Xu, HQ; Zhang, TJ (2013). Assessment of consistency in forest-dominated vegetation observations between ASTER and Landsat ETM plus images in subtropical coastal areas of southeastern China. AGRICULTURAL AND FOREST METEOROLOGY, 168, 1-9.
Vegetation is one of the most important components of the global ecosystem. It plays a vital role for global energy balances and climate modulations. Timely and precisely monitoring vegetation changes has become an increasing need. Among various satellite-based earth observation systems, the Landsat and ASTER sensors are most commonly used in mesoscale vegetation observations. Nevertheless, the quantitative relationship between the two sensors in measuring vegetation has not been investigated in detail. Therefore, this paper aims to investigate how well ASTER vegetation measurements replicate ETM+ vegetation measurements, and more important, how much difference there is in the measurements between the two sensors. The study utilised three date-coincident image pairs of the two sensors, covering the subtropical coastal areas of southeastern China. The approach was achieved by evaluating the consistence of the normalised difference vegetation index (NDVI) data and the NDVI-estimated vegetation area between the two sensors. A further inter-type comparison among forest, paddy and grass has also been carried out to examine whether there is any variation in the consistence between different vegetation types. Results suggest that the ASTER sensor produces similar vegetation measurements to ETM+ in the study areas. Nevertheless, differences have also been observed. The study found an overall lower spectral NDVI measurement of ASTER than ETM+ based on their differences in mean NDVI value and estimated vegetation area. This suggests that the ASTER sensor is less sensitive to vegetation characteristics. Compared with ETM+, the ASTER sensor produces lower NDVI values by up to 8.1% and estimates less vegetation area by up to 4.1%. Inter-type analysis indicates that among the three test vegetation types, forest has the highest degree of agreement in NDVI measurements between the two sensors, whereas grass has the greatest difference in mean NDVI value between both sensors. All these differences are worth considering when the vegetation measurement results of ASTER have to be used together with those of ETM+ for a synergistic scientific application. This often occurs when using the ASTER sensor data for Landsat data continuity in scientific applications in order to fill the gaps in observation. As such, a data conversion between the two sensors is recommended. This paper demonstrated that the conversion using the model equations obtained in this study could reduce the root mean square errors and improve data agreement between the two sensor NDVIs. The study shows that the revealed differences in vegetation measurements between both sensors are due largely to the difference in the relative spectral response functions of the NDVI-related near-infrared bands between the two sensors. (C) 2012 Elsevier B.V. All rights reserved.