Optimizing collapsed pipes mapping: Effects of DEM spatial resolution

Abstract Finding a digital elevation model (DEM) of suitable spatial resolution is vital to investigate piping erosion using aerial remote-sensing platforms like unmanned aerial vehicles (UAV). Previous studies have implied that the best spatial resolution is a DEM with the most detail. This study evaluates piping-affected areas with five DEMs (1, 5, 10, 20, and 30 m resolutions) with three trained machine-learning methods: support vector machine (SVM), maximum entropy (ME), and boosted regression tree (BRT). This method enables the identification of the specific impacts caused by changing pixel resolution to guide the selection of the most effective DEM. This study employs piping morphometry data to predict the locations of completely collapsed pipes. The performance of the methods for mapping of pipes was assessed against a piping inventory map. The results demonstrate that the finest resolution DEM is not always the most useful. Though 1 m-resolution DEMs show the most detail, the best performance was the 5 m-resolution DEM when tested for all three mapping models. The 5 m-resolution DEM-SVM combination was the best predictor of known piping sites (AUC = 81.0%). The 5-m DEM-ME was second most effective model (AUC = 75.8%). And 5-m DEM-BRT was third (AUC = 72.9%). Applying more DEM derivatives may increase confidence in the selection of the most appropriate resolution.

[1]  H. Pourghasemi,et al.  Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. , 2017, The Science of the total environment.

[2]  A. Maritan,et al.  Applications of the principle of maximum entropy: from physics to ecology , 2010, Journal of physics. Condensed matter : an Institute of Physics journal.

[3]  B. Schröder,et al.  A functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy , 2011 .

[4]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[5]  K. S. Jayappa,et al.  Prioritization of sub-basins based on geomorphology and morphometricanalysis using remote sensing and geographic informationsystem (GIS) techniques , 2011 .

[6]  S. Keesstra,et al.  How can statistical and artificial intelligence approaches predict piping erosion susceptibility? , 2019, The Science of the total environment.

[7]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[8]  Hamid Reza Pourghasemi,et al.  Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping , 2018, Geoderma.

[9]  Jay C. Bell,et al.  Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling , 2001 .

[10]  Eun-Sung Chung,et al.  An index-based robust decision making framework for watershed management in a changing climate. , 2014, The Science of the total environment.

[11]  H. Pourghasemi,et al.  Prediction of the landslide susceptibility: Which algorithm, which precision? , 2018 .

[12]  M. Marjanović,et al.  Landslide susceptibility assessment using SVM machine learning algorithm , 2011 .

[13]  H. Pourghasemi,et al.  Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran , 2016 .

[14]  H. Pourghasemi,et al.  Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances , 2013, Natural Hazards.

[15]  Bin Zhao,et al.  A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants , 2011, Ecol. Informatics.

[16]  Hamid Reza Pourghasemi,et al.  Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms , 2018, Applied Sciences.

[17]  Wanchang Zhang,et al.  Assessing Water Scarcity Using the Water Poverty Index (WPI) in Golestan Province of Iran , 2018, Water.

[18]  Hamid Reza Pourghasemi,et al.  Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion , 2017 .

[19]  D. Bui,et al.  Shallow landslide susceptibility assessment using a novel hybrid intelligence approach , 2017, Environmental Earth Sciences.

[20]  Robert E. Schapire,et al.  The Boosting Approach to Machine Learning An Overview , 2003 .

[21]  R. Selvakumar,et al.  Watershed prioritization of Palar sub-watershed based on the morphometric and land use analysis , 2014, Journal of Mountain Science.

[22]  J. Adinarayana,et al.  Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: A remote sensing and GIS approach , 2014 .

[23]  Laura Coco,et al.  From Slope Morphometry to Morphogenetic Processes: An Integrated Approach of Field Survey, Geographic Information System Morphometric Analysis and Statistics in Italian Badlands , 2016 .

[24]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[25]  I. Woodhouse,et al.  Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries , 2017 .

[26]  Charles K. Toth,et al.  ANALYZING THE EFFECTS OF SPATIAL RESOLUTION FOR SMALL LANDSLIDE SUSCEPTIBILITY AND HAZARD MAPPING , 2014 .

[27]  Martin Kehl,et al.  Loess chronology of the Caspian Lowland in Northern Iran , 2009 .

[28]  Anita Bernatek-Jakiel,et al.  Impact of piping on gully development in mid-altitude mountains under a temperate climate: A dendrogeomorphological approach , 2018 .

[29]  Aykut Akgün.  A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey , 2012 .

[30]  Chong Xu,et al.  GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China , 2012 .

[31]  Min Fan,et al.  Spatial and Temporal Analysis of Hydrological Provision Ecosystem Services for Watershed Conservation Planning of Water Resources , 2014, Water Resources Management.

[32]  Takashi Oguchi,et al.  Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest , 2016 .

[33]  Jean Poesen,et al.  Subsurface erosion by soil piping: significance and research needs , 2018, Earth-Science Reviews.

[34]  David G. Tarboton,et al.  A New Method for Determination of Most Likely Landslide Initiation Points and the Evaluation of Digital Terrain Model Scale in Terrain Stability Mapping , 2006 .

[35]  Saro Lee,et al.  Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. , 2019, The Science of the total environment.

[36]  Hamid Reza Pourghasemi,et al.  Spatial modelling of gully erosion in Mazandaran Province, northern Iran , 2018 .

[37]  Shakil Ahmad Romshoo,et al.  Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed , 2014, Environmental Monitoring and Assessment.

[38]  Seyed Amir Naghibi,et al.  A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping , 2017 .

[39]  Vijendra Kumar Pandey,et al.  Sedimentological characteristics and application of machine learning techniques for landslide susceptibility modelling along the highway corridor Nahan to Rajgarh (Himachal Pradesh), India , 2019, CATENA.

[40]  Vinay Kumar Dadhwal,et al.  Multi-Criteria Decision Making Approach for Watershed Prioritization Using Analytic Hierarchy Process Technique and GIS , 2013, Water Resources Management.

[41]  Mahyat Shafapour Tehrany,et al.  Flood susceptibility assessment using GIS-based support vector machine model with different kernel types , 2015 .

[42]  Dieu Tien Bui,et al.  A novel hybrid artificial intelligence approach for flood susceptibility assessment , 2017, Environ. Model. Softw..

[43]  Hamid Reza Pourghasemi,et al.  Spatial Modeling of Gully Erosion Using Linear and Quadratic Discriminant Analyses in GIS and R , 2019, Spatial Modeling in GIS and R for Earth and Environmental Sciences.

[44]  Joseph Holden,et al.  Application of ground‐penetrating radar to the identification of subsurface piping in blanket peat , 2002 .

[45]  M. Jain,et al.  Estimation of Sediment Yield and Areas of Soil Erosion and Deposition for Watershed Prioritization using GIS and Remote Sensing , 2010 .

[46]  Hamid Reza Pourghasemi,et al.  Spatial modelling of gully headcuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree) , 2019, Geomorphology.

[47]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[48]  F. A. Vega,et al.  Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop , 2015 .

[49]  Benjamin L. Ruddell,et al.  Applying Information Theory in the Geosciences to Quantify Process Uncertainty, Feedback, Scale , 2013 .

[50]  Dirk Hoffmeister,et al.  Geological controlling soil organic carbon and nitrogen density in a hillslope landscape, semiarid area of Golestan province, Iran , 2017 .

[51]  R. Schlögel,et al.  Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models , 2018 .

[52]  B. Keshavarzi,et al.  A POSSIBLE LINK BETWEEN MINERALOGY OF LOESS DEPOSITS AND HIGH INCIDENCE RATE OF ESOPHAGEAL CANCER IN GOLESTAN PROVINCE OF IRAN , 2014 .

[53]  Seth M. Dabney,et al.  Soil pipe collapses in a loess pasture of Goodwin Creek watershed, Mississippi: role of soil properties and past land use , 2015 .

[54]  Jean Poesen,et al.  Factors controlling the spatial distribution of soil piping erosion on loess-derived soils: A case study from central Belgium , 2010 .

[55]  Tongxin Zhu,et al.  Tunnel development over a 12 year period in a semi‐arid catchment of the Loess Plateau, China , 2003 .

[56]  R. Bryan,et al.  The significance of soil piping processes: inventory and prospect , 1997 .

[57]  Anita Bernatek‐Jakiel,et al.  Combining geomorphological mapping and near surface geophysics (GPR and ERT) to study piping systems , 2016 .

[58]  Wei Chen,et al.  Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility , 2019, CATENA.

[59]  Giovanni B. Crosta,et al.  Techniques for evaluating the performance of landslide susceptibility models , 2010 .

[60]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[61]  Paul Zukowskyj,et al.  Moderating accurate topographic EDM survey with expert-derived planimetric geomorphological information: a case study mapping soil pipes, Mocatán, SE Spain , 2008 .

[62]  H. Pourghasemi,et al.  Multi-hazard probability assessment and mapping in Iran. , 2019, The Science of the total environment.

[63]  Hamid Reza Pourghasemi,et al.  GIS‐based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi‐arid environment: Golestan Province, NE Iran , 2019, Land Degradation & Development.

[64]  Henrique N. Cabral,et al.  Predicting fish species richness in estuaries: Which modelling technique to use? , 2015, Environ. Model. Softw..

[65]  Liyang Xiong,et al.  Effects of DEM resolution on the accuracy of gully maps in loess hilly areas , 2019, CATENA.

[66]  H. Pourghasemi,et al.  Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions , 2018, Bulletin of Engineering Geology and the Environment.

[67]  J. Poesen.  Soil erosion in the Anthropocene: Research needs , 2018 .

[68]  Hamid Reza Pourghasemi,et al.  Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran. , 2019, The Science of the total environment.

[69]  Matthew R. Bennett,et al.  Subsidence Structures Associated with Subaerial Desiccation-Crack Piping and Their Role in Drainage Evolution on a Drained Proglacial Lake Bed: Hagavatn, Iceland , 2000 .

[70]  Glenn V. Wilson,et al.  Spatial distribution of pipe collapses in Goodwin Creek Watershed, Mississippi , 2013 .

[71]  Jean Poesen,et al.  Prediction of spatial patterns of collapsed pipes in loess-derived soils in a temperate humid climate using logistic regression , 2011 .

[72]  H. A. Nefeslioglu,et al.  Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey) , 2008 .

[73]  Tian Yingjie,et al.  Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks , 2010 .

[74]  T. Svoray,et al.  Predicting gully initiation: comparing data mining techniques, analytical hierarchy processes and the topographic threshold , 2012 .

[75]  Marco Borga,et al.  The influence of grid resolution on the prediction of natural and road-related shallow landslides , 2014 .