scholarly journals Application of Sentinel Images 2-A in Estimation of Potassium in Potatoes

2022 ◽  
Vol 9 (1) ◽  
pp. 141-146
Author(s):  
Deffi Armita ◽  
Aditya Nugraha Putra ◽  
S Sudarto ◽  
Istika Nita ◽  
Hana Kusumawati ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Nutrient Deficiency ◽  
Grid Method ◽  
Nutrient Content ◽  
Potato Production ◽  
Estimation Model ◽  
Validation Test ◽  
Significant Difference ◽  
Regression Test ◽  
Sentinel 2

Potato production in Indonesia decreased by 2.43% from 1,314,657 in 2019 and 1,282,768 tons in 2020. One of the causes of the decline in potato production is a lack of potassium. Potassium nutrient deficiency can be caused by fertilization that is not yet precise and is still done conventionally. The purpose of this study was to estimate the nutrient content of potassium using Sentinel 2-A. This study observed 50 points that were determined through the free grid method. Sentinel 2-A was transformed into GLI, GNDVI, NDVI which is the vegetation index and NDSI, and SAVI which is the soil index. The results showed that plant K correlated with GLI CS index (r = -0,46), NDVI CS (r = -0,48) and NDSI CS (r = -0,46). NDVI CS (R2 =2 3%) is the most accurate index in estimating the nutrient content of Potassium than GLI CS (R2 = 21%) and NDSI CS (R2 = 21%). Based on the results of the plant K regression test and NDVI CS, the regression equation y = 1,8003 + (-0,5716 NDVI CS) was obtained. The results of the validation test showed that the t table (-3.18) > t count (2.15) so that there is a significant difference in the estimation results of potassium with the results of potassium obtained in the field. Based on the results of the validation test which were significantly different, the productivity estimation model could not be used to estimate the potassium nutrient in potatoes.

ESTIMASI KANDUNGAN FOSFOR PADA TANAMAN NANAS MENGGUNAKAN UNMANNED AERIAL VEHICLE (UAV) DI PT. GREAT GIANT PINEAPPLE, LAMPUNG

2021 ◽  
Vol 8 (2) ◽  
pp. 427-435
Author(s):  
Revaldy Andika ◽  
Retno Suntari
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nutrient Content ◽  
Aerial Photographs ◽  
Estimation Model ◽  
P Deficiency ◽  
Distribution Maps ◽  
Stunted Growth ◽  
Significant Difference ◽  
Plantation Company

PT. Great Giant Pineapple (PT. GGP) is the largest pineapple plantation company in Indonesia, with a land area of approximately ±33,000 ha and dominated by soil types in the form of Ultisols. Soil fertility at PT. GGP tends to have relatively low nutrient content, one of which is phosphorus due to Al fixation. The nutrient P in pineapple is used to stimulate root growth, accelerate the ripening of fruit and seeds. Symptoms arising from P deficiency will experience stunted growth (stunted), and the pineapple will become imperfect. This study aimed to estimate the P nutrient content in pineapple plants using vegetation indexes in the form of GNDVI (Green Normalized Difference Vegetation Index). The study was carried out by taking aerial photographs and samples of pineapple plants in the 1 months phase before forcing and 1 months after forcing (F-1 and F+1), laboratory analysis, statistical analysis, and making distribution maps. The results showed that the vegetation index could estimate the nutrient content of P using the best estimation model. This was evidenced from the results of the correlation test which shows a very strong and real relationship of 0.81-0.82 with the regression test results of 66%-67%. In addition, the results of the validation test using the paired t-test showed that the t-count was smaller than the t-table of 2.30, which means that the estimated GNDVI vegetation index and the P nutrient content of pineapple plants showed no significant difference.

scholarly journals PEMANFAATAN INDEKS VEGETASI UNTUK ESTIMASI KANDUNGAN KALIUM PADA TANAMAN NANAS (Ananas comosus) MENGGUNAKAN UAV (Unmanned Aerial Vehicle) DI PT. GREAT GIANT PINEAPPLE, LAMPUNG

2020 ◽  
Vol 8 (1) ◽  
pp. 91-99
Author(s):  
Dita Khairunnisa ◽  
Mochtar Lutfi Rayes ◽  
Christanti Agustina
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Nutrient Status ◽  
Nutrient Content ◽  
Aerial Photographs ◽  
Ananas Comosus ◽  
P Value ◽  
Distribution Maps ◽  
Significant Difference ◽  
Aerial Vehicle

PT Great Giant Pineapple (PT. GGP) is the largest pineapple production company in Indonesia. One of the nutrients that pineapple plants really need is potassium (K). K plays a key role in carbohydrate metabolism and transport of photosynthates from source to sink. Remote sensing technology has been developed to estimate nutrient status, one of which is using an Unmanned Aerial Vehicle (UAV). This study aims to estimate the K nutrient content in pineapple plants using vegetation indexes in the form of NDVI (Normalyzed Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and OSAVI (Optimized of Soil Adjusted Vegetation Index). The research was carried out by taking aerial photographs and samples of pineapple plants in the 5 months phase before forcing up to 2 months after forcing (F-5 to F + 2), laboratory analysis, statistical analysis, and making distribution maps. The results showed that the relationship between the vegetation index value and K plant was the strongest and most significant is in 1 month before forcing phase (F-1) with the same r value for the three indices vegetation (r=0.867). The results of the regression analysis between the NDVI, SAVI and OSAVI values with K plant were 75.17%, 75.18% and 75.17%, respectively. The calculation of the K estimate using three methods yields no different values. The validation results using paired t test (t count -0.63; t table 2.31; p-value 0.544) where the K content in the measured plants and the estimation results showed no significant difference with the measurement results.

scholarly journals Research on apple orchard classification and yield estimation model based on GF-1 and Sentinel-2

2021 ◽  
Vol 248 ◽  
pp. 03080
Author(s):  
Yu Liu ◽  
Xiaoping Wang ◽  
Jiaxin Qian
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Rapid Development ◽  
Research Area ◽  
Shaanxi Province ◽  
Yield Estimation ◽  
Estimation Model ◽  
Apple Industry ◽  
Apple Production ◽  
Sentinel 2

China is a big apple planting country and attaches great importance to the development of apple industry in agricultural economy. There are many mountainous areas in Shaanxi Province, which has obvious geographical advantages and is one of the important areas for apple production in my country. A quick and effective forecast of the apple output in Shaanxi Province can not only strengthen the management of apple planting and production, improve the varieties of apple production, and improve the quality of apple production, but also provide technical support for regional agricultural departments to expand the apple market and improve the base construction. It is of great significance to promote the rapid development of my country's apple planting industry. In this study, Luochuan County, Yana’s City, Shaanxi Province was used as the research area, using GF-1 and Sentinel-2 multispectral remote sensing images and their vegetation indices from 2013 to 2019, and using RF to extract orchards in the research area. Secondly, combining the classification results with rainfall, temperature, sunshine hours, air pressure, humidity, wind speed, drought indicators and remote sensing vegetation index, using RFR and SVR methods, establish a comprehensive production estimation model suitable for Luochuan County apples, and compare different types Model accuracy. The main conclusions are drawn through the research: Using RF classification method can effectively extract the luochuan orchard distribution and high precision, based on RFR and SVR method combined with meteorological factor, the drought index and remote sensing vegetation index to establish basic quite, crop yield estimation model precision machine learning regression algorithm for subsequent apple luochuan orchard management, and provide strong decision basis for the development of apple industry.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Annually Urban Fractional Vegetation Cover Dynamic Mapping in Hefei, China (1999–2018)

2021 ◽  
Vol 13 (11) ◽  
pp. 2126
Author(s):  
Yuliang Wang ◽  
Mingshi Li
Keyword(s):  
Standard Deviation ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Dynamic Range ◽  
Dynamic Change ◽  
Estimation Model ◽  
Sparse Vegetation ◽  
Fractional Vegetation Cover ◽  
Dynamic Mapping ◽  
Multi Temporal

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

scholarly journals Multi-Year NDVI Values as Indicator of the Relationship between Spatiotemporal Vegetation Dynamics and Environmental Factors in the Qaidam Basin, China

2021 ◽  
Vol 13 (7) ◽  
pp. 1240
Author(s):  
Junpeng Lou ◽  
Guoyin Xu ◽  
Zhongjing Wang ◽  
Zhigang Yang ◽  
Sanchuan Ni
Keyword(s):  
Environmental Factors ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Qaidam Basin ◽  
Elevation Gradient ◽  
Practical Significance ◽  
Elevation Gradients ◽  
Relative Contribution ◽  
Significant Difference ◽  
Different Altitudes

The Qaidam Basin is a unique and complex ecosystem, wherein elevation gradients lead to high spatial heterogeneity in vegetation dynamics and responses to environmental factors. Based on the remote sensing data of Moderate Resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM) and Global Land Data Assimilation System (GLDAS), we analyzed the spatiotemporal variations of vegetation dynamics and responses to precipitation, accumulative temperature (AT) and soil moisture (SM) in the Qaidam Basin from 2001 to 2016. Moreover, the contribution of those factors to vegetation dynamics at different altitudes was analyzed via an artificial neural network (ANN) model. The results indicated that the Normalized Difference Vegetation Index (NDVI) values in the growing season showed an overall upward trend, with an increased rate of 0.001/year. The values of NDVI in low-altitude areas were higher than that in high-altitude areas, and the peak values of NDVI appeared along the elevation gradient at 4400–4600 m. Thanks to the use of ANN, we were able to detect the relative contribution of various environmental factors; the relative contribution rate of AT to the NDVI dynamic was the most significant (35.17%) in the low-elevation region (< 2900 m). In the mid-elevation area (2900–3900 m), precipitation contributed 44.76% of the NDVI dynamics. When the altitude was higher than 3900 m, the relative contribution rates of AT (39.50%) and SM (38.53%) had no significant difference but were significantly higher than that of precipitation (21.97%). The results highlight that the different environmental factors have various contributions to vegetation dynamics at different altitudes, which has important theoretical and practical significance for regulating ecological processes.

scholarly journals Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuai Che ◽  
Guoying Du ◽  
Ning Wang ◽  
Kun He ◽  
Zhaolan Mo ◽  
...  
Keyword(s):  
High Throughput ◽  
Vegetation Index ◽  
Multispectral Imaging ◽  
Vegetation Indices ◽  
Spectral Imaging ◽  
Shielding Effect ◽  
Quadratic Model ◽  
Biomass Estimation ◽  
Marine Aquaculture ◽  
Significant Difference

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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