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首页> 外文期刊>ISPRS International Journal of Geo-Information >Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents
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Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents

机译:Pansat-8影像的全屏锐化及其在植被绿色度和冠层含水量计算中的应用

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摘要

Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 images and calculate vegetation greenness (i.e., normalized difference vegetation index (NDVI)), canopy structure (i.e., enhanced vegetation index (EVI)), and canopy water content (i.e., normalized difference water index (NDWI))-related variables. Our proposed methods consisted of: (i) evaluating the relationships between PAN band (0.503–0.676 μm) with a spatial resolution of 15 m and individual MS bands of Landsat-8 from blue (i.e., acquiring in the range 0.452–0.512 μm), green (i.e., 0.533–0.590 μm), red (i.e., 0.636–0.673 μm), near infrared (NIR: 0.851–0.879 μm), shortwave infrared-I (SWIR-I: 1.566–1.651 μm), and SWIR-II (2.107–2.294 μm) bands with a spatial resolution of 30 m; (ii) determining the suitable individual MS bands to be enhanced into the spatial resolution of the PAN band; and (iii) calculating several vegetation greenness and canopy moisture indices (i.e., NDVI, EVI, NDWI-I, and NDWI-II) at 15 m spatial resolution and subsequent validation using their equivalent-values at a spatial resolution of 30 m. Our analysis revealed that strong linear relationships existed between the PAN and most of the MS individual bands of interest except NIR. For example, r 2 values were 0.86–0.89 for blue band; 0.89–0.95 for green band; 0.84–0.96 for red band; 0.71–0.79 for SWIR-I band; and 0.71–0.83 for SWIR-II band. As a result, we performed smoothing filter-based intensity modulation method of pan-sharpening to enhance the spatial resolution of 30 m to 15 m. In calculating the vegetation indices, we used the enhanced MS images and resampled the NIR to 15 m. Finally, we evaluated these indices with their equivalents at 30 m spatial resolution and observed strong relationships (i.e., r 2 values in the range 0.98–0.99 for NDVI, 0.95–0.98 for EVI, 0.98–1.00 for NDWI).
机译:泛锐化是将较高空间分辨率的全色(PAN)与较低空间分辨率的多光谱(MS)图像融合以创建较高空间分辨率的MS图像的过程。在这里,我们的总体目标是全景处理Landsat-8图像并计算植被的绿色度(即归一化差异植被指数(NDVI)),冠层结构(即增强的植被指数(EVI))和冠层含水量(即归一化差异水指数(NDWI))相关变量。我们提出的方法包括:(i)评价空间分辨率为15 m的PAN波段(0.503–0.676μm)与蓝色的Landsat-8的各个MS波段之间的关系(即,在0.452–0.512μm范围内采集) ,绿色(即0.533–0.590μm),红色(即0.636–0.673μm),近红外(NIR:0.851–0.879μm),短波红外I(SWIR-1:1.566–1.651μm)和SWIR- II(2.107–2.294μm)波段,空间分辨率为30 m; (ii)确定要增强到PAN频段的空间分辨率的合适的各个MS频段; (iii)在15 m的空间分辨率下计算几种植被的绿色度和冠层湿度指数(即NDVI,EVI,NDWI-I和NDWI-II),然后使用其等效值在30 m的空间分辨率下进行验证。我们的分析表明,PAN与除NIR以外的大多数MS单个感兴趣波段之间存在强线性关系。例如,蓝色带的r 2值为0.86-0.89;绿色频段为0.89–0.95;红色波段为0.84–0.96; SWIR-1频段为0.71-0.79; SWIR-II频段为0.71-0.83。结果,我们执行了基于平滑滤波器的泛锐化强度调制方法,以提高30 m至15 m的空间分辨率。在计算植被指数时,我们使用了增强型MS图像并将NIR重采样到15 m。最后,我们以30 m的空间分辨率评估了这些指数及其当量值,并观察到了很强的关系(即,NDVI的r 2值在0.98-0.99之间,EVI的r 2值在0.95-0.98之间,NDWI的r2值在0.98-1.00之间)。

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