Estimation of flood damage to rice production in North Korea in 1995

Okamoto, K.; Yamakawa, Shuji; Kawashima, Hiroyuki

doi:10.1080/014311698216332

Cited by 43 publications

(28 citation statements)

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“…Geographic Information System (GIS) data have been proved to be important to enhance the accuracy of land use and land cover classification [13,14]. RS data with coarse and medium resolution are widely used in rice cultivation research [12,[15][16][17][18][19][20][21][22][23][24][25][26]. However, the number of conducted studies on rice using high resolution RS images was limited in the past two decades [23,27,28].…”

Section: Introductionmentioning

confidence: 99%

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Zhao

Lenz-Wiedemann

Yuan

et al. 2015

IJGI

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Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error 237(RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

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Section: Introductionmentioning

confidence: 99%

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Zhao

Lenz-Wiedemann

Yuan

et al. 2015

IJGI

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“…Therefore it is very critical to obtain timely and accurate information about the location and disaster situation (Miranda et al 1988, Okamoto et al 1998. Although IRS AWiFS and IRS LISS-III satellite passes occurred over the Leh region on 7 August 2010, to obtain high resolution coverage and also to obtain cloud free data, emergency programming of both IRS Cartosat-1 and RISAT-2 over the Leh region was done, which was not a regular pass.…”

Section: Satellite Emergency Programmingmentioning

confidence: 99%

Potential of high resolution satellite data for disaster management: a case study of Leh, Jammu & Kashmir (India) flash floods, 2010

Bhatt

Rao

Manjusree

et al. 2011

Geomatics, Natural Hazards and Risk

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The town of Leh and its adjoining areas were affected by severe flash floods and mudslides triggered by a cloud burst over the Leh region on 6 August 2010. Identification of the area affected by flash floods is a challenging task under unfavourable weather conditions, collapse of communication systems and damage to transportation systems. Rapid satellite based assessment of high resolution satellite data from Indian Remote Sensing (IRS) satellites Cartosat-1, Cartosat-2B and RISAT-2 (microwave satellite) was carried out for identification of the locations affected by flash floods in the Leh region. Severe damage to roads, houses, canals and agricultural fields is observed from the analysis of Choglamsar, the worst affected. The present paper discusses pre-and post-event satellite based changes observed due to the flash floods and mudslides in the Leh region, in general and in Choglamsar village, in particular.

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“…Remote sensing is a practical way to map rice paddies and offers advantages over partial ground surveys [4]. In the monsoon season, Asian countries such as Thailand are often obscured by cloud cover.…”

Section: Introductionmentioning

confidence: 99%

Application of Fine Quad-Polarization Radarsat-2 Data and Support Vector Machines to Classify Off-Season Rice Paddies

Waisurasingha¹

2018

GEOMATE

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ABSTRACT:In monsoon areas in Asia, most seasonal rice paddies are destroyed annually by flooding. To secure rice production, farmers in these areas grow off-season rice crops, which can be produced only with irrigated water. However, due to prevailing drought conditions, the cultivation of off-season rice crops cannot commence simultaneously. The availability of irrigated water is the critical factor for farmers in deciding when to begin off-season cultivation. To assess off-season rice production, a map of the cultivated area is required. Using multi-polarization/multi-temporal RADARSAT-2 data, this study aims to estimate and map the off-season rice paddies. The potential of using support vector machine classification to map off-season rice paddies with C-band SAR data was also established. Multi-temporal fine quad-polarization RADARSAT-2 data can be effectively used to estimate off-season rice cultivated areas.

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Estimation of flood damage to rice production in North Korea in 1995

Cited by 43 publications

References 0 publications

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Potential of high resolution satellite data for disaster management: a case study of Leh, Jammu & Kashmir (India) flash floods, 2010

Application of Fine Quad-Polarization Radarsat-2 Data and Support Vector Machines to Classify Off-Season Rice Paddies

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