Paddy rice mapping in red river delta region using landsat 8 images: Preliminary results

Chuc, Man Duc; Anh, Nguyen Hoang; Thuy, Nguyen Thanh; Hung, Bui Quang; Thanh, Nguyen Thi Nhat

doi:10.1109/kse.2017.8119460

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…In comparison to the statistics, our rice map difference is 1879 ha which corresponds to 0.3% in total if excluding Quang Ninh province, since the largest error of about 10,665 ha was reported above. Besides, our rice area estimation difference compared to the statistics was more positive than the available studies in the RRD [29,30,33] (see Table 7). For SAR data, Duy et al reported that the maximum planted area for all seasons in 5 years from 2007 to 2011 for 10 provinces excluding Quang Ninh with a total difference compared to the statistical database of 67,500 hectares (from 900 hectares to 20,400 hectares) [33].…”

Section: The Combination Of Two Channels Following Equation Vv−v Hmentioning

confidence: 59%

“…The estimated rice area reported in the study is 216,784 hectares for 2016, which is about 18,916 hectares of difference in comparison to GSO statistics. Regarding optical satellite images, Landsat 8 was used with the XGBoost classifier to obtain a rice map for the RRD in 4 years from 2013 to 2016 excluding Quang Ninh [29]. The rice maps in the study were underestimated in the statistics (38,554 hectares from 2013 to 2016).…”

Section: The Combination Of Two Channels Following Equation Vv−v Hmentioning

confidence: 96%

“…A combination of data from Landsat-8, Sentinel-1A and PALSAR-2 data was utilized to produce a rice map using a Random Forest (RF) classifier for greenhouse gas emission evaluation in the RRD [28]. Due to the high frequent cloud cover in this area, Chuc et al proposed a compositing method for Landsat-8 images, which is used for rice mapping with XGBoost classifier later [29]. Other studies utilized time-series SAR imagery for rice classification using ML techniques including SVM [30,31], Simple Linear Iterative Clustering super-pixel algorithm [32], decision tree based on phenology analysis and the thresholding approach [33].…”

Section: Monitoring Rice and Flooding Using Satellite Imagesmentioning

confidence: 99%

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Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery

Anh

Man

et al. 2019

Remote Sensing

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The Red River Delta (RRD), including 11 provinces, is one of the four largest rice-growing areas in Vietnam. Tropical storms often occur and cause serious flooding from May to October annually in the RRD, which strongly affects the productivity of the summer–autumn rice, one of two main rice crops. Therefore, the rapid assessment of damaged rice area by flooding inundation is critical for farmers and the government. In this study, we proposed a methodology for quick estimation of rice areas damaged by flooding using Sentinel 1A (S1A) imagery. Firstly, the latest rice map was produced. Then, a Near Real-Time (NRT) flood map, which is estimated from S1A images at the closest time to a flooding event, was generated by excluding the yearly permanent map from the temporal water map. Our experiment was conducted for the assessment of damaged rice area by flooding from the tropical storm named Son-Tinh, which happened on 19–21 July 2018. A Support Vector Machine (SVM) classifier was applied on time-series of S1A VV with VH data (VVVH) to obtain a rice map for the winter-spring season of 2018 with 90.5% Overall Accuracy (OA) and 2.37% difference (12,544 ha) from the General Statistics Office (GSO) of Vietnam’s reports for the whole region. Then, the Otsu thresholding method was applied for permanent water surface extraction and NRT flood mapping. The estimated damaged area was compared to available provincial and communal statistics for validation and further analysis. Right after the Son-Tinh storm, the estimation of inundated rice was approximately 50% of the total rice area in the RRD (271,092 ha). As a result, rice damage level strongly corresponds to the inundation period. In addition, the rice-flooding frequency map over the RRD was estimated to show rice fields suffering a high risk of flooding during the rainy season in the RRD. Our experiment’s results highlight the potential of using Synthetic-Aperture Radar (SAR) imagery for fast monitoring and assessment of paddy rice areas affected by flooding at a large scale in the RRD region.

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Section: The Combination Of Two Channels Following Equation Vv−v Hmentioning

confidence: 59%

Section: The Combination Of Two Channels Following Equation Vv−v Hmentioning

confidence: 96%

Section: Monitoring Rice and Flooding Using Satellite Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery

Anh

Man

et al. 2019

Remote Sensing

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Paddy rice mapping in Red River Delta, Vietnam, using Sentinel 1/2 data and machine learning algorithms

Ngo

Bui

Phan

et al. 2023

Journal of Spatial Science

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Land-cover Mapping from Sentinel Time-Series Imagery on the Google Earth Engine: A Case Study for Hanoi

Bui¹,

Phan²,

Nguyen³

2020

2020 7th NAFOSTED Conference on Information and Computer Science (NICS)

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Paddy rice mapping in red river delta region using landsat 8 images: Preliminary results

Cited by 4 publications

References 6 publications

Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery

Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery

Paddy rice mapping in Red River Delta, Vietnam, using Sentinel 1/2 data and machine learning algorithms

Land-cover Mapping from Sentinel Time-Series Imagery on the Google Earth Engine: A Case Study for Hanoi

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