Mapping Shrub Coverage in Xilin Gol Grassland with Multi-Temporal Sentinel-2 Imagery

Abstract

In recent decades, shrubs dominated by the genus Caragana have expanded in a large area in Xilin Gol grassland, Inner Mongolia, China. This study comprehensively evaluated the performances of multiple factors for mapping shrub coverage across the Xilin Gol grassland based on the spectral and temporal signatures of Sentinel-2 imagery, and for the first time produced a large-scale shrub coverage mapping result in this region. Considering the regional differences and gradients in the types and sizes of shrub in the study area, the study area was divided into three subregions based on precipitation data, i.e., west, middle and east regions. The shrub coverage estimation accuracy from dry- and wet-year data, different types of vegetation indices (VIs) and multiple regression methods were compared in each subregion, and the key phenological periods were selected. We also compared the accuracy of four mapping strategies, which were pairwise combinations of zoning (i.e., subregions divided by precipitation) and non-zoning, and full time series of VIs and key phenological period. Results show that the mapping accuracy in a dry year (2017) is higher than that in a wet year (2018). The optimal VIs and key phenological periods show high spatial variability. In terms of mapping strategies, the accuracy of zoning is higher than that of non-zoning. The root mean square error (RMSE), overall accuracy (OA) and recall for ‘zoning + full time series (or + key phenological period)’ strategy were 0.052 (0.055), 76.4% (79.7%) and 91.7% (94.6%), respectively, while for ‘non-zoning + full time series (or + key phenological period)’ strategy were 0.057 (0.060), 75.5% (74.6%) and 91.7% (88.6%), respectively. The mapping using VIs in key phenological periods is better than that of using full time series in the low-value prediction of shrub cover. Based on the strategy of ‘zoning + key phenological period’, the shrub coverage map of the whole region was generated with a RMSE of 0.055, OA of 80% and recall of 95%. This study not only provides the first large-scale mapping data of shrub coverage, but also provides reference for shrub dynamic monitoring in this area.

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

“…Similar results were achieved for High-Arctic shrubs [11,12] and for alpine meadow plants [13][14][15]. Sentinel-2 images were also successfully used to identify multi-temporal shrubland areas in Inner Mongolia, China [16] and in deforested equatorial areas of Gabon [17]. In the first case, the authors [16] described the importance of the phenological factor in identifying shrubs.…”

“…This was also confirmed by Calvão and Palmeirim [9], who observed a summer stress (an intense radiation and a water deficit), which changed the registered spectra of different types of vegetation included in the shrubby formations. It was also noted, as by previous researchers Gan et al [16], that shrub land cover classification is not an easy task using remote sensing tools. In general, the shrub canopy is heterogeneous due to combinations with other targets (grasses, soil, single trees and shades), and an additional difficulty is the problematic separation of shrubs from grasslands, as they show similar spectral characteristics.…”

“…Sentinel-2 images were also successfully used to identify multi-temporal shrubland areas in Inner Mongolia, China [16] and in deforested equatorial areas of Gabon [17]. In the first case, the authors [16] described the importance of the phenological factor in identifying shrubs. For this purpose, the authors used the gaussian process regression (GPR, determining similarity between samples) and random forest regression (RFR, indicating a degree of mean square error (MSE) reduction of feature variables), which were the vegetation indices presented below.…”

“…The conducted analyses confirmed that early spring (early April), summer (late June-mid-July) and late October are optimal periods for shrubs analysis (in our case, various growing periods were not analyzed, as we had access to the data acquired during a single AISA flight campaign, and the Sentinel-2 image was selected in accordance with the airborne data). Gan et al [16] also selected relevant vegetation indices for this type of analysis, which were NDTI (Normalized Difference Tillage Index), GNDVI (Green Normalized Difference Vegetation Index), NDVI, NDSVI (Normalized Difference Senescent Vegetation Index), EVI2 (two-band Enhanced Vegetation Index), MSAVI, NDPI (Normalized Difference Phenology Index), NDGI (Normalized Difference Greenness Index) and NDMI (Normalized Difference Moisture Index). As the outcome, Caragana shrubs were identified with the best overall accuracy, achieving 91.7%, and RMSE achieved 5.0-5.5% (validation-test patterns).…”

“…As the outcome, Caragana shrubs were identified with the best overall accuracy, achieving 91.7%, and RMSE achieved 5.0-5.5% (validation-test patterns). A significant role of increasing the results were found with the NDTI and NDGI indices [16]. In the case of Gabonese research, authors tested an increase of DEM and NDVI on the accuracy of Sentinel-2 image classification carried out with the Random Forest (RF) algorithm.…”