Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Rice is the most important food crop in Taiwan. Early information on rice-growing conditions is thus vital for estimating rice production to guarantee national food security and grain exports. The rice-harvested area is conventionally inspected twice a year by costly interpretation of aerial photographs and intensive labor-field surveys. However, such methods of rice monitoring are inadequate for providing the government with timely information on rice-cultivated conditions. This study aims to use time series of Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral data to develop a machine-learning approach for the early prediction of rice-growing fields in Taiwan. An object-based random forest (OBRF) was developed to process remotely sensed data for rice-cropping seasons from 2018 to 2021. The prediction results compared with the reference data showed that rice-growing fields could be accurately predicted before harvest, about three months for the first crop and two months for the second crop. The F-score and Kappa coefficient values achieved for the first crop were 0.87 and 0.85, and those for the second crop were 0.72 and 0.71, respectively. These findings were reaffirmed by close agreement between the official statistics and the rice area estimated from the satellite data, with the correlation coefficient of determination (R 2 ) value greater than 0.96. A large portion of the first crop's rice areas was abandoned or converted to upland crop cultivation in the second crop, which was confirmed by a visual interpretation of Landsat images and official statistics. Ultimately, this study proved the efficacy of using Sentinel-1/2 images and OBRF for the early prediction of rice-cultivated fields in Taiwan. Quantitative and geographical information produced from such methods was essential for the early estimation of rice production to nationally address food security concerns.

show abstract

Classification and spatio-temporal evolution analysis of coastal wetlands in the Liaohe Estuary from 1985 to 2023: based on feature selection and sample migration methods

Ke,

Tan,

et al. 2024

Front. For. Glob. Change

View full text Add to dashboard Cite

Coastal wetlands are important areas with valuable natural resources and diverse biodiversity. Due to the influence of both natural factors and human activities, the landscape of coastal wetlands undergoes significant changes. It is crucial to systematically monitor and analyze the dynamic changes in coastal wetland cover over a long-term time series. In this paper, a long-term time series coastal wetland remote sensing classification process was proposed, which integrated feature selection and sample migration. Utilizing Google Earth Engine (GEE) and Landsat TM/ETM/OLI remote sensing image data, the selected feature set is combined with the sample migration method to generate the training sample set for each target year. The Simple Non-Iterative Clustering-Random Forest (SNIC-RF) model was ultimately employed to accurately map wetland classes in the Liaohe Estuary from 1985 to 2023 and quantitatively evaluate the spatio-temporal pattern change characteristics of wetlands in the study area. The findings indicate that: (1) After feature selection, the accuracy of the model reached 0.88, and the separation of the selected feature set was good. (2) After sample migration, the overall accuracy of sample classification in the target year ranged from 87 to 94%, along with Kappa coefficients of 0.84 to 0.92, thereby ensuring the validity of classification sample migration. (3) SNIC-RF classification results showed better performance of wetland landscape. Compared with RF classification, the overall classification accuracy was increased by 0.69–5.82%, and the Kappa coefficient was increased by 0.0087–0.0751. (4) From 1985 to 2023, there has been a predominant trend of natural wetlands being converted into artificial wetlands. In recent years, this transition has occurred more gently. Finally, this study offers valuable insights into understanding changes and trends in the surface ecological environment of the Liaohe Estuary. The research method can be extended to other types of wetland classification and the comprehensive application of coastal wetland in hydrology, ecology, meteorology, soil, and environment can be further explored on the basis of this research, laying strong groundwork for shaping policies on ecological protection and restoration.

show abstract

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Cited by 5 publications

References 69 publications

Optimizing Feature Selection for Solar Park Classification: Approaches with OBIA and Machine Learning

Optimizing Feature Selection for Solar Park Classification: Approaches with OBIA and Machine Learning

Machine learning-based early prediction of rice-growing fields using multi-temporal Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral data

Classification and spatio-temporal evolution analysis of coastal wetlands in the Liaohe Estuary from 1985 to 2023: based on feature selection and sample migration methods

Contact Info

Product

Resources

About