# Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

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## Abstract

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## 1. Introduction

## 2. Data Sets and Methods

#### 2.1. Study Area

^{2}(Figure 1). Suide County is situated in the key soil erosion region of the Loess Plateau and is one of the focal areas of soil loss and erosion control. In addition, an experimental scientific station for ecology and environment is set up in the study area, thus facilitating field works.

#### 2.2. Data Acquisition

#### 2.3. Overview of Extraction

#### 2.4. Selection of Terrain Indexes

_{max}= the maximum elevation in the neighborhood

_{mean}= the mean elevation in the neighborhood

#### 2.5. Segmentation

#### 2.6. Classification

#### 2.6.1. Selection and Calculation of Classification Features

_{x}axis and computed in a counter-clockwise direction [32]. In this research, a set of GLCM variables was considered as the pre-selected classification feature indexes; the set includes (1) GLCM contrast, measuring diversity and dominance; (2) GLCM correlation; and (3) GLCM homogeneity, measuring similarity; and (4) GLCM entropy; and (5) GLCM angular second moment (ASM), assessing chaos and order lines (Table 1). These texture variables were based on a calculation over all directions. In this study, the GLCM features were calculated on topographic data rather than imagery because compared with the texture from imagery, the texture from topographic data reflects the topographic relief of the real terrace morphology without vegetation cover. Among the topographic indexes, AC with clear texture determined by visual inspection in the terrace region (see Figure 3 in Section 2.4) was used to calculate the GLCM features.

^{®}data mining and prediction analysis tool [40]. The boxplot method, which could show the distribution of feature variable values and thereby reflect the function of these features in distinguishing between terrace and non-terrace objects, was used to validate further the rationality of the topographic classification features. The boxplot of the topographic classification features was calculated on the basis of the above training samples.

#### 2.6.2. Classification Rules and Validation

## 3. Results

#### 3.1. Results of Terrain Indexes Selection

#### 3.2. Characteristics of Produced Segmentation

#### 3.3. Characteristics of Topographic Classification Features

#### 3.4. Characteristics of Two Classifications

#### 3.4.1. DEM-Based Classification

#### 3.4.2. Dem and Image-Based Classification

## 4. Discussion

#### 4.1. Rationality of the Proposed Method

#### 4.2. Application of the Proposed Method

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Location of the study area: (

**a**) location of the Loess Plateau in China; (

**b**) location of the study area and cities in the Loess Plateau; (

**c**) digital terrain map of the study area and the location of the sample plot for displaying the result of the terrain index analysis and segmentation; (

**d**) terrace distribution (red region) in the sample plot.

**Figure 3.**Calculation results of the terrain indexes. PN—positive and negative terrain index; AC—accumulative curvature; CVE—the coefficient of variation in elevation; SOS—slope of slope; TR—terrain roughness. White—for high values; black—for low values.

**Figure 4.**Segmentation results with the scale parameters of 25 to 150 and the shape and compactness values of 0.1 and 0.5, respectively. The three values under each figure represent the scale, shape, and compactness. The terrace areas are highlighted by red lines.

**Figure 5.**Estimation of scale parameter (ESP) output graphs showing local variance against rate of change. The orange vertical lines indicate suggested scale.

**Figure 6.**Segmentation results with the four scales (58, 64, 75, and 94) generated from the ESP and the shape and compactness values of 0.1 and 0.5, respectively. The three values under each figure represent the scale, shape, and compactness. The terrace areas are highlighted by red lines.

**Figure 7.**Importance of topographic classification features obtained by classification and regression tree algorithm. GLCM ASM—gray-level co-occurrence matrix angular second moment; V_PN—variance of positive and negative terrain indexes; V_SOS—variance of slope of slope; V_Slope—variance of slope; M_AC—mean of accumulative curvature; M_SOS—mean of slope of slope; M_Slope—mean of slope; M_PN—mean of positive and negative terrain indexes.

**Figure 8.**Boxplot of GLCM features, including GLCM ASM, GLCM contrast, GLCM contrast, GLCM correlation, GLCM entropy, and GLCM homogeneity. “1”—terraces; “2”—non-terraces.

**Figure 9.**Boxplot of the mean and variance values of the terrain indexes. M_AC—mean of accumulative curvature; M_SOS—mean of slope of slope; M_Slope—mean of slope; M_PN—mean of positive and negative terrain indexes; V_AC—variance of accumulative curvature; V_SOS—variance of slope of slope; V_Slope—variance of slope; V_PN—variance of positive and negative terrain index. “1”—terraces; “2”—non-terraces.

**Figure 11.**Terrace distribution in the study area. (

**a**) Terrace distribution via extraction based on DEMs; (

**b**) terrace distribution by the extraction based on DEMs and image; (

**c**) actual terrace distribution.

**Table 1.**Variables of GLCM [39].

Feature | Equation | Explanation |
---|---|---|

Angular Second Moment (ASM) | ${\displaystyle \sum}_{i=0}^{L-1}}{\displaystyle {\displaystyle \sum}_{j=0}^{L-1}}P{\left(i,j\right)}^{2$ | Angular second moment (ASM) is a measure of the homogeneity of an image. High values of ASM or energy occur when the window is highly orderly. |

Contrast | ${\displaystyle \sum}_{n=0}^{L-1}}{n}^{2}\left\{{\displaystyle {\displaystyle \sum}_{i=0}^{L-1}}{\displaystyle {\displaystyle \sum}_{j=0}^{L-1}}P\left(i,j\right)\right\$ | This measure of contrast or local intensity variation favors contributions from P(i, j) away from the diagonal, i.e., i! = j. |

Homogeneity | $\displaystyle \sum}_{i=0}^{L-1}}{\displaystyle {\displaystyle \sum}_{j=0}^{L-1}}\frac{P\left(i,j\right)}{1+{\left(i-j\right)}^{2$ | Homogeneity measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal. |

Entropy | $-{\displaystyle {\displaystyle \sum}_{i=0}^{L-1}}{\displaystyle {\displaystyle \sum}_{j=0}^{L-1}}P\left(i,j\right)\mathrm{log}P\left(i,j\right)$ | Inhomogeneous scenes exhibit low first-order entropy, whereas homogeneous scenes exhibit high entropy. |

Correlation | $\displaystyle \sum}_{i=0}^{L-1}}{\displaystyle {\displaystyle \sum}_{j=0}^{L-1}}\frac{ijP\left(i,j\right)-{u}_{1}{u}_{2}}{{\sigma}_{1}{{\sigma}_{2}}^{2$ | Correlation is a measure of gray-level linear dependence between pixels at specified positions relative to each other. |

AC | Slope | CVE | SOS | TR | PN | |
---|---|---|---|---|---|---|

AC | 1 | |||||

Slope | −0.003 | 1 | ||||

CVE | 0.004 | 0.913 | 1 | |||

SOS | −0.011 | 0.212 | 0.247 | 1 | ||

TR | −0.001 | 0.931 | 0.936 | 0.231 | 1 | |

PN | −0.387 | −0.010 | −0.009 | 0.002 | −0.029 | 1 |

Combination of Indexes | PN, SOS, CVE, AC | PN, SOS, Slope, AC | PN, SOS, TR, AC |
---|---|---|---|

Entropy | 3.278 | 13.189 | 10.680 |

Classification | Reference | Class. Totals | Prod. Ac. | User. Ac. | Omi. Er. | Com. Er. | |
---|---|---|---|---|---|---|---|

Terraces | Non-Terraces | ||||||

Terraces | 260575 | 116343 | 376918 | 0.835 | 0.691 | 0.309 | 0.166 |

Non-terraces | 51719 | 1245789 | 1297508 | 0.915 | 0.960 | 0.040 | 0.085 |

Ref. Totals | 312293 | 1362135 | 1674428 |

Classification | Reference | Class. Totals | Prod. Ac. | User. Ac | Omi. Er. | Com. Er. | |
---|---|---|---|---|---|---|---|

Terraces | Non-Terraces | ||||||

Terraces | 257549 | 46198 | 303747 | 0.825 | 0.848 | 0.152 | 0.175 |

Non-terraces | 54742 | 1316039 | 1370781 | 0.966 | 0.960 | 0.040 | 0.034 |

Ref. Totals | 312293 | 1362135 | 1674428 |

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## Share and Cite

**MDPI and ACS Style**

Zhao, H.; Fang, X.; Ding, H.; Josef, S.; Xiong, L.; Na, J.; Tang, G. Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis. *ISPRS Int. J. Geo-Inf.* **2017**, *6*, 157.
https://doi.org/10.3390/ijgi6060157

**AMA Style**

Zhao H, Fang X, Ding H, Josef S, Xiong L, Na J, Tang G. Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis. *ISPRS International Journal of Geo-Information*. 2017; 6(6):157.
https://doi.org/10.3390/ijgi6060157

**Chicago/Turabian Style**

Zhao, Hanqing, Xuan Fang, Hu Ding, Strobl Josef, Liyang Xiong, Jiaming Na, and Guoan Tang. 2017. "Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis" *ISPRS International Journal of Geo-Information* 6, no. 6: 157.
https://doi.org/10.3390/ijgi6060157