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Classification of Forest Vegetation Type Using Fused NDVI Time Series Data Based on STNLFFM | IEEE Conference Publication | IEEE Xplore

Classification of Forest Vegetation Type Using Fused NDVI Time Series Data Based on STNLFFM


Abstract:

Forest resources play an important ecological role on the earth. The traditional forest resource inventory methods are grueling and time-consuming. It is insufficient by ...Show More

Abstract:

Forest resources play an important ecological role on the earth. The traditional forest resource inventory methods are grueling and time-consuming. It is insufficient by using single-date remote sensing data to identify the forest vegetation types. Fortunately, multitemporal images have advantages of seasonal variation pattern in different forest vegetation. Duchang County as a study case, GF-1, Landsat8 and MODIS NDVI data were fused to obtain enough spatial and temporal resolution for forest types distinguished by utilizing STNLFFM model and Savitzky-Golay filter. Then, the forest types were classified with the aid of forest inventory data by the SVM method. The classification results showed that the overall accuracy of twice confusion (82%) are significantly better than that of single fusion (72%) only based on Landsat8 and MODIS data. It proved that the more complete temporal data series with higher spatial resolution is very vital even they are derived from different sensors.
Date of Conference: 28 July 2019 - 02 August 2019
Date Added to IEEE Xplore: 14 November 2019
ISBN Information:

ISSN Information:

Conference Location: Yokohama, Japan
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
The Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

1. INTRODUCTION

As one of the most vital components in terrestrial ecosystem, forest resources are not only the crucial material foundation for the country, but also the indispensable renewable resources for the establishment of ecological environment and economic construction [1]. They have many ecological functions, such as water conservation and air purification, even to affect the regional environment [2]. In order to accomplish the scientific management and effective utilization of forest resources, it is necessary to obtain a large amount of information of forest resources accurately, quickly and efficiently [3]. In early time, forest vegetation classification was often based on measured data adopting visual interpretation, which bring about a series of problems, such as heavy workload, time-consuming, poor timeliness and low accuracy. Remote sensing technology provides a possibility way for monitoring the forest resources with its dynamic, convenient, high-quality and efficiently characteristics [4]. Recent years, compared with the single-date images, time-series vegetation indices have a more efficiency tendency to improve the classification accuracy, such as the normalized-difference vegetation index (NDVI) [5]. However, due to the restriction of acquiring data with both high spatial resolution and high temporal resolution, it is advisable to fuse data from multiple sensors, thereby gaining the rich informative remote sensing data [6]. The spatio-temporal data fusion technique includes the spatial and temporal nonlocal filter-based fusion model (STNLFFM) [7], the spatial and temporal adaptive-reflectance fusion model (STARFM) [8] and so forth. The STARFM algorithm is most widely used. However, due to giving more attention on the reflectance changes in the multitemporal images, STNLFFM model performs a higher prediction accuracy.

The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
The Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
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