Integrated Use of Existing Global Land Cover Datasets for Producing a New Global Land Cover Dataset with a Higher Accuracy: A Case Study in Eurasia

Naijia Zhang, Ryutaro Tateishi

Center of Environmental Remote Sensing (CEReS), Chiba University, Chiba, Japan.

DOI: 10.4236/ars.2013.24039                

Abstract

It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.

Keywords

Global Land Cover; GLCNMO; Training Data; Accuracy

1. Introduction

Global mapping plays an important role in the areas such as monitoring the major environmental phenomena, environmental protection as well as sustainable growth. An accurate global map could also contribute to the establishment of a global spatial data infrastructure, for future research and many other scientific purposes.

Until present, many global land cover projects have been carried out. Examples are that the IGBP DISCover dataset was based on the Advanced Very High Resolution Radiometer (AVHRR) from 1992 to 1993 [1], and the land cover product of the University of Maryland (UMD) was based on the same data from AVHRR, distinguished 14 classes [2]. In 2002, Boston University produced the MODIS land cover data using MODIS 1-km satellite data on board the Terra satellite [3]. The Global Land Cover 2000 (GLC2000) was based on SPOT-VEGETATION data from November 1999 to December 2000 [4,5]. Global Land Cover by National Mapping Organizations (GLCNMO) was based on 2003 data from MODIS, which was produced by Center for Environmental Remote Sensing (CEReS, Chiba University) [6]. In 2009, cooperating with an international network of partners (including EEA, FAO, GOFC-GOLD, IGB, JRC and UNEP), the European Space Agency (ESA) produced GLOBCOVER. Unlike other datasets, GLOBCOVER presents a higher resolution (300 m) than any previous global satellite derived maps [7].

Besides many studies on a single datasets, various researches have also tried to compare the exiting different global land cover datasets. In 2006, a spatial comparison of four satellite derived 1 km global land cover datasets (IGBP, UMD, MODIS LC, GLC2000) was conducted by generalizing a global land cover legend [8]. Another comparison between the exiting 1 km datasets was conducted in 2008 [9]. Purpose of those comparisons is trying to develop the integrated use of different datasets. For example, areas having the high agreement from the various existing global datasets were to be served as the reference data for training area selections by Chandra Giri et al.’s study in 2005 [10].

However, the integrated uses so far have mostly focused on the areas with high accuracy. There are large areas with low accuracy, which seem to have been ignored. If the accuracy of these areas could be improved to a higher level, theoretically a better global land cover datasets can be expected and the potential usage can be discovered within those accuracy-improved areas. Therefore, a question of “How to improve the accuracy level of certain areas” has been raised, which is also the key objective of this paper.

2. Methodology

As figure 1 shown, this study has utilized the four global land cover datasets: 1) MODIS LC (v004), 2) GLC2000 (v1.1), 3) GLCNMO (2003) and 4) GLOBCOVER (2009). The detail information of classes of each datasets is provided in Appendix.

This study used these datasets to separate the high accurracy area and the low accuracy area. Next, for the reclassification purpose, the low accuracy area has been checked cautiously to collect the training data. Two classification methods (Maximum likelihood method and decision tree method) have been adopted to produce the accuracy result as well as to compare. Finally, the accuracy comparison has been done between the results and the existing datasets.

2.1. Preprocessing

As mentioned above, there is a resolution difference between MODIS LC (v004), GLC2000 (v1.1), GLCNMO (2003) and GLOBCOVER (2009). Therefore, to be able to compare, the first step was to resample them all to the same resolution, which was a 300 m resolution same as GLOBCOVER (2009).

Next step was to reconcile the different legends (Table 1), again due to the differences among those four datasets. Most classes (i.e. some part of the forest, urban, bare land and water bodies etc.) were translated well. However, the “mixed classes” were difficult to correspond with each other. In this study, the correspondences were mainly based on the GLCNMO’s classes [11-17].

Table 1 shows the pixel-by-pixel comparison of four maps.

Conflicts of Interest

The authors declare no conflicts of interest.

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