scholarly journals PENGEMBANGAN PENGGUNAAN PENGINDERAAN JAUH UNTUK ESTIMASI PRODUKSI PADI (STUDI KASUS KABUPATEN BEKASI)

2019 ◽  
Vol 19 (1) ◽  
pp. 6-12
Author(s):  
Eka Rudiana ◽  
Ernan Rustiadi ◽  
Muhammad Firdaus ◽  
Dede Dirgahayu
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Rice Yield ◽  
Equation Model ◽  
Rice Production ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Enhanced Vegetation Index ◽  
The One ◽  
The Relationship

The utilization of remote sensing imagery such Landsat-8 (OLI) to estimate harvested area and yield using Enhanced Vegetation Index (EVI) parameter is a new approach to estimate regional rice production. Based on the analysis of the satellite imagery acquisition during May-August 2015, the estimation of rice harvested area in Bekasi District during July-October 2015 is 15.86 thousand ha or 7.74 thousand ha (32.79%) lower than BPS figures in the same period. Based on the relationship between yield (from the crop cutting survei, BPS) and EVI maximum, the equation model for rice yield estimation is: Yield (qu ha-1) = 36.818 + 44.965 EVImax. R2 value is 0.809. Based on the model, the estimation of rice yield in Bekasi District during July-October 2015 is 47.40 qu ha-1. Compared to the data published by BPS, the result is 12.66 qu ha-1 lower than the yield figure in subround I 2015, 6.77 qu ha-1 lower than the one in subround II 2015, 10.15 qu ha-1 lower than the one subround III 2015, and 6.62 qu ha-1 lower than the one in January-December 2015. Meanwhile, based on satellite imagery analysis, the estimation of rice production in the period of July-October 2015 is 75.16 thousand tons of GKG or 55.35 thousand tons of GKG (42.41%) lower than BPS figures during the same period. Keywords: Enhanced Vegetation Index, Landsat-8 (OLI), rice production estimation

scholarly journals Integration of Leaf Water Content Index (LWCI) and Enhanced Vegetation Index (EVI) for Stress Detection of Rice Plant Using Landsat 8 Satellitte Imagery

2019 ◽  
Author(s):  
Abdi Sukmono ◽  
Arief Laila Nugraha ◽  
Hana Sugistu Firdaus
Keyword(s):  
Water Content ◽  
Satellite Imagery ◽  
Stress Level ◽  
Agricultural Intensification ◽  
Vegetation Index ◽  
Leaf Water ◽  
Rice Fields ◽  
Landsat 8 ◽  
Enhanced Vegetation Index ◽  
Rice Consumption

Rice is the main staple food for Indonesian society. Almost 95% of Indonesians consume rice. Along with the increasing population in Indonesia, the level of rice consumption each year has increased. But on the other hand, the amount of paddy fields has decreased due to the development of settlements and industry. Consequently, the business of fulfilling rice consumption needs should prioritize agricultural intensification method. This agricultural intensification program requires good supporting data. One of the supporting data required is a plant health condition that can be represented in data on rice stress levels. Monitoring the stress level of rice plants can be done using remote sensing methods based on satellite imagery. One of them is Landsat-8 satellite imagery with certain algorithm. In this research, a modification algorithm of Rice Paddy Stress Index (RPSI) was obtained by integrating Leaf Water Canopy Index (LWCI) and Enhanced Vegetation Index (EVI). LWCI is used as a representation of water content in vegetation and EVI is used as a representation of the greenish level of plants associated with chlorophyll content. Plants that experience a decrease in health will decrease the content of chlorophyll and water. The results of this study indicate that in 2015 planting season 2 in Kendal Regency there are 1696.26 ha of rice fields indicated experiencing stress and 3493.85 Ha of rice fields have a potential stress. The result of validation test shows that RPSI algorithm method has 75% accuracy for determining rice stress level.

scholarly journals Application of geo-informatics engineering to estimate above-ground carbon sequestration

2018 ◽  
Vol 38 (3) ◽  
pp. 303-308
Author(s):  
Teerawong Laosuwan ◽  
Yannawut Uttaruk ◽  
Tanutdech Rotjanakusol ◽  
Kusuma Arsasana
Keyword(s):  
Carbon Sequestration ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Equations ◽  
Landsat 8 Oli ◽  
Green Vegetation ◽  
Simple Ratio ◽  
The Relationship

This research aims to estimate above-ground carbon sequestration of orchards by using the data collected from Landsat 8 OLI. Regression equations are applied to study the relationship between the amount of above-ground carbon sequestration and vegetation indices from Landsat 8 OLI, in which the data was collected in 2015 in 3 methods: 1) Difference Vegetation Index (DVI), 2) Green Vegetation Index (GVI), and 3) Simple Ratio (SR). The results are as follows: 1) By DVI method, it results in the equation y = 0.3184e0.0482x and the coefficient of determination R² = 0.8457. The amount of the above-ground sequestration calcula-tion's result is 213.176 tons per rai. 2) Using the GVI method, it results in the equation y = 0.2619e0.0489x and the coefficient of determination R²=0.8763. The amount of the above-ground sequestration calculation's result is 220.510 tons per rai. 3) Using the SR method, it results in the equation y = 0.8900e0.0469x and the coefficient of determination R² = 0.7748. The amount of the above-ground sequestration calculation's result is 234.229 tons per rai.

scholarly journals Transformasi Indeks Vegetasi Citra LANDSAT 8 OLI untuk Pemetaan Musim Tanam pada Lahan Sawah di Kabupaten Buru, Provinsi Maluku

2020 ◽  
Vol 16 (2) ◽  
pp. 197-205
Author(s):  
Nunung N Tatisina ◽  
Willem A Siahaya ◽  
Johanes P Haumahu
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Rice Fields ◽  
Paddy Fields ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Accuracy Test ◽  
Planting Season

The use of satellite imagery in agriculture has been widely used, for example in plantation areas or in rice fields. Satellite imagery can estimate the area of paddy fields and estimate rice production; however, the application of satellites for mapping the planting season in rice fields using Landsat 8 OLI imagery and NDVI (Normal Difference Vegetation Index) transformation has not been widely undertaken. This study aimed to analyze the growing season in paddy fields based on the results of NDVI analysis of Multitemporal Landsat 8 OLI imagery. Based on the results of research analyzed from the spectral value of NDVI images recorded in March and May 2020 then added with the results of observations   and farmer interviews in September 2020, it was found that there were color differences in NDVI images caused by differences in planting time or plant age at the time of recording the image. The planting season in rice fields in the Buru regency was generally two times per year or five times per two years. The increase in the rice planting season could be realized due to the availability of water in the field that was sufficient for the growth of rice plants, both from irrigation and rainfall. Based on the interpretation of the images used and field checks, it was found that the accuracy test results showed the overall accuracy of 88%. The overall accuracy value is considered correct if it exceeds the tolerance limit given, which is ≥ 80%. Keywords: Buru Regency, growing season, Landsat 8, NDVI, paddy fields   ABSTRAK Pemanfaatan citra satelit pada bidang pertanian telah banyak dilakukan, misalnya pada areal perkebunan ataupun pada areal persawahan. Citra satelit dapat menduga luas lahan sawah sampai dengan menduga produksi padi; tetapi pemanfaatan citra satelit untuk pemetaan musim tanam di lahan sawah menggunakan citra Landsat 8 OLI dan transformasi NDVI (Normalized Difference Vegetation Index) sampai saat ini belum banyak dilakukan. Penelitian ini bertujuan untuk Menganalisis musim tanam di lahan sawah berdasarkan hasil analisis NDVI Citra Landsat 8 OLI Multitemporal. Berdasarkan hasil penelitian yang dianalisis dari nilai spektral citra NDVI perekaman bulan Maret dan Mei tahun 2020 ditambah dengan hasil pengamatan serta wawancara petani pada bulan September 2020 didapatkan adanya perbedaan warna pada citra NDVI yang diakibatkan oleh perbedaan waktu tanam ataupun umur tanaman pada saat perekaman citra. Musim tanam pada lahan sawah di Kabupaten Buru umumnya dua kali per tahun atau lima kali per dua tahun. Peningkatan musim tanam padi dapat terealisasi karena ketersediaan air di lapangan untuk mencukupi pertumbuhan tanaman padi, baik yang bersumber dari air irigasi maupun curah hujan. Berdasarkan interpretasi citra yang digunakan dan pengecekan lapangan didapatkan bahwa hasil uji   akurasi menunjukkan hasil overall accuracy sebesar 88%. Nilai overall accuracy dianggap benar jika melebihi batas toleransi yang diberikan yakni ≥ 80%. Kata kunci:  Kabupaten Buru, Landsat 8, lahan sawah, musim tanam, NDVI

scholarly journals Pemanfaatan Data Satelit Landsat 8 Untuk Menduga Produktivitas Tanaman Padi (Studi Kasus Kabupaten Karawang)

2020 ◽  
Vol 16 (2) ◽  
pp. 178
Author(s):  
Bustomi Bustomi ◽  
Dede Dirgahayu Domiri ◽  
Slamet Abadi ◽  
Kasdi Pringadi
Keyword(s):  
Vegetation Index ◽  
Crop Productivity ◽  
Equation Model ◽  
Landsat 8 ◽  
Linear Regression Models ◽  
Department Of Agriculture ◽  
Rice Productivity ◽  
Average Value ◽  
Landsat Satellite ◽  
The Relationship

This study aims to know prediction the productivity of rice by using Landsat Satellite data 8 in Karawang District. The research method used was descriptive, infrensial and modeling. For a presumes the productivity of the rice being estimated by using the Enhanced Vegetation Index (EVI). Prediction of productivity based on linear regression models between EVI from satellite imagery analysis results with the highest productivity of the rice plant of the Department of Agriculture Karawang District. The results showed that the analysis of Landsat 8 Satellite images obtained the average EVI value from 2017 and 2018, in 2017 the average EVI value was 0.36. while in the year 2018 average value of EVI was 0.48. Estimates of rice productivity in Karawang District 2017 and 2018 were obtained by using the regression equation model the relationship between EVI value and rice productivity yielding Anova obtained Sig = 0.000 <0.05, so that a significant model means the model can be used to estimate rice crop productivity. The z-Test Two Sample for Means statistical test for productivity on the EVI model and in the field shows that in 2017 Zhit = -0.0015 and 2018 Zhit = -0.0002 with areas of rejection and acceptance H0 then Zhit is located in the reception area which produces both results not real difference. This shows that the equation model can be said to be close to the yield of rice productivity in Karawang District and the prediction of rice productivity in the Karawang District in 2019 which is equal to 7.447 tons / ha.

ANALISIS PERBANDINGAN METODE NORMALIZED VEGETATION INDEX DAN MAXIMUM LIKELIHOOD CLASSIFICATION UNTUK ANALISIS RUANG TERBUKA HIJAU (Studi Kasus di Kota Pekalongan, Jawa Tengah)

2021 ◽  
pp. 513
Author(s):  
Mohammad Slamet Sigit Prakoso ◽  
Rizki Dwi Safitri
Keyword(s):  
Maximum Likelihood ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maximum Likelihood Classification

Ruang Terbuka Hijau (RTH) adalah suatu tempat yang luas dan terbuka yang dimaksudkan untuk penghijauan suatu kota, di mana di dalamnya ditumbuhi pepohonan. Dalam analisis ruang terbuka hijau dapat menggunakan beberapa metode, di antaranya yaitu metode Normalized Difference Vegetation Index (NDVI) dan metode Maximum Likelihood Classification. Tujuan penelitian ini untuk mengetahui perbedaan hasil dari analisis metode NDVI dan Maximum Likelihood Classification yang digunakan untuk mengetahui ruang terbuka hijau di Kota Pekalongan. Metode yang digunakan pada penelitian ini yaitu dengan menggunakan metode NDVI dan metode Maximum Likelihood Classification. Data yang digunakan yaitu Citra Landsat 8 OLI. Pengolahan data menggunakan software Arcgis 10.3. Hasil dari pengolahan berupa peta ruang terbuka hijau dari masing - masing metode. Secara kuantitatif dari hasil perhitungan luas metode NDVI, luas permukiman sebesar 3.016,53 ha, persawahan 609,39 ha, hutan kota 573,3 ha, dan badan air seluas 482,04 ha. Sedangkan untuk metode Maximum Likelihood Classification didapatkan hasil luas permukiman 2.278,26 ha, persawahan 1.141,83 ha, hutan kota 738,18 ha, dan badan air seluas 522,99 ha. Berdasarkan luasan RTH terhadap luas Kota Pekalongan, pada metode NDVI sebesar 25,2%, sedangkan untuk metode Maximum Likelihood Classification sebesar 40,1%. Dari hasil analisis diperoleh perbedaan luasan yang cukup signifikan yaitu pada luasan persawahan dan permukiman. Perbedaan hasil analisis terjadi akibat perbedaan klasifikasi warna citra pada saat pengolahan data.

scholarly journals ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

2018 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Made Arya Bhaskara Putra ◽  
I Wayan Nuarsa ◽  
I Wayan Sandi Adnyana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Image Data ◽  
Research Area ◽  
Rice Production ◽  
Landsat 8 ◽  
Estimation Model ◽  
Accuracy Test ◽  
Accuracy Level

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

scholarly journals Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest

2019 ◽  
Vol 8 (2) ◽  
pp. 56 ◽  
Author(s):  
Maliheh Arekhi ◽  
Cigdem Goksel ◽  
Fusun Balik Sanli ◽  
Gizem Senel
Keyword(s):  
Vegetation Index ◽  
Data Retrieval ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Spring Season ◽  
Landsat 8 Oli ◽  
Atmospheric Effect ◽  
Spatial Consistency ◽  
The Mean ◽  
Sentinel 2

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.

scholarly journals Effect of Image Fusion on Vegetation Index Quality—A Comparative Study from Gaofen-1, Gaofen-2, Gaofen-4, Landsat-8 OLI and MODIS Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1550 ◽  
Author(s):  
Prakash Ghimire ◽  
Deng Lei ◽  
Nie Juan
Keyword(s):  
Image Fusion ◽  
Vegetation Index ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Pass Filter ◽  
Modis Imagery

In recent years, the use of image fusion method has received increasing attention in remote sensing, vegetation cover changes, vegetation indices (VIs) mapping, etc. For making high-resolution and good quality (with low-cost) VI mapping from a fused image, its quality and underlying factors need to be identified properly. For example, same-sensor image fusion generally has a higher spatial resolution ratio (SRR) (1:3 to 1:5) but multi-sensor fusion has a lower SRR (1:8 to 1:10). In addition to SRR, there might be other factors affecting the fused vegetation index (FVI) result which have not been investigated in detail before. In this research, we used a strategy on image fusion and quality assessment to find the effect of image fusion for VI quality using Gaofen-1 (GF1), Gaofen-2 (GF2), Gaofen-4 (GF4), Landsat-8 OLI, and MODIS imagery with their panchromatic (PAN) and multispectral (MS) bands in low SRR (1:6 to 1:15). For this research, we acquired a total of nine images (4 PAN+5 MS) on the same (almost) date (GF1, GF2, GF4 and MODIS images were acquired on 2017/07/13 and the Landsat-8 OLI image was acquired on 2017/07/17). The results show that image fusion has the least impact on Green Normalized Vegetation Index (GNDVI) and Atmospherically Resistant Vegetation Index (ARVI) compared to other VIs. The quality of VI is mostly insensitive with image fusion except for the high-pass filter (HPF) algorithm. The subjective and objective quality evaluation shows that Gram-Schmidt (GS) fusion has the least impact on FVI quality, and with decreasing SRR, the FVI quality is decreasing at a slow rate. FVI quality varies with types image fusion algorithms and SRR along with spectral response function (SRF) and signal-to-noise ratio (SNR). However, the FVI quality seems good even for small SRR (1:6 to 1:15 or lower) as long as they have good SNR and minimum SRF effect. The findings of this study could be cost-effective and highly applicable for high-quality VI mapping even in small SRR (1:15 or even lower).

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