Decision tree algorithm of automatically extracting mangrove forests information from Landsat 8 OLI imagery

doi:10.6046/gtzyyg.2016.02.28

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Abstract

NDMI (normalized difference moisture index) is widely used to assess and retrieve vegetation liquid water content. In this study, decision tree method was employed to automatically extract mangrove forests information combining the NDMI and MNDPI (modified normalized difference pond index), modified according to the mangrove characteristics, with Landsat8 OLI imagery acquired at Shankou mangrove national ecosystem nature reserve in Guangxi. The research results show that mangrove forests spectra consist of vegetation and wetland characteristics due to the unique near-shore coastal habitat of mangrove forests. MNDPI and NDMI can represent the spectral contrast between shortwave infrared region and visible region, near infrared region respectively. Therefore, the two spectral indices can successfully be employed to extract wetland vegetation and effectively discriminate mangrove forests from other land cover types. The decision tree method effectively extracted mangrove forests information by combining the classification features of MNDPI and NDMI and using Landsat8 OLI remotely sensed data. The commission error and omission error of mangrove forests were 5.34% and 1.69% respectively.

Keywords apple leaf hyperspectral content of nitrogen(N) back propagation(BP)neural network

:	TP751.1
	TP753

Issue Date: 14 April 2016

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	AN Jing
	YAO Guoqing
	ZHU Xicun

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AN Jing,YAO Guoqing,ZHU Xicun. Decision tree algorithm of automatically extracting mangrove forests information from Landsat 8 OLI imagery[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 182-187.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.02.28 OR https://www.gtzyyg.com/EN/Y2016/V28/I2/182

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