An unmanned aerial vehicle (UAV) exhibits many advantages in remote sensing because of its high efficiency

high resolution

low cost

and simple operation.However

most UAVs carry only visible-light true-color sensors

which only contain red

green

and blue bands.Generating some of the most commonly used visible-near infrared-based vegetation indices

such as normalized difference vegetation index (NDVI) and soil-adjusted vegetation index

is difficult.Although hyperspectral and multispectral sensors can produce the previously mentioned indices

the high cost and complexity of data acquisition hinder the further development of UAV technology in the field of vegetation remote sensing.A new vegetation index

which can fully utilize visible-light true-color image in the HSL color space

called Normalized Hue and Lightness Vegetation Index (NHLVI)

has been proposed to solve this problem. The characteristics of hue and lightness of different objects in the HSL color space model were analyzed within visible-light true-color image

and the hue (H) and lightness (L) components were selected because of their weak correlations.After their normalization

NHLVI was constructed on the basis of the form of NDVI to enhance the vegetation information.A total of 88 visible true-color and 163 multispectral images

which covered a test area with different vegetation types and coverage

were acquired through a UAV flight campaign to verify the validity of this new vegetation index.The structure from motion algorithm was used to mosaic the UAV images

and the digital orthophoto map of the test area was produced.Then

the NHLVI and several commonly used visible vegetation indices

i.e.

normalized green-red difference index (NGRDI)

excess green

vegetation index

color index of vegetation

excess green minus excess red

combination

and combination 2

were calculated.Hyperspectral datasets were simultaneously collected using the ASD HandHeld2 during the UAV flight and were resampled to match visible true-color and multispectral sensors in accordance with their spectral response function.For the multispectral image

the digital number value was converted to reflectance through the empirical line method.All the vegetation indices calculated from visible true-color UAV imagery were compared with NDVIs generated from ASD and UAV multispectral data.The receiver operating characteristic (ROC) curve was employed to analyze and determine the threshold to extract and compare vegetation information further.The results from different vegetation indices were compared. The correlation coefficient () was used to evaluate the performance of all the vegetation indices from visible-light true-color imagery.The correlation coefficient(s) between different visible true-color-based vegetation indices and ASD-based and/or multispectral UAV-based NDVIs show that a high correlation exists between the proposed vegetation index and NDVI

with a correlation coefficient () of 0.776 8

followed by the correlation between NGRDI and NDVI (=0.687 4).The ROC curve was utilized to explore the capability of the proposed vegetation index to extract vegetation information.NGRDI and NDVI were selected and compared with NHLVI.First

the area under the ROC curve was calculated for NHLVI (0.777)

and it is smaller than that for NDVI (0.815) but larger than that for NGRDI (0.681).This finding indicates that NHLVI outperforms NGRDI in terms of vegetation extraction.Second

vegetation information was extracted by the predefined threshold and compared between different vegetation indices through visual interpretation.Given the difficulties in threshold determination

the ROC curve method was selected to decide the threshold.By selecting the point on the cutoff between the curve and a line with slope equals to 1

the sensitivity and specificity were maximized whereas the omission and commission errors were minimized.Finally

quantitative analysis was performed

400 random points were positioned on the UAV visible-light true-color image to derive the NHLVI

and the NDVI was extracted from the multispectral UAV image as a reference to evaluate the vegetation extraction accuracy.The overall vegetation extraction accuracy using NHLVI is 82.25%

which is higher than that of NGRDI (79.75%) and lower than that of NDVI (84.00%).The commission error of NHLVI is 6.64%

and the omission error is 22.82%.Notably

the proposed vegetation index

compared with the other vegetation indices

e.g.

NGRDI

performs outstandingly in sparsely vegetated areas. The NHLVI was proposed in this study.This index takes advantage of the HSL color space transformation and makes good use of high spatial resolution visible-light true-color images

e.g.

UAV images.Experimental results show that the proposed vegetation index

compared with several commonly used vegetation indices

possesses a stronger correlation with NDVI and exhibits the same value range

i.e.

[-1

1].Vegetation cover information can be extracted using the threshold set by the ROC method.The proposed method can achieve outstanding results compared with other vegetation indices

particularly in sparsely vegetated areas

and presents nearly the same accuracy as NDVI.Overall

NHLVI is suitable for vegetation extraction from visible-light true-color images from UAVs and provides a new method for the application of UAV-based remote sensing.Future works will focus on its application to different types of vegetation and verification in different sensors.