Judul: Karakterisasi Indeks Band Warna Tampak dari Citra Aerial Nirawak untuk Mengestimasi False NDVI pada Vegetasi Padi.
Abstrak: Indonesia’s climate conditions, characterized by the presence of clouds during the rainy season and most of the short-period crops (maize, soybean, rice) established, cloud are main problem for satellite to generate clear imagery. The use of aircraft or drones can also be used in remote sensing as an alternative data. However, it is necessary to develop other technologies based on aerial photographs from UAV or drone to generate more frequent information, reliable and detailed to support decision-making for each crop. Hence, drone with RGB imagery could be optimized to generate information as problem solver similar with Normalized Digital Vegetation Index. Thus, this study aimed at examining the correlation between vegetation indexes resulted from satellite Sentinel 2A imagery and visible band (RGB) imagery from UAV, estimating NDVI value by using false NDVI (NGRDI) generated from visible band, and estimating the rice growth phase (days after transplanting) by approaching NDVI and false NDVI values. By using drone flight at 60 meter above the ground with interval 10 days following the schedule of Sentinel 2A crossing the research location at Pasir Kaliki village, the imagery were acquired consistently. The study has discovered that NDVI showed higher number in result of vegetation index compared to NGRDI with correlation coefficient is 0.944625. The regression model resulted as y=4.7722x+0.3845 and MAPE value expresses as 26.74%, where the regression model with Pearson’s correlation coefficient value is 0.877885. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice vegetation map reveal a high mapping accuracy with the corresponding R2 being as high as 0.7429; however, the mapped rice vegetation accuracy might influenced by other physical factors such as water reflectant, sunlight and the RGB camera limitation itself. Nonetheless, the highest values of NGRDI only reach 0.2 while NDVI can attain at 0.9 at the peak of vegetative phase of rice growth stage. This means that Green Band have limitation in detecting vegetation index. In relation to the different approaches performed, it is noted that the average trend line on both NDVI and NGRDI shown the similarity tendency in all growth stage., Kondisi iklim Indonesia yang ditandai dengan adanya awan pada musim hujan dan sebagian besar tanaman berumur pendek (jagung, kedelai, padi) terbentuk, awan merupakan masalah utama bagi satelit untuk menghasilkan citra yang jelas. Penggunaan pesawat terbang atau drone juga dapat digunakan dalam penginderaan jauh sebagai data alternatif. Namun, perlu dikembangkan teknologi lain berdasarkan foto udara dari UAV atau drone untuk menghasilkan informasi yang lebih sering, andal, dan terperinci untuk mendukung pengambilan keputusan untuk setiap tanaman. Oleh karena itu, drone dengan citra RGB dapat dioptimalkan untuk menghasilkan informasi sebagai pemecah masalah yang serupa dengan Normalized Digital Vegetation Index. Oleh karena itu, penelitian ini mengkaji korelasi antara indeks vegetasi, dari citra satelit Sentinel 2A dan citra pita tampak (RGB) dari UAV, mengestimasi nilai NDVI dengan menggunakan false NDVI (NGRDI) yang dihasilkan dari pita tampak, dan mengestimasi fase pertumbuhan padi (hari setelah transplantasi) dengan mendekati nilai NDVI dan NDVI palsu. Citra diperoleh secara konsisten dengan menggunakan drone terbang pada ketinggian 60 meter di atas permukaan tanah dengan
Keyword: NDVI, NGRDI, Unmanned Aerial Vehicle, Sentinel, Rice Growth Stage