Analysing variety of vegetation indices values using different methods for mapping oil palm closed-canopy composition in southern Riau Province, Indonesia

2012 ◽  
Author(s):  
Fatwa Ramdani
Keyword(s):  
Oil Palm ◽  
Vegetation Indices ◽  
Closed Canopy ◽  
Canopy Composition

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals Detection of Diseases in Oil Palm Plantations in the Brazilian Amazon through Orbital Image

2020 ◽  
Vol 12 (4) ◽  
pp. 138
Author(s):  
João Almiro Corrêa Soares ◽  
Artur Vinícius Ferreira dos Santos ◽  
Paulo Roberto Silva Farias ◽  
Leidiane Ribeiro Medeiros ◽  
Adriano Anastácio Cardoso Gomes
Keyword(s):  
Decision Making ◽  
Palm Oil ◽  
Oil Palm ◽  
Large Scale ◽  
Vegetation Indices ◽  
Brazilian Amazon ◽  
The State ◽  
Excellent Correlation ◽  
New Techniques

The detection of diseases in oil palm crops in the Brazilian Amazon represents a great challenge for the management of this crop in Brazil. The plantations in the State of Pará provide inputs for the food, cosmetics, agro-energy and biofuel industries, supplying Brazilian markets. In recent years, several factors such as pests, diseases and climate have interfered in the development of oil palm in the region, generating the need to adopt new techniques to detect and monitor such issues. In this work, spectral enhancements were carried out by simple reflectance and vegetation indices for four plots cropped on Companhia Palmares da Amazônia (CPA) farm, owned by Agropalma S.A. company in the municipality of Acará, in the state of Pará. The results allowed the identification of expressive patterns minimum and maximum reflectances of the studied plots, correlating with occurrences of diseases. The EVI index showed an excellent correlation with the occurrence of diseases. However, the NDVI and SAVI indexes showed adequate adjustments with the occurrence of diseases in 2017. The areas corresponding to the L36 and H27 plots showed higher occurrences of diseases, based on the analysis of reflectance through vegetation indices. It is concluded that the reflectance enhancements, NDVI, SAVI and EVI obtained by orbital sensors are efficient in the detection of diseases in the plots. The results allowed the identification of diagnostic anomalies of stresses in the plots, either by disease or other factor, allowing the decision making in an adequate time, therefore avoiding large scale eradication in the extensive areas in commercial palm oil plantations in Brazil.

scholarly journals Identification and classification of cloud computing-based vegetation index values on several lands used in Bogor Regency, Indonesia

2021 ◽  
Vol 918 (1) ◽  
pp. 012011
Author(s):  
H S Aprilianti ◽  
R A Ari ◽  
A Ranti ◽  
M F Aslam
Keyword(s):  
Land Use ◽  
Cloud Computing ◽  
Oil Palm ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Vegetation Indices ◽  
Threshold Value ◽  
Vegetation Types ◽  
Computing Platform ◽  
Use Studies

Abstract Understanding the threshold value classification from various vegetation types may help distinguish spectral reflectance differences in detailed land use studies. However, conducting all of the processes requires relatively large resources regarding manual computation, which could be surpassed by cloud computing. Unfortunately, in Bogor Regency, there is still a lack of research that studies the threshold value of various vegetation types related to forestry and plantation sectors. Land use categories were classified, and threshold values were determined, especially for selected vegetation types including teak, oil palm, rubber, pine, bamboo, and tea based on several vegetation indices in Bogor Regency using the Cloud-Computing platform. The data source was retrieved from 10-meters Sentinel-2 Satellite median imagery of January 2019 - June 2021. Land use maps were generated using Random Forest Algorithm from composite images. Meanwhile, the threshold value of each vegetation type was calculated from the average and standard deviation of NDVI, SAVI, EVI, ARVI, SLAVI, and GNDVI index. The result of the study showed forest and plantation area covers about 158,168.13 ha or 48.92 % of the study area. NDVI was found suitable to identify teak, SLAVI for rubber and pine, EVI for bamboo and tea, and GNDVI for oil palm vegetation.

scholarly journals Plot-Based Classification of Macronutrient Levels in Oil Palm Trees with Landsat-8 Images and Machine Learning

2021 ◽  
Vol 13 (11) ◽  
pp. 2029
Author(s):  
Zhi Hong Kok ◽  
Abdul Rashid Bin Mohamed Shariff ◽  
Siti Khairunniza-Bejo ◽  
Hyeon-Tae Kim ◽  
Tofael Ahamed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Oil Palm ◽  
Vegetation Indices ◽  
Model Performance ◽  
Landsat 8 ◽  
Palm Trees ◽  
Class Separability ◽  
Resolution Imaging

Oil palm crops are essential for ensuring sustainable edible oil production, in which production is highly dependent on fertilizer applications. Using Landsat-8 imageries, the feasibility of macronutrient level classification with Machine Learning (ML) was studied. Variable rates of compost and inorganic fertilizer were applied to experimental plots and the following nutrients were studied: nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg) and calcium (Ca). By applying image filters, separability metrics, vegetation indices (VI) and feature selection, spectral features for each plot were acquired and used with ML models to classify macronutrient levels of palm stands from chemical foliar analysis of their 17th frond. The models were calibrated and validated with 30 repetitions, with the best mean overall accuracy reported for N and K at 79.7 ± 4.3% and 76.6 ± 4.1% respectively, while accuracies for P, Mg and Ca could not be accurately classified due to the limitations of the dataset used. The study highlighted the effectiveness of separability metrics in quantifying class separability, the importance of indices for N and K level classification, and the effects of filter and feature selection on model performance, as well as concluding RF or SVM models for excessive N and K level detection. Future improvements should focus on further model validation and the use of higher-resolution imaging.

scholarly journals Supplementary material to "High-resolution global map of smallholder and industrial closed-canopy oil palm plantations"

2020 ◽  
Author(s):  
Adrià Descals ◽  
Serge Wich ◽  
Erik Meijaard ◽  
David L. A. Gaveau ◽  
Stephen Peedell ◽  
...  
Keyword(s):  
High Resolution ◽  
Oil Palm ◽  
Closed Canopy ◽  
Supplementary Material

scholarly journals Generating the Baseline in the Early Detection of Bud Rot and Red Ring Disease in Oil Palms by Geospatial Technologies

2020 ◽  
Vol 12 (19) ◽  
pp. 3229
Author(s):  
Mauricio Viera-Torres ◽  
Izar Sinde-González ◽  
Mariluz Gil-Docampo ◽  
Vladimir Bravo-Yandún ◽  
Theofilos Toulkeridis
Keyword(s):  
Early Detection ◽  
Gross Domestic Product ◽  
Oil Palm ◽  
Vegetation Index ◽  
Agricultural Sector ◽  
Vegetation Indices ◽  
Spatial Behavior ◽  
Domestic Product ◽  
The Republic ◽  
Aircraft System

Oil palm cultivation in Ecuador is important for the agricultural sector. As a result of it, the country generates sources of employment in some of the most vulnerable zones; it contributes 0.89% of the gross domestic product and 4.35% of the agricultural gross domestic product. In 2017, a value of USD $252 million was generated by exports, and palm contributed 4.53% of the agricultural gross domestic product (GDP). It is estimated that 125,000 hectares of palm were lost in the Republic of Ecuador due to Red Ring Disease (RRD) and specifically Bud Rot (BR). The current study aimed to generate an early detection of BR and RRD in oil palm. Image acquisition has been performed using Remotely Piloted Aircraft System (RPAS) with Red, Green, and Blue (RGB) cannons, and multispectral cameras, in study areas with and without the presence of the given disease. Hereby, we proposed two phases. In phase A, a drone flight has been conducted for processing and georeferencing. This allowed to obtain an orthomosaic that serves as input for obtaining several vegetation indices of the healthy crop. The data and products obtained from this phase served as a baseline to perform comparisons with plantations affected by BR and RRD and to differentiate the palm varieties that are used by palm growers. In phase B, the same process has been applied three times with an interval of 15 days in an affected plot, in order to identify the symptoms and the progress of them. A validation for the diseases detection has been performed in the field, by taking Global Positioning System (GPS) points of the palms that presented symptoms of BR and RRD, through direct observation by field experts. The inputs obtained in each monitoring allowed to analyze the spatial behavior of the diseases. The values of the vegetation indices obtained from Phase A and B aimed to establish the differences between healthy and diseased palms, with the purpose of generating the baseline of early responses of BR and RRD conditions. However, the best vegetation index to detect the BR was the Visible Atmospherically Resistant Index (VARI).

scholarly journals Using SPOT-7 for Nitrogen Fertilizer Management in Oil Palm

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 133
Author(s):  
Mohammad Yadegari ◽  
Redmond R. Shamshiri ◽  
Abdul Rashid Mohamed Shariff ◽  
Siva K. Balasundram ◽  
Benjamin Mahns
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Accuracy Assessment ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
P Value ◽  
Statistical Parameters ◽  
N Content ◽  
Regression Formula ◽  
Non Destructive

Environmental concerns are growing about excessive applying nitrogen (N) fertilizers, especially in oil palm. Some conventional methods which are used to assess the amount of nutrient in oil palm are time-consuming, expensive, and involve frond destruction. Remote sensing as a non-destructive, affordable, and efficient method is widely used to detect the concentration of chlorophyll (Chl) from canopy plants using several vegetation indices (VIs) because there is an influential relation between the concentration of N in the leaves and canopy Chl content. The objectives of this research are to (i) evaluate and compare the performance of various vegetation indices (VIs) for measuring N status in oil palm canopy using SPOT-7 imagery (AIRBUS Defence & Space, Ottobrunn, Germany) to (ii) develop a regression formula that can predict the N content using satellite data to (iii) assess the regression formula performance on testing datasets by testing the coefficient of determination between the predicted and measured N contents. SPOT-7 was acquired in a 6-ha oil palm planted area in Pahang, Malaysia. To predict N content, 28 VIs based on the spectral range of SPOT-7 satellite images were evaluated. Several regression models were applied to determine the highest coefficient of determination between VIs and actual N content from leaf sampling. The modified soil-adjusted vegetation index (MSAVI) generated the highest coefficient of determination (R2 = 0.93). MTVI1 and triangular VI had the highest second and third coefficient of determination with N content (R2 = 0.926 and 0.923, respectively). The classification accuracy assessment of the developed model was evaluated using several statistical parameters such as the independent t-test, and p-value. The accuracy assessment of the developed model was more than 77%.

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