Abstract
Abstract. Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. This study aims to explore the capabilities of Sentinel-2 data over Landsat-8 Operational Land Imager (OLI) data for vegetation mapping. Two combination of Sentinel-2 dataset have been considered, first combination is 4-band dataset at 10m resolution which consists of NIR, R, G and B bands, while second combination is generated by stacking 4 bands having 10 m resolution along with other six sharpened bands using Gram-Schmidt algorithm. For Landsat-8 OLI dataset, six multispectral bands have been pan-sharpened to have a spatial resolution of 15 m using Gram-Schmidt algorithm. Random Forest (RF) and Maximum Likelihood classifier (MLC) have been selected for classification of images. It is found that, overall accuracy achieved by RF for 4-band, 10-band dataset of Sentinel-2 and Landsat-8 OLI are 88.38 %, 90.05 % and 86.68 % respectively. While, MLC give an overall accuracy of 85.12 %, 87.14 % and 83.56 % for 4-band, 10-band Sentinel and Landsat-8 OLI respectively. Results shown that 10-band Sentinel-2 dataset gives highest accuracy and shows a rise of 3.37 % for RF and 3.58 % for MLC compared to Landsat-8 OLI. However, all the classes show significant improvement in accuracy but a major rise in accuracy is observed for Sugarcane, Wheat and Fodder for Sentinel 10-band imagery. This study substantiates the fact that Sentinel-2 data can be utilized for mapping of vegetation with a good degree of accuracy when compared to Landsat-8 OLI specifically when objective is to map a sub class of vegetation.
Highlights
Mapping of vegetation with precision is a key task for managing natural resources as well as plays an important role in various protection and restoration programs
Sentinel-2 imagery is classified using 4-band and 10-band data and classification performance is compared with pan-sharpened Landsat-8 Operational Land Imager (OLI) imagery using Random Forest (RF) and Maximum Likelihood classifier (MLC) classifier
It can be observed visually by comparing Landsat-8 OLI image classified by MLC with Landsat-8 OLI classified by RF (Figure 2) shows that maximum crop land is misclassified as other crops
Summary
Mapping of vegetation with precision is a key task for managing natural resources as well as plays an important role in various protection and restoration programs. There are many studies for Land Use Land Cover classification as well some of them are dedicated to vegetation mapping used various supervised and unsupervised algorithms in pixel based or object based frameworks (Belgiu, 2018; Chuang, 2016; Nay, 2018; Colkesen, 2017; Li, 2014). Launched Sentinel satellite is receiving much attention due to its fine spatial resolution, fast revisit time, global coverage, last but not least free availability makes it a great choice for various applications in the field of remote sensing (Wang, 2016). This study aims to explore the capabilities of Sentinel-2 data vis-a-vis Landsat-8 OLI data for vegetation mapping. The objective of this paper is to explore the potential of Sentinel for vegetation mapping in comparison to Landsat-8 OLI data. This paper is structured as follows: Section 2 presents the study areas and the data; Section 3 describe the classifier; Section 4 is dedicated to the results analysis; Section 5 highlights the main findings of the study as conclusion
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