Research on forage hyperspectral image recognition based on F-SVD and XGBoost

Zhao, Xuanhe; Pan, Xin; Ma, Yong; Wang, Yan

doi:10.1051/matecconf/202133606027

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Between two nonlinear models, XGBoost reported considerable advantages in predictive accuracy over RF. This result was consistent with recently published studies [43,54]. As XGBoost is effective in high-dimension data analysis, it is becoming a reliable method in vegetation parameter modelling using UAV-based hyperspectral data [52].…”

Section: Discussionsupporting

confidence: 92%

Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

et al. 2022

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Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.

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Section: Discussionsupporting

confidence: 92%

Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

et al. 2022

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“…Acquisition. In order to realize the comprehensive disaster reduction capability evaluation of regional disaster points in a complex environment based on hyperspectral remote sensing monitoring image technology, the method of adaptive global tone mapping was used to collect regional disaster points' geographic remote sensing images in a complex environment, and combined with the edge detection method [8,9], an automatic segmentation model of spatiotemporal distribution characteristics and texture distribution domain of regional disaster points' geographic remote sensing images in a complex environment was established. e filtering detection and analysis model of spatiotemporal distribution characteristics texture distribution domain of regional disaster point remote sensing images under the complex environmental background is constructed, and the pixels per inch sampling structure of spatiotemporal distribution characteristics texture distribution domain of regional disaster point remote sensing images under the complex environmental background is obtained [10].…”

Section: Hyperspectral Remote Sensing Monitoring Imagementioning

confidence: 99%

Evaluation Model of Regional Comprehensive Disaster Reduction Capacity under Complex Environment

Wang

Lin

2022

Journal of Environmental and Public Health

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In order to realize the evaluation of regional comprehensive disaster reduction capacity in a complex environment, an evaluation model of regional comprehensive disaster reduction capacity in a complex environment based on remote sensing monitoring and data image feature analysis is proposed. According to the geographical location and scale of disaster spots and the parameter analysis of the model of disaster-bearing bodies around the disaster spots, the remote sensing monitoring method is adopted to extract the geographical remote sensing images of regional disaster spots in a complex environment. The collected geographical remote sensing images of regional disaster points under the complex environmental background are filtered and preprocessed, and the texture parameters of the geographical remote sensing images of regional disaster points under the complex environmental background are recognized by combining the method of image texture feature extraction. Based on the method of tone mapping, the rapid filtering and feature analysis of the geographical remote sensing images of regional disaster points under the complex environmental background are carried out, and the time, position, damage, and so on in the geographical remote sensing images of regional disaster points under the complex environmental background are analyzed. By using the method of parameter analysis and gradient operator operation, a comparison model of geographical remote sensing images of regional disaster points under the complex environmental background is established, and the reliability evaluation of regional comprehensive disaster reduction ability under the complex environmental background is realized according to the method of contrast and detail significance enhancement. The test shows that this method has high accuracy in evaluating regional comprehensive disaster reduction capability under a complex environment, high accuracy in marking the geographical location of regional disaster points under a complex environment, and good fusion performance and reliability of regional comprehensive disaster reduction capability evaluation parameters.

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“…For example, Zhao et al collected the pure quadrat of artificial grassland in Inner Mongolia with hyperspectral imager. After dimensional-reduction processing of hyperspectral data, machine learning classification algorithm was used to classify six types of grass species [5]. UAV remote sensing technology is also known as the third generation of remote sensing technology.…”

Section: Introductionmentioning

confidence: 99%

Grassland botanical species identification based on MTMF model

Huang

Wang

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Grassland plays an important role in regional economic development and terrestrial ecosystem security. Remote sensing technology is fast, efficient and low cost. The use of remote sensing technology to discriminate grassland species is an important way to monitor the population dynamics and botanical community succession in grassland. Such information is conducive to the timely and accurate detection of changes in the grassland ecological environment and provides an important reference for the scientific management of grassland ecosystems and the construction of an ecologically aware civilization. In this paper, based on the UAV hyperspectral remote sensing image of natural grassland in Inner Mongolia and the linear spectral mixture model, the MTMF (mixture tuned matched filtering) model was used for grass species identification, and the results were verified with FVC (fractional vegetation cover) estimation. The results showed that this method could achieve effective identification and extraction of the dominant species of Leymus chinensis in the study area.

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Research on forage hyperspectral image recognition based on F-SVD and XGBoost

Cited by 4 publications

References 6 publications

Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

Evaluation Model of Regional Comprehensive Disaster Reduction Capacity under Complex Environment

Grassland botanical species identification based on MTMF model

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