Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Abstract: Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of “vegetation recovery”. This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs) and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA). A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN), accumulative curvature slope (AC), and slope of slope (SOS) were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM) analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART) analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and spectrum characteristics, demonstrating huge potential in hilly-gully loess region with similarly complex terrain and diverse vegetation covers.

[1]  Jörgen Wallerman,et al.  Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM , 2012 .

[2]  Bo Yang,et al.  Analysis of comprehensive benefits of transforming slope farmland to terraces on the Loess Plateau: A case study of the Yangou Watershed in Northern Shaanxi Province, China , 2011 .

[3]  L. Gómez Chova,et al.  Land cover classification of VHR airborne images for citrus grove identification , 2011 .

[4]  Maria Pateraki,et al.  Orthophoto generation using IKONOS imagery and high-resolution DEM: a case study on volcanic hazard monitoring of Nisyros Island (Greece) , 2002 .

[5]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[6]  Dirk Tiede,et al.  ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data , 2010, Int. J. Geogr. Inf. Sci..

[7]  Ahmed El-Rabbany,et al.  AIRBORNE MULTISPECTRAL LIDAR DATA FOR LAND-COVER CLASSIFICATION AND LAND/WATER MAPPING USING DIFFERENT SPECTRAL INDEXES , 2016 .

[8]  Sharon A. Robinson,et al.  Using an Unmanned Aerial Vehicle (UAV) to capture micro-topography of Antarctic moss beds , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Chang Zhi-yan.  AUTO-CLASSIFICATION OF LANDFORM IN THREE GORGES REGION BASED ON SRTM DEM , 2014 .

[10]  Long Yi,et al.  Research on the slope spectrum of the Loess Plateau , 2008 .

[11]  Hu Ding,et al.  Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau , 2016, ISPRS Int. J. Geo Inf..

[12]  Kimberly M Fornace,et al.  Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology. , 2014, Trends in parasitology.

[13]  Giovanni Cargnello,et al.  Traditional landscape and rural development: comparative study in three terraced areas in northern, central and southern Italy to evaluate the efficacy of GAEC standard 4.4 of cross compliance , 2011 .

[14]  R. C. Frohn,et al.  Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery , 2011 .

[15]  Geoffrey J. Hay,et al.  A multiscale geographic object-based image analysis to estimate lidar-measured forest canopy height using Quickbird imagery , 2011, Int. J. Geogr. Inf. Sci..

[16]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[17]  Claire Baffaut,et al.  Development and Application of Algorithms for Simulating Terraces within SWAT , 2013 .

[18]  H. J. H. Kux,et al.  OBJECT-BASED IMAGE ANALYSIS OF WORLDVIEW-2 SATELLITE DATA FOR THE CLASSIFICATION OF MANGROVE AREAS IN THE CITY OF SÃO LUÍS, MARANHÃO STATE, BRAZIL , 2012 .

[19]  André Streilein,et al.  FOR THE VERIFICATION OF COUNTRYWIDE TERRAIN AND SURFACE MODELS , 2003 .

[20]  Liu Guobin,et al.  Analysis of comprehensive benefits of transforming slope farmland to terraces on the Loess Plateau: A case study of the Yangou Watershed in Northern Shaanxi Province, China , 2011 .

[21]  Marco Dubbini,et al.  Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments , 2013, Remote. Sens..

[22]  Lei Ai,et al.  Modeling the impacts of integrated small watershed management on soil erosion and sediment delivery: A case study in the Three Gorges Area, China , 2012 .

[23]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[24]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[25]  O. Csillik,et al.  Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[26]  Wei Song,et al.  Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China , 2014, Int. J. Geogr. Inf. Sci..

[27]  Guoan Tang,et al.  Positive and negative terrains on northern Shaanxi Loess Plateau , 2010 .

[28]  B. S. Tan,et al.  Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data , 2015, PloS one.

[29]  Nicola Fohrer,et al.  Field data-based implementation of land management and terraces on the catchment scale for an eco-hydrological modelling approach in the Three Gorges Region, China , 2016 .

[30]  Yi Long,et al.  Research on the slope spectrum of the Loess Plateau , 2008 .

[31]  J. Gong,et al.  Sloping farmland identification using hierarchical classification in the Xi-He region of China , 2013 .

[32]  C. Burnett,et al.  A multi-scale segmentation/object relationship modelling methodology for landscape analysis , 2003 .

[33]  Clemens Eisank,et al.  Local variance for multi-scale analysis in geomorphometry , 2011, Geomorphology.

[34]  Geoffrey J. Hay,et al.  Object-based Image Analysis : Strengths , Weaknesses , Opportunities and Threats ( Swot ) , 2006 .

[35]  R A Diaz-Varela,et al.  Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. , 2014, Journal of environmental management.

[36]  Boun Suy Tan,et al.  Correction: Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data , 2016, PloS one.

[37]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[38]  Mohamed Sultan,et al.  The geomorphological evidences of subsidence in the Nile Delta: Analysis of high resolution topographic DEM and multi-temporal satellite images , 2017 .

[39]  José A. Martínez-Casasnovas,et al.  Influence of the EU CAP on terrain morphology and vineyard cultivation in the Priorat region of NE Spain , 2010 .

[40]  Wang Qin-xiang.  Extracting Features of Soil and Water Conservation Measures from Remote Sensing Images of Different Resolution Levels:Accuracy Analysis , 2012 .

[41]  Arko Lucieer,et al.  Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..

[42]  Todd H. Skaggs,et al.  Comparison of measured and simulated water storage in dryland terraces of the Loess Plateau, China , 2009 .

[43]  Emanuele Frontoni,et al.  Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery , 2011, Int. J. Geogr. Inf. Sci..

[44]  S. M. Jong,et al.  Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography , 2014 .

[45]  Willem Bouten,et al.  Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping , 2011 .

[46]  Qiao Yuliang.  Classification System of 1:1 000 000 Digital Loess Geomorphology in China , 2006 .

[47]  M. Arabi,et al.  Representation of agricultural conservation practices with SWAT , 2008 .

[48]  Dan Steinberg,et al.  THE HYBRID CART-LOGIT MODEL IN CLASSIFICATION AND DATA MINING , 1998 .

[49]  Irene Marzolff,et al.  Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco , 2012, Remote. Sens..

[50]  Noemí Lana-Renault,et al.  Effects of farming terraces on hydrological and geomorphological processes. A review , 2015 .

[51]  P. Tarolli,et al.  Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography , 2013 .

[52]  Coen J. Ritsema.  Introduction: soil erosion and participatory land use planning on the Loess Plateau in China , 2003 .

[53]  Jean-Stéphane Bailly,et al.  Potential of linear features detection in a Mediterranean landscape from 3D VHR optical data: Application to terrace walls , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[54]  J. Nicolau,et al.  Abandonment of agricultural land: an overview of drivers and consequences , 2007 .

[55]  G. Pardini,et al.  Land use change effects on abandoned terraced soils in a Mediterranean catchment, NE Spain , 2003 .

[56]  Susan L. Ustin,et al.  Mapping of freshwater lake wetlands using object-relations and rule-based inference , 2012, Chinese Geographical Science.

[57]  Tang Guoa,et al.  Progress of DEM and digital terrain analysis in China , 2014 .

[58]  Xiu Yi,et al.  The soil water dynamics and hydraulic processes of crops with plastic film mulching in terraced dryland fields on the Loess Plateau , 2016, Environmental Earth Sciences.

[59]  Josef Strobl,et al.  What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS , 2001 .