Research on the Temporal and Spatial Changes and Driving Forces of Rice Fields Based on the NDVI Difference Method

Jinglian Tian, Yongzhong Tian (), Wenhao Wan, Chenxi Yuan, Kangning Liu and Yang Wang
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Jinglian Tian: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Yongzhong Tian: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Wenhao Wan: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Chenxi Yuan: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Kangning Liu: Chongqing Geomatics and Remote Sensing Center, Chongqing 400715, China
Yang Wang: Chongqing Soil and Water Conservation Monitoring Centre, Chongqing 400715, China

Agriculture, 2024, vol. 14, issue 7, 1-22

Abstract: Rice is a globally important food crop, and it is crucial to accurately and conveniently obtain information on rice fields, understand their spatial patterns, and grasp their dynamic changes to address food security challenges. In this study, Chongqing’s Yongchuan District was selected as the research area. By utilizing UAVs (Unmanned Aerial Vehicles) to collect multi-spectral remote sensing data during three seasons, the phenological characteristics of rice fields were analyzed using the NDVI (Normalized Difference Vegetation Index). Based on Sentinel data with a resolution of 10 m, the NDVI difference method was used to extract rice fields between 2019 and 2023. Furthermore, the reasons for changes in rice fields over the five years were also analyzed. First, a simulation model of the rice harvesting period was constructed using data from 32 sampling points through multiple regression analysis. Based on the model, the study area was classified into six categories, and the necessary data for each region were identified. Next, the NDVI values for the pre-harvest and post-harvest periods of rice fields, as well as the differences between them, were calculated for various regions. Additionally, every year, 35 samples of rice fields were chosen from high-resolution images provided by Google. The thresholds for extracting rice fields were determined by statistically analyzing the difference in NDVI values within the sample area. By utilizing these thresholds, rice fields corresponding to six harvesting regions were extracted separately. The rice fields extracted from different regions were merged to obtain the rice fields for the study area from 2019 to 2023, and the accuracy of the extraction results was verified. Then, based on five years of rice fields in the study area, we analyzed them from both temporal and spatial perspectives. In the temporal analysis, a transition matrix of rice field changes and the calculation of the rice fields’ dynamic degree were utilized to examine the temporal changes. The spatial changes were analyzed by incorporating DEM (Digital Elevation Model) data. Finally, a logistic regression model was employed to investigate the causes of both temporal and spatial changes in the rice fields. The study results indicated the following: (1) The simulation model of the rice harvesting period can quickly and accurately determine the best period of remote sensing images needed to extract rice fields. (2) The confusion matrix shows the effectiveness of the NDVI difference method in extracting rice fields. (3) The total area of rice fields in the study area did not change much each year, but there were still significant spatial adjustments. Over the five years, the spatial distribution of gained rice fields was relatively uniform, while the lost rice fields showed obvious regional differences. In combination with the analysis of altitude, it tended to grow in lower areas. (4) The logistic regression analysis revealed that gained rice fields tended to be found in regions with convenient irrigation, flat terrain, lower altitude, and proximity to residential areas. Conversely, lost rice fields were typically located in areas with inconvenient irrigation, long distance from residential areas, low population, and negative topography.

Keywords: rice fields; NDVI; logistic regression model; driving forces; Chongqing; NDVI difference method (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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