Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning

Haoyu Wang,Xin Zhang,Xiang Zhao,Xiaozheng Du,Donghai Wu

doi:10.3390/rs11141639

Abstract

Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuous land cover classification products for many years is mostly after the year 2000, and there is a lack of long-term continuously annual land cover classification products before 2000. In this study, a long time series classification data extraction model is established using a bidirectional long-term and short-term memory network (Bi-LSTM). In the model, quantitative remote sensing products combined with DEM, nighttime lighting data, and latitude and longitude elevation data were used. We applied this model in China and obtained China’s 1982–2017 0.05° land cover classification product. The accuracy assessment results of the test data show that the overall accuracy is 84.2% and that the accuracies of wetland, water, glacier, tundra, city and bare soil reach 92.1%, 92.0%, 94.3%, 94.6% and 92.4%, respectively. For the first time, this study used a variety of long time series data, especially quantitative remote sensing products, for the classification of features. At the same time, it also acquired long time series land cover classification products, including those from the year 2000. This study provides new ideas for the establishment of higher-resolution long time series land cover classification products.

Highlights

With population growth, economic development, and various factors, land cover information has been identified as one of the key data components in many aspects of global change research and environmental applications [1,2]
After obtaining the long time series land cover classification product set, this study studies the percentage of long time series land cover category extraction
We can find that quantitative remote sensing parameters are more important for classification than NDVI statistical indicators

Summary

Introduction

Economic development, and various factors, land cover information has been identified as one of the key data components in many aspects of global change research and environmental applications [1,2]. Large-scale land cover classification and mapping provides a source of data for many of the research works on global change and is an important input variable to global change models (such as net productivity models, ecosystem metabolic models, and carbon cycle models). Most global change models need to be supported by large areas of land cover information [3,4]. For two maps covering long time spans, there is a lack of corresponding process information, and the long time series of land cover datasets can capture the complexity of ground changes [5] while quantifying these changes. Long time series land cover classification data are of great significance for land change monitoring [6], identification, and planning assessment [7,8]

Methods

Results

Discussion

Conclusion