Abstract
The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully-affected areas detection is the basic work in this region for gully erosion assessment and monitoring. For the first time, an unmanned aerial vehicle (UAV) was applied to extract gully features in this region. Two typical catchments in Changwu and Ansai were selected to represent loess tableland and loess hilly regions, respectively. A high-powered quadrocopter (md4-1000) equipped with a non-metric camera was used for image acquisition. InPho and MapMatrix were applied for semi-automatic workflow including aerial triangulation and model generation. Based on the stereo-imaging and the ground control points, the highly detailed digital elevation models (DEMs) and ortho-mosaics were generated. Subsequently, an object-based approach combined with the random forest classifier was designed to detect gully-affected areas. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The overall extraction accuracy in Changwu and Ansai achieved was 84.62% and 86.46%, respectively, which indicated the potential of the proposed workflow for extracting gully features. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.
Highlights
Soil erosion is a serious environmental problem which causes high economic costs [1]
The present study aims to: (1) apply the unmanned aerial vehicle (UAV) on the Chinese Loess Plateau to generate the high-resolution digital elevation models (DEMs) and ortho-mosaics; and (2) investigate the object-based method for detection of gully-affected areas by using UAV-acquired dataset
The two methods employed in this paper (Figure 2) are: (1) UAV photogrammetry for obtaining high-resolTuhtieotnwDo EmMethaonddsoermthpolo-ymeodsianicths;isapnadp(e2r)(oFibgjuecret-2b)aasreed: (d1e) tUeActViopnhooftoggurlalym-mafefetrcytefdoraorbetaasinbiyngusing high-resolution DEM and ortho-mosaics; and (2) object-based detection of gully-affected areas by generuasteindgdgaetanseeratste. dDudraitnagsedtsa.taDaucrqinugisditaiotan,asctqruicitsirteiognu,lasttiroicntsrsehgouulaltdiobnes osbhsoeurlvdedbefroombseoruvteddoofrrosmurvey to indoouotdroimorasguervpeyrotcoeisnsdionogr simo athgeatptrhoceeasscicnugrsaoctyhamt ethtreiaccsccuarnacsyamtiestfryictshceanresqautisirfyemtheenrtesq.uFiroermdeenttes.ction part, oFobrjedcetteedc-tiboanspedarta,popbjreocatecdh-baansdedraapnpdroomachfoarnedstra(RndFo)mclafosrseisfite(rRwF)ecrleasasipfipelriewderwe iatphptlwiedowexitphetrwimo ents for opetxipmeirziminegntssefgomr oepnttiamtiozinngansedgcmlaesnstaifiticoantiaonndsctleapsssi.fication steps
Summary
Soil erosion is a serious environmental problem which causes high economic costs [1]. Many approaches have been proposed for monitoring and predicting soil erosion at different scales [2,3,4,5]. Latest measurement technologies, such as terrestrial laser scanning (TLS) [13], airborne laser scanning (ALS) [14], 3D photo-reconstruction [15], and total station [16], are adopted. Both TLS and ALS are able to efficiently acquire high resolution dataset for erosion monitoring and related fluvial geomorphology studies [3,17,18]. The heavy fieldwork complicates the application of such method for a large region [18]
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