Document Type

Article

Publication Date

11-2020

DOI

10.3390/rs12233880

Publication Title

Remote Sensing

Volume

12

Issue

23

Pages

3880 (1-29 pp.)

Abstract

Accurate vegetation detection is important for many applications, such as crop yield estimation, landcover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

Comments

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Original Publication Citation

Kwan, C., Gribben, D., Ayhan, B., Li, J., Bernabe, S., & Plaza, A. (2020). An accurate vegetation and non-vegetation differentiation approach based on land cover classification. Remote Sensing, 12(23), 1-29, Article 3880. https://doi.org/10.3390/rs12233880

ORCID

0000-0003-0091-6986 (Li)

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