Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region

Yang, Shuai; Feng, Qisheng; Liang, Tiangang; Liu, Baokang; Zhang, Wenjuan; Xie, Hongjie

doi:10.1016/j.rse.2017.10.011

Cited by 183 publications

(152 citation statements)

References 42 publications

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“…Accurate estimation of rice parameters, like above ground biomass (AGB), are very important for rice growth monitoring, the assessment of rice nutrition status, and the prediction of rice yield [2]. Traditional methods to estimate biomass are both labor-intensive and time-consuming, and are difficult to apply over large areas [3]. Remote sensing is a non-contact and non-destructive measuring method that can acquire both spectral and structural properties of the target at different spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

Jiang

Fang

et al. 2019

Remote Sensing

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Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g/m2, MAE = 236.49 g/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R2 = 0.86, RMSE = 178.37 g/m2, MAE = 127.34 g/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.

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Section: Introductionmentioning

confidence: 99%

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

Jiang

Fang

et al. 2019

Remote Sensing

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“…In addition, it is impossible to collect all the grassland biomass information within a satellite pixel for constructing the biomass estimation model. A more rational method is to set 3 to 5 quadrats in a pixel, and then average the sampling points to estimate the pixel grassland biomass [16,[32][33][34][35][36]. As a result, spatial scale mismatching occurs between ground-based observation data and satellite data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

et al. 2018

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Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m 2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R 2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R 2 = 0.89, RMSE = 91.48 g/m 2 , rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m 2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.

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“…The number of lines in the network, which was computed using Pajek, was applied as the dependent variable, while the 23 static features were independent. Then the mean impact value (MIV) [9,10,11] exported by the BP neural network was used to screen the independent variables.…”

Section: The Static Features Of the Chromatin Image Network Of Nucleimentioning

confidence: 99%

Network analysis of lymphocyte nucleus staining image —Data mining of lymphocyte image

D¹,

Yang²,

Xiong³

et al. 2018

Preprint

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Background: A complex network has been studied and applied in various disciplines. As network analysis and image processing are based on matrices, this research analysed the changes in the chromatin image of lymphocyte nuclei in peripheral blood of humans using a network motif and 1 *: Joint first author. yangxinglin. 123@163.com. Tel:+86 851 86753314 This study was supported by research grants from the Guiyang,Sci-Tech plan(2018)1-40, Taizhou 1401ky51 2 static features (static parameters), so as to complete image classification with network method.Methods: Image processing technology was used to establish a chromatin image network of a cell nucleus; Network analysis tool Pajek was used to display the special motif of an isolated structural hole with different symmetric line values; afterwards, the frequency of occurrence of this structural hole in patients with nasopharyngeal carcinoma and AIDS, and healthy people was computed. Then by applying the network static features as variables, the chromatin images of stained lymphocytes from the three groups of people were classified and recognised by using an extreme learning machine (ELM). Results: The frequency of occurrence of the isolated structural hole with different symmetric line values was adopted to distinguish the structures of the chromatins of peripheral blood lymphocytes in patients with nasopharyngeal carcinoma and AIDS, and healthy people. Similarly, The static features of the chromatin image network of a cell nucleus were applied to classify and recognise the morphological and structural changes in chromatins for peripheral blood lymphocytes in the three groups of people. Conclusion: The surface chemical and physical characteristics, as well as the polymerisation link status of biomacromolecules such as DNA, RNA, and protein in the lymphocyte nucleus change under certain pathological conditions. The change influences the combination of small molecular staining materials and any associated biomacromolecules. Therefore, various macroscopic and microscopic changes were found in the chromatin images of the cell nucleus. The microscopic changes include the variations of the extent of staining of chromatin in the nuclei, coarseness and direction of the texture therein, the size of stained conglomerations, etc. These changes contribute to the differences in chromatin image networks among the same type of cells across the three groups.Based on this, the model can be used to classify and reorganise certain diseases. The results prove that using complex network to analyse the chromatin structure of a cell nucleus is of significance.

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Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region

Cited by 183 publications

References 42 publications

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

Network analysis of lymphocyte nucleus staining image —Data mining of lymphocyte image

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