Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1575-1579, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1575-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1575-1579, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1575-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

THE ANALYSIS OF BURROWS RECOGNITION ACCURACY IN XINJIANG'S PASTURE AREA BASED ON UAV VISIBLE IMAGES WITH DIFFERENT SPATIAL RESOLUTION

D. Sun1,2, J. H. Zheng1,2,3, T. Ma1,2, J. J. Chen4, and X. Li4 D. Sun et al.
  • 1College of Resource and Environment Science, Xinjiang University, Shengli Road 666, Urumchi, China
  • 2Key Laboratory of Oasis Ecology Ministry of Education, Shengli Road 666, Urumchi, China
  • 3Institute of Arid Ecology and Environment Xinjiang University, Shengli Road 666, Urumchi, China
  • 4Xinjiang Uygur Autonomous Region locust & rodent forecast & control Center, Xinhua South Road 23, Urumchi, China

Keywords: Rodent disaster monitoring, UAV, Low altitude remote sensing, Multi-spatial scale, Object-based image analysis, Grassland protection

Abstract. The rodent disaster is one of the main biological disasters in grassland in northern Xinjiang. The eating and digging behaviors will cause the destruction of ground vegetation, which seriously affected the development of animal husbandry and grassland ecological security. UAV low altitude remote sensing, as an emerging technique with high spatial resolution, can effectively recognize the burrows. However, how to select the appropriate spatial resolution to monitor the calamity of the rodent disaster is the first problem we need to pay attention to. The purpose of this study is to explore the optimal spatial scale on identification of the burrows by evaluating the impact of different spatial resolution for the burrows identification accuracy. In this study, we shoot burrows from different flight heights to obtain visible images of different spatial resolution. Then an object-oriented method is used to identify the caves, and we also evaluate the accuracy of the classification. We found that the highest classification accuracy of holes, the average has reached more than 80 %. At the altitude of 24 m and the spatial resolution of 1cm, the accuracy of the classification is the highest We have created a unique and effective way to identify burrows by using UAVs visible images. We draw the following conclusion: the best spatial resolution of burrows recognition is 1 cm using DJI PHANTOM-3 UAV, and the improvement of spatial resolution does not necessarily lead to the improvement of classification accuracy. This study lays the foundation for future research and can be extended to similar studies elsewhere.