Abstract

Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale.

Highlights

  • IntroductionAs the largest terrestrial ecosystem on earth, grasslands play a crucial role in regulating climate, conserving water, protecting biodiversity, and promoting livestock development [1,2]

  • The optimal scale obtained by the stepwise evolution analysis (SEA) method was 177, and its corresponding Overall Goodness F-measure (OGF) value was very close to the peak of the OGF curve

  • To visually examine the performance of segmentation based on SEA, we compared the segmentation results of three sub-regions within the study area (Figure 8c) at scales larger and smaller than 177 with the OGF value of 0.67 (i.e., 155 and 221) with that of scale 177

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Summary

Introduction

As the largest terrestrial ecosystem on earth, grasslands play a crucial role in regulating climate, conserving water, protecting biodiversity, and promoting livestock development [1,2]. Grassland communities are considered the fundamental unit of grassland ecosystems [3]. Accurate classification of grassland communities is important for humans to understand and study grassland areas, and provides an important basis for rational use, effective conservation, and sustainable development [4]. Researchers can obtain accurate information on the distribution of different grasslands through sampling and obtaining field records. This method is costly and time-consuming when applied either repetitively or in large landscapes [5]

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