Long Time-Series Mapping and Change Detection of Coastal Zone Land Use Based on Google Earth Engine and Multi-Source Data Fusion

Dong Chen,Zhenyu Shen,Shaobo Sun,Yafei Wang,Jinfeng Liao,Jiezhi Chen

doi:10.3390/rs14010001

Abstract

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.

Highlights

We proposed a framework for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion
A land-use classification system of coastal zone based on human production, living, and ecology was constructed, and initial classification of long time-series images was carried out using random forest algorithms based on multi-source big data
InIn this study, we used multi-source data fusion and prior knowledge toto improve land use classification, which addresses the limitation of existing classification relying only use classification, which addresses the limitation of existing classification relying only on on spectral information of features

Summary

Introduction

About 75% of the world’s large cities and 70% of industrial capital and population are concentrated within the 100-km-width coastal zones. Concentrated populations and economic activities lead to significant land-use changes in coastal zones [1,2,3]. The rapid expansion of human activities such as off-shore aquaculture, coastal tourism, coastal infrastructure construction, and reclamation has led to great changes in coastal zone land use, triggering a series of ecological and environmental problems such as contamination of the coastal zone environment, massive degradation of wetlands, and destruction of biodiversity and habitats [4,5]. The complicated land-use changes call for remote sensing

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Conclusion