# Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Data

#### 2.1. Study Area

#### 2.2. Data

## 3. Methodology

#### 3.1. Preprocessing Remote Sensing Data

#### 3.2. Extracting the Maize Planting Distribution

#### 3.3. Exploring the Spatio-Temporal Variation in Maize Plantation Distribution

#### 3.4. Exploring the Influencing Factors of Maize Plantation Distribution Spatio-Temporal Variation

- Calculate the q statistic values of two factors ${X}_{1}$ and ${X}_{2}$: $q\left({X}_{1}\right)$ and $q\left({X}_{2}\right)$.
- Overlay the two layers ${X}_{1}$ and ${X}_{2}$ to obtain the composite layer ${X}_{1}\cap {X}_{2}$.
- Calculate the q statistic values of composite layer ${X}_{1}\cap {X}_{2}$: $q\left({X}_{1}\cap {X}_{2}\right)$
- Compare $q\left({X}_{1}\right)$, $q\left({X}_{2}\right)$ and $q\left({X}_{1}\cap {X}_{2}\right)$

## 4. Results

#### 4.1. Maize Plantation Distribution

#### 4.2. Spatio-Temporal Variation in Maize Plantation Distribution

#### 4.2.1. Temporal Variation of Maize Plantation

#### 4.2.2. Spatial Distribution Characteristics and Changes in Maize Plantation

#### 4.3. Spatio-Temporal Variation in Influencing Factors of Maize Plantation Distribution

#### 4.3.1. Results of the Partial Correlation Analysis

#### 4.3.2. Results of Geographic Detector Analysis

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AE | Agriculture Employees |

AGP | Agriculture Gross Product |

CART | Classification And Regression Tree |

CCF | Consumption of Chemical Fertilizers |

CFMASK | C Function of Mask |

CNLULC | Chinese Land Use And Land Cover |

EC | Electricity Consumed in rural areas |

IA | Irrigated Area |

GDP | Gross Regional Product |

GEE | Google Earth Engine |

KDE | Kernel Density Estimation |

LUT | Land Cover Transformation |

MPP | Maize Plantation Proportion |

NRH | Number of Rural Households |

NRL | Number of Rural Laborers |

PAF | Percentage of Area with Flood prevention measures |

Probability Density Function | |

PRE | Precipitation |

PRS | Atmospheric Pressure |

QA | Quality Assessment |

RF | Random Forest |

RHU | Relative Humidity |

SDE | Standard Ellipse analysis |

SH | Spatial Heterogeneity |

SR | Surface Reflectance |

SSD | Sunshine Duration |

TEM | Temperature |

WIN | Wind Speed |

## Appendix A

Year | smileCart | smileRandomForest | ||
---|---|---|---|---|

Overall Accuracy | Kappa | Overall Accuracy | Kappa | |

2013 | 0.9724 | 0.9436 | 0.9628 | 0.9107 |

2014 | 0.9444 | 0.8849 | 0.9328 | 0.9045 |

2015 | 0.9882 | 0.9761 | 0.9784 | 0.9478 |

2016 | 0.9592 | 0.9183 | 0.9652 | 0.9246 |

2017 | 0.9870 | 0.9739 | 0.9723 | 0.9548 |

2018 | 0.9783 | 0.9563 | 0.9457 | 0.9369 |

2019 | 0.9643 | 0.9245 | 0.9367 | 0.8974 |

2020 | 0.9800 | 0.9584 | 0.9124 | 0.8753 |

**Figure A1.**The maize plantation distribution from 2013 to 2020 (

**a1**–

**h1**) and the maize plantation proportion at the county level from 2013 to 2020 (

**a2**–

**h2**).

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**Figure 3.**The expectations of the kernel density estimation function from 2013 to 2020 (

**a**) and the kernel density estimation function from 2013 to 2020 (

**b**).

**Figure 4.**Linear regression trend of MPP at the county level (

**a**) and the proportion of counties with increasing or decreasing trend across different latitudes and landform regions (

**b**).

**Figure 7.**Correlation coefficient between maize plantation proportion (MPP) and meteorological factors including precipitation ((

**a**) PRE), atmospheric pressure ((

**b**) PRS), relative humidity ((

**c**) RHU), sunshine duration ((

**d**) SSD), temperature ((

**e**) TEM), wind speed ((

**f**) WIN) and the proportion of counties with positive or negative correlation across different latitudes and landform regions.

**Figure 8.**Dominant relevant meteorological factors (

**a**) and the proportion of meteorological factors as each county’s most relevant factor across different latitudes and landform regions (

**b**).

**Figure 9.**Annual (

**a**), monthly (

**b**), comprehensive (

**c**) geographic factor detector analysis and comprehensive geographic interactive detector analysis (

**d**).

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**MDPI and ACS Style**

Guo, R.; Zhu, X.; Zhang, C.; Cheng, C. Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China. *Remote Sens.* **2022**, *14*, 3590.
https://doi.org/10.3390/rs14153590

**AMA Style**

Guo R, Zhu X, Zhang C, Cheng C. Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China. *Remote Sensing*. 2022; 14(15):3590.
https://doi.org/10.3390/rs14153590

**Chicago/Turabian Style**

Guo, Rui, Xiufang Zhu, Ce Zhang, and Changxiu Cheng. 2022. "Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China" *Remote Sensing* 14, no. 15: 3590.
https://doi.org/10.3390/rs14153590