High-resolution Unmanned Aerial Vehicles Imagery Research Articles

Automated Stock Volume Estimation Using UAV-RGB Imagery.

Forests play a critical role in the global carbon cycle, with carbon storage being an important carbon pool in the terrestrial ecosystem with tree crown size serving as a versatile ecological indicator influencing factors such as tree growth, wind resistance, shading, and carbon sequestration. They help with habitat function, herbicide application, temperature regulation, etc. Understanding the relationship between tree crown area and stock volume is crucial, as it provides a key metric for assessing the impact of land-use changes on ecological processes. Traditional ground-based stock volume estimation using DBH (Diameter at Breast Height) is labor-intensive and often impractical. However, high-resolution UAV (unmanned aerial vehicle) imagery has revolutionized remote sensing and computer-based tree analysis, making forest studies more efficient and interpretable. Previous studies have established correlations between DBH, stock volume and above-ground biomass, as well as between tree crown area and DBH. This research aims to explore the correlation between tree crown area and stock volume and automate stock volume and above-ground biomass estimation by developing an empirical model using UAV-RGB data, making forest assessments more convenient and time-efficient. The study site included a significant number of training and testing sites to ensure the performance level of the developed model. The findings underscore a significant association, demonstrating the potential of integrating drone technology with traditional forestry techniques for efficient stock volume estimation. The results highlight a strong exponential correlation between crown area and stem stock volume, with a coefficient of determination of 0.67 and mean squared error (MSE) of 0.0015. The developed model, when applied to estimate cumulative stock volume using drone imagery, demonstrated a strong correlation with an R2 of 0.75. These results emphasize the effectiveness of combining drone technology with traditional forestry methods to achieve more precise and efficient stock volume estimation and, hence, automate the process.

Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle.

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

The consecutive submesoscale sea surface processes observed by an unmanned aerial vehicle (UAV) were used to decompose into spatial waves and current features. For the image decomposition, the Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) method was employed to disintegrate multicomponent signals identified in sea surface optical images into modulated signals characterized by their amplitudes and frequencies. These signals, referred to as Bidimensional Intrinsic Mode Functions (BIMFs), represent the inherent two-dimensional oscillatory patterns within sea surface optical data. The BIMFs, separated into seven modes and a residual component, were subsequently reconstructed based on the physical frequencies. A two-dimensional Fast Fourier Transform (2D FFT) for each high-frequency mode was used for surface wave analysis to illustrate the wave characteristics. Wavenumbers (Kx, Ky) ranging between 0.01–0.1 radm−1 and wave directions predominantly in the northeastward direction were identified from the spectral peak ranges. The Optical Flow (OF) algorithm was applied to the remaining consecutive low-frequency modes as the current signal under 0.1 Hz for surface current analysis and to estimate a current field with a 1 m spatial resolution. The accuracy of currents in the overall region was validated with in situ drifter measurements, showing an R-squared (R2) value of 0.80 and an average root-mean-square error (RMSE) of 0.03 ms−1. This study proposes a novel framework for analyzing individual sea surface dynamical processes acquired from high-resolution UAV imagery using a multidimensional signal decomposition method specialized in nonlinear and nonstationary data analysis.

Monitoring the spatial–temporal distribution of invasive plant in urban water using deep learning and remote sensing technology

Despite water ecosystems being capable of sustaining biodiversity and enhancing the overall resilience of the urban environment, they are highly susceptible to biological invasions. Invasive aquatic plants (IAPs) threaten the natural environment by reducing the diversity of native aquatic plants and animal communities. Detecting IAPs and mapping their distribution is crucial for the protection of urban water ecosystems. This study is the first of its kind to use high-resolution unmanned aerial vehicle (UAV) imagery and deep learning approaches to monitor the expansion of Pistia stratiotes (water lettuce). A DJI Matrice 300 RTK was utilized to capture time-series images with a resolution of 0.018 m on the Guanyin Lake, Cangshan District, Fuzhzou, China, during an outbreak and subsequent rapid growth stage of water lettuce. Three deep learning model architectures and three backbones were combined to detect water lettuce. Model performance and the ability to generalize the model were evaluated to determine the optimal model for water lettuce detection from time-series high-resolution UAV imagery. Results show that the DeepLabv3 + model with ResNet-34 achieved superior performance in detecting water lettuce from time-series imagery, yielding an average accuracy of 90.24 % (85.33 %≤F1_score ≤ 96.54 %) for water lettuce detection on five different dates. For the UAV image acquired on September 26th, the U-Net model with ResNet-18 yielded the highest accuracy (F1_score = 92.46 %), but it was not the optimal model for multi-temporal water lettuce detection on subsequent dates. The distribution of water lettuce can have large variations at different times, with an average change rate of 49.50 % every two days and the highest change rate up to 60.33 %. The study demonstrates that the combination of UAV imagery and a deep learning model can achieve excellent accuracy for water lettuce monitoring and provide a method to map IAPs in dynamic urban water systems over time.

In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery.

In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs). The proposed model seeks to harness the spatiotemporal dynamics inherent in high-resolution UAV imagery to improve the accuracy of the cotton yield prediction. The CNN component adeptly extracts spatial features from UAV-derived imagery, capturing intricate details related to crop health and growth, modeling temporal dependencies, and facilitating the recognition of trends and patterns over time. Research experiments were carried out in a cotton field at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, with three replications evaluating four irrigation treatments (rainfed, full irrigation, percent deficit of full irrigation, and time delay of full irrigation) on cotton yield. The prediction revealed that the proposed CNN regression models outperformed conventional CNN models, such as AlexNet, CNN-3D, CNN-LSTM, ResNet. The proposed CNN model showed state-of-art performance at different image scales, with the R2 exceeding 0.9. At the cotton row level, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.08 pounds per row and 7.76%, respectively. At the cotton grid level, the MAE and MAPE were 0.05 pounds and 10%, respectively. This shows the proposed model's adaptability to the dynamic interplay between spatial and temporal factors that affect cotton yield. The authors conclude that integrating UAV-derived imagery and CNN regression models is a potent strategy for advancing precision agriculture, providing growers with a powerful tool to optimize cultivation practices and enhance overall cotton productivity.

Effect of high-resolution satellite and UAV imagery plot pixel resolution in wheat crop yield prediction

ABSTRACT Accurate crop performance assessment and yield prediction in plant breeding programmes can aid decision-making to improve productivity and product quality during crop selection and management. Grain yield is a complex trait, which is a function of the genotype-environment interaction. While using digital remote sensing traits to assess crop performance and predict yield, the characteristics of the sensing tools and approaches can influence prediction performance. In this study, two sensing scales, an unmanned aerial vehicle (UAV) equipped with a ten-band multispectral camera and high-resolution (~0.31 m) WorldView-3 satellite imagery, were used to monitor spring and winter wheat breeding trails in two growing seasons (2020 and 2021). The breeding plots were planted in three different plot sizes (about 1.5 × 5.0 m, 3.0 × 11.0 m, and 4.5 × 11.0 m in spring wheat, and about 1.5 × 3.0 m, 3.0 × 7.3 m, and 4.5 × 7.3 m in winter wheat), with each having 12 varieties and three replications per variety. The spectral and vegetation indices (VI) were extracted from the datasets, and machine learning models for yield prediction (partial least squares regression, least absolute shrinkage selector operator regression, and random forest regression) were evaluated. With multiscale approaches, a moderate to strong correlation of VI data between high-resolution satellite and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) was found in most cases. The yield prediction accuracies using the extracted data from the high-resolution satellite (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤ 0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) were also comparable. These findings inform the applications of high-resolution satellite imagery in breeding programmes, considering that the plot size would influence yield prediction accuracies.

Mapping the Distribution of High-Value Broadleaf Tree Crowns through Unmanned Aerial Vehicle Image Analysis Using Deep Learning

High-value timber species with economic and ecological importance are usually distributed at very low densities, such that accurate knowledge of the location of these trees within a forest is critical for forest management practices. Recent technological developments integrating unmanned aerial vehicle (UAV) imagery and deep learning provide an efficient method for mapping forest attributes. In this study, we explored the applicability of high-resolution UAV imagery and a deep learning algorithm to predict the distribution of high-value deciduous broadleaf tree crowns of Japanese oak (Quercus crispula) in an uneven-aged mixed forest in Hokkaido, northern Japan. UAV images were collected in September and October 2022 before and after the color change of the leaves of Japanese oak to identify the optimal timing of UAV image collection. RGB information extracted from the UAV images was analyzed using a ResU-Net model (U-Net model with a Residual Network 101 (ResNet101), pre-trained on large ImageNet datasets, as backbone). Our results, confirmed using validation data, showed that reliable F1 scores (&gt;0.80) could be obtained with both UAV datasets. According to the overlay analyses of the segmentation results and all the annotated ground truth data, the best performance was that of the model with the October UAV dataset (F1 score of 0.95). Our case study highlights a potential methodology to offer a transferable approach to the management of high-value timber species in other regions.

Review of Deep Learning Algorithms for Urban Remote Sensing Using Unmanned Aerial Vehicles (UAVs)

Abstract: This study conducts a comprehensive review of Deep Learning-based approaches for accurate object segmentation and detection in high-resolution imagery captured by Unmanned Aerial Vehicles (UAVs). The methodology employs three different existing algorithms tailored to detect roads, buildings, trees, and water bodies. These algorithms include Res-UNet for roads and buildings, DeepForest for trees, and WaterDetect for water bodies. To evaluate the effectiveness of this approach, the performance of each algorithm is compared with state-of-the-art (SOTA) models for each class. The results of the study demonstrate that the methodology outperforms SOTA models in all three classes, achieving an accuracy of 93% for roads and buildings using Res-U-Net, 95% for trees using DeepForest, and an impressive 98% for water bodies using WaterDetect. The paper utilizes a Deep Learning-based approach for accurate object segmentation and detection in high-resolution UAV imagery, achieving superior performance to SOTA models, with reduced overfitting and faster training by employing three smaller models for each task

Deep Learning of High-Resolution Unmanned Aerial Vehicle Imagery for Classifying Halophyte Species: A Comparative Study for Small Patches and Mixed Vegetation

Recent advances in deep learning (DL) and unmanned aerial vehicle (UAV) technologies have made it possible to monitor salt marshes more efficiently and precisely. However, studies have rarely compared the classification performance of DL with the pixel-based method for coastal wetland monitoring using UAV data. In particular, many studies have been conducted at the landscape level; however, little is known about the performance of species discrimination in very small patches and in mixed vegetation. We constructed a dataset based on UAV-RGB data and compared the performance of pixel-based and DL methods for five scenarios (combinations of annotation type and patch size) in the classification of salt marsh vegetation. Maximum likelihood, a pixel-based classification method, showed the lowest overall accuracy of 73%, whereas the U-Net classification method achieved over 90% accuracy in all classification scenarios. As expected, in a comparison of pixel-based and DL methods, the DL approach achieved the most accurate classification results. Unexpectedly, there was no significant difference in overall accuracy between the two annotation types and labeling data sizes in this study. However, when comparing the classification results in detail, we confirmed that polygon-type annotation was more effective for mixed-vegetation classification than the bounding-box type. Moreover, the smaller size of labeling data was more effective for detecting small vegetation patches. Our results suggest that a combination of UAV-RGB data and DL can facilitate the accurate mapping of coastal salt marsh vegetation at the local scale.

An Effective Approach for Automatic River Features Extraction Using High-Resolution UAV Imagery

The effects of climate change are causing an increase in the frequency and extent of natural disasters. Because of their morphological characteristics, rivers can cause major flooding events. Indeed, they can be subjected to variations in discharge in response to heavy rainfall and riverbank failures. Among the emerging methodologies that address the monitoring of river flooding, those that include the combination of Unmanned Aerial Vehicle (UAV) and photogrammetric techniques (i.e., Structure from Motion-SfM) ensure the high-frequency acquisition of high-resolution spatial data over wide areas and so the generation of orthomosaics, useful for automatic feature extraction. Trainable Weka Segmentation (TWS) is an automatic feature extraction open-source tool. It was developed to primarily fulfill supervised classification purposes of biological microscope images, but its usefulness has been demonstrated in several image pipelines. At the same time, there is a significant lack of published studies on the applicability of TWS with the identification of a universal and efficient combination of machine learning classifiers and segmentation approach, in particular with respect to classifying UAV images of riverine environments. In this perspective, we present a study comparing the accuracy of nine combinations, classifier plus image segmentation filter, using TWS, also with respect to human photo-interpretation, in order to identify an effective supervised approach for automatic river features extraction from UAV multi-temporal orthomosaics. The results, which are very close to human interpretation, indicate that the proposed approach could prove to be a valuable tool to support and improve the hydro-geomorphological and flooding hazard assessments in riverine environments.

Mapping of Rumex obtusifolius in nature conservation areas using very high resolution UAV imagery and deep learning

Rumex obtusifolius (Rumex or broad leaved dock) is one of the most common weeds in grasslands. It spreads quickly, lowers the nutritional value of the grass, and is poisonous for livestock due to its oxalic acid content. Mapping it is important before any control treatment is applied. Current methods for mapping Rumex either involve manual work or the utilization of ground robots, which are not efficient in large fields. This study investigated the feasibility of using aerial images from unmanned aerial vehicles (UAV) and deep learning to map Rumex in grasslands. Seven pre-trained CNN models were tested using transfer learning on UAV images acquired at 10 m, 15 m, and 30 m height. Based on Cross Validation results, MobileNet performed the best in detecting Rumex, with an F1-Score of 78.36% and an AUROC of 93.74%, at 10 m height. At 15 m, the detection performance was relatively lower (F1-score = 72.00%, AUROC = 88.67%), but the results showed that the performance can increase with more data. Experiments also showed that Rumex detection was dependent on the flight height since the algorithm was unable to detect the plants at 30 m height. The code and the datasets used in this work were released in an open access repository to contribute to the advances in grassland management using UAV technology.

Corn planting quality assessment in very high-resolution RGB UAV imagery using Yolov5 and Python

Abstract. Uniform plant spacing along crop rows is a primary concern in maximising yield in precision agriculture, and research has shown that variation in this spacing uniformity has a detrimental effect on productive potential. This irregularity needs to be evaluated as early and efficiently as possible to facilitate effective decision-making. Traditionally, variation in seedling spacing is sampled manually on site, however recent technological developments have made it possible to refine, scale and automate this process. Using machine-learning (ML) object detection techniques, plants can be detected in very high-resolution RGB (redgreen-blue) imagery acquired by an unmanned aerial vehicle (UAV), and after processing and geometric analysis of the results a measurement of the variability in intra-row plant distances can be obtained. This proposed technique is superior to traditional methods since the sampling can be made over more area in less time, and the results are more representative and objective. The main benefits are speed, accuracy and cost reduction. This work aims to demonstrate the feasibility of automatically assessing sowing quality in any number of images, using ML object detection and the Shapely Python library for geometrical analysis. The prototype model can detect 99.35% of corn plants in test data from the same field, but also detects 1.89% false positives. Our geometric analysis algorithm has been shown to be robust in finding planting rows orientation and interplant lines in test cases. The result is a coefficient of variation (CV) calculated per sample image, which can be visualised geographically to support decision-making.

Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery

BackgroundLeaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.MethodsTo investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.ResultsThe results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.ConclusionsThe method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles

Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m&lt;sup&gt;-2&lt;/sup&gt;, and per-PFT RMSE ranged from 17.14-164.3 g m&lt;sup&gt;-2&lt;/sup&gt;. Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.

Estimation of bell pepper evapotranspiration using two-source energy balance model based on high-resolution thermal and visible imagery from unmanned aerial vehicles

Crop evapotranspiration (ET) is a crucial component of energy and water budgets. The accurate determination of ET is vital for agricultural water management. Several satellite-based ET models have been developed to map ET at the field to regional scales. The spatial resolution of the satellite observations, particularly thermal-infrared imagery, is insufficient to estimate ET for small (<1 ha) agricultural fields. With unmanned aerial vehicle (UAV) technology advancement, high spatial and temporal images can be acquired with UAVs to monitor ET for small fields or even at a canopy scale. The aim of this study was the evaluation of the two-source energy balance (TSEB) model to estimate daily and seasonal crop (bell pepper) ET (ETTSEB) using high-resolution visible and thermal UAV imagery. Also, the impact of using different pixel resolutions (40, 50, 60, and 70 cm) with TSEB is compared with ET values derived using a soil water budget approach (ETSWD) with a profile soil water content. The results of this study showed that there is a high correlation between ETTSEB and ETSWD values (R2 = 0.73 for daily, R2 = 0.98 for seasonal). The root mean square error values for daily and seasonal ETTSEB are 0.62 mm day − 1 and 11.46 mm season − 1, respectively. The sensitivity of TSEB output to the spatial resolution indicated that different pixel resolutions do not significantly impact ET estimates. We suggest that the TSEB model has a real potential for agricultural water management applications for small agricultural fields using high-resolution UAV multispectral and thermal images.

Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy.

Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf–canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LCC), leaf area index (LAI) and canopy chlorophyll content (CCC) of sweet and silage maize throughout one growing season. Two different retrieval methods were tested: (i) applying the RTM inversion scheme to mean reflectance data derived from single breeding plots (mean reflectance approach) and (ii) applying the same inversion scheme to an orthomosaic to separately retrieve the target variables for each pixel of the breeding plots (pixel-based approach). For LCC retrieval, soil and shaded pixels were removed by applying simple vegetation index thresholding. Retrieval of LCC from UAV data yielded promising results compared to ground measurements (sweet maize RMSE = 4.92 μg/cm2, silage maize RMSE = 3.74 μg/cm2) when using the mean reflectance approach. LAI retrieval was more challenging due to the blending of sunlit and shaded pixels present in the UAV data, but worked well at the early developmental stages (sweet maize RMSE = 0.70m2/m2, silage RMSE = 0.61m2/m2 across all dates). CCC retrieval significantly benefited from the pixel-based approach compared to the mean reflectance approach (RMSEs decreased from 45.6 to 33.1 μg/m2). We argue that high-resolution UAV imagery is well suited for LCC retrieval, as shadows and background soil can be precisely removed, leaving only green plant pixels for the analysis. As for retrieving LAI, it proved to be challenging for two distinct varieties of maize that were characterized by contrasting canopy geometry.

Comparative Evaluation of Land Surface Temperature Images from Unmanned Aerial Vehicle and Satellite Observation for Agricultural Areas Using In Situ Data

Remotely-sensed data are a source of rich information and are valuable for precision agricultural tasks such as soil quality, plant disease analysis, crop stress assessment, and allowing for better management. It is necessary to validate the accuracy of land surface temperature (LST) that is acquired from an unmanned aerial vehicle (UAV) and satellite-based remote sensing and verify these data by a comparison with in situ LST. Comprehensive studies at the field scale are still needed to understand the suitability of UAV imagery and resolution, for which ground measurement is used as a reference. In this study, we examined the accuracy of surface temperature data that were obtained from a thermal infrared (TIR) sensor placed on a UAV. Accordingly, we evaluated the LST from the Landsat 8 satellite for the same specific periods. We used contact thermometers to measure LSTs in situ for comparison and evaluation. Between 18 August and 2 September 2020, UAV imagery and in situ measurements were carried out. The effectiveness of high-resolution UAVs imagery and of Landsat 8 imagery was evaluated by considering a regression and correlation coefficient analysis. The data from the satellite photography was compared to the UAV imagery using statistical metrics after it had been pre-processed. Ground control points (GCPs) were collected to create a rigorous geo-referenced dataset of UAV imagery that could be compared to the geo-referenced satellite and aerial imagery. The UAV TIR LST showed higher accuracy (R2 0.89, 0.90, root-mean-square error (RMSE) 1.07, 0.70 °C) than the Landsat LST accuracy (R2 0.70, 0.73, (RMSE) 0.78 °C). The relationship between LST and the available soil water content (SWC) was also observed. The results suggested that the UAV-SMC correlation was negative (−0.85) for the image of DOY 230, while this value remains approximately constant (−0.86) for the DOY 245. Our results showed that satellite imagery that was coherent and correlated with UAV images could be useful to assess the general conditions of the field while the UAV favors localized circumscribed areas that the lowest resolution of satellites missed. Accordingly, our results could help with urban area and environmental planning decisions that take into account the thermal environment.

Quantum neural network-based multilabel image classification in high-resolution unmanned aerial vehicle imagery

Quantum neural network-based multilabel image classification in high-resolution unmanned aerial vehicle imagery

Sensitivity of Landsat NDVI to subpixel vegetation and topographic components in glacier forefields: assessment from high-resolution multispectral UAV imagery

Recently, deglaciated landscapes are ideal natural arenas to investigate ecological succession processes. However, ground data acquisition remains complicated as glacier forefields are often difficult to access and fieldwork possibilities remain limited. Remote sensing offers an opportunity to bypass this issue and increase spatial and temporal coverage of ecological parameters. The Landsat satellites (5 to 8) provide reflectance data for the past 40 years, which align with recent phenomena of glacier retreat and related ecological and geomorphological dynamics in glacier forefields. Difficulties remain as information retrieved from 30-m Landsat pixels are the result of a mixture of objects influencing reflectance signals. Here, we used a submeter multispectral unmanned aerial vehicle (UAV) image of the Glacier noir foreland, France, to assess the sensitivity of Landsat normalized difference vegetation index (NDVI) to subpixel vegetation and topographic components. We found a twofold linear relationship (a = 0.456) and high sensitivity between fractional vegetation cover (FVC) and Landsat NDVI with detection of low vegetation changes (FVC > 5 % ) at low NDVI values (<0.1) (F-score = 0.75). We also showed that vegetation height and subpixel topographic heterogeneity leads to misestimation of vegetation cover as quantified by Landsat NDVI. Overall, our comparative analysis using very-high resolution UAV imagery provides support for the use of widely available Landsat imagery for investigating vegetation dynamics in glacier forefields.

UAV reveals substantial but heterogeneous effects of herbivores on Arctic vegetation

Understanding how herbivores shape plant biomass and distribution is a core challenge in ecology. Yet, the lack of suitable remote sensing technology limits our knowledge of temporal and spatial impacts of mammal herbivores in the Earth system. The regular interannual density fluctuations of voles and lemmings are exceptional with their large reduction of plant biomass in Arctic landscapes during peak years (12–24%) as previously shown at large spatial scales using satellites. This provides evidence that herbivores are important drivers of observed global changes in vegetation productivity. Here, we use a novel approach with repeated unmanned aerial vehicle (UAV) flights, to map vegetation impact by rodents, indicating that many important aspects of vegetation dynamics otherwise hidden by the coarse resolution of satellite images, including plant–herbivore interactions, can be revealed using UAVs. We quantify areas impacted by rodents at four complex Arctic landscapes with very high spatial resolution UAV imagery to get a new perspective on how herbivores shape Arctic ecosystems. The area impacted by voles and lemmings is indeed substantial, larger at higher altitude tundra environments, varies between habitats depending on local snow cover and plant community composition, and is heterogeneous even within habitats at submeter scales. Coupling this with spectral reflectance of vegetation (NDVI), we can show that the impact on central ecosystem properties like GPP and biomass is stronger than currently accounted for in Arctic ecosystems. As an emerging technology, UAVs will allow us to better disentangle important information on how herbivores maintain spatial heterogeneity, function and diversity in natural ecosystems.