using ASTER global digital elevation model data

Tropical mountainous areas not only provide substantial carbon storage and play an important role in global biological diversity, but also provide basic livelihood for a large number of poor ethnic minorities. However, there is no unified and explicit definition for mountainous areas. The local elevation range(LER) is a crucial structural parameter for delineating mountainous areas. However, current LER products are limited by the subjective selection of an optimum statistical window or coarser spatial resolution of topographical data. In this study, we presented an approach using thresholds for three topographic parameters, elevation, slope, and LER, derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM) to redelineate the vast mountainous areas of mainland Southeast Asia(MSEA). The mean change-point analysis method was applied to determine the optimum statistical window of the 1 arc second(approximately 30 m)-resolution GDEM LER. The results showed that: First, the optimum statistical window is 38 × 38 cell units(width × height) in a rectangular neighborhood, or an area of about 1.30 km^2 for calculating GDEM LER in MSEA. Second, the LER of more than 80% of the area ranges from 30 m to 499 m in MSEA. The LERs in the northern and northwestern MSEA are greater than their counterparts in the south and east. Third, the area of the re-delineated mountainous areas was 83.52 × 10~4 km^2, about 38.71% of the total area. Spatially, the mountainous areas are mainly distributed in the north and northeast of MSEA. The re-delineated 30-m resolution map of the mountainous areas will serve as a topographical dataset for monitoring mountainrelated land surface changes in MSEA. The parameter-modified mountain extraction procedure can be expanded to delineate global mountainous areas.

The variation in basal channels and basal melt rates of Pine Island Ice Shelf

In recent years,there has been a significant acceleration in the thinning,calving and retreat of the Pine Island Ice Shelf(PIIS).The basal channels,results of enhanced basal melting,have the potential to significantly impact the stability of the PIIS.In this study,we used a variety of remote sensing data,including Landsat,REMA DEM,ICESat-1 and ICESat-2 satellite altimetry observations,and Ice Bridge airborne measurements,to study the spatiotemporal changes in the basal channels from 2003 to 2020 and basal melt rate from 2010 to 2017 of the PIIS under the Eulerian framework.We found that the basal channels are highly developed in the PIIS,with a total length exceeding 450 km.Most of the basal channels are ocean-sourced or groundingline-sourced basal channels,caused by the rapid melting under the ice shelf or near the groundingline.A raised seabed prevented warm water intrusion into the eastern branch of the PIIS,resulting in a lower basal melt rate in that area.In contrast,a deepsea trough facilitates warm seawater into the mainstream and the western branch of the PIIS,resulting in a higher basal melt rate in the main-stream,and the surface elevation changes above the basal channels of the mainstream and western branch are more significant.The El Ni?o event in 2015–2016 possibly slowed down the basal melting of the PIIS by modulating wind field,surface sea temperature and depth seawater temperature.Ocean and atmospheric changes were driven by El Ni?o,which can further explain and confirm the changes in the basal melting of the PIIS.

arcPycor:an open-source automated GIS tool to co-register elevation datasets

Subtraction of elevation datasets(e.g.digital elevation models(DEMs)and non-continuous elevation points)acquired at different times is a useful method to monitor landform surface change.Due to heavy post-processing of these elevation datasets,multi-source errors are introduced into the resulting elevation data products.To improve the estimation of elevation change,co-registration of elevation datasets is a prerequisite.This paper presents an open-source automated GIS tool(arc Pycor)for co-registering elevation datasets.arc Pycor is coded in Python 2.7 and is run via Arc GIS for Desktop.The performances of arc Pycor have been evaluated using a series of experiments.In benchmark tests,the resolved co-registration vectors of arc Pycor are compared to the predefined shift vectors obtained by artificially misaligning the slave DEMs from the master elevation datasets.Results show that arc Pycor is able to co-register DEMs with relative high accuracy and can well align slave DEMs to non-continuous elevation points,which indicates its robustness in co-registering of elevation datasets.arc Pycor is also able to co-register multi-sourced DEMs of different resolutions in mountain areas.

Morphodynamics and sediment connectivity index in an unmanaged,debris-flow prone catchment:a through time perspective

Torrential processes are among the main actors responsible for sediment production and mobility in mountain catchments.For this reason,the understanding of preferential pathways for sediment routing has become a priority in hazard assessment and mitigation.In this context,the sediment Connectivity Index(IC)enables to analyse the existing linkage between sediment sources and the selected target(channel network or catchment outlet).The IC is a grid-based index that allows fast computation of sediment connectivity based on landscape information derived from a single Digital Terrain Model(DTM).The index computation is based on the log-ratio between an upslope and a downslope component,including information about drainage area,slope,terrain roughness,and distance to the analysis target(e.g.outlet).The output is a map that highlights the degree of structural connectivity of sediment pathways over analysed catchments.Until now,these maps are however rarely used to help defining debris-flow hazard maps,notably due to a lack of guidelines to interpret the IC spatial distribution.This paper proposes an exploitation procedure along profiles to extract more information from the analysis of mapped IC values.The methodology relies on the analysis of the IC and its component variables along the main channel profile,integrated with information about sediment budgeting derived from Difference of DEMs(DoD).The study of connectivity was applied in the unmanaged sub-catchment(without torrent control works)of the Rio Soial(Autonomous Province of Trento–NE Italy)to understanding the geomorphic evolution of the area after five debris flows(in ten years)and the related changes of sediment connectivity.Using a recent DTM as validation,we demonstrated how an IC analysis over the older DTM can help predicting geomorphic changes and associated hazards.The results show an IC aptitude to capture geomorphic trajectories,anticipate debris flow deposits in a specific channel location,and depict preferential routing pathways.

The morphological changes of basal channels based on multisource remote sensing data at the Pine Island Ice Shelf

The basal channel is a detailed morphological feature of the ice shelf caused by uneven basal melting.This kind of specifically morphology is widely distributed in polar ice shelves.It is an important research object of sea-ice interaction and plays a vital role in studying the relationship between the ice sheet/ice shelf and global warming.In this paper,high-resolution remote sensing image and ice penetration data were combined to extract the basal channel of the Pine Island Ice Shelf.The depth variation of Pine Island Ice Shelf in the recent 20 years was analyzed and discussed by using ICESat-1,ICESat-2,and IceBridge data.Combined with relevant marine meteorological elements(sea surface temperature,surface melting days,circumpolar deep water and wind)to analyze the basal channel changes,the redistribution of ocean heat is considered to be the most important factor affecting the evolution and development of the basal channel.

Land Use Land Cover Dynamics of Oba Hills Forest Reserve, Nigeria, Employing Multispectral Imagery and GIS

Land use Land cover (LULC) has undergone progressive changes worldwide over the years. However, there is limited information available about these changes in Oba Hills Forest Reserve, Nigeria. The existing spatial analysis of the forest excluded important land use classes like settlements. Therefore, this study aimed at assessing the dynamics of LULC in Oba Hills Forest Reserve between 1987 and 2019. Images from Landsat 5, Landsat 7, and Landsat 8 for the years 1987, 2001, 2013, and 2019 were obtained and subjected to preprocessing and classification using the maximum likelihood algorithm, change detection, and Normalized Differential Vegetation Index (NDVI). The coordinates of specific benchmark locations and other points were acquired for ground-truthing and developing Digital Elevation Model (DEM). Three distinct LULC classes were identified: forest, bare land (including open spaces, agriculture, rocks, and grasslands), and built-up areas. The forest cover in the reserve gradually decreased from 56% in 1987 to 47% in 2019, resulting in a total area loss of 455.4 hectares. Correspondingly, the other LULC classes experienced exponential expansion. Bare land increased from 44% in 1987 to 52% in 2019, while the built-up area expanded by 57.28 hectares. These changes are attributed to prevalent anthropogenic activities such as agriculture, grazing, logging, firewood collection, and population growth within the catchment area. The declining NDVI values in the forest reserve, from 0.52 to 0.44 within the years of assessment, further substantiated the substantial loss of forest cover. The DEM and topographical map highlighted notable steep slopes and elevations of up to over 550 m above sea level (asl) within the reserve, which have implications for forest growth and dynamics. In conclusion, this study reveals extensive rates of forest cover changes into bare land, primarily for agriculture, and settlements, and offers further recommendations to reverse the trend.

Characteristics and influencing factors of proximity distance index on the northern Shaanxi Loess Plateau in China

Topographic feature points and lines are the framework of topography,and their spatial distance relationship is an breakthrough in the study of topographical geometry,internal structure and development level.Proximity distance(PD)is an indicator to describe the distance between the gully source point(GSP)and the watershed boundary.In the upstream catchment area,PDs can be expressed by the streamline proximity distance(SPD),as well as by the horizontal proximity distance(HPD)and the vertical proximity distance(VPD)in the horizontal and vertical dimensions,respectively.The series of indicators(e.g.,SPD,HPD and VPD)are important for quantifying the geomorphological development process of a loess basin because of the headward erosion of loess gullies.In this study,the digital elevation model data with 5 m resolution and a digital topographic analysis method are used for the statistical analyses of the SPD,VPD and HPD in 50 sample areas of 6 geomorphic types in the Loess Plateau of northern Shaanxi.The spatial characteristics and the influencing factors are also analysed.Results show that:1)Central tendencies for the HPDs and the VPDs for the whole study area and the six typical loess landforms are evident.2)Spatial patterns of the HPDs and the VPDs exhibit evident trends and zonal distributions over the whole study area.3)The HPDs have a strong positive correlation with gully density(GD)and hypsometric integral.The VPDs also correlates with GD to an extent.Vegetation cover,mean annual precipitation and loess thickness have stronger effects on the VPD than on the HPD.

Deriving the slope-mean shielded astronomical solar radiation spectrum and slope-mean possible sunshine duration spectrum over the Loess Plateau

Solar radiation is often shielded by terrain relief, especially in mountainous areas, before reaching the surface of the Earth. The objective of this paper is to study the spatial structures of the shielded astronomical solar radiation(SASR) and the possible sunshine duration(PSD) over the Loess Plateau. To this end, we chose six test areas representing different landforms over the Loess Plateau and the software package of Matlab was used as the main computing platform. In each test area, 5-m-resolution digital elevation model established from 1:10,000 scale topographic maps was used to compute the corresponding slope, SASR and PSD. Then, we defined the concepts of the slope-mean SASR spectrum and the slope-mean PSD spectrum, and proposed a method to extract them from the computed slope, SASR and PSD over rectangular analysis windows. Using this method, we found both spectrums in a year or in a season for each of the four seasons in the six test areas. Each spectrum was found only when the area of the corresponding rectangular analysis window was greater than the corresponding stable area of the spectrum. The values of the two spectrums decreased when the slope increased.Furthermore, the values of the stable areas of the spectrums in a year or in a season were positively correlated with the variable coefficients of the slope or the profile curvature. The values of the stable areas of the two spectrums in a year or in a season may represent the minimum value of test areas for corresponding future research on the spatial structures of the SASR or PSD. All the findings herein suggest that the spatial structures of the PSD and the SASR are caused by the interactions between solar radiation and terrain relief and that the method for extracting either spectrum is effective for detecting their spatial structures. This study may deepen our understanding of the spatial structure of solar radiation and help us further explore the distribution of solar energy in mountainous regions.

Analysis of the Quality of Daily DEM Generation with Geosynchronous InSAR

Up-to-date digital elevation model(DEM)products are essential in many fields such as hazards mitigation and urban management.Airborne and low-earth-orbit(LEO)space-borne interferometric synthetic aperture radar(InSAR)has been proven to be a valuable tool for DEM generation.However,given the limitations of cost and satellite repeat cycles,it is difficult to generate or update DEMs very frequently(e.g.,on a daily basis)for a very large area(e.g.,continental scale or greater).Geosynchronous synthetic aperture radar(GEOSAR)satellites fly in geostationary earth orbits,allowing them to observe the same ground area with a very short revisit time(daily or shorter).This offers great potential for the daily DEM generation that is desirable yet thus far impossible with space-borne sensors.In this work,we systematically analyze the quality of daily GEOSAR DEM.The results indicate that the accuracy of a daily GEOSAR DEM is generally much lower than what can be achieved with typical LEO synthetic aperture radar(SAR)sensors;therefore,it is important to develop techniques to mitigate the effects of errors in GEOSAR DEM generation.

Estimation of leaf color variances of Cotinus coggygria based on geographic and environmental variables

Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(SVM)algorithms.Results reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not obvious.The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of C.coggygria in a shrubbery with a complex geographical environment in the absence of human activity.

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.Methods:Here,we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China.We used Landsat time-series observations,Advanced Land Observing Satellite(ALOS)Phased Array L-band Synthetic Aperture Radar(PALSAR)data,and National Forest Inventory(NFI)plot measurements,to generate the forest AGB maps at three time points(1992,2002 and 2010)showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong,China.Results:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable manner.The root mean square error of the plotlevel AGB validation was between 15.62 and 53.78 t∙ha^(−1),the mean absolute error ranged from 6.54 to 32.32 t∙ha^(−1),the bias ranged from−2.14 to 1.07 t∙ha^(−1),and the relative improvement over the random forest algorithm was between 3.8%and 17.7%.The largest coefficient of determination(0.81)and the smallest mean absolute error(6.54 t∙ha^(−1)were observed in the 1992 AGB map.The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010.By adding elevation as a covariable,the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals,because co-kriging resulted in better interpolation results in the valleys and plains of the study area.Conclusions:Validation of the three AGB maps with an independent dataset indicated that the random forest/cokriging performed best for AGB prediction,followed by random forest coupled with ordinary kriging(random forest/ordinary kriging),and the random forest model.The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography.The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

A New Algorithm to Automatically Extract the Drainage Networks and Catchments Based on Triangulation Irregular Network Digital Elevation Model

A new algorithm to automatically extract drainage networks and catchments based on triangulation irregular networks(TINs) digital elevation model(DEM) was developed. The flow direction in this approach is determined by computing the spatial gradient of triangle and triangle edges. Outflow edge was defined by comparing the contribution area that is separated by the steepest descent of the triangle. Local channels were then tracked to build drainage networks. Both triangle edges and facets were considered to construct flow path. The algorithm has been tested in the site for Hawaiian Island of Kaho'olawe, and the results were compared with those calculated by ARCGIS as well as terrain map. The reported algorithm has been proved to be a reliable approach with high efficiency to generate well-connected and coherent drainage networks.

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.

Design Bistatic Interferometric DEM Generation Algorithm and Its Theoretical Accuracy Analysis for LuTan-1 Satellites

LuTan-1(LT-1)is a constellation with two full-polarization L-band radar satellites designed by China,and the first satellite was scheduled to be launched in December 2021 and the second one in January 2022.The LT-1 will be operated for deformation monitoring in repeat-pass mode,and for DEM generation in bistatic mode,improving self-sufficiency of SAR data for the field of geology,earthquake,disaster reduction,geomatics,forestry and so on.In this paper,we focused on designing an algorithm for interferometric DEM generation using LT-1 bistatic satellites.The basic principle,main error sources and errors control of the DEM generation algorithm of LT-1 were systematically analyzed.The experiment results demonstrated that:①The implemented algorithm had rigorous resolution with a theoretic accuracy better than 0.03 m for DEM generation.②The errors in satellite velocity and Doppler centroid had no obvious effect on DEM accuracy and they could be neglected.While the errors in position,baseline,slant range and interferometric phase had a significant effect on DEM accuracy.And the DEM error caused by baseline error was dominated,followed by the slant range error,interferometric phase error and satellite position error.③To obtain an expected DEM accuracy of 2 m,the baseline error must be strictly controlled and its accuracy shall be 1.0 mm or better for Cross-Track and Normal-Direction component,respectively.And the slant range error and interferometric phase error shall be reasonably controlled.The research results were of great significance for accurately grasping the accuracy of LT-1 data products and their errors control,and could provide a scientific auxiliary basis for LT-1 in promoting global SAR technology progress and the generation of high-precision basic geographic data.

Obtaining morphometric variables from gullies using two methods of interpolation laser scanner data:the case study of Vassouras,Brazil

Gully erosion is a worldwide problem of land degradation and water quality,and it is also frequent in Brazil.Typically,anthropic influence is the major driver of gully evolution.To study and monitor gullies it is necessary to use specific instruments and methods to obtain accurate information.The objective of this study was to use Terrestrial Laser Scanning(TLS) to create digital elevation model(DEM) accurately and define morphometric variables that characterize gullies in a mountainous relief.Two different interpolations were evaluated using the Topogrid and GridSurfaceCreate algorithms to elaborate DEM.Topographic profile for gullies was used to assess modeling quality.The DEM of the Gully 1(G1) from the Topogrid algorithm estimated soil loss of 49%,whereas the GridSurfaceCreate algorithm estimated a soil loss of97%,in a period of 1 year.The estimated soil loss for the Gully 2(G2) was 14% from the Topogrid,and 8%from the GridSurfaceCreate algorithm.The GridSurfaceCreate algorithm underestimated the volume to area ratio for G2 due to a failure on interpolating a region of low point representativity.The Topogrid algorithm represented better the terrain irregularities,as observed through the topographic profiles traced in three regions of G1 and G2.Statistical analysis showed that the GridSurfaceCreate algorithm presented lower accuracy in estimating elevations.The underestimation trend of this algorithm was also observed in G2.The gullies showed considerable soil losses,which may reduce the areas suitable for agricultural activities,and silting up of water courses.The Topogrid algorithm presented satisfactory results,denoting great potential to produce morphometric data of gullies.

GIS tools for preliminary debris-flow assessment at regional scale

The assessment of the areas endangered by debris flows is a major issue in the context of mountain watershed management. Depending on the scale of analysis, different methods are required for the assessment of the areas exposed to debris flows.While 2-D numerical models are advised for detailed mapping of inundation areas on individual alluvial fans, preliminary recognition of hazard areas at the regional scale can be adequately performed by less data-demanding methods, which enable priority ranking of channels and alluvial fans at risk by debris flows. This contribution focuses on a simple and fast procedure that has been implemented for regionalscale identification of debris-flow prone channels and prioritization of the related alluvial fans. The methodology is based on the analysis of morphometric parameters derived from Digital Elevation Models(DEMs). Potential initiation sites of debris flows are identified as the DEM cells that exceed a threshold of slope-dependent contributing area. Channel reaches corresponding to debris flows propagation, deceleration and stopping conditions are derived from thresholds of local slope. An analysis of longitudinal profiles is used for the computation of the runout distance of debris flows. Information on erosion-resistant bedrock channels and sediment availability surveyed in the field are taken into account in the applications. A set of software tools was developed and made available(https://github.com/Hydrogeomorphology Tools) to facilitate the application of the procedure. This approach, which has been extensively validated by means of field checks, has been extensively applied in the eastern Italian Alps. This contribution discusses potential and limitations of the method in the frame of the management of small mountain watersheds.

Aspect in Topography to Enhance Fine-detailed Landform Element Extraction on High-resolution DEM

The value of the high-resolution data lies in the high-precision information discovery.The fine-detailed landform element extraction is thus the basis of high-fidelity application of the high-resolution digital elevation models(DEMs).However,the results of landform element extraction generated by classical methods might be ungrounded on high-resolution DEMs.This paper presents our research on using the aspect to reinforce the applicability and robustness of the classical approaches in landform element extraction.First,according to the research of pattern recognition,we assume that aspect-enhanced landform representation is robust to rotation,scaling and affine variance.To testify the role of aspect,we respectively integrated the aspect into three classical approaches:mean curvaturebased fuzzy classification,elevation-based feature descriptor,and object-based segmentation.In the experiment,based on four types of high-resolution DEMs(1 m,2 m,4 m and 8 m),we compare each classical approaches and their corresponding aspect-enhanced approaches based on extracting the rims of two craters having different landforms,and the ridgelines and valleylines of a region covered by few vegetables and man-made buildings.In comparison to the results generated by curvature-based fuzzy classification,the aspect enhanced curvature-based fuzzy classification can effectively filter a number of noises outperforms the curvature-based one.Otherwise,the aspect-enhanced feature descriptor can detect more landform elements than the elevation-based feature descriptor.Moreover,the aspect-based segmentation can detect the main structure of landform,while the boundaries segmented by classical approaches are messing and meaningless.The systematic experiments on meter-level resolution DEMs proved that the aspect in topography could effectively to improve the classical method-system,including fuzzy-based classification,feature descriptors-based detection and object-based segmentation.The value of aspect is significantly great to be worthy of attentions in landform representation.