Land cover change and multiple remotely sensed datasets consistency in China

Abstract

Introduction

Although numerous land cover datasets can act as references for understanding land cover change in China, the inconsistencies between the datasets can also provide understanding. Previous studies on the consistency between land cover datasets have mostly focused on land cover type consistencies and have ignored data consistencies in land cover change.

Outcomes

Therefore, we aim to analyse the consistencies in land cover changes through likelihood assessment methods. We compared the spatiotemporal changes in forest, grassland, cropland, and bare land in the Climate Change Initiative land cover dataset (CCI-LC), Moderate-resolution Resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), China’s National Land Use and Cover Change (CNLUCC), Globeland30 and Global Land Cover Fine Surface Covering 30 (GLC-FCS30) datasets in 2010. The results showed that the percentages and changes in each land cover type in MCD12Q1 were different from those in the other datasets.

Discussion

For example, the proportion of grassland in MCD12Q1 was the highest, reaching 48.04%. The places with high consistency were the places where the land cover types were concentrated, and the bare land had the highest consistency. However, the consistency of China’s land cover change was quite low, and the percentage of low consistency was more than 87% from 2000-2018. Comparison of the data with the global artificial impervious area (GAIA) and Hansen-Global Forest Change (Hansen-GFC) datasets showed that the percentage of high construction gain consistency (38.83%) was higher than the forest change consistency, and the percentage forest loss high consistency (8.85%) was lower than the forest gain high consistency (12.76%).

Conclusion

The results not only provide a basis for the use of land cover datasets but also give a clearer understanding of the pattern of land cover changes.

KEYWORDS:

• Spatiotemporal characteristic
• MCD12Q1
• CCI-LC
• consistency assessment
• RMSE

Acknowledgments

This research was financially supported by the National Natural Science Foundation of China (42101287), the Science and Technology Project of Inner Mongolia Autonomous Region, China (NMKJXM202109), Bayannur Ecological Governance and Green Development Academician Expert Workstation (YSZ2018-1), the Shandong Provincial Natural Science Foundation (ZR2019BD045) and Qufu Normal University Dissertation Research and Innovation Fund (LWCXS202121).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [42101287]; Qufu Normal University Dissertation Research and Innovation Fund [LWCXS202121]; Bayannur Ecological Governance and Green Development Academician Expert Workstation [YSZ2018-1]; Shandong Provincial Natural Science Foundation [ZR2019BD045]; the Science and Technology Project of Inner Mongolia Autonomous Region, China [NMKJXM202109].