Mapping Annual Land Use and Land Cover Changes in the Yangtze Estuary Region Using an Object-Based Classification Framework and Landsat Time Series Data

Jinquan Ai,Dajun Li,Chao Zhang,Lijuan Chen

doi:10.3390/su12020659

Abstract

A system understanding of the patterns, causes, and trends of long-term land use and land cover (LULC) change at the regional scale is essential for policy makers to address the growing challenges of local sustainability and global climate change. However, it still remains a challenge for estuarine and coastal regions due to the lack of appropriate approaches to consistently generate accurate and long-term LULC maps. In this work, an object-based classification framework was designed to mapping annual LULC changes in the Yangtze River estuary region from 1985–2016 using Landsat time series data. Characteristics of the inter-annual changes of LULC was then analyzed. The results showed that the object-based classification framework could accurately produce annual time series of LULC maps with overall accuracies over 86% for all single-year classifications. Results also indicated that the annual LULC maps enabled the clear depiction of the long-term variability of LULC and could be used to monitor the gradual changes that would not be observed using bi-temporal or sparse time series maps. Specifically, the impervious area rapidly increased from 6.42% to 22.55% of the total land area from 1985 to 2016, whereas the cropland area dramatically decreased from 80.61% to 55.44%. In contrast to the area of forest and grassland, which almost tripled, the area of inland water remained consistent from 1985 to 2008 and slightly increased from 2008 to 2016. However, the area of coastal marshes and barren tidal flats varied with large fluctuations.

Highlights

Estuaries are the interfaces between marine, freshwater, and terrestrial ecosystems and are welcomed as intermediate transitional zones with various ecosystem service functions [1]
An accuracy assessment of the land use and land cover (LULC) products indicated that the map accuracies from the object-based classification framework approach were high (Supplementary Table S1)
An object-based classification framework that integrates object-based image analysis, hierarchical classification, and updating and backdating approaches was developed for mapping long-term LULC changes in the Yangtze River estuary region from 1985–2016 using annual Landsat time series images

Summary

Introduction

Estuaries are the interfaces between marine, freshwater, and terrestrial ecosystems and are welcomed as intermediate transitional zones with various ecosystem service functions [1]. Affected by climate change and rapidly urbanization, the estuarine and coastal regions are facing with many degradation risks, such as wetland shrinking, environmental pollution and coastal erosion [2,3] To alleviate these problems, long-term dense monitoring of land use and land cover (LULC) change is of crucial important, because it provides essential information for depicting the history, current situation and future of LULC change, and for understanding biogeochemical processes and the mechanisms of LULC changes [4,5]. LULC maps derived from coarse resolution imagery (such as EOS-MODIS/NOAA-AVRHH data) have high temporal frequency and large coverage, but its spatial resolution is too coarse to track detail change of LULC [5] Against this background, Landsat data with 16 days temporal resolution and 30 m spatial resolution, which provides the longest and most systematic historical data [10], is potentially more suitable than other data sources for monitoring dense LULC dynamics at large estuarine regions

Methods

Results

Discussion

Conclusion