Use of GIS and Remote Sensing in Mapping Rice Areas and Forecasting Its Production at Large Geographical Extent
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AbstractRice is one of the staple foods for more than three billion people worldwide. Here, the overall objective was the development of rice area mapping and forecasting its production using primarily geographic information system (GIS) and remote sensing technology, and its implementation over a large geographical extent. In this thesis, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250m spatial resolution was used in conjunction with other GIS datasets during the period 2007-2012. For mapping the rice area during the entire growing season (i.e., January-May), the results demonstrated a reasonable agreements between the proposed method and ground-based estimates at both country level [i.e., relative error (RE) in the range -0.83 to 1.42%] and district-level (i.e., co-efficient of determination, r2 in the range 0.69 to 0.89) during the period 2010-2012. The rice yield forecasting consisted of two methods: (i) the first method was based on images acquired from plantation to the peak greenness stage, that is, six consecutive images during the growing season (i.e., January 1 to April 6/7); and (ii) the development of second method utilized two images during the initial (i.e., January 1 to January 16) and peak greenness (i.e., March 23/24 to April 6/7). In both methods, initially I delineated the rice area and then forecasted the yield before harvesting. The rice area mapping and forecasting its production based on the first method demonstrated good agreements between the model and ground-based area estimates during 2010-2012 [i.e., r2 ≥ 0.93; root mean square error (RMSE) in between 32,237 to 36,040 ha at the 23 district-levels; and RE in the range -0.26 to 0.50% at country level]. However, the spatial distribution of rice areas produced very well for all the districts except for six districts (i.e., average relative error of -3-43% using data acquired during the entire growing season and from plantation to the peak greenness). Also, good agreements were found, i.e., r2, RMSE, and RE were in the range of 0.71 to 0.77, 0.25 to 0.59 Mton/ha, and -0.21 to 14.65%, respectively between forecasted and ground-based yields estimates during 2010-2012 period. In addition, strong agreements were also observed using the second method while compared with ground-based area estimates during 2010-2012 [i.e., r2, RMSE, in between 0.93 to 0.95; 30,519 to 37,451 ha respectively at the 23 district-levels, respectively; and RE -2.87 to 3.60%, at the country-level]. The spatial distribution of rice area derived by the model demonstrated well for all 23 districts with the ground-based estimates (i.e., average RE under 13%). I also found good agreements (i.e., r2, RMSE, were in between 0.76 to 0.86; 0.21 to 0.29 Mton/ha at the 23 district-levels, respectively; and RE of 0.81 to 5.41% Mton/ha, at the country-level) between forecasted and ground-based yields during 2010-2012 period. Despite the effectiveness of my proposed methods, I strongly recommend that these methods should thoroughly be evaluated prior to implement in other geographical locations.
CitationMosleh, M. (2015). Use of GIS and Remote Sensing in Mapping Rice Areas and Forecasting Its Production at Large Geographical Extent (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/28608
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