Identification and Area Information Extraction of Oat Pasture Based on GEE—A Case Study in the Shandan Racecourse (China)

Abstract

Forage grass is very important for food security. The development of artificial grassland is the key to solving the shortage of forage grass. Understanding the spatial distribution of forage grass in alpine regions is of great importance for guiding animal husbandry and the rational selection of forage grass management measures. With its powerful computing power and complete image data storage, Google Earth Engine (GEE) has become a new method to address remote sensing data collection difficulties and low processing efficiency. High-resolution mapping of pasture distributions on the Tibetan Plateau (China) is still a difficult problem due to cloud disturbance and mixed planting of forage grass. Based on the GEE platform, Sentinel-2 data and three classifiers, this study successfully mapped the oat pasture area of the Shandan Racecourse (China) on the eastern Tibetan Plateau over 3 years from 2019 to 2021 at a resolution of 10 m based on cultivated land identification. In this study, the key phenology windows were determined by analysing the time series differences in vegetation indices between oat pasture and other forage grasses in the Shandan Racecourse, and monthly scale features were selected as features for oat pasture identification. The results show that the mean Overall Accuracy (OA) of Random Forest (RF) classifier, Support Vector Machine (SVM) classifier, and Classification and Regression Trees (CART) classifier are 0.80, 0.69, and 0.72 in cultivated land identification, respectively, with corresponding the Kappa coefficients of 0.74, 0.58, and 0.62. The RF classifier far outperforms the other two classifiers. In oat pasture identification, the RF, SVM and CART classifiers have high OAs of 0.98, 0.97, and 0.97 and high Kappa values of 0.95, 0.94, and 0.95, respectively. Overall, the RF classifier is more suitable for our research. The oat pasture areas in 2019, 2020 and 2021 were 347.77 km2 (15.87%), 306.19 km2 (13.97%) and 318.94 km2 (14.55%), respectively, with little change (1.9%) from year to year. The purpose of this study was to explore the identification model of forage grass area in alpine regions with a high spatial resolution, and to provide technical and methodological support for information extraction of the forage grass distribution status on the Tibetan Plateau.

An Overview of the Special Issue “Remote Sensing Applications in Vegetation Classification”

“…A significant part of the research focused on areas in Asia, mostly China [1,2,5,6,6,11,14]. Five studies were conducted in Europe [3,8,9,12,16], three in North America [7,10,16], and one in Africa-Nigeria [4].…”

“…The published manuscripts covered a wide range of topics regarding vegetation classification using remote sensing techniques. In 14 research papers, the classifications were performed to identify different types of vegetation [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Different types of crops were analysed most frequently [2][3][4]6,6,9,12].…”

“…In 14 research papers, the classifications were performed to identify different types of vegetation [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Different types of crops were analysed most frequently [2][3][4]6,6,9,12]. In three cases, the authors recognized different types of grasslands [8,11,14], whereas trees were the object of the studies in two papers [1,10].…”

“…Generally, it can be stated that most of the methods use optical data [2][3][4][5][6]6,[8][9][10][11]14]; however, one employs radar data [12]. In nine cases, free Copernicus data Sentinel-1, Sentinel-2 or both were analysed [2][3][4][5][6]8,9,12,13]. The optical Sentinel-2 data were used to classify crops [2,13], pastures [6] and semi-natural vegetation [5].…”

“…In nine cases, free Copernicus data Sentinel-1, Sentinel-2 or both were analysed [2][3][4][5][6]8,9,12,13]. The optical Sentinel-2 data were used to classify crops [2,13], pastures [6] and semi-natural vegetation [5].…”