Search Results (2,843)

The leaf area index (LAI) is a direct indicator of crop canopy growth and serves as an indirect measure of crop yield. Unmanned aerial vehicles (UAVs) offer rapid collection of crop phenotypic data across multiple time points, providing crucial insights into the evolving dynamics of the LAI essential for crop breeding. In this study, the variation process of the maize LAI was investigated across two locations (XD and KZ) using a multispectral sensor mounted on a UAV. During a field trial involving 399 maize inbred lines, LAI measurements were obtained at both locations using a random forest model based on 28 variables extracted from multispectral imagery. These findings indicate that the vegetation index computed by the near-infrared band and red edge significantly influences the accuracy of the LAI prediction. However, a prediction model relying solely on data from a single observation period exhibits instability (R² = 0.34–0.94, RMSE = 0.02–0.25). When applied to the entire growth period, the models trained using all data achieved a robust prediction of the LAI (R² = 0.79–0.86, RMSE = 0.12–0.18). Although the primary variation patterns of the maize LAI were similar across the two fields, environmental disparities changed the variation categories of the maize LAI. The primary factor contributing to the difference in the LAI between KZ and XD lies in soil nutrients associated with carbon and nitrogen in the upper soil. Overall, this study demonstrated that UAV-based time-series phenotypic data offers valuable insight into phenotypic variation, thereby enhancing the application of UAVs in crop breeding. Full article

The advancement of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has opened the way to large-scale data collection for the calculation of vegetation indices (VIs). These vegetation indexes (VIs) are useful for agricultural monitoring, as they highlight the inherent characteristics of vegetation and optimize the spatial and temporal evaluation of different crops. The experiment tested three coffee genotypes (Catuaí 62, E237 and Iapar 59) under five water regimes: (1) FI 100 (year-round irrigation with 100% replacement of evapotranspiration loss), (2) FI 50 (year-round irrigation with 50% evapotranspiration replacement), (3) WD 100 (no irrigation from June to September (dry season) and, thereafter, 100% evapotranspiration replacement), (4) WD 50 (no irrigation from June to September (water stress) and, thereafter, 50% evapotranspiration replacement) and (5) rainfed (no irrigation during the year). The irrigated treatments were watered with irrigation and precipitation. Most indices were highest in response to full irrigation (FI 100). The values of the NDVI ranged from 0.87 to 0.58 and the SAVI from 0.65 to 0.38, and the values of these indices were lowest for genotype E237 in the rainfed areas. The indices NDVI, OSAVI, MCARI, NDRE and GDVI were positively correlated—very strongly with photosynthesis (A) and strongly with transpiration (E) of the coffee trees. On the other hand, temperature-based indices, such as canopy temperature and the TCARI index correlated negatively with A, E and stomatal conductance (gs). Under full irrigation, the tested genotypes did not differ between the years of evaluation. Overall, the index values of Iapar 59 exceeded those of the other genotypes. The use of VIs to evaluate coffee tree performance under different water managements proved efficient in discriminating the best genotypes and optimal water conditions for each genotype. Given the economic importance of coffee as a crop and its susceptibility to extreme events such as drought, this study provides insights that facilitate the optimization of productivity and resilience of plantations under variable climatic conditions. Full article

This paper presents the results of a three-year survey (2021–2023), conducted in an area of approximately 356 km² in Iraqi Kurdistan with the aim of identifying previously undetected archaeological sites. Thanks to the development of a multi-temporal approach based on open multispectral satellite data, greater effectiveness was achieved for the recognition of archaeological sites when compared to the use of single archival or freely accessible satellite images, which are typically employed in archaeological research. In particular, the Google Earth Engine services allowed for the efficient utilization of cloud computing resources to handle hundreds of remote sensing images. Using different datasets, namely Landsat 5, Landsat 7 and Sentinel-2, several products were obtained by processing entire stacks of images acquired at different epochs, thus minimizing the adverse effects on site visibility caused by vegetation, crops and cloud coverage and permitting an effective visual inspection and site recognition. Furthermore, spectral signature analysis of every potential site complemented the method. The developed approach was tested on areas that belong to the Land of Nineveh Archaeological Project (LoNAP) and the Upper Greater Zab Archaeological Reconnaissance (UGZAR) project, which had been intensively surveyed in the recent past. This represented an additional challenge to the method, as the most visible and extensive sites (tells) had already been detected. Three years of direct ground-truthing in the field enabled assessment of the outcomes of the remote sensing-based analysis, discovering more than 60 previously undetected sites and confirming the utility of the method for archaeological research in the area of Northern Mesopotamia. Full article

River inlets, deltas, and estuaries represent delicate ecosystems highly susceptible to climate change impacts. While significant progress has been made in understanding the morphodynamics of these environments in recent decades, the development of models still requires thorough testing and data integration. In this context, remote sensing emerges as a potent tool, providing crucial data and the ability to monitor temporal changes. In this paper, an integrated approach combining remote sensing and morphodynamic modeling is proposed to assess river systems comprehensively. By utilizing multispectral or RGB optical imagery from satellites or UAVs for river classification and remotely derived bathymetry, echo sounder data for ground truth, and photogrammetric modeling of emerged areas, we outline a procedure to create an integrated and continuous digital terrain model (DTM) of a riverbed, paying particular attention to the wet–dry interface. This method enables us to identify the river centerline, its width, and its slope variations. Additionally, by applying a linear morphodynamic model that considers the spatial variability of river morphology commonly found in estuarine environments, it is possible to predict the wavelength and migration rate of sediment bars. This approach has been successfully applied to recreate the DTM and monitor the morphodynamics of the seaward reach of the Roya River (Italy). Full article

Fractional vegetation cover (FVC) plays a significant role in assessing ecological quality and protection, as well as soil and water conservation. As a typical rare-earth resource county in China, Dingnan County has experienced rapid development due to rare-earth mining, resulting in significant alterations to vegetation cover. To elucidate the spatio-temporal changes in vegetation within Dingnan County over the past 35 years and the effects of natural and human factors on these changes, the spatial and temporal variations in FVC were analyzed using Landsat-TM/OLI multispectral images taken in 1988, 1995, 1997, 2002, 2006, 2013, 2017, and 2023. The findings indicate that (1) vegetation coverage in Dingnan County decreased from 1988 to 2002, followed by a gradual increase; (2) high vegetation cover is predominantly found in forested areas that maintain their natural state, while the central town and mining areas exhibit generally low coverage; (3) there are regional differences in the relationship between vegetation cover and environmental factors in Dingnan County. This research facilitates the alignment of ion-type rare-earth mining with ecological protection, thereby promoting the sustainable development of the mining area and providing scientific guidance for local governments to formulate more effective management and protection strategies for the mining ecosystem. Additionally, this research offers a scientific foundation for mining areas globally to develop sustainable policies and informed decision-making regarding environmental protection and sustainable development. Full article

The sustainable development of agriculture is closely related to the adoption of precision agriculture techniques, and accurate crop classification is a fundamental aspect of this approach. This study explores the application of machine learning techniques to crop classification by integrating RGB images and multispectral data acquired by UAVs. The study focused on five crops: rice, soybean, red bean, wheat, and corn. To improve classification accuracy, the researchers extracted three key feature sets: band values and vegetation indices, texture features extracted from a grey-scale co-occurrence matrix, and shape features. These features were combined with five machine learning models: random forest (RF), support vector machine (SVM), k-nearest neighbour (KNN) based, classification and regression tree (CART) and artificial neural network (ANN). The results show that the Random Forest model consistently outperforms the other models, with an overall accuracy (OA) of over 97% and a significantly higher Kappa coefficient. Fusion of RGB images and multispectral data improved the accuracy by 1–4% compared to using a single data source. Our feature importance analysis showed that band values and vegetation indices had the greatest impact on classification results. This study provides a comprehensive analysis from feature extraction to model evaluation, identifying the optimal combination of features to improve crop classification and providing valuable insights for advancing precision agriculture through data fusion and machine learning techniques. Full article

Farmland shelterbelt plays an important role in protecting farmland and ensuring stable crop yields, and it is mainly distributed in the form of bands and patches; different forms of distribution have different impacts on farmland, which is an important factor affecting crop yields. Therefore, high-precision classification of banded and patch farmland shelterbelt is a prerequisite for analyzing its impact on crop yield. In this study, we explored the effectiveness and transferability of an improved Prototypical Network model incorporating data augmentation and a convolutional block attention module for extracting banded and patch farmland shelterbelt in Northeast China, and we analyzed the potential of applying it to the production of large-scale farmland shelterbelt products. Firstly, we classified banded and patch farmland shelterbelt under different sample window sizes using the improved Prototypical Network in the source domain study area to obtain the optimal sample window size and the optimal classification model. Secondly, fine-tuning transfer learning and learning from scratch directly were used to classify the banded and patch farmland shelterbelt in the target domain study area, respectively, to evaluate the extraction model’s migratability. The results showed that classification of farmland shelterbelt using the improved Prototypical Network is very effective, with the highest extraction accuracy under the 5 × 5 sample window; the accuracies of the banded and patch farmland shelterbelt are 92.16% and 90.91%, respectively. Using the fine-tuning transfer learning method in the target domain can classify the banded and patch farmland shelterbelt with high accuracy, above 95% and 89%, respectively. The proposed approach can provide new insight into farmland shelterbelt classification and farmland shelterbelt products obtained from freely accessible Sentinel-2 multispectral images. Full article

This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km². The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring. Full article

This paper presents the mission analysis of ³Cat-8, a 6-Unit CubeSat mission being developed by the UPC NanoSat Lab for ionospheric research. The primary objective of the mission is to monitor the ionospheric scintillation of the aurora, and to perform several technological demonstrations. The satellite incorporates several novel systems, including a deployable Fresnel Zone Plate Antenna (FZPA), an integrated PocketQube deployer, a dual-receiver GNSS board for radio occultation and reflectometry experiments, and a polarimetric multi-spectral imager for auroral emission observations. The mission design, the suite of payloads, and the concept of operations are described in detail. This paper discusses the current development status of ³Cat-8, with several subsystems already developed and others in the final design phase. It is expected that the data gathered by ³Cat-8 will contribute to a better understanding of ionospheric effects on radio wave propagation and demonstrate the feasibility of compact remote sensors in a CubeSat platform. Full article

Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice. Full article

The leaf area index (LAI) and leaf chlorophyll content (LCC) are key indicators of crop photosynthetic efficiency and nitrogen status. This study explores the integration of UAV-based multispectral (MS) and thermal infrared (TIR) data to improve the estimation of maize LAI and LCC across different growth stages, aiming to enhance nitrogen (N) management. In field trials from 2022 to 2023, UAVs captured canopy images of maize under varied water and nitrogen treatments, while the LAI and LCC were measured. Estimation models, including partial least squares regression (PLS), convolutional neural networks (CNNs), and random forest (RF), were developed using spectral, thermal, and textural data. The results showed that MS data (spectral and textural features) had strong correlations with the LAI and LCC, and CNN models yielded accurate estimates (LAI: R² = 0.61–0.79, RMSE = 0.02–0.38; LCC: R² = 0.63–0.78, RMSE = 2.24–0.39 μg/cm²). Thermal data reflected maize growth but had limitations in estimating the LAI and LCC. Combining MS and TIR data significantly improved the estimation accuracy, increasing R² values for the LAI and LCC by up to 23.06% and 19.01%, respectively. Nitrogen dilution curves using estimated LAIs effectively diagnosed crop N status. Deficit irrigation reduced the N uptake, intensifying the N deficiency, while proper water and N management enhanced the LAI and LCC. Full article

In the future, the cultivation of maize will become more and more prominent. As the world’s demand for food and animal feeding increases, remote sensing technologies (RS technologies), especially unmanned aerial vehicles (UAVs), are developing more and more, and the usability of the cameras (Multispectral-MS) installed on them is increasing, especially for plant disease detection and severity observations. In the present research, two different maize hybrids, P9025 and sweet corn Dessert R78 (CS hybrid), were employed. Four different treatments were performed with three different doses (low, medium, and high dosage) of infection with corn smut fungus (Ustilago maydis [DC] Corda). The fields were monitored two times after the inoculation—20 DAI (days after inoculation) and 27 DAI. The orthomosaics were created in WebODM 2.5.2 software and the study included five vegetation indices (NDVI [Normalized Difference Vegetation Index], GNDVI [Green Normalized Difference Vegetation Index], NDRE [Normalized Difference Red Edge], LCI [Leaf Chlorophyll Index] and ENDVI [Enhanced Normalized Difference Vegetation Index]) with further analysis in QGIS. The gathered data were analyzed using R-based Jamovi 2.6.13 software with different statistical methods. In the case of the sweet maize hybrid, we obtained promising results, as follows: the NDVI values of CS 0 were significantly higher than the high-dosed infection CS 10.000 with a mean difference of 0.05422 *** and a p value of 4.43 × 10⁻⁵ value, suggesting differences in all of the levels of infection. Furthermore, we investigated the correlations of the vegetation indices (VI) for the Dessert R78, where NDVI and GNDVI showed high correlations. NDVI had a strong correlation with GNDVI (r = 0.83), a medium correlation with LCI (r = 0.56) and a weak correlation with NDRE (r = 0.419). There was also a strong correlation between LCI and GNDVI, with r = 0.836. NDRE and GNDVI indices had the correlation coefficients with a CCoeff. of r = 0.716. For hybrid separation analyses, useful results were obtained for NDVI and ENDVI as well. Full article

This paper presents the Hierarchical Spectral–Spatial Transformer (HSST) network, a novel approach applicable to both drone-based and broader remote sensing platforms for integrating hyperspectral (HSI) and multispectral (MSI) imagery. The HSST network improves upon conventional multi-head self-attention transformers by integrating cross attention, effectively capturing spectral and spatial features across different modalities and scales. The network’s hierarchical design facilitates the extraction of multi-scale information and employs a progressive fusion strategy to incrementally refine spatial details through upsampling. Evaluations on three prominent hyperspectral datasets confirm the HSST’s superior efficacy over existing methods. The findings underscore the HSST’s utility for applications, including drone operations, where the high-fidelity fusion of HSI and MSI data is crucial. Full article

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: R_pre = 0.8143, RMSEP = 171.9 kg/hm²) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: R_pre = 0.7655, RMSEP = 188.3 kg/hm²) from a multispectral camera. The best models for yield prediction were found by applying BPNN (R_pre = 0.8114, RMSEP = 172.6 kg/hm²) built from optimal EWs and ELM (R_pre = 0.8118, RMSEP = 170.9 kg/hm²) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture. Full article

Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R² of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R² and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas. Full article