NEW MODIS VEGETATION INDEX FOR BORO RICE MODEL USING 3D PLOT AND K-NN: BANGLADESH HAOR REGION PERSPECTIVE

被引：0

作者：

Kalpoma, Kazi A. ^{[1
]}

Chowdhury, Anik ^{[1
]}

Arony, Nowshin Nawar ^{[2
]}

Nowshin, Mehjabin ^{[1
]}

Kudoh, Jun-ichi ^{[1
]}

机构：

[1] Ahsanullah Univ Sci & Technol, Dept CSE, Dhaka, Bangladesh

[2] Tohoku Univ, Ctr Northeast Asian Studies, Sendai, Miyagi, Japan

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

关键词：

Remote sensed satellite image; MODIS; Boro rice; Haor; Vegetation Index (VI); Normalized Vegetaion Index (NDVI); Enhanced Vegetation Index (EVI); Optimized Soil-Adjusted Vegetation Index (OSAVI);

D O I：

10.1109/igarss.2019.8898950

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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 efficient 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 F-1 score 0:5175.

引用

页码：7322 / 7325

页数：4