TY - GEN
T1 - New Modis Vegetation Index for Boro Rice Model Using 3d Plot and K-NN
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
AU - Kalpoma, Kazi A.
AU - Chowdhury, Anik
AU - Arony, Nowshin Nawar
AU - Nowshin, Mehjabin
AU - Kudoh, Jun Ichi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This paper demonstrates an approach to develop a prediction based model for forecasting Boro rice areas in the haor region of Bangladesh. Forecasting the rice areas can contribute in creating a centralized monitoring system for planning effi-cient storage and proper utilization methods. This leads to the development of proposing a new vegetation index (VI). The approach considers a new vegetation index combining NDVI (Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index 2) and OSAVI (Optimized Soil-Adjusted Vegetation Index) for latest version MODIS (version-6) data. The method will forecast total Boro rice areas at the beginning of the Boro season (Dec-Jan) which is more than 3 months earlier from harvesting time without using any ground truth data. 3 Dimensional plotting method and k-Nearest Neighbor classifier have been used on only sowing period (Dec-Jan) data to predict Boro rice pixels. Our new VI has achieved an accuracy of 72%, recall 0.7020, precision 0.4183 and F1 score 0.5175.
AB - This paper demonstrates an approach to develop a prediction based model for forecasting Boro rice areas in the haor region of Bangladesh. Forecasting the rice areas can contribute in creating a centralized monitoring system for planning effi-cient storage and proper utilization methods. This leads to the development of proposing a new vegetation index (VI). The approach considers a new vegetation index combining NDVI (Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index 2) and OSAVI (Optimized Soil-Adjusted Vegetation Index) for latest version MODIS (version-6) data. The method will forecast total Boro rice areas at the beginning of the Boro season (Dec-Jan) which is more than 3 months earlier from harvesting time without using any ground truth data. 3 Dimensional plotting method and k-Nearest Neighbor classifier have been used on only sowing period (Dec-Jan) data to predict Boro rice pixels. Our new VI has achieved an accuracy of 72%, recall 0.7020, precision 0.4183 and F1 score 0.5175.
KW - Boro rice
KW - Enhanced Vegetation Index (EVI)
KW - Haor
KW - MODIS
KW - Normalized Vege-taion Index (NDVI)
KW - Opti-mized Soil-Adjusted Vegetation Index (OSAVI)
KW - Remote sensed satellite image
KW - Vegetation Index (VI)
UR - http://www.scopus.com/inward/record.url?scp=85077682713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077682713&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8898950
DO - 10.1109/IGARSS.2019.8898950
M3 - Conference contribution
AN - SCOPUS:85077682713
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 7322
EP - 7325
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 July 2019 through 2 August 2019
ER -