Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline–Alkali Soil

Zhao, Yue; Zhang, Zhuopeng; Zhu, Hui; Ren, Jianhong

doi:10.3390/ijerph19116556

Cited by 8 publications

(5 citation statements)

References 67 publications

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“…In addition, the kurtosis and skewness coefficients for the different texture features ranged from −1.008 to 3.401 and −1.853 to 1.452, respectively. From the extracted GLCM texture features and the GLCM texture analysis for desiccation cracking soils in natural conditions by Zhao et al [45], it can be seen that although it differed slightly, the overall distribution of all 12 texture features was still poorly skewed and well-concentrated, which conformed to the characteristics of a normal distribution. Figure 7 shows the cross-correlation coefficients among the 12 texture features from crack patterns of all 57 soil samples.…”

Section: Glcm Texture Featuresmentioning

confidence: 88%

“…In order to describe the texture characteristics more scientifically, 12 specific texture features were calculated from the extracted GLCMs using the equations proposed by Haralick et al [44], including contrast (CON), angular second moment (ASM), entropy (ENT), homogeneity (HOM), correlation (COR), cluster shade (CS), cluster prominence (CP), max probability (MP), sum average (SA), sum entropy (SE), sum variance (SV), and information of correlation (IC). Note that in order to effectively consider the computational complexity and fully maintain the GLCM texture information, five pixels were selected as the GLCM step size according to the research proposed by Zhao et al [45]. Moreover, after considering the effect of the directions, the GLCM texture features computed from 0 • , 45 • , 90 • , and 135 • were averaged for further analysis.…”

Section: Texture Featurementioning

confidence: 99%

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Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation

Zhang

Zhou

et al. 2023

Remote Sensing

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Soil salinity is one of the parameters used for determining the extent of soil salinization. During water evaporation, the surface of salt-affected soils in the Songnen Plain, China, exhibits obvious shrinkage and cracking phenomena due to the high salt content. The aim of this current study is to quantify the influence of the salt content on the surface shrinkage–cracking process and to achieve quantitative extraction of soil salinity parameters based on different crack parameter types. In order to achieve the above objectives, a controlled shrinkage–cracking experiment was conducted. Subsequently, three kinds of crack characteristics such as crack length, box-counting dimension, and 12 gray-level co-occurrence matrix (GLCM) texture features were quantitatively extracted from the standard binary crack patterns. In order to predict the soil physical–chemical properties of salt-affected soils in the Songnen Plain, three models such as multiple linear regression (MLR), multiple stepwise regression (MSR), and artificial neural network (ANN) were developed and compared based on the crack length, box-counting dimension, and the first two principal components of GLCM texture features. The results show that the extent of desiccation cracks was determined by soil salinity since the water film caused by exchangeable cations and the thickness of DDL determined by soil salinity can promote desiccation cracking. Although the three methods have high prediction accuracy for Na+, electrical conductivity (EC), and total soil salinity, the ANN-based method showed the best prediction with R2 values for Na+, EC, and soil salinity as high as 0.91, 0.91, and 0.89, and ratio of performance to deviation (RPD) values for Na+, EC, and soil salinity corresponding to 2.96, 3.47, and 2.95.

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Section: Glcm Texture Featuresmentioning

confidence: 88%

Section: Texture Featurementioning

confidence: 99%

Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation

Zhang

Zhou

et al. 2023

Remote Sensing

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“…The grayscale matrix method is the most mature texture extraction method, which can describe the spatial distribution and structural characteristics of each grayscale pixel of the image and is widely used to extract texture feature values of remote sensing images [23]. Although 18 GLCM outputs are provided in GEE, in order to avoid dimensional disaster, five indicators with high correlation with soil salinity, namely Correlation (COR), Contrast (CON), Dissimilarity (DIS), Entropy (ENT), and Angular Second Moment (ASM), were selected as texture features based on preliminary experiments and existing research references [53,54]. And the B2, B8, and gray value bands of the Sentinel-2 were used as the computational bands.…”

Section: Index Formula Referencesmentioning

confidence: 99%

Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China

Zhang,

Zhao,

Yang

et al. 2024

Land

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(1) Monitoring salinized soil in saline–alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R2 = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions.

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“…This approach has been rarely used in the field of soil science; however, some studies tested the feasibility of image texture features from GLCM to determine the correlation between soil moisture conditions and the intensity of the pixel in laboratory conditions [46] while, in the open field, they tested image texture parameters from Sentinel-1 for soil moisture retrieval [47]. Recently, Zhao et al [48] used the GLCM texture analysis to describe the surface cracking conditions of soda saline-alkali soil and quantitatively studied the responses of GLCM texture features to soil salinity.…”

Section: Introductionmentioning

confidence: 99%

Using Image Texture Analysis to Evaluate Soil–Compost Mechanical Mixing in Organic Farms

et al. 2023

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Soil amendments (e.g., compost) require uniform incorporation in the soil profile to benefit plants. However, machines may not mix them uniformly throughout the upper soil layer commonly explored by plant roots. The study focuses on using image texture analysis to determine the level of mixing uniformity in the soil following the passage of two kinds of harrows. A 12.3-megapixel DX-format digital camera acquired images of soil/expanded polystyrene (in the laboratory) and soil/compost mixtures (in field conditions). In the laboratory, pictures captured the soil before and during the simulated progressive mixing of expanded polystyrene particles. In field conditions, images captured the exposed superficial horizons of compost-amended soil after the passage of a combined spike-tooth–disc harrow and a disc harrow. Image texture analysis based on the gray-level co-occurrence matrix calculated the sums of dissimilarity, contrast, entropy, and uniformity metrics. In the laboratory conditions, the progressive mixing resulted in increased image dissimilarity (from 1.15 ± 0.74 × 106 to 1.65 ± 0.52 × 106) and contrast values (from 2.69 ± 2.06 × 106 to 5.67 ± × 1.93 106), almost constant entropy (3.50 ± 0.25 × 106), and decreased image uniformity (from 6.65 ± 0.31 × 105 to 4.49 ± 1.36 × 105). Using a tooth-disc harrow in the open field resulted in higher dissimilarity, contrast, entropy (+73.3%, +62.8%, +16.3%), and lower image uniformity (−50.6%) than the disc harrow, suggesting enhanced mixing in the superficial layer.

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Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline–Alkali Soil

Cited by 8 publications

References 67 publications

Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation

Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation

Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China

Using Image Texture Analysis to Evaluate Soil–Compost Mechanical Mixing in Organic Farms

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