Normalized Green Red Difference Index Research Articles

Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice

Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management.

A UAV and S2A data-based estimation of the initial biomass of green algae in the South Yellow Sea

Previous studies have shown that the initial biomass of green tide was the green algae attaching to Pyropia aquaculture rafts in the Southern Yellow Sea. In this study, the green algae was identified with unmanned aerial vehicle (UAV), an biomass estimation model was proposed for green algae biomass in the radial sand ridge area based on Sentinel-2A image (S2A) and UAV images. The result showed that the green algae was detected highly accurately with the normalized green-red difference index (NGRDI); approximately 1340 tons and 700 tons of green algae were attached to rafts and raft ropes respectively, and the lower biomass might be the main cause for the smaller scale of green tide in 2017. In addition, UAV play an important role in raft-attaching green algae monitoring and long-term research of its biomass would provide a scientific basis for the control and forecast of green tide in the Yellow Sea.

RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields

In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Additionally, to compare the performance of the method, two indices were used, specifically, the M/MGT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images; namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. The best weed detection performance was obtained by the NN with the fused image, with M/MGT index between 80 and 108% and MP between 70 and 85%.

Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems

ABSTRACTAccurate, reliable, and cost-effective methods of evaluating forest regeneration success are needed to improve forest inventories and silvicultural operations. While traditional surveys are relatively inexpensive and meet current data requirements, their annual coverage of over 1 million hectares in British Columbia alone are operationally and logistically intensive. To improve the efficiency and utility of forest regeneration inventories, the incorporation of multi-temporal monitoring linked to years since planting (YSP) could help improve understanding of rates and characteristics of vegetative succession while providing a means to evaluate the economic and operational success of management actions on public land. In this study, we evaluate the potential of utilizing Unmanned Aerial System (UAS)-acquired very high spatial resolution imagery to provide spatial, spectral, and structural information on forest regeneration in previously clear-cut stands near Nakusp and Quesnel, British Columbia, Canada. Three stands approximately 5, 10, and 15 YSP were chosen at both sites. Using wall-to-wall UAS-acquired red–green–blue (RGB) imagery, dense Digital Aerial Photogrammetric (DAP) point clouds were produced providing forest structure information. Spectral data in the form of Visible Vegetation Indices (VVI) including the Normalized Green Red Difference Index (NGRDI), visible atmospherically resistant index (VARIg), and Green Leaf Indices (GLIx) were computed. Spectral and structural information from the VVI and DAP were combined to perform Object-Based Image Analyses (OBIA) facilitating supervised classifications of forest cover into conifer, deciduous, and ground classes. Independent classifications were performed on each stand, yielding high overall accuracies (86–95% for Nakusp; 93–95% for Quesnel). Spectral and structural differences amongst classes and YSP were analysed. Height and area coverage of conifers were found to increase with YSP in both sites (0.7–2.7 m for Nakusp; 0.3–2.2 m for Quesnel), while VVI metrics were shown to be more successful than standard RGB at differentiating forest cover through time. The results of this study indicate that UAS-acquired imagery has a potential niche for quickly, accurately, and reliably providing highly detailed spatial, spectral, and structural information on forest regeneration. Methodology and data products from this study show promise for benefiting silvicultural monitoring and operations while improving multi-temporal forest inventory knowledge.

Selection of Optimal Vegetation Indices and Regression Model for Estimation of Rice Growth Using UAV Aerial Images

Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to select optimal vegetation indices and regression model for estimating of rice growth using UAV images. This study was conducted using a fixed- wing UAV (Model : Ebee) with Cannon S110 and Cannon IXUS camera during farming season in 2016 on the experiment field of National Institute of Crop Science. Before heading stage of rice, there were strong relationships between rice growth parameters (plant height, dry weight and LAI (Leaf Area Index)) and NDVI (Normalized Difference Vegetation Index) using natural exponential function (R≥0.97). After heading stage, there were strong relationships between rice dry weight and NDVI, gNDVI (green NDVI), RVI (Ratio Vegetation Index), CI-G (Chlorophyll Index-Green) using quadratic function (R≤-0.98). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images. RVI (Ratio vegetation index), NDVI (Normalized difference vegetation index), CVI (Chlorophyll vegetation index), gNDVI (Greent normalized difference vegetation index, CI-G (Chlorophyll Index- Geen), NGRDI (Normalized green red difference index), GLI (Green leaf index), VARI (Visible atmospherically resistant index). Relationship between vegetation indices and rice growth parameter (LAI).

Crop water stress maps for an entire growing season from visible and thermal UAV imagery

Abstract. This study investigates whether a water deficit index (WDI) based on imagery from unmanned aerial vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and late growing season are included to investigate whether the WDI has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI differs from the more commonly applied crop water stress index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature).Lightweight thermal and RGB (red–green–blue) cameras were mounted on a UAV on three occasions during the growing season 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the normalized green–red difference index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI is assessed. Final WDI maps had a spatial resolution of 0.25 m.It was found that the UAV-based WDI is in agreement with measured stress values from an eddy covariance system. Further, the WDI is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI accounts for areas of ripe crops that no longer have the same need for irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing season.

Detecting interactive effects of N fertilization and heat stress on maize productivity by remote sensing techniques

Abstract The objective of this study was to compare the performance of two different remotely sensed techniques in detecting the effects of terminal heat stress and N fertilization on final maize aerial biomass (AB) and grain yield (GY). The study was conducted under field conditions for two consecutive growing seasons. Six N treatments combining three doses [0, 100, 200 Kg N ha −1 ] and two timings [at V4 and at 15 days before silking] were applied. Within each N treatment three heat treatments were applied (pre-flowering, post-flowering and the control treatment at ambient air temperature). Remote sensing measurements were taken with a multispectral band camera to measure the normalized difference vegetation index (NDVI) and a digital Red/Green/Blue (RGB) camera to measure the normalized green red difference index (NGRDI). Both indices failed to predict the GY of pre-flowering heat-treated plants due to grain set establishment problems that could not be detected by vegetation indices which are designed to capture differences in green canopy area. In contrast, both the NGRDI and the NDVI correlated positively with GY and AB in the control heat treatment and to a lesser extent in the post-flowering heat treatment. Under the control heat treatment, the NGRDI exhibited higher correlations with AB and GY than the NDVI across the N fertilization treatments. Since the NGRDI is formulated based only on the reflectance in the visible regions (VIS) of the spectrum (Green and Red) without dependence on the near infrared regions (NIR), it performs better than the NDVI. This is because it overcame the reported saturation patterns at high leaf area index and was more efficient at capturing even small differences in leaf colour (chlorophyll content) due to the different applied N treatments. Also, the NGRDI seemed to be a more seasonally independent parameter than the NDVI, which is more affected by temporal variability within the field, and thus the NGRDI predicted AB and GY better than the NDVI when combining the data of the two growing seasons.

The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions

Improving durum wheat performance to abiotic stresses is often limited by a lack of proper monitoring methods in support of crop management and efficient phenotyping tools for breeding. The objectives of this study were: (1) comparing the performance under contrasting water treatments of different physiological traits, which evaluate plant growth and water status; and (2) understanding how these traits can predict grain yield (GY) performance under contrasting water conditions. Thus, five modern durum wheat genotypes were subjected to rainfed (RF) and supplemental irrigation (SI) treatments. Two categories of physiological traits were tested; (1) the vegetation indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Green Red Difference Index (NGRDI); and (2) the stable carbon and oxygen isotope compositions (δ13C and δ18O) of different plant parts. The NGRDI at anthesis and the δ13C of mature grains were the traits best correlated (positively and negatively, respectively) with GY. Both traits in combination explained at least 50% of variability in GY within each water treatment. The produced path models for RF and SI conditions highlighted the particular role of NGRDI and δ13C in predicting GY. In addition, the study showed the potential of using vegetation indices derived from digital Red-Green-Blue (RGB) images as a low-cost technique for assessing aerial biomass (AB) and GY under different water availabilities.

Long-Term Distribution Patterns of Chlorophyll-a Concentration in China’s Largest Freshwater Lake: MERIS Full-Resolution Observations with a Practical Approach

A new empirical Chl-a algorithm has been developed and validated for the largest freshwater lake of China (Poyang Lake) using a normalized green-red difference index (NGRDI), where the uncertainty was estimated to be <45% for Chl-a ranging between 1.3 and 10.5 mg·m−3. The combined approach of using the NGRDI algorithm and atmospherically-corrected Medium Resolution Imaging Spectrometer (MERIS) data showed an advantage over other popular approaches. The algorithm was then applied to 325 carefully-selected MERIS full-resolution (300-m) scenes between 2003 and 2012, with pixels of extreme turbidity (NGRDI < 0.06, corresponding to >~25 mg·L−1 total suspended sediments or TSS) masked. The long-term Chl-a distribution showed significant spatial gradient and temporal variability, with Chl-a ranging between 2.4 ± 0.2 mg·m−3 in April and 4.4 ± 1.0 mg·m−3 in July and no significant increasing or decreasing trend during the 10-year period. In waters where Chl-a was retrievable (i.e., where TSS is <25 mg·L−1), Chl-a concentration indicated a significant negative correlation with TSS concentration on a seasonal scale and a significant positive correlation with precipitation over the years. Potential eutrophic regions in the southern and eastern lake, thought to be results of limited water exchange with the main lake, were delineated based on the occurrence frequency of high Chl-a (>10 mg·m−3) in summer. The study not only provides, for the first time, synoptic baseline information on the lake’s Chl-a distributions and potential eutrophic regions, but also demonstrates a practical approach that might be extended to assess eutrophication conditions in other inland waters.

A new spectral index to detect Poaceae grass abundance in Mongolian grasslands

The objectives of the present study were to develop a new index based on remotely-sensed data for detecting the abundance of grasses in the family Poaceae, which has a high palatability for livestock in Mongolia, and to map the distribution of these grasses in the semi-arid Mongolian steppes. We measured ground-based spectral reflectance of pure plant leaves – including Poaceae grasses – and soils, as well as in-situ in the Mongolian grasslands. The hyper-spectral data, taken by a spectroradiometer, were converted into four multi-spectral bands (i.e., blue, green, red, and NIR) to simulate satellite-based imagery data. In order to magnify the characteristics of the spectral signal of Poaceae, NGBDI (Normalized Green-Blue Difference Index), NGRDI (Normalized Green-Red Difference Index), NDVI (Normalized Difference Vegetation Index), NNBDI (Normalized NIR-Blue Difference Index) were calculated from the four multi-spectral reflectance values. Poaceae Abundance Index (PAI) was derived by combining these four normalized difference indices. PAI was found out to be a good indicator to discriminate Poaceae grass from the other plant spectral data.

Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status

Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may outweigh the benefits of the information. Hobbyists have been acquiring aerial photography from radio-controlled model aircraft; we evaluated these very-low-cost, very high-resolution digital photography for use in estimating nutrient status of corn and crop biomass of corn, alfalfa, and soybeans. Based on conclusions from previous work, we optimized an aerobatic model aircraft for acquiring pictures using a consumer-oriented digital camera. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for the same reflectance because of differences in the exposure settings selected by the digital camera. To account for differences in exposure a Normalized Green–Red Difference Index [(NGRDI = (Green DN − Red DN)/(Green DN + Red DN)] was used; this index was linearly related to the normalized difference of the green and red reflectances, respectively. For soybeans, alfalfa and corn, dry biomass from zero to 120 g m−2 was linearly correlated to NGRDI, but for biomass greater than 150 g m−2 in corn and soybean, NGRDI did not increase further. In a fertilization experiment with corn, NGRDI did not show differences in nitrogen status, even though areas of low nitrogen status were clearly visible on late-season digital photographs. Simulations from the SAIL (Scattering of Arbitrarily Inclined Leaves) canopy radiative transfer model verified that NGRDI would be sensitive to biomass before canopy closure and that variations in leaf chlorophyll concentration would not be detectable. There are many advantages of model aircraft platforms for precision agriculture; currently, the imagery is best visually interpreted. Automated analysis of within-field variability requires more work on sensors that can be used with model aircraft platforms.