Growing Stock Volume Estimation for Daiyun Mountain Reserve Based on Multiple Linear Regression and Machine Learning

Wei, Jinhuang; Fan, Zhongmou

doi:10.3390/su141912187

Cited by 2 publications

(3 citation statements)

References 39 publications

(37 reference statements)

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“…MLR is considered the most common form of linear regression, which practically utilizes multiple variables (Zhang et al, 2022).The MLR formula (Eq. 3) is as follows (Wei et al, 2022): NDVI = a0+a1x1+a2x2+…+anxn Eq. 3 In the above, y is the NDVI value; a0, a1, •••, an are the model-fitting parameters; x1, •••, xn are the pixel variables; and n is the number of variables.…”

Section: Filling the Gaps Of The Ndvi Time Seriesmentioning

confidence: 99%

Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data

Faisal

Makar²

2022

JANS

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The presence of gaps or missing values in time series prevents the practical use of such data. The current research aims at developing a simplified, straightforward technique for gap-filling the time series data of the Normalize Difference Vegetation Index (NDVI) generated using Moderate Resolution Imaging Spectroradiometer (MODIS). This research assumes that a relationship exists between the pixel location, date of acquisition and its NDVI value within a defined timeline. Therefore, two relatively simple methods were tested: the Multiple Linear Regression (MLR) analysis and the Artificial Neural Networks (ANN)to fill the NDVI missing values. While MLR is a well-known simple statistical method, the ANN has been successfully applied for the analysis of various scientific data, including the gap-filling of time series data. Nevertheless, ANN proved its supremacy in such approach. The accuracy of estimation utilizing the developed ANN model reached an average of r2 of 0.8, while the average accuracy of MLR was about 0.3. Nevertheless, the developed model could only be applied within the same timeframe of the images used for developing the model. Otherwise, the accuracy of determination was reduced significantly. The results showed that according to its performance, ANN are promising for filling missing data of NDVI time series and could be applied to any other vegetation indices as well.

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Section: Filling the Gaps Of The Ndvi Time Seriesmentioning

confidence: 99%

Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data

Faisal

Makar²

2022

JANS

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“…Developing appropriate inversion models is also a crucial step in estimating AGB. Parametric and non-parametric models are the two most commonly used models in AGB inversion [16,25]. The former often includes a limited number of parameters, such as multivariate linear regression (MLR) and generalized linear models (GLM), which require the addition of restrictive hypothesis functions between features and AGB [25,26].…”

Section: Introductionmentioning

confidence: 99%

“…Parametric and non-parametric models are the two most commonly used models in AGB inversion [16,25]. The former often includes a limited number of parameters, such as multivariate linear regression (MLR) and generalized linear models (GLM), which require the addition of restrictive hypothesis functions between features and AGB [25,26]. However, due to the inherent complexity in the relationship between AGB and RS data, parametric models often exhibit limited accuracy [27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

Zhang,

Zhou,

Zhang

et al. 2024

Forests

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The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel framework based on freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data. Firstly, we designed new indices through the formula analogous with vegetation index calculation to integrate multidimensional spectral and structural information. Then, leveraging the simplicity of computational principles, a pigeon-inspired optimization algorithm (PIO) was introduced into a bi-directional long short-term memory neural network (PIO-BiLSTM), which achieved the set objective function through repeated iteration and validation to obtain the optimal model parameters. Finally, to verify the framework’s effect, we conducted experiments in two different tree species and compared another seven classical optimization algorithms and machine learning models. The results indicated that the new indices significantly improved the inversion accuracy of all models in both categories, and the PIO-BiLSTM model achieved the highest accuracy (Category-1: R2 = 0.8055, MAE = 8.8475 Mg·ha−1, RMSE = 12.2876 Mg·ha−1, relative RMSE = 18.1715%; Category-2: R2 = 0.7956, MAE = 1.7103 Mg·ha−1, RMSE = 2.2887 Mg·ha−1, relative RMSE = 9.3000%). Compared with existing methods, the proposed framework greatly reduced the labor costs in parameter selection, and its potential uncertainty also decreased by up to 9.0%. Furthermore, the proposed method has a strong generalization ability and is independent of tree species, indicating its great potential for future forest AGB inversion in wider regions with diverse forest types.

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Growing Stock Volume Estimation for Daiyun Mountain Reserve Based on Multiple Linear Regression and Machine Learning

Cited by 2 publications

References 39 publications

Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data

Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

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