Abstract
Land cover data is crucial for earth system modelling, natural resources management, and conservation planning. Remotely sensed time-series data capture dynamic behavior of vegetation, and have been widely used for land cover mapping. Temporal profiles of vegetation index (VI), especially normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are the most used features derived from time-series spectral data. Whether NDVI or EVI is optimal to generate temporal profiles has not been evaluated. The universal normalized vegetation index (UNVI), a relatively new index with all spectral bands incorporated, has been proved to be more effective than several commonly used satellite-derived VIs in some application scenarios. In this study, we explored the ability of UNVI time series for discriminating different vegetation types in Chaoyang prefecture, northeast China, in comparison with normalized NDVI, EVI, triangle vegetation index (TVI), and tasseled cap transformation greenness (TCG). These five indices were calculated using Landsat 8 surface reflectance data, and two comparative experiments were conducted. The first experiment analyzed class separabilities using pairwise JM (Jeffries–Matusita) distance as indicator, and the results showed that UNVI was superior to EVI, TVI, and TCG, and almost equivalent to NDVI, especially during the peak of vegetation growing season and for the most indistinguishable vegetation pair broadleaf and shrubs. The second experiment compared the vegetation classification accuracies using the features of these VI temporal profiles and the corresponding phenological parameters, and the results showed that UNVI can better classify the five major vegetation in Chaoyang prefecture than other four indices. Therefore, we conclude that UNVI time series has considerable potential for regional land cover mapping, and we recommend that the use of the UNVI is considered in the future time series related studies.
Highlights
Identifying, characterizing, and mapping land cover is essential for earth system modelling and natural resources planning [1,2]
In order to get a general understanding of the phenological characteristics of the five major vegetation types, we calculated the averaged vegetation index (VI) of the selected typical pixels at each time point
universal normalized vegetation index (UNVI) and normalized difference vegetation index (NDVI) of conifers kept high values throughout the year, while enhanced vegetation index (EVI), triangle vegetation index (TVI), and TCG were relatively low. This is because EVI, TVI, TCG are more sensitive to the reflectance of the near-infrared (NIR) spectral band, and conifer is characterized by strong absorption and strong scattering (“photon trap” caused by the structure of a coniferous shoot) in the NIR band [73]
Summary
Identifying, characterizing, and mapping land cover is essential for earth system modelling and natural resources planning [1,2]. While multispectral image data from a single date suffers high level of spectral confusion or spectral similarity between different cover types, multi-temporal data sets, which are more accessible to the remote sensing community because of the advent of cloud-computing [6] and resources like Google Earth Engine [7], have proved to be more effective and accurate for vegetation discrimination and classification [8,9,10] With both spectral and temporal profiles incorporated, time series and their derivations such as vegetation indices (VIs) and further derived vegetation phenological parameters greatly enrich available information for vegetation identification. Recently more efforts have been focused on multi-temporal images for land cover mapping [11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
