Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 150, Issue -, Pages 226-244Publisher
ELSEVIER
DOI: 10.1016/j.isprsjprs.2019.02.022
Keywords
Unmanned aerial vehicle; Vegetation indices; Ultrahigh ground-resolution image; Image textures; Gray-tone spatial-dependence matrix; Reproductive growth stages
Categories
Funding
- National Key Research and Development Program of China [2016YFD020060306]
- National Natural Science Foundation of China [61661136003, 41471351, 41771370]
- Beijing Natural Science Foundation, China [6182011]
- High-resolution Earth Observation Project of China [03-Y20A04-9001-17/18, 30-Y20A07-9003-17/18]
- Special Funds for Technology innovation capacity building - Beijing Academy of Agriculture and Forestry Sciences, China [KJCX20170423]
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When dealing with multiple growth stages, estimates of above-ground biomass (AGB) based on optical vegetation indices (VIs) are difficult for two reasons: (i) optical VIs saturate at medium-to-high canopy cover, and (ii) organs that grow vertically (e.g., biomass of reproductive organs and stems) are difficult to detect by canopy spectral VIs. Although several significant improvements have been made for estimating AGB by using narrow band hyperspectral VIs, synthetic aperture radar, laser intensity direction and ranging, the crop surface model technique, and combinations thereof, applications of these new techniques have been limited by cost, availability, data-processing difficulties, and high dimensionality. The present study thus evaluates the use of ultrahigh-ground-resolution image textures, VIs, and combinations thereof to make multiple temporal estimates and maps of AGB covering three winter-wheat growth stages. The selected gray-tone spatial-dependence matrix based image textures (e.g., variance, entropy, data range, homogeneity, second moment, dissimilarity, contrast, correlation) are calculated from 1-, 2-, 5-, 10-, 15-, 20-, 25-, and 30-cm-ground-resolution images acquired by using an inexpensive RGB sensor mounted on an unmanned aerial vehicle (UAV). Optical-VI data were obtained by using a ground spectrometer to analyze UAV-acquired RGB images. The accuracy of AGB estimates based on optical VIs varies, with validation R-2: 0.59-0.78, root mean square error (RMSE): 1.22-1.59 t/ha, and mean absolute error (MAE): 1.03-1.27 t/ha. The most accurate AGB estimate was obtained by combining image textures and VIs, which gave R-2: 0.89, MAE: 0.67 t/ha, and RMSE: 0.82 t/ha. The results show that (i) the eight selected textures from ultrahigh-ground-resolution images were significantly related to AGB, (ii) the combined use of image textures from 1- to 30-cm-ground-resolution images and VIs can improve the accuracy of AGB estimates as compared with using only optical VIs or image textures alone; and (iii) high AGB values from winter-wheat reproductive growth stages can be accurately estimated by using this method; (iv) high estimates of winter-wheat AGB (8-14 t/ha) using the proposed combined method (DIS1, SE30, B460, B560, B670, EVI2 using MSR) show a 22.63% (nRMSE) improvement compared with using only spectral VIs (LCI, NDVI using MSR), and a 21.24% (nRMSE) improvement compared with using only image textures (COR1, DIS1, SE30, EN30 using MSR). Thus, the combined use of image textures and VIs can help improve estimates of AGB under conditions of high canopy coverage.
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