Abstract
An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies.
Highlights
This study aims to generate an accurate and detailed map of the vegetation type of the permafrost region on the central Qinghai-Tibet Plateau (QTP) based on field samples and remote sensing data
The random forest (RF) classifier was selected in this study to predict the vegetation type, which was implemented in R [43]
The matrix showed that only one misclassified type was found in alpine swamp meadow (ASM) and alpine desert (AD), two types were found in alpine meadow (AM) and alpine steppe (AS), and misclassifications occurred only in the adjacent types for each type
Summary
High-accuracy vegetation mapping can provide the exact spatial distribution pattern of vegetation from local to global scales at a given time point or over a continuous period [2,3]. The rapid development of remote sensing techniques and data processing methods has significantly promoted large-scale and high-accuracy vegetation mapping. Over the past several decades, large amounts of remote sensing images have been acquired by a range of sensors with different platforms, spectral ranges, resolutions, and revisit frequencies [9]. These data, catalyzed by the accessibility of cloud computing platforms (e.g., Google Earth Engine), support multipurpose vegetation mapping [9,10]. The massive increases and continuous updating of data sources and their related processing techniques could meet the high accuracy required of vegetation mapping work for multiscale and multitarget studies
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