Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (12): 2436-2444.doi: 10.12082/dqxxkx.2020.190744

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The Spatial Distribution Pattern of Rock in Rocky Desertification Area based on Unmanned Aerial Vehicle Imagery and Object-oriented Classification Method

ZHANG Zhihui1(), LIU Wen1,2,*(), LI Xiaohan1, ZHU Jingxuan1, ZHANG Hongtao1, YANG Dong3, XU Chaohao3, XU Xianli3   

  1. 1. College of Resources and Environment Science, Hunan Normal University, Changsha 410081, China
    2. Key Laboratory of Geospatial Big Data Mining and Application, Changsha 410081, China
    3. Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
  • Received:2019-12-02 Revised:2020-03-02 Online:2020-12-25 Published:2021-02-25
  • Contact: LIU Wen E-mail:zhihui616713@163.com;liuwenww@gmail.com
  • Supported by:
    National Key R&D Program of China(2016YFC0502400);National Key Research and Development Program(41501478)

Abstract:

The change of landscape pattern has an important influence on the material and energy flows of ecological environment. Quantifying the landscape pattern of rocky desertification in a karst area is very important for understanding the development of rocky desertification. Rocky desertification is a dynamic land degradation process, which is a comprehensive reflection of vegetation, bedrock, soil cover, and other surface factors. Particularly, exposed bedrock acts as a key indicator of karst rocky desertification. In this study, spatial distribution of rock patches with varied bare-rock ratio (11%, 20%, 29%, and 48%) is characterized using an Unmanned Aerial Vehicle (UAV) image in a rocky desertification area in Guizhou Province. The classification methods for this small-scale UAV image include unsupervised classification, supervised classification, and object-oriented classification. The existing supervised and unsupervised classification methods based on pixels cannot meet the requirements of accurate extraction of rocky desertification information in karst rocky desertification area with complex geological environment. So an optimal classification method is selected to classify the UAV image of rocky desertification in our study. Our results show that the object-oriented classification method has higher accuracy than the others, which reduces the “salt-and-pepper phenomenon” caused by complicated topography. Based on object-oriented classification, the UAV image is interpreted first, and the distribution characteristics of rock patches with different bare-rock rates (i.e., 11%, 20%, 29% and 48%) are quantified by combining various indices in landscape ecology including landscape patch index, landscape shape index, and landscape fragmentation index. Generally, the average patch area of rock is negatively correlated with bare-rock rate. With the increase of bare-rock rate in different rocky desertification areas, the number of rock patches gradually increase with increasing rock shape index and rock fragmentation index, which indicates the increase of rocky desertification. The exposed bare rocks in this karst area show different distribution patterns and characteristics under different rocky desertification rates. The higher the rate of bare rock is, the higher the degree of rock fragmentation is, with a relatively scattering distribution of rock patches. Analyzing the rock distribution for a rocky desertification area can provide support for the evaluation and management of rocky desertification areas. Since the changes of small-scale, small-patch landscape pattern in rocky desertification areas can affect the ecological processes, our small-scale study also provides better understanding of future processes of rocky desertification and the development of rocky desertification at regional scale.

Key words: rocky desertification, landscape pattern, patch size, distribution pattern, object-oriented classification, UAV, classification, bare rates, karst area