scholarly journals Developing flood vulnerability curve for rice crop using remote sensing and hydrodynamic modeling

2021 ◽  
Vol 54 ◽  
pp. 102058
Author(s):  
Vempi Satriya Adi Hendrawan ◽  
Daisuke Komori
Keyword(s):  
Remote Sensing ◽  
Hydrodynamic Modeling ◽  
Rice Crop ◽  
Flood Vulnerability ◽  
Vulnerability Curve

Prioritization of Flood Vulnerability Zones Using Remote Sensing and GIS for Hydrological Modelling

2018 ◽  
Vol 68 (2) ◽  
pp. 176-190
Author(s):  
Mohd Talha Anees ◽  
K. Abdullah ◽  
M.N.M. Nawawi ◽  
Nik Norulaini Nik A. Rahman ◽  
Ahmad Zuhdi Ismail ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hydrological Modelling ◽  
Remote Sensing And Gis ◽  
Flood Vulnerability

scholarly journals Flood Vulnerability Assessment of Afikpo South Local Government Area, Ebonyi State, Nigeria

2019 ◽  
pp. 331-342
Author(s):  
Endurance Okonufua ◽  
Olabanji O. Olajire ◽  
Vincent N. Ojeh
Keyword(s):  
Remote Sensing ◽  
Local Government ◽  
Multicriteria Analysis ◽  
Drainage Density ◽  
Fuzzy Membership ◽  
Rainfall Runoff ◽  
Flood Vulnerability ◽  
Vulnerability Map ◽  
Flood Vulnerability Assessment ◽  
Vulnerable Zones

The study was conducted in Afikpo South Local Government covering a total area of 331.5km2. Remote sensing and Geographic Information System (GIS) were integrated with multicriteria analysis to delineate the flood vulnerable areas. Seven criteria were considered; rainfall, runoff, slope, distance to drainage, drainage density, landuse and landcover, and soil. The various criteria were fit into fuzzy membership classes based on their effect in causing flood. The fuzzy members of all criteria were then overlaid to generate the flood vulnerability map. The result of the flood vulnerability map shows that very low vulnerable zones cover 86.7% of the total area, low vulnerable zones cover 1.6% of the total area, moderate vulnerable zones cover 2.17% of the total area, highly vulnerable zones cover 2.3% of the total area while very highly vulnerable zones cover 7.3% of the total area. Built up was used as a measure of the effect of flooding on human lives and properties in Afikpo South Local Government. Built up covers a total area of 38.6km2. Over sixty eight (69.8%) of built up lies in very low vulnerable zone, 3% lies in low vulnerable zone, 3.7% lies in moderate vulnerable zone, 0.6% lies in highly vulnerable zone and 17.9% lies in very highly vulnerable zone. The study provides information on target areas that may be affected by flood in Afikpo South Local Government. This information is useful for decision making on flood early warning and preparedness as well as in mitigation preparedness within Afikpo LGA.

scholarly journals Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

2015 ◽  
Vol XL-7/W3 ◽  
pp. 85-92 ◽  
Author(s):  
S. Pazhanivelan ◽  
P. Kannan ◽  
P. Christy Nirmala Mary ◽  
E. Subramanian ◽  
S. Jeyaraman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Accuracy Assessment ◽  
Rice Crop ◽  
Policy Decisions ◽  
Simulation Accuracy ◽  
Sustainable Solutions ◽  
Rice Area ◽  
Sar Imagery ◽  
Crop Parameters

Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87- 92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85- 96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

scholarly journals QUALITY CHECKING OF CROP CUTTING EXPERIMENTS USING REMOTE SENSING DATA: A CASE STUDY FOR RICE CROP IN ODISHA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 461-466
Author(s):  
S. K. Dubey ◽  
D. Mandloi ◽  
A. S. Gavli ◽  
A. Latwal ◽  
R. Das ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Rice Crop ◽  
High Yield ◽  
Equal Proportion ◽  
Yield Data ◽  
Crop Condition ◽  
Crop Variety ◽  
Cutting Experiments

<p><strong>Abstract.</strong> Under Pradhan Mantri Fasal Bima Yojana (PMFBY), a large number of Crop Cutting Experiments (CCEs) were conducted by Odisha State for Kharif Rice in the year 2016 and 2017. The present study was carried out to examine the quality of the performed CCEs using statistical methods and Remote Sensing (RS) technique. Total 24389 and 34725 CCEs were conducted. After removing outliers, 22083 and 26848 CCE points were analyzed for the year 2016 and 2017, respectively. Multi-date RISAT-1 (2016) and Sentinel-1A (2017) satellite data were used for generating the Kharif Rice crop mask, which was used to get NDVI and NDWI values for Rice pixels, from MODIS VI products. The values of these indices were divided into four strata from highest A, followed by B, C, and D (Lowest Value) based on the range (minimum and maximum) of values. The CCE based yield data were then divided into four yield strata of equal proportion. Yield and RS (NDVI+NDWI) based strata were combined to examine whether the CCE Points having high yield fall under good NDVI zone or vice versa. The results showed that there was strong match between CCE strata and the vegetation index strata in both the years. Therefore, it could be be concluded that RS based indices have the capability to assess the quality/accuracy of CCEs. Furthermore, the large variety of information available with CCEs such that crop variety, crop condition, water sources, stress conditions etc., can be used as input parameters to train any model to predict better results.</p>

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