UNVI-Based Time Series for Vegetation Discrimination Using Separability Analysis and Random Forest Classification

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.

The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination

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Methodology of wheat lodging annotation based on semi-automatic image segmentation algorithm

“…The value of JM distance ranges from 0 to 2. A large J-M distance indicates that the difference between the two types of ground objects is more significant, thus contributing to the separation of the two types [32] . Table 4 lists the classification of JM distance feature separability.…”

Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification

“…Spectral separability refers to quantifying the degree of separation between LC classes in different remote sensing images. Jeffries-Matusita (JM) distance is the indicator of separability commonly used in the applications of remote sensing [61][62][63][64][65]. JM distance was recommended to be more reliable for assessing separability [66] and demonstrated increased reliability for classes within homogenous distributions [67].…”