[논문]Comparative Analysis of Supervised and Phenology-Based Approaches for Crop Mapping: A Case Study in South Korea

Ehsan Rahimi; Chuleui Jung

doi:10.7780/kjrs.2024.40.2.5

Comparative Analysis of Supervised and Phenology-Based Approaches for Crop Mapping: A Case Study in South Korea 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.40 no.2, 2024년, pp.179 - 190

Ehsan Rahimi (Agricultural Science and Technology Institute, Andong National University) , Chuleui Jung (Agricultural Science and Technology Institute, Andong National University)

Abstract ▼ AI-Helper

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.

주제어

표/그림 (8)

그림 Fig. 1. Land use map of South Korea (Buchhorn et al., 2020) and the geographic location of the study area Angyo-ri in Andong City, South Korea.
표 Table 1. Distribution of Sentinel dataset across different months of the year
그림 Fig. 2. The NDVI dynamics curve shows nine phonological metrics. DOY: day of the year.
표 Table 2. Definition of the phonological indices calculated in the study (Filippa et al., 2016; Rahimi, 2024)
그림 Fig. 3. Examples of phenological maps calculated for the study area: (a) Min, (b) Max, (c) SOS, (d) EOS, (e) Timelength, (f) TMin, (g) TMax, and (h) AUC.
그림 Fig. 4. The NDVI dynamics curve showing different crops.
그림 Fig. 5. Crop classified maps of the study area: (a) MaxLike, (b) pixel-based phenology, and (c) segment-based phenology.
표 Table 3. The classification accuracy of the crop maps (based on 400 random points)

AI 본문요약
AI-Helper

제안 방법

The comparison of these two approaches allowed for a comprehensive evaluation of their performance in the study area. Additionally, the study employed field surveys and ground truth data to assess the accuracy of the classified maps, utilizing metrics such as overall accuracy and the kappa coefficient. The results of the study demonstrated that the phenology-based approach, particularly the segment-based method, achieved acceptable classification accuracies, indicating its effectiveness in differentiating various crop types.
These points were then cross-referenced with Google Earth imagery and the previously collected field data to determine the actual crop type for each point. By comparing the assigned crop type from the classified map with the actual crop type, and confusion matrix, the overall accuracy and kappa coefficients were calculated as evaluation metrics for the classification accuracy.
To perform this step, we conducted a field survey of the study area in August 2022 to visually identify and locate the different crop types present. Given that the study area was primarily dominated by rice crops, we focused on recording the locations of other crops using GPS devices. These recorded locations served as training samples in the ENVI software, which allowed us to train the MaxLike algorithm for the accurate classification of crop types in the study area.
In regions like South Korea with a high frequency of rainy and cloudy days, particularly during the peak vegetation growth period, and where agricultural parcels are predominantly small (less than 1 hectare), mapping crops using phenology-based approaches can be challenging. In this study, we aim to compare the effectiveness of a phenology-based approach and a supervised approach (Maximum likelihood classification termed MaxLike) algorithm, in crop classification.
They also observed that incorporating a multi-temporal approach enhanced the accuracy for specific crop types. The researchers then shifted towards using object-based methods after initially employing simple pixel-based classification. The comparison revealed that object-based methods exhibited higher ability and performance compared to pixel-based methods for land-cover classification (Ma et al.

이론/모형

In the supervised classification step, we employed the MaxLike algorithm at the pixel level to compare the results with the phenology-based approach. To perform this step, we conducted a field survey of the study area in August 2022 to visually identify and locate the different crop types present.
Given that the study area was primarily dominated by rice crops, we focused on recording the locations of other crops using GPS devices. These recorded locations served as training samples in the ENVI software, which allowed us to train the MaxLike algorithm for the accurate classification of crop types in the study area.

성능/효과

1(a) remarkable similarity can be observed, indicating the usefulness of the “Max” metric in crop classification
3e) map shows similarities with the “SOS” metric, both of which can assist in differentiating farms
5a) and pixel-based (Fig. 5b) classification maps compared to the segment-based map (Fig. 5c).
9 ha is in Andong City which is the capital of Gyeongbuk Province in South Korea. After conducting field surveys in the study area, several crop types were identified, including rice, pepper, potato, corn, and man-made structures such as greenhouses covered with plastic. These classes were deemed relevant for further analysis and were therefore considered in the subsequent stages of the study.
This process aimed to group similar pixels based on their spectral characteristics and spatial relationships. As a result, an output shapefile consisting of 4,699 segments was generated. Each segment represented a distinct region within the study area, characterized by homogeneous attributes such as color and texture.
The phenology-based approach leverages the patterns and trends in vegetation indices over time to differentiate between different crop types, leading to improved classification results. By considering spatial relationships and aggregating pixels into segments, the segment-based approach was able to capture more coherent and meaningful agricultural parcels, resulting in enhanced classification accuracy and smoother boundaries. These results highlight the importance of selecting appropriate classification methods that leverage temporal and spatial information for accurate crop mapping.
We proposed phenology-based classification as a potential approach to mitigate the challenge of the limited availability of cloud-free images. By incorporating phenological information from satellite imagery, we can enhance the accuracy and robustness of crop classification models, thereby overcoming the limitations posed by cloud cover and ensuring the reliability of land cover mapping efforts.
However, it shows limitations in accurately classifying other crops, such as Man-made and Pepper, where the classification accuracy is lower. Conversely, the Pixel-based and Segment-based classifications exhibit higher overall accuracy, ranging from 0.92 to 0.93, and kappa coefficients ranging from 0.81 to 0.85. These methods demonstrate improved performance in classifying all crop types, including Man-made, Pepper, and Potato, with higher accuracy compared to the MaxLike method.
In our study, we aimed to compare supervised classification with phenology-based approaches of pixel-based and segment-based methods, in regions with challenges such as rainy days and limited availability of cloud-free images, such as South Korea. Despite the advantage of supervised classification in terms of shorter processing time, we found that the MaxLike method yielded lower classification accuracy compared to phenology-based approaches. As a result, our study suggests using segment-based phenology mapping as a more suitable approach for regions like South Korea, where continuous cloud-free satellite images are less likely to be available.
This highlights the significance of having comprehensive and well-distributed temporal data for reliable analysis using phenology-based methods. Despite this limitation, the study findings indicated that phenology-based approaches still outperformed supervised methods in regions with frequent rainfall and cloudy conditions, such as South Korea. This suggests that the inherent temporal dynamics captured by phenological metrics can compensate for the difficulties posed by adverse weather conditions, leading to improved accuracy in classifying crops compared to traditional supervised methods.
However, we encountered a limitation in using phenology-based approaches to determine suitable thresholds for differentiating crops. Due to a lack of adequately sampled NDVI data at critical time points, there was a possibility of missing valuable information, making it challenging to establish precise classification thresholds. This highlights the significance of having comprehensive and well-distributed temporal data for reliable analysis using phenology-based methods.
The phenology-based approach, utilizing phenological metrics derived from remote sensing data, showcased a higher ability to accurately classify different crops. In contrast, the pixel-based method exhibited lower performance in crop classification than segment-based. The findings suggest that incorporating phenological information and temporal dynamics into the classification process can enhance the accuracy and effectiveness of crop mapping.
In particular, the MaxLike method demonstrates high accuracy in classifying Rice, with values ranging from 0.89 to 0.93. However, it shows limitations in accurately classifying other crops, such as Man-made and Pepper, where the classification accuracy is lower.
No potential conflict of interest relevant to this article was reported.
These methods demonstrate improved performance in classifying all crop types, including Man-made, Pepper, and Potato, with higher accuracy compared to the MaxLike method. Overall, the Segment-based classification shows greater consistency and accuracy across all crop classes, suggesting their effectiveness in accurately mapping land cover types within the study area.
By utilizing these two techniques, we aimed to map crops based on their phenological characteristics. The comparison between the pixel-based and segment-based approaches allowed us to assess their respective abilities and determine which technique performed better in this study.
The researchers then shifted towards using object-based methods after initially employing simple pixel-based classification. The comparison revealed that object-based methods exhibited higher ability and performance compared to pixel-based methods for land-cover classification (Ma et al., 2017). However, supervised classification in remote sensing does have some important weaknesses.
In contrast, the pixel-based method exhibited lower performance in crop classification than segment-based. The findings suggest that incorporating phenological information and temporal dynamics into the classification process can enhance the accuracy and effectiveness of crop mapping. The phenology-based approach leverages the patterns and trends in vegetation indices over time to differentiate between different crop types, leading to improved classification results.
The findings suggest that incorporating phenological information and temporal dynamics into the classification process can enhance the accuracy and effectiveness of crop mapping. The phenology-based approach leverages the patterns and trends in vegetation indices over time to differentiate between different crop types, leading to improved classification results. By considering spatial relationships and aggregating pixels into segments, the segment-based approach was able to capture more coherent and meaningful agricultural parcels, resulting in enhanced classification accuracy and smoother boundaries.
This study revealed that among the methods evaluated, the phenology-based approach demonstrated superior performance compared to the classical supervised. The phenology-based approach, utilizing phenological metrics derived from remote sensing data, showcased a higher ability to accurately classify different crops. In contrast, the pixel-based method exhibited lower performance in crop classification than segment-based.
Additionally, the study employed field surveys and ground truth data to assess the accuracy of the classified maps, utilizing metrics such as overall accuracy and the kappa coefficient. The results of the study demonstrated that the phenology-based approach, particularly the segment-based method, achieved acceptable classification accuracies, indicating its effectiveness in differentiating various crop types.
, 2005). These findings highlight the valuable insights that can be gained by utilizing crop phenology in agricultural remote sensing and its potential for enhancing mapping and monitoring capabilities.
85. These methods demonstrate improved performance in classifying all crop types, including Man-made, Pepper, and Potato, with higher accuracy compared to the MaxLike method. Overall, the Segment-based classification shows greater consistency and accuracy across all crop classes, suggesting their effectiveness in accurately mapping land cover types within the study area.
This suggests that the inherent temporal dynamics captured by phenological metrics can compensate for the difficulties posed by adverse weather conditions, leading to improved accuracy in classifying crops compared to traditional supervised methods. These results underscore the potential of phenology-based approaches in regions with diverse weather patterns, where relying solely on supervised methods may be less effective due to factors like cloud cover and atmospheric variations. However, it is crucial to acknowledge the limitations of the phenology-based approach, particularly the need for comprehensive and well-distributed temporal data to ensure accurate determination of thresholds and reliable crop classification.
, 2020). These satellites provide 10-meter resolution data and revisit the same location every five days, which has significantly improved the acquisition of cloud-free images. Despite this progress, retrospective analyses using such dense high-resolution datasets are still not readily accessible.
(2012) found that support vector machine classifiers with specific kernels were particularly successful in classifying crops in their dataset. They also observed that incorporating a multi-temporal approach enhanced the accuracy for specific crop types. The researchers then shifted towards using object-based methods after initially employing simple pixel-based classification.
As a result, our study suggests using segment-based phenology mapping as a more suitable approach for regions like South Korea, where continuous cloud-free satellite images are less likely to be available. This highlights the potential of phenology-based methods in overcoming the limitations posed by adverse weather conditions and limited data availability for accurate crop classification in such regions.
This study revealed that among the methods evaluated, the phenology-based approach demonstrated superior performance compared to the classical supervised. The phenology-based approach, utilizing phenological metrics derived from remote sensing data, showcased a higher ability to accurately classify different crops.
Despite this limitation, the study findings indicated that phenology-based approaches still outperformed supervised methods in regions with frequent rainfall and cloudy conditions, such as South Korea. This suggests that the inherent temporal dynamics captured by phenological metrics can compensate for the difficulties posed by adverse weather conditions, leading to improved accuracy in classifying crops compared to traditional supervised methods. These results underscore the potential of phenology-based approaches in regions with diverse weather patterns, where relying solely on supervised methods may be less effective due to factors like cloud cover and atmospheric variations.
Each method’s accuracy is evaluated for various crop classes, including Rice, Man-made, Pepper, Potato, and Corn. Under the MaxLike classification, the overall accuracy ranges from 0.89 to 0.93, with corresponding kappa coefficients between 0.74 and 0.85. The kappa coefficient is a measure of agreement between the classified maps and the ground truth data, with values closer to 1 indicating a higher level of agreement.

후속연구

Despite the advantage of supervised classification in terms of shorter processing time, we found that the MaxLike method yielded lower classification accuracy compared to phenology-based approaches. As a result, our study suggests using segment-based phenology mapping as a more suitable approach for regions like South Korea, where continuous cloud-free satellite images are less likely to be available. This highlights the potential of phenology-based methods in overcoming the limitations posed by adverse weather conditions and limited data availability for accurate crop classification in such regions.
, 2020). By leveraging these datasets, researchers can gain comprehensive insights into the temporal patterns and changes in crop growth stages overtime. Therefore, we used Sentinel-2A images from the year 2022 for both phenology-based and supervised classification approaches.
This study compared the application of phenology-based approaches and supervised classification techniques for crop mapping in a specific study area. By utilizing remote sensing data and analyzing various phenology metrics, the study aimed to understand the effectiveness of these methods in accurately identifying and mapping different crops. The phenology-based approach relied on calculating NDVI values and extracting phenology metrics to characterize crop growth patterns.
These satellites provide 10-meter resolution data and revisit the same location every five days, which has significantly improved the acquisition of cloud-free images. Despite this progress, retrospective analyses using such dense high-resolution datasets are still not readily accessible. The availability of Sentinel-2 images provides a valuable resource for acquiring time series imagery.
However, it is crucial to acknowledge the limitations of the phenology-based approach, particularly the need for comprehensive and well-distributed temporal data to ensure accurate determination of thresholds and reliable crop classification. Future studies could focus on enhancing data collection strategies and developing robust techniques for threshold determination to further enhance the performance of phenology-based approaches in challenging environmental conditions.
In the supervised classification step, we employed the MaxLike algorithm at the pixel level to compare the results with the phenology-based approach. To perform this step, we conducted a field survey of the study area in August 2022 to visually identify and locate the different crop types present. Given that the study area was primarily dominated by rice crops, we focused on recording the locations of other crops using GPS devices.

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표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

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