Tracking the dynamics of paddy rice cultivation practice through MODIS time series and PhenoRice algorithm

2021 ◽  
Vol 307 ◽  
pp. 108538
Author(s):  
Nirajan Luintel ◽  
Weiqiang Ma ◽  
Yaoming Ma ◽  
Binbin Wang ◽  
Jie Xu ◽  
...  
2021 ◽  
Vol 259 ◽  
pp. 112394
Author(s):  
Huijin Yang ◽  
Bin Pan ◽  
Ning Li ◽  
Wei Wang ◽  
Jian Zhang ◽  
...  

2018 ◽  
Vol 18 (11) ◽  
pp. 2859-2876 ◽  
Author(s):  
Nguyen Van Khanh Triet ◽  
Nguyen Viet Dung ◽  
Bruno Merz ◽  
Heiko Apel

Abstract. Flooding is an imminent natural hazard threatening most river deltas, e.g. the Mekong Delta. An appropriate flood management is thus required for a sustainable development of the often densely populated regions. Recently, the traditional event-based hazard control shifted towards a risk management approach in many regions, driven by intensive research leading to new legal regulation on flood management. However, a large-scale flood risk assessment does not exist for the Mekong Delta. Particularly, flood risk to paddy rice cultivation, the most important economic activity in the delta, has not been performed yet. Therefore, the present study was developed to provide the very first insight into delta-scale flood damages and risks to rice cultivation. The flood hazard was quantified by probabilistic flood hazard maps of the whole delta using a bivariate extreme value statistics, synthetic flood hydrographs, and a large-scale hydraulic model. The flood risk to paddy rice was then quantified considering cropping calendars, rice phenology, and harvest times based on a time series of enhanced vegetation index (EVI) derived from MODIS satellite data, and a published rice flood damage function. The proposed concept provided flood risk maps to paddy rice for the Mekong Delta in terms of expected annual damage. The presented concept can be used as a blueprint for regions facing similar problems due to its generic approach. Furthermore, the changes in flood risk to paddy rice caused by changes in land use currently under discussion in the Mekong Delta were estimated. Two land-use scenarios either intensifying or reducing rice cropping were considered, and the changes in risk were presented in spatially explicit flood risk maps. The basic risk maps could serve as guidance for the authorities to develop spatially explicit flood management and mitigation plans for the delta. The land-use change risk maps could further be used for adaptive risk management plans and as a basis for a cost–benefit of the discussed land-use change scenarios. Additionally, the damage and risks maps may support the recently initiated agricultural insurance programme in Vietnam.


2021 ◽  
Vol 13 (19) ◽  
pp. 3994
Author(s):  
Lu Xu ◽  
Hong Zhang ◽  
Chao Wang ◽  
Sisi Wei ◽  
Bo Zhang ◽  
...  

The elimination of hunger is the top concern for developing countries and is the key to maintain national stability and security. Paddy rice occupies an essential status in food supply, whose accurate monitoring is of great importance for human sustainable development. As one of the most important paddy rice production countries in the world, Thailand has a favorable hot and humid climate for paddy rice growing, but the growth patterns of paddy rice are too complicated to construct promising growth models for paddy rice discrimination. To solve this problem, this study proposes a large-scale paddy rice mapping scheme, which uses time-series Sentinel-1 data to generate a convincing annual paddy rice map of Thailand. The proposed method extracts temporal statistical features of the time-series SAR images to overcome the intra-class variability due to different management practices and modifies the U-Net model with the fully connected Conditional Random Field (CRF) to maintain the edge of the fields. In this study, 758 Sentinel-1 images that covered the whole country from the end of 2018 to 2019 were acquired to generate the annual paddy rice map. The accuracy, precision, and recall of the resultant paddy rice map reached 91%, 87%, and 95%, respectively. Compared to SVM classifier and the U-Net model based on feature selection strategy (FS-U-Net), the proposed scheme achieved the best overall performance, which demonstrated the capability of overcoming the complex cultivation conditions and accurately identifying the fragmented paddy rice fields in Thailand. This study provides a promising tool for large-scale paddy rice monitoring in tropical production regions and has great potential in the global sustainable development of food and environment management.


EUGENIA ◽  
2017 ◽  
Vol 23 (1) ◽  
Author(s):  
Moulwy F. Dien ◽  
Daisy S. Kandowangko

ABSTRACTThe experiment was conducted using a survey method at 4 locations/district in the Southeast Minahasa Regency is Belang,Tombatu, North Tombatu, and East Tombatu. Each location/districts determined three paddy fields (repeats) as a place of observation and sampling. The study lasted for 10 months ie from January to October 2015. Sampling is done diagonally to the respective fields. So one rice field consists of 5 sub-plot as a point of sampling Samples are larvae present in the leaf roll. Implementation of the sampling carried out on rice plants vegetative phase once a week for 6 weeks. The results showed that the average population of C. medinalis (per-10 clumps) on paddy rice cultivation in Southeast Minahasa Regency highest found in the location of the North Tombatu 10.99, then Eastern Tombatu 10.44, Belang 10.43 and lows in the Tombatu 0.94. Observations of percentage of pests C. medinalis highest in Southeast Minahasa Regency found in the sample locations in the North Tombatu which reached 33.95%, Belang 32.51%, Eastern Tombatu 31.86%, and the lowest in the Tombatu 4.08%. Keywords : rice, Cnaphalocrosis medinalis


2014 ◽  
Vol 494-495 ◽  
pp. 119-128 ◽  
Author(s):  
Alessandra Fusi ◽  
Jacopo Bacenetti ◽  
Sara González-García ◽  
Annamaria Vercesi ◽  
Stefano Bocchi ◽  
...  

2016 ◽  
Vol 8 (5) ◽  
pp. 434 ◽  
Author(s):  
Kersten Clauss ◽  
Huimin Yan ◽  
Claudia Kuenzer
Keyword(s):  

1963 ◽  
Vol 1963 (2) ◽  
pp. 94-99
Author(s):  
Masuzi MIYAHARA ◽  
Masao ARAI
Keyword(s):  

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