[Objectives] With the object-oriented classification techniques for low-altitude unmanned aerial vehicle(UAV) remote sensing image data

this study monitored soil and water conservation for development and construction projects

to provide technical supports for soil and water conservation monitoring.[Methods] Unmanned multi-rotor aircraft was used to obtain the low altitude remote sensing images in soil and water conservation monitoring target areas. Digital surface models were constructed by oblique photography. The optimal segmentation scale parameters were selected by the estimating the scale parameter(ESP) segmentation scale evaluation tool

and supervised classification by the nearest neighbor classification of multivariate feature space metrics was used. The classification accuracy was verified through the location information validation method and error matrix.[Results] The total accuracy and the Kappa coefficients of target area in monitoring the soil and water conservation were 86.10% and 0.841

respectively. This result could meet the precision requirements.[Conclusion] The object-oriented classification techniques used for low-altitude UAV remote sensing image data achieved fast

accurate identification and classification in the monitoring of soil and water conservation for development and construction projects.