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All Journal Jurnal Keteknikan Pertanian Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)
Taufiq Yuliawan
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Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Environmental Science

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Selecting The Most Optimum Sentinel-2A Based Vegetation Index to Estimate the Leaf Area Index of Three Rice Cultivars Oxa Aspera Endiviana; Impron; Yudi Setiawan; Harry Imantho; Slamet Widodo Sugiarto; Taufiq Yuliawan
Jurnal Keteknikan Pertanian Vol. 10 No. 3 (2022): Desember 2022
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.010.3.200-214

Abstract

The estimation of leaf area index (LAI) becomes important as LAI is one of the parameters in analyzing the crop growth model. Crop growth has different characteristics and it’s strongly influenced by environmental conditions and factors. The growth tends to occur in a short period and covers a large area. Therefore, an approach to analyzing the pattern of changes in crop growth based on LAI spatially is needed. Remote sensing offers an effective and efficient approach to monitoring crop growth characteristics, which can be done in a time series with a wide area coverage by detecting and monitoring the physical characteristics of the crop. The most famous and commonly used parameters to estimate LAI are vegetation indices which are usually calculated based on the ratio of the red and NIR wavelength, known as a spectral signature. The objectives of the research are to examine the Spatio-temporal correlation between LAI of three rice cultivars Sentinel-2A based vegetation indices and to select the most optimum vegetation index in estimating LAI. The field experiment was set up comprising 81 plots, each had a size of 10m x 10 m to resemble a pixel of Sentinel-2A imagery. The results of the analysis show that the vegetation index has a strong correlation with LAI. The Comparison of the four calculated vegetation indices in estimating LAI was performed using a linear regression model and followed by comparing R-squared, RMSE, and Correctness. In general, the EVI2 vegetation index provides the most optimum representation in capturing crop growth patterns based on LAI compared to NDVI, ARVI and SAVI vegetation indices calculated from Sentinel-2A satellite imagery indicated by the better-validated model with the result of RMSE value are 1.12 on V1, 1.11 on V2 and 0.70 on V3. The result of EVI2 Correctness also showed the highest value compared to the other vegetation indices with values of more than 60%, 64.15% on V1, 65.51% on V2, and 78.69% on V3. Further analysis by separating two growth stages could overcome the bias that appears in the LAI data for one life of the crop cycle which is indicated by the decrease of RMSE value on each cultivar planted for both vegetative and generative phases, except for cultivar V3 for the generative phase. The separation of data into two growth stages also increase the percentage value of correctness reaching a number above 60% and there was a value that reached 87%.
Visible Band Optimation of Unmanned Aerial Vehicle for Estimating Synthetic NDVI on Rice Vegetation Azizah Nur Islam Al Rosyid; I Wayan Astika; Yudi Setiawan; Kikin Hamzah Muttaqin; Impron; Harry Imantho; Slamet Widodo Sugiarto; Oxa Aspera Endiviana; Taufiq Yuliawan
Jurnal Keteknikan Pertanian Vol. 10 No. 3 (2022): Desember 2022
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.010.3.281-290

Abstract

Serntinel 2A provide Normalized Difference Vegetation Index (NDVI) to be used as an estimate of soil fertility, plant varieties and productivity. The weakness of satellite data is that the data obtained is often inaccurate due to cloud cover, especially in tropical countries with high rainfall such as Indonesia. The use of unmanned aerial vehicle (UAV) as an alternative data have limitation as it captured Red Green Blue (RGB) imagery. The research was conducted from July to September 2020 at Pasir Kaliki Village, District of Rawamerta, Karawang Regency, West Java province. The study has discovered that NDVI showed higher number in result of vegetation index compared to Normalized Green-Red Difference Index (NGRDI) with correlation coefficient is 0.94. The regression model resulted as y=4.78x+0.38 and MAPE value expresses as 26.74%, where the regression model with Pearson’s correlation coefficient value is 0.88. 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 0.74; 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.
Comparisons of Growth, Yield, and Meteorological Properties of Rice Canopy under Double-Row (Jajar Legowo and Jejer Manten) and Tile Transplanting Systems Taufiq Yuliawan; Nazif Ichwan; Augustine Ukpoju; Fadli Irsyad; Hiroki Oue
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 14 No 2 (2024): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.14.2.325

Abstract

Over the past decade, the Indonesian government has been recommending double-row transplanting systems, i.e., Jajar Legowo (JL) and Jejer Manten (JM), to increase rice production. These systems have been reported to obtain higher yield of Indica rice cultivars than the standard tile (TL) system, primarily due to the border effect. However, scientific investigations of the border effect in these transplanting systems remain limited. This study was conducted during the summer seasons in Japan in 2022 and 2023 to observe the plant growth and yield of a Japonica rice cultivar, Nikomaru, and to investigate differences in meteorological properties, such as intercepted solar radiation (SRint). The study found that higher plant competition for light in JM and JL caused a lower tiller number and above-ground biomass (Wt) per hill than in TL. However, due to denser planting, JM and JL obtained higher tiller numbers, Wt, and sink capacity per unit area than TL. Additionally, the denser canopy in JM and JL compared to TL increased SRint by the whole canopy, even though the space between the double rows was wider. Although SRint was not significantly different amongthe systems, higher normalized difference vegetation index in JM and JL was strongly correlated with a higher sink filling rate due to the healthier canopy absorbing more solar radiation. The synergistic effect of higher sink capacity and sink filling rate led to higher yields in JM and JL than in TL. This study suggests that JM is the best transplanting system for increasing rice yield.
Co-Authors Augustine Ukpoju Azizah Nur Islam Al Rosyid Fadli Irsyad Harry Imantho Hiroki Oue I Wayan Astika Impron Kikin Hamzah Muttaqin Nazif Ichwan Oxa Aspera Endiviana Oxa Aspera Endiviana Slamet Widodo Sugiarto Yudi Setiawan