Abstract
In recent years, the use of image fusion method has received increasing attention in remote sensing, vegetation cover changes, vegetation indices (VIs) mapping, etc. For making high-resolution and good quality (with low-cost) VI mapping from a fused image, its quality and underlying factors need to be identified properly. For example, same-sensor image fusion generally has a higher spatial resolution ratio (SRR) (1:3 to 1:5) but multi-sensor fusion has a lower SRR (1:8 to 1:10). In addition to SRR, there might be other factors affecting the fused vegetation index (FVI) result which have not been investigated in detail before. In this research, we used a strategy on image fusion and quality assessment to find the effect of image fusion for VI quality using Gaofen-1 (GF1), Gaofen-2 (GF2), Gaofen-4 (GF4), Landsat-8 OLI, and MODIS imagery with their panchromatic (PAN) and multispectral (MS) bands in low SRR (1:6 to 1:15). For this research, we acquired a total of nine images (4 PAN+5 MS) on the same (almost) date (GF1, GF2, GF4 and MODIS images were acquired on 2017/07/13 and the Landsat-8 OLI image was acquired on 2017/07/17). The results show that image fusion has the least impact on Green Normalized Vegetation Index (GNDVI) and Atmospherically Resistant Vegetation Index (ARVI) compared to other VIs. The quality of VI is mostly insensitive with image fusion except for the high-pass filter (HPF) algorithm. The subjective and objective quality evaluation shows that Gram-Schmidt (GS) fusion has the least impact on FVI quality, and with decreasing SRR, the FVI quality is decreasing at a slow rate. FVI quality varies with types image fusion algorithms and SRR along with spectral response function (SRF) and signal-to-noise ratio (SNR). However, the FVI quality seems good even for small SRR (1:6 to 1:15 or lower) as long as they have good SNR and minimum SRF effect. The findings of this study could be cost-effective and highly applicable for high-quality VI mapping even in small SRR (1:15 or even lower).
Highlights
Image fusion has received increasing attention as simultaneous purchase of a PAN and MS image from a high-resolution satellite is usually very costly [1]
In GF2-GF2 fusion, for most of the vegetation indices (VIs) like Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Optimized Soil Adjusted Vegetation Index (OSAVI) and Soil Adjusted Vegetation Index (SAVI); GS fusion has a lower effect on fused vegetation index (FVI) quality
In GF2 to Landsat-8 Operational Land Imager (OLI) (30 m, 40 m, 48 m and 60 m) fusion, there is a uniform relationship between spatial resolution ratio (SRR) and FVI quality (CC and root mean square error (RMSE)) for Atmospherically Resistant Vegetation Index (ARVI) (92.56%, 22.59%), (92.11%, 23.05%), (91.88% and 23.43%) and (91.17% and 23.45%) in SRR 1:8, 1:10, 1:12 and 1:15 respectively (Table 7)
Summary
Image fusion has received increasing attention as simultaneous purchase of a PAN and MS image from a high-resolution satellite is usually very costly [1] It is necessary as many sensors acquire information about the Earth in either only PAN (EROS-A, EROS-B, Worldview-1) or only MS (Rapid Eye) mode [2]. A high-resolution and good-quality VI mapping is necessary [1] for monitoring the earth’s vegetative cover as a precise radiometric measure of green vegetation [12]. Such high-resolution VIs derived from satellite images area useful data source for many agricultural, environmental, and climate studies [13]. It is important to assess the quality of the fused image before using it for various applications of remote sensing like VI mapping [14]
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