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In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures. [ABSTRACT FROM AUTHOR]

This paper presents a method for mosaicking unmanned aerial vehicle (UAV) multispectral images. The main purpose of the proposed method is to reduce spatial distortion in the mosaicking process and increase robustness and the speed of the operation. Most UAV multispectral images have multiple bands, and in every band, ground targets have a variety of reflection characteristics that will result in diverse feature quality for feature matching. In this research, an information entropy-based evaluation method is used to select the optimal band for feature matching among the UAV images. To produce more robust matching results for the following alignment step, the evaluation method takes the contrast and spatial distribution of the feature points into consideration at the same time. In most common image mosaicking processes, the digital orthophoto map (DOM) is generated to achieve maximum spatial accuracy. During this process, the original image data will experience considerable irregular resampling, and the process is also unstable in some circumstances. The alignment step uses a simplified projection model that treats the ground as planar is provided, by which the alignment parameters are applied directly to the images instead of generating 3D points, to avoid irregular resampling and unstable 3D reconstruction. The proposed method is proved to be more efficient and accurate and has lower spectral distortion than state-of-the-art mosaicking software. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Vehicles (UAV) for photogrammetric surveying has recently gained enormous popularity. Images taken from UAVs are used for generating Digital Surface Models (DSMs) and orthorectified images. In the glaciological context, these can serve for quantifying ice volume change or glacier motion. This study focuses on the accuracy of UAV-derived DSMs. In particular, we analyze the influence of the number and disposition of Ground Control Points (GCPs) needed for georeferencing the derived products. A total of 1321 different DSMs were generated from eight surveys distributed on three glaciers in the Swiss Alps during winter, summer and autumn. The vertical and horizontal accuracy was assessed by cross-validation with thousands of validation points measured with a Global Positioning System. Our results show that the accuracy increases asymptotically with increasing number of GCPs until a certain density of GCPs is reached. We call this the optimal GCP density. The results indicate that DSMs built with this optimal GCP density have a vertical (horizontal) accuracy ranging between 0.10 and 0.25m (0.03 and 0.09 m) across all datasets. In addition, the impact of the GCP distribution on the DSM accuracy was investigated. The local accuracy of a DSM decreases when increasing the distance to the closest GCP, typically at a rate of 0.09m per 100-m distance. The impact of the glacier's surface texture (ice or snow) was also addressed. The results show that besides cases with a surface covered by fresh snow, the surface texture does not significantly influence the DSM accuracy. [ABSTRACT FROM AUTHOR]

Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification. [ABSTRACT FROM AUTHOR]

The Kobresia pygmaea (KP) community is a key succession stage of alpine meadow degradation on the Qinghai–Tibet Plateau (QTP). However, most of the grassland classification and mapping studies have been performed at the grassland type level. The spatial distribution and impact factors of KP on the QTP are still unclear. In this study, field measurements of the grassland vegetation community in the eastern part of the QTP (Counties of Zeku, Henan and Maqu) from 2015 to 2019 were acquired using unmanned aerial vehicle (UAV) technology. The machine learning algorithms for grassland vegetation community classification were constructed by combining Gaofen satellite images and topographic indices. Then, the spatial distribution of KP community was mapped. The results showed that: (1) For all field observed sites, the alpine meadow vegetation communities demonstrated a considerable spatial heterogeneity. The traditional classification methods can hardly distinguish those communities due to the high similarity of their spectral characteristics. (2) The random forest method based on the combination of satellite vegetation indices, texture feature and topographic indices exhibited the best performance in three counties, with overall accuracy and Kappa coefficient ranged from 74.06% to 83.92% and 0.65 to 0.80, respectively. (3) As a whole, the area of KP community reached 1434.07 km2, and accounted for 7.20% of the study area. We concluded that the combination of satellite remote sensing, UAV surveying and machine learning can be used for KP classification and mapping at community level. [ABSTRACT FROM AUTHOR]

By combining the spectral and texture features of images captured by unmanned aerial vehicles (UAVs), the accurate and timely detection of wheat Fusarium head blight (FHB) can be realized. This study presents a methodology to select the optimal window size of the gray-level co-occurrence matrix (GLCM) to extract texture features from UAV images for FHB detection. Host conditions and the disease distribution were combined to construct the model, and its overall accuracy, sensitivity, and generalization ability were evaluated. First, the sensitive spectral features and bands of the UAV-derived hyperspectral images were obtained, and then texture features were selected. Subsequently, spectral features and texture features extracted from windows of different sizes were input to classify the area of severe FHB. According to the model comparison, the optimal window size was obtained. With the collinearity between features eliminated, the best performance of the logistic model reached, with an accuracy, F1 score, and area under the receiver operating characteristic curve of 0.90, 0.79, and 0.79, respectively, when the window size of the GLCM was 5 × 5 pixels on May 3, and of 0.90, 0.83, and 0.82, respectively, when the size was 17 × 17 pixels on May 8. The results showed that the selection of an appropriate GLCM window size for texture feature extraction enabled more accurate disease detection. [ABSTRACT FROM AUTHOR]

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones. [ABSTRACT FROM AUTHOR]

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way. [ABSTRACT FROM AUTHOR]

Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC. [ABSTRACT FROM AUTHOR]

The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery. [ABSTRACT FROM AUTHOR]

The fine classification of crops is critical for food security and agricultural management. There are many different species of crops, some of which have similar spectral curves. As a result, the precise classification of crops is a difficult task. Although the classification methods that incorporate spatial information can reduce the noise and improve the classification accuracy, to a certain extent, the problem is far from solved. Therefore, in this paper, the method of spatial–spectral fusion based on conditional random fields (SSF-CRF) for the fine classification of crops in UAV-borne hyperspectral remote sensing imagery is presented. The proposed method designs suitable potential functions in a pairwise conditional random field model, fusing the spectral and spatial features to reduce the spectral variation within the homogenous regions and accurately identify the crops. The experiments on hyperspectral datasets of the cities of Hanchuan and Honghu in China showed that, compared with the traditional methods, the proposed classification method can effectively improve the classification accuracy, protect the edges and shapes of the features, and relieve excessive smoothing, while retaining detailed information. This method has important significance for the fine classification of crops in hyperspectral remote sensing imagery. [ABSTRACT FROM AUTHOR]