Monitoring Lodging Extents of Maize Crop Using Multitemporal GF-1 Images

Qu, Xu zhou; Shi, Dong Po; Gu, Xiaohe; Sun, Qian; Hu, Xuqian; Yang, Xin; Pan, Yuchun

doi:10.1109/jstars.2022.3170345

Cited by 17 publications

(5 citation statements)

References 63 publications

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“…Notably, the reflectance of the blue band was clearly lower after DLS correction than CRP correction. Although few VIs that are in common use include the blue band, these data are nevertheless important for crop classification, lodging monitoring, and vegetation mapping [35][36][37].…”

Section: Removing the Impact Of Correction Methods From Irradiance Va...mentioning

confidence: 99%

Radiometric Correction of Multispectral Field Images Captured under Changing Ambient Light Conditions and Applications in Crop Monitoring

Xue

Ming

Xin

et al. 2023

Drones

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Applications of unmanned aerial vehicle (UAV) spectral systems in precision agriculture require raw image data to be converted to reflectance to produce time-consistent, atmosphere-independent images. Complex light environments, such as those caused by varying weather conditions, affect the accuracy of reflectance conversion. An experiment was conducted here to compare the accuracy of several target radiance correction methods, namely pre-calibration reference panel (pre-CRP), downwelling light sensor (DLS), and a novel method, real-time reflectance calibration reference panel (real-time CRP), in monitoring crop reflectance under variable weather conditions. Real-time CRP used simultaneous acquisition of target and CRP images and immediate correction of each image. These methods were validated with manually collected maize indictors. The results showed that real-time CRP had more robust stability and accuracy than DLS and pre-CRP under various conditions. Validation with maize data showed that the correlation between aboveground biomass and vegetation indices had the least variation under different light conditions (correlation all around 0.74), whereas leaf area index (correlation from 0.89 in sunny conditions to 0.82 in cloudy days) and canopy chlorophyll content (correlation from 0.74 in sunny conditions to 0.67 in cloudy days) had higher variation. The values of vegetation indices TVI and EVI varied little, and the model slopes of NDVI, OSAVI, MSR, RVI, NDRE, and CI with manually measured maize indicators were essentially constant under different weather conditions. These results serve as a reference for the application of UAV remote sensing technology in precision agriculture and accurate acquisition of crop phenotype data.

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Section: Removing the Impact Of Correction Methods From Irradiance Va...mentioning

confidence: 99%

Radiometric Correction of Multispectral Field Images Captured under Changing Ambient Light Conditions and Applications in Crop Monitoring

Xue

Ming

Xin

et al. 2023

Drones

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“…Tis paper selects fve-fold cross-validation to obtain the optimal feature combination. Table 3 displays the pseudo-code of the RF-RFECV algorithm [22].…”

Section: Recursive Feature Elimination With Cross-validationmentioning

confidence: 99%

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

Zhao,

Yang,

Han

et al. 2024

International Transactions on Electrical Energy Systems

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High-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system based on the downsampling factor (DF) and approximation derivatives (AD). The signals at multiple sampling frequencies were analysed using the DF, and the AD approach was used to generate various levels of detail and approximation coefficients. Initially, the signals were processed with different DF values. The first, second, and third order derivatives of the generated signals were calculated by the AD method. Next, the entropy features of these signals were computed, and the Random Forest-Recursive feature elimination with cross-validation (RF-RFECV) algorithm was used to select a high-quality feature subset. Finally, an ensemble classifier consisting of Light Gradient Boosting Machine (LightGBM), K Nearest Neighbor (KNN), and Naive Bayes (NB) classifiers was utilized to identify UHVDC faults. The MATLAB/Simulink simulation software was used to develop a ±800 kV UHVDC transmission line model and perform simulation experiments with various fault locations and types. Based on the experiments, it has been established that the suggested approach is highly precise in detecting several faults on UHVDC transmission lines. The method is capable of accurately identifying low or high resistance faults, irrespective of their incidence, and is remarkably resistant to transitional resistance. Furthermore, it exhibits excellent performance in identifying faults using a small sample size and is highly reliable.

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“…Recursive Feature Elimination (RFE), an efficient approach to selecting feature combinations, determines the optimal feature combination. It trains on the initial feature space of the classification algorithm model, figures out how important each feature is during training, and builds the model by eliminating the less important features one by one until the best combination of features is found [39]. The GINI coefficient is used to evaluate the importance of each feature, and it is calculated as follows [40]:…”

Section: B Classification Frameworkmentioning

confidence: 99%

Fine Extraction of Plateau Wetlands Based on a Combination of Object-Oriented Machine Learning and Ecological Rules: A Case Study of Dianchi Basin

Cai,

Tang,

Sima

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Wetland ecosystems are essential to the preservation of biodiversity. Plateau wetland ecosystems are vital components of wetland ecosystems, characterized by diverse wetland types and fragmented land distribution. In the extraction of plateau wetlands, there are such issues as inaccuracy in classification, inadequately fine categories, and difficulty in extracting vegetated wetlands. The aim of this study was to establish a new classification framework for extracting detailed information about plateau wetlands, and the Dianchi Basin was used as the study area. Using Gaofen-2 (GF-2) imagery from 2019-2021, land cover information was extracted by applying nearest neighbor classification and random forest classification. Wetlands were then extracted from the land cover data using ecological rule classification, and a detailed wetland map of the Dianchi Basin was obtained in 2020 with a 1 m resolution. Results showed that the production accuracies of forest wetlands, shrub wetlands, meadow wetlands, rivers, ponds, reservoirs, and lakes in the Dianchi Basin were 89.4%, 87.9%, 91.4%, 90.7%, 89.9%, 92.9%, and 95.9%, respectively, and the user accuracies were 94.9%, 92.4%, 92.6%, 95.4%, 94.2%, 91.0%, and 99.4%, respectively. Compared to the CAS_Wetlands, this study categorized the five categories of plateau wetlands into seven types with greater specificity and increased the spatial resolution of wetland mapping from 30 m to 1 m. This study can provide a new reference framework for wetland extraction and support the conservation of plateau wetland ecosystems.

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Monitoring Lodging Extents of Maize Crop Using Multitemporal GF-1 Images

Cited by 17 publications

References 63 publications

Radiometric Correction of Multispectral Field Images Captured under Changing Ambient Light Conditions and Applications in Crop Monitoring

Radiometric Correction of Multispectral Field Images Captured under Changing Ambient Light Conditions and Applications in Crop Monitoring

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

Fine Extraction of Plateau Wetlands Based on a Combination of Object-Oriented Machine Learning and Ecological Rules: A Case Study of Dianchi Basin

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