A comprehensive change detection method for updating land cover data base

doi:10.6046/gtzyyg.2018.01.22

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Abstract

Describing, quantifying and monitoring land use/land cover change play an important role in the global and environmental change investigation. Based on a comprehensive change detection method (CCDM), the authors mapped the land cover changes in the period between 2011 and 2016 over the Landsat image (Path33/Row33). CCDM integrates some spectral change based detection algorithms, which encompasses the multi-index integrated change analysis (MIICA) model and the Zone model, with an emphasis or core on MIICA. By calculating four spectral indices including change vector (CV), relative change vector maximum (RCVMAX), differenced normalized burn ratio (dNBR) and differenced normalized difference vegetation index (dNDVI), the land cover changes were extracted from the bi-temporal imagery. According to the previous and the current land cover change trends, coupled with the changes in results from MIICA and Zone models, the accuracies of change detection results by CCDM were evaluated. The results show that there is an accuracy of 96% for the category of no-change, and 40% for change category, with an overall accuracy of 68%. The CCDM is a simple, easily realized and widely used model to capture the potential land cover changes caused by the diverse natural and anthropengic disturbances in different landscapes.

Keywords comprehensive change detection method(CCDM) land cover change remote sensing detection accuracy assessment

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Issue Date: 08 February 2018

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	Jiaojiao DIAO
	Xinye GONG
	Mingshi LI

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Jiaojiao DIAO,Xinye GONG,Mingshi LI. A comprehensive change detection method for updating land cover data base[J]. Remote Sensing for Land & Resources, 2018, 30(1): 157-165.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.22 OR https://www.gtzyyg.com/EN/Y2018/V30/I1/157

Fig.1 NLCD land cover classification from Landsat image in 2011 and location of study area

Tab.1 Detailed information of Landsat remote sensing data

Fig.2 Technical flowchart of CCDM model

Fig.3 Flowchart of MIICA model

Tab.2 Rule of threshold determination of MIICA

Fig.4 Flowchart of Zone model

Fig.5 Flowchart of integrating MIICA and Zone Models to map land cover change

Fig.6-1 Change detection processes demonstrated for subset-1 area

Fig.6-2 Change detection processes demonstrated for subset-1 area

Fig.7-1 Change detection processes illustrated for subset-2 area

Fig.7-2 Change detection processes illustrated for subset-2 area

Tab.3 Accuracy assessment results of 100 samples for Landsat P033/R033 image

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