Mr. Jun Mi Profile

Jun Mi

at Institute of Remote Sensing and Digital Earth CAS

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SPIE Journal Paper | 26 February 2022

Impact of geometric misregistration in GlobeLand30 on land-cover change analysis, a case study in China

Jun Mi, Liangyun Liu, Xiao Zhang, Xidong Chen, Yuan Gao, Shuai Xie

JARS, Vol. 16, Issue 01, 014516, (February 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.014516

KEYWORDS: Sensors, Data modeling, Analytical research, Image registration, Error analysis, Remote sensing, Raster graphics, Earth observing sensors, Vegetation, Statistical methods

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Accurate information about land-cover change (LCC) is important in many different fields. GlobeLand30 is a widely used global land-cover product that can support change analysis. However, since the data sources used in GlobeLand30 products are acquired by different sensors, it is hard to achieve accurate geometric registration between classified images; as a result, spurious changes may be detected. For this reason, we aimed to investigate the effects of geometric misregistration on LCC detection using the 2010 and 2020 GlobeLand30 products for the whole of China to quantitatively understand the impact of geometric misregistration in GlobeLand30 on the analysis of LCC. First, we quantified the effect of the misregistration using a block-statistical approach that calculates the difference in the change ratio based on a pixel-by-pixel comparison method together with a method that estimates the proportional change in classes within 1-km blocks (a block-by-block comparison). The results obtained using the pixel-by-pixel comparison method and the block-by-block method give land-change ratios for China as 10.6% and 6.95%, respectively, which indicate that about 3.65% of the changes may be spurious changes caused by geometric misregistration. We then analyzed the relationship between the landscape heterogeneity and the spurious changes using the Geodetector model. The results show that landscape heterogeneity explains the spatial distribution of spurious changes due to the misregistration effect to a certain extent and show a positive correlation. In terms of the Shannon diversity index, the explanatory ability reaches more than 50% in most subregions. Our work provides a quantitative analysis of the influence of geometric misregistration on LCC measurements and has an important reference value for the updating of land-cover mapping used in change detection.

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