Hyperspectral Unmanned Aerial Vehicles Research Articles

Remote retrieval of dissolved organic carbon in rivers using a hyperspectral drone system

ABSTRACT Rivers act as the principal channels for transporting terrigenous dissolved organic carbon (DOC) to lakes and reservoirs. Satellite remote sensing-based river monitoring is difficult due to the narrow river form and high spatiotemporal heterogeneity of DOC components. The unique advantages of unmanned aerial vehicles (UAVs) facilitate river DOC concentration monitoring. The DOC concentration in 8 major tributaries (average width: 109.62 m) and shoreside of the Lake Chaohu Basin were retrieved via a hyperspectral UAV. The results showed that (1) the DOC concentration was significantly correlated with the water remote sensing reflectance ( R rs ) of 402, 429-438, 440–451 and 458–462 nm in the blue band (r2: 0.11 to 0.13; p<0.05), and 620–621 and 623–693 nm in the red band (r2: 0.12 to 0.20; p<0.05). The water quality parameters chlorophyll-a (Chl-a) and suspended particulate matter (SPM) and environmental parameters wind speed and temperature 3 days delay sampling date, also showed a significant correlation. (2) The random forest regression (RFR) model attained the best performance (r2: 0.64; RMSE: 0.30 mg/L; MAPE: 7.02%). (3) DOC concentration in Lake Chaohu Basin was highest in the northeast (8.19 mg/L), followed by the northwest and west (7.13 mg/L), and it was lowest in the south (6.70 mg/L).

Observations of HONO and its precursors between urban and its surrounding agricultural fields: The vertical transports, sources and contribution to OH

The insufficient study on vertical observations of main atmospheric reactive nitrogen oxides (NO2 and HONO) posed a great challenge to evaluate their intertransport between urban and agricultural areas, and to further learn the atmospheric nitrogen chemistry and the atmospheric oxidation capacity at high altitudes. A stereoscopic measurement campaign (satellite remote sensing, hyperspectral unmanned aerial vehicle (UAV) remote sensing and MAX-DOAS observation) was performed in a typical inland city Hefei and its surrounding agricultural fields from June to October 2022. Average aerosol vertical profiles exhibited a Gaussian shape above 100 m with maximum values of 0.67 km−1 and 0.55 km−1 at 300–400 m layer at Anhui University (AHU) and Changfeng (CF), respectively. The distinct layered structure was mainly attributed to regional transport. Average H2O and NO2 vertical profiles all showed a Gaussian shape and an exponential shape at AHU and CF, respectively. Moreover, the diurnal evolution of H2O profiles performed one peak and bi-peak patterns at AHU and CF, respectively, whereas the diurnal evolution of NO2 at two stations all exhibited bi-peak patterns attributed to vehicle emissions. Average HONO vertical profiles showed an exponential shape and a Gaussian shape at AHU and CF, respectively. Higher HONO (> 0.05 ppb) above 1.0 km at 14:00–16:00 was observed at CF. The transport flux analysis showed that the northern transport flux always larger than southern transport flux for aerosol and H2O. The maximum northern transport fluxes appeared at 300 m and surface for aerosol and H2O, respectively. It indicated that surrounding agricultural fields was an important source of atmospheric H2O of city. The southern transport flux was larger than northern transport flux for NO2, with a maximum net transport flux of 9.20 ppb m s−1 at 100 m. It demonstrated that NO2 transported from urban areas was an important source of NO2 in agricultural fields. For HONO, the southern transport flux was larger than northern transport flux under 100 m, whereas it was opposite above 100 m. It indicated that the HONO distributed at high altitudes at agricultural fields had potential to enhance the atmospheric oxidation capacity of urban area. The net horizontal transport fluxes of HONO of our defined cropland were 5.25 μg m−2 s−1 and -3.65 μg m−2 s−1 during non-fertilization and fertilization periods, respectively. It indicated that the cropland could obviously export HONO to surrounding atmosphere during the fertilization period. Deducing the contribution of direct emission, heterogeneous process was a major source of HONO at urban and agricultural areas. The average surface conversion rate of NO2-to-HONO (CHONO) was 0.01467 h−1, and this value decreased with the increase of height at urban station. While average surface CHONO was 0.0322 h−1 at agricultural fields, which was ~1.2–2.8 times higher than that at urban area. The CHONO at agricultural fields significantly increased with the increase of height. The average CHONO at 1.0 km was ~2.0–3.6 times higher than that at surface. That suggested that the heterogeneous process was the main HONO source at high altitudes at CF, and this process obviously correlated with aerosol and H2O. The higher OH production from HONO (P(OH)HONO) occurred at 0–200 m and 100–400 m with averaged values of 0.31 ppb h−1 and 0.39 ppb h−1 at AHU and CF, respectively. The high P(OH)HONO above 1.0 km at CF from September to October was strongly correlated with high O3 (> 80 ppb). This study emphasized the importance of the stereoscopic of HONO on the analysis of its distribution, evolution, source and atmospheric oxidizing contribution.

Inversion analysis of soil nitrogen content using hyperspectral images with different preprocessing methods

Unmanned aerial vehicle (UAV) hyperspectral data can provide accurate and detailed spectral information on the surface of features, leading to a better understanding of the physiological characteristics and optical properties of objects. Compared to satellite hyperspectral, UAV hyperspectral has a greater image spatial resolution, but it also collects more impurity information along with more image detail information. The nitrogen (N) content in shallow soils is of great significance for crop growth, and most of its remote sensing inversions are currently based on satellite images, whereas relatively few studies have used hyperspectral UAV images in the near-infrared band for inversions. Based on this, we propose a high-accuracy inversion model based on UAV hyperspectral imagery using an elastic net (EN) dimensionality reduction algorithm combined with a decision tree (DT) model. In this study, a hyperspectral imager carried by a drone was used to acquire the surface soil spectral data of winter wheat in the study area after sowing and fertilizing, and the measured N content of the sampled soil was determined by chemical analysis methods. We used various pre-processing methods to denoise and reduce the dimensionality of the original hyperspectral images and utilized a DT regression model to construct an inversion model of N content with different preprocessing results. Then we compared the accuracy of the models and used the optimal model to obtain the spatial distribution map of soil N content in the study area. The results show that (1) the dimensionality reduction of hyperspectral data can effectively remove redundant information and prevent data overfitting. Among them, the successive projections algorithm and correlation analysis method are more suitable for analyzing pre-processed spectral data, whereas the Lasso and EN algorithms are more suitable for processing original spectral data. (2) For the original spectral images, the EN dimensionality reduction algorithm (R2 of the EN-Unt-DT inversion model is 0.930, RMSE is 0.045) performed better than the other three algorithms. For the pre-processed spectral images, the successive projections algorithm (R2 of the SPA-Pre-DT inversion model is 0.906, RMSE is 0.055) performed better than the other three algorithms. The constructed EN-Unt-DT inversion model had the highest fitting accuracy. The research results provide a corresponding technical reference for the inversion of soil N content using UAV NIR hyperspectral data.

Early Detection of Dendroctonus valens Infestation at Tree Level with a Hyperspectral UAV Image

The invasive pest Dendroctonus valens has spread to northeast China, causing serious economic and ecological losses. Early detection and disposal of infested trees is critical to prevent its outbreaks. This study aimed to evaluate the potential of an unmanned aerial vehicle (UAV)-based hyperspectral image for early detection of D. valens infestation at the individual tree level. We compared the spectral characteristics of Pinus tabuliformis in three states (healthy, infested and dead), and established classification models using three groups of features (reflectance, derivatives and spectral vegetation indices) and two algorithms (random forest and convolutional neural network). The spectral features of dead trees were clearly distinct from those of the other two classes, and all models identified them accurately. The spectral changes of infested trees occurred mainly in the visible region, but it was difficult to distinguish infested from healthy trees using random forest classification models based on reflectance and derivatives. The random forest model using spectral vegetation indices and the convolutional neural network model performed better, with an overall accuracy greater than 80% and a recall rate of infested trees reaching 70%. Our results demonstrated the great potential of hyperspectral imaging and deep learning for the early detection of D. valens infestation. The convolutional neural network proposed in this study can provide a reference for the automatic detection of early D. valens infestation using UAV-based multispectral or hyperspectral images in the future.

Damage Diagnosis of Pinus yunnanensis Canopies Attacked by Tomicus Using UAV Hyperspectral Images

It remains challenging to control Tomicus spp., a pest with fast spreading capability, leading to the death of large numbers of Pinus yunnanensis (Franch.) and posing a severe threat to ecological security in southwest China. Therefore, it is crucial to effectively and accurately monitor the damage degree for Pinus yunnanensis attacked by Tomicus spp. at large geographical scales. Airborne hyperspectral remote sensing is an effective, accurate means to detect forest pests and diseases. In this study, we propose an innovative and precise classification framework to monitor the damage degree of Pinus yunnanensis infected by Tomicus spp. using hyperspectral UAV (unmanned aerial vehicle) imagery with machine learning algorithms. First, we revealed the hyperspectral characteristics of Pinus yunnanensis from a UAV-based hyperspectral platform. We obtained 22 vegetation indices (VIs), 4 principal components, and 16 continuous wavelet transform (CWT) features as the damage degree sensitive features. We classified the damage degree of Pinus yunnanensis canopies infected by Tomicus spp. via three methods, i.e., discriminant analysis (DA), support vector machine (SVM), and backpropagation (BP) neural network. The results showed that the damage degree detected from the BP neural network, combined with 16 CWT features, achieved the best performance (training accuracy: 94.05%; validation accuracy: 94.44%).

Effect of crop spectra purification on plant nitrogen concentration estimations performed using high-spatial-resolution images obtained with unmanned aerial vehicles

Estimating plant nitrogen concentrations (PNCs) with remote sensing technology is critical for ensuring precise field nitrogen (N) management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolutions that can be used to clearly identify soil and vegetation. Previously, many spectral indices were designed to remove soil effects to obtain optimal PNC predictions. Herein, we attempt to enhance the PNC prediction accuracy only by removing soil pixels in high-resolution images. Thus, we aimed to collect a dataset containing different crops and image types to investigate whether removing soil pixels to purify crop spectra can improve PNC estimations. For this purpose, N fertilizer experiments were conducted on cotton ( Gossypium hirsutum L.), wheat ( Triticum aestivum L.) and maize ( Zea mays L.), and multispectral and hyperspectral UAV images and PNCs were collected at different growth stages. The multispectral images had actual high spatial resolutions, while the hyperspectral images had virtual high spatial resolutions constructed by fusing high resolution panchromatic images and coarse resolution hyperspectral images. These images represent two typical UAV image types. First, for each crop, the relative changes and driving forces associated with the purified and nonpurified spectra were analyzed under different growth stage, N treatment. Then, three commonly used methods, the spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods were used to design PNC prediction model using purified and nonpurified spectra respectively. The results showed the differences between purified and nonpurified spectra were affected by the proportion of crop pixel, sunlit soil pixel and sunshade soil pixel in image. This influence had various trends and magnitudes among different N treatment, growth stages and crop types. It is better to remove soil pixels in imagery, when designing PNC prediction model for plants across growth stages, crop types or even in a single growth stage. The results from actual high spatial resolution images demonstrated this point, with the best PNC prediction model from purified spectra. When considering virtual high spatial resolution image, as the spectrum obtained for each vegetation pixel still represented a mixed vegetation and soil spectrum, removing soil pixels showed no improved performance for PNC estimation. These results provide a reference for others to reasonably choose an optimal data-processing method for constructing PNC prediction models. • Plant nitrogen concentration can be estimated using UAV-based images. • Purified and nonpurified spectra exist significant difference. • Plant nitrogen concentration can be better estimated using purified spectra.

Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha−1. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500–550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the “green peak”, a “red valley” increase, and a redshift appearing in the “red edge” position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R2, RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R2, RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC.

Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data

The species richness and biodiversity of vegetation in Hungary are increasingly threatened by invasive plant species brought in from other continents and foreign ecosystems. These invasive plant species have spread aggressively in the natural and semi-natural habitats of Europe. Common milkweed (Asclepias syriaca) is one of the species that pose the greatest ecological menace. Therefore, the primary purpose of the present study is to map and monitor the spread of common milkweed, the most common invasive plant species in Europe. Furthermore, the possibilities to detect and validate this special invasive plant by analyzing hyperspectral remote sensing data were investigated. In combination with field reference data, high-resolution hyperspectral aerial images acquired by an unmanned aerial vehicle (UAV) platform in 138 spectral bands in areas infected by common milkweed were examined. Then, support vector machine (SVM) and artificial neural network (ANN) classification algorithms were applied to the highly accurate field reference data. As a result, common milkweed individuals were distinguished in hyperspectral images, achieving an overall accuracy of 92.95% in the case of supervised SVM classification. Using the ANN model, an overall accuracy of 99.61% was achieved. To evaluate the proposed approach, two experimental tests were conducted, and in both cases, we managed to distinguish the individual specimens within the large variety of spreading invasive species in a study area of 2 ha, based on centimeter spatial resolution hyperspectral UAV imagery.

Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

The monitoring of winter wheat Fusarium head blight via rapid and non-destructive measures is important for agricultural production and disease control. Images of unmanned aerial vehicles (UAVs) are particularly suitable for the monitoring of wheat diseases because they feature high spatial resolution and flexible acquisition time. This study evaluated the potential to monitor Fusarium head blight via UAV hyperspectral imagery. The field site investigated by this study is located in Lujiang County, Anhui Province, China. The hyperspectral UAV images were acquired on 3 and 8 May 2019, when wheat was at the grain filling stage. Several features, including original spectral bands, vegetation indexes, and texture features, were extracted from these hyperspectral images. Based on these extracted features, univariate Fusarium monitoring models were developed, and backward feature selection was applied to filter these features. The backpropagation (BP) neural network was improved by integrating a simulated annealing algorithm in the experiment. A multivariate Fusarium head blight monitoring model was developed using the improved BP neural network. The results showed that bands in the red region provide important information for discriminating between wheat canopies that are either slightly or severely Fusarium-head-blight-infected. The modified chlorophyll absorption reflectance index performed best among all features, with an area under the curve and standard deviation of 1.0 and 0.0, respectively. Five commonly used methods were compared with this improved BP neural network. The results showed that the developed Fusarium head blight monitoring model achieved the highest overall accuracy of 98%. In addition, the difference between the producer accuracy and user accuracy of the improved BP neural network was smallest among all models, indicating that this model achieved better stability. These results demonstrate that hyperspectral images of UAVs can be used to monitor Fusarium head blight in winter wheat.

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

Detection of REEs with lightweight UAV-based hyperspectral imaging

Rare earth elements (REEs) supply is important to ensure the energy transition, e-mobility and ultimately to achieve the sustainable development goals of the United Nations. Conventional exploration techniques usually rely on substantial geological field work including dense in-situ sampling with long delays until provision of analytical results. However, this approach is limited by land accessibility, financial status, climate and public opposition. Efficient and innovative methods are required to mitigate these limitations. The use of lightweight unmanned aerial vehicles (UAVs) provides a unique opportunity to conduct rapid and non-invasive exploration even in socially sensitive areas and in relatively inaccessible locations. We employ drones with hyperspectral sensors to detect REEs at the earth’s surface and thus contribute to a rapidly evolving field at the cutting edge of exploration technologies. We showcase for the first time the direct mapping of REEs with lightweight hyperspectral UAV platforms. Our solution has the advantage of quick turn-around times (< 1 d), low detection limits (< 200 ppm for Nd) and is ideally suited to support exploration campaigns. This procedure was successfully tested and validated in two areas: Marinkas Quellen, Namibia, and Siilinjärvi, Finland. This strategy should invigorate the use of drones in exploration and for the monitoring of mining activities.

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea

The non-spatial information of cadastral maps must be repeatedly updated to monitor recent changes in land property and to detect illegal land registrations by tax evaders. Since non-spatial information, such as land category, is usually updated by field-based surveys, it is time-consuming and only a limited area can be updated at a time. Although land categories can be updated by remote sensing techniques, the update is typically performed through manual analysis, namely through a visually interpreted comparison between the newly generated land information and the existing cadastral maps. A cost-effective, fast alternative to the current surveying methods would improve the efficiency of land management. For this purpose, the present study analyzes the discrepancy between the existing cadastral map and the actual land use. Our proposed method operates in two steps. First, an up-to-date land cover map is generated from hyperspectral unmanned aerial vehicle (UAV) images. These images are effectively classified by a hybrid two- and three-dimensional convolutional neural network. Second, a discrepancy map, which contains the ratio of the area that is being used differently from the registered land use in each parcel, is constructed through a three-stage inconsistency comparison. As a case study, the proposed method was evaluated using hyperspectral UAV images acquired at two sites of Jeonju in South Korea. The overall classification accuracies of six land classes at Sites 1 and 2 were 99.93% and 99.75% and those at Sites 1 and 2 are 39.4% and 34.4%, respectively, which had discrepancy ratios of 50% or higher. Finally, discrepancy maps between the land cover maps and existing cadastral maps were generated and visualized. The method automatically reveals the inconsistent parcels requiring updates of their land category. Although the performance of the proposed method depends on the classification results obtained from UAV imagery, the method allows a flexible modification of the matching criteria between the land categories and land coverage. Therefore, it is generalizable to various cadastral systems and the discrepancy ratios will provide practical information and significantly reduce the time and effort for land monitoring and field surveying.

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.

Use of principal components of UAV-acquired narrow-band multispectral imagery to map the diverse low stature vegetation fAPAR

The fraction of absorbed photosynthetically active radiation (fAPAR) is an important plant physiological index that is used to assess the ability of vegetation to absorb PAR, which is utilized to sequester carbon in the atmosphere. This index is also important for monitoring plant health and productivity, which has been widely used to monitor low stature crops and is a crucial metric for food security assessment. The fAPAR has been commonly correlated with a greenness index derived from spaceborne optical imagery, but the relatively coarse spatial or temporal resolution may prohibit its application on complex land surfaces. In addition, the relationships between fAPAR and remotely sensed greenness data may be influenced by the heterogeneity of canopies. Multispectral and hyperspectral unmanned aerial vehicle (UAV) imaging systems, conversely, can provide several spectral bands at sub-meter resolutions, permitting precise estimation of fAPAR using chemometrics. However, the data pre-processing procedures are cumbersome, which makes large-scale mapping challenging. In this study, we applied a set of well-verified image processing protocols and a chemometric model to a lightweight, frame-based and narrow-band (10 nm) UAV imaging system to estimate the fAPAR over a relatively large cultivated land area with a variety of low stature vegetation of tropical crops along with native and non-native grasses. A principal component regression was applied to 12 bands of spectral reflectance data to minimize the collinearity issue and compress the data variation. Stepwise regression was employed to reduce the data dimensionality, and the first, third and fifth components were selected to estimate the fAPAR. Our results indicate that 77% of the fAPAR variation was explained by the model. All bands that are sensitive to foliar pigment concentrations, canopy structure and/or leaf water content may contribute to the estimation, especially those located close to (720 nm) or within (750 nm and 780 nm) the near-infrared spectral region. This study demonstrates that this narrow-band frame-based UAV system would be useful for vegetation monitoring. With proper pre-flight planning and hardware improvement, the mapping of a narrow-band multispectral UAV system could be comparable to that of a manned aircraft system.

Boresight Calibration of GNSS/INS-Assisted Push-Broom Hyperspectral Scanners on UAV Platforms

Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative to conventional remote sensing platforms such as manned aircraft and satellites. In order to employ this emerging technology in fields such as high-throughput phenotyping and precision agriculture, direct georeferencing of hyperspectral data using onboard integrated global navigation satellite systems (GNSSs) and inertial navigation systems (INSs) is required. Directly deriving the scanner position and orientation requires the spatial and rotational relationship between the coordinate systems of the GNSS/INS and hyperspectral scanner to be measured. The spatial offset (lever arm) between the scanner and GNSS/INS unit can be measured manually. However, the angular relationship (boresight angles) between the scanner and GNSS/INS coordinate systems, which is more critical for accurate generation of georeferenced products, is difficult to establish. This paper presents three calibration approaches to estimate the boresight angles relating hyperspectral push-broom scanner and GNSS/INS coordinate systems. For reliable/practical estimation of the boresight angles, this paper starts with establishing the optimal/minimal flight and control/tie point configuration through a bias impact analysis starting from the point positioning equation. Then, an approximate calibration procedure utilizing tie points in overlapping scenes is presented after making some assumptions about the flight trajectory and topography of covered terrain. Next, two rigorous approaches are introduced – one using ground control points and other using tie features. The approximate/rigorous approaches are based on enforcing the collinearity and coplanarity of the light rays connecting the perspective centers of the imaging scanner, object point, and the respective image points. To evaluate the accuracy of the proposed approaches, estimated boresight angles are used for orthorectification of six hyperspectral UAV dataset acquired over an agricultural field. Qualitative and quantitative evaluations of the results have shown significant improvement in the derived orthophotos to a level equivalent to the ground sampling distance of the used scanner (namely, 3–5 cm when flying at 60 m).

Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications

Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral-temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.