Analysis of Landsat-8 OLI Imagery for Estimating Exposed Bedrock Fractions in Typical Karst Regions of Southwest China Using a Karst Bare-Rock Index

作者:Pei, J (Pei, Jie)[ 1,2 ] ; Wang, L (Wang, Li)[ 1 ] ; Huang, N (Huang, Ni)[ 1 ] ; Geng, J (Geng, Jing)[ 2,3 ] ; Cao, JH (Cao, Jianhua)[ 4,5 ] ; Niu, Z (Niu, Zheng)[ 1,2 ]

REMOTE SENSING

卷: 10  期: 9

文献号: 1321

DOI: 10.3390/rs10091321

出版年: SEP 2018

摘要

Karst rocky desertification (KRD) has become the primary ecoenvironmental problem in the karst regions of southwest China. The rapid and efficient acquisition of exposed bedrock fractions (EBF) is crucial for the monitoring and assessment of KRD degree and distribution within the highly heterogeneous landscapes. Remote-sensing indices provide a useful method for the quick mapping of the EBF at large scales. The currently available rock indices, however, are faced with insensitivity to bedrock change characteristics, which greatly limits their performances and suitability. To address this problem, we proposed a novel karst bare-rock index (KBRI) that applies shortwave-infrared (SWIR) and near-infrared (NIR) bands from Landsat-8 OLI imagery to maximally distinguish between exposed bedrock and other land cover types in southwest China. A linear regression model was thus established between KBRI and the EBF derived from in situ measurements. The model developed here was then validated with an independent experiment and applied over a large geographic area to produce regional maps of EBF in southwest China. Experimental results showed good performance on root mean square error (5.59%), mean absolute error (4.63%), root mean absolute percentage error (13.59%), and coefficient of determination (0.72), respectively. The advantages of the proposed method are reflected in its simplicity and minimal requirements for auxiliary data while still achieving comparatively better accuracy than existing related indices. Thus, the KBRI has the great potential for the application in other regions around the world with the similar geological backgrounds, thereby helping to address the similar or other related environmental issues. Results of this study provide baseline data for the KRD assessment and karst-ecosystem management in southwest China.

通讯作者地址: Wang, L; Niu, Z (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, POB 9718,Datun Rd, Beijing 100101, Peoples R China.

通讯作者地址: Niu, Z (通讯作者)

Univ Chinese Acad Sci, Yuquan Rd 19, Beijing 100049, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, POB 9718,Datun Rd, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Yuquan Rd 19, Beijing 100049, Peoples R China

[ 3 ] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Datun Rd, Beijing 100101, Peoples R China

[ 4 ] Chinese Acad Geol Sci, Inst Karst Geol, Key Lab Karst Dynam, Qixing Rd, Guilin 541004, Peoples R China

[ 5 ] UNESCO, Int Res Ctr Karst, Qixing Rd, Guilin 541004, Peoples R China