Abstract
Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover 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).
Highlights
The normalized difference vegetation index (NDVI) [1,2] has been applied to the monitoring of land use and land cover change [3] drought, desertification [4], desertification [5], soil erosion [6], vegetation fires [7], biodiversity and conservation [8], and soil organic carbon (SOC) [9]
We propose a new approach to vegetation detection based on land cover classification
Hyperspectral data have been used for various land cover classification applications, our experiments showed that the vegetation detection performance using hyperspectral data did not yield better results than those obtained using only the RGB and near infrared (NIR) bands
Summary
The normalized difference vegetation index (NDVI) [1,2] has been applied to the monitoring of land use and land cover change [3] drought, desertification [4], desertification [5], soil erosion [6], vegetation fires [7], biodiversity and conservation [8], and soil organic carbon (SOC) [9]. NDVI has been widely used due to its simplicity, its accuracy still has room for improvement. Land cover classification using multispectral and hyperspectral images [12,13,14,15,16] has been widely used for urban growth monitoring, land use monitoring, flood and fire damage assessment, etc. In many land cover classification papers, researchers use all of the multispectral and hyperspectral bands for classification. There have been some investigations [17,18,19] that only use a few bands such as RGB and NIR bands, and yet can still achieve reasonable classification accuracy. In [17], it was found that synthetic bands using Extended Multi-attribute Profiles (EMAP) [20,21,22,23] can help improve the land classification performance quite significantly
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