scholarly journals QUALITY CHECKING OF CROP CUTTING EXPERIMENTS USING REMOTE SENSING DATA: A CASE STUDY FOR RICE CROP IN ODISHA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 461-466
Author(s):  
S. K. Dubey ◽  
D. Mandloi ◽  
A. S. Gavli ◽  
A. Latwal ◽  
R. Das ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Rice Crop ◽  
High Yield ◽  
Equal Proportion ◽  
Yield Data ◽  
Crop Condition ◽  
Crop Variety ◽  
Cutting Experiments

<p><strong>Abstract.</strong> Under Pradhan Mantri Fasal Bima Yojana (PMFBY), a large number of Crop Cutting Experiments (CCEs) were conducted by Odisha State for Kharif Rice in the year 2016 and 2017. The present study was carried out to examine the quality of the performed CCEs using statistical methods and Remote Sensing (RS) technique. Total 24389 and 34725 CCEs were conducted. After removing outliers, 22083 and 26848 CCE points were analyzed for the year 2016 and 2017, respectively. Multi-date RISAT-1 (2016) and Sentinel-1A (2017) satellite data were used for generating the Kharif Rice crop mask, which was used to get NDVI and NDWI values for Rice pixels, from MODIS VI products. The values of these indices were divided into four strata from highest A, followed by B, C, and D (Lowest Value) based on the range (minimum and maximum) of values. The CCE based yield data were then divided into four yield strata of equal proportion. Yield and RS (NDVI+NDWI) based strata were combined to examine whether the CCE Points having high yield fall under good NDVI zone or vice versa. The results showed that there was strong match between CCE strata and the vegetation index strata in both the years. Therefore, it could be be concluded that RS based indices have the capability to assess the quality/accuracy of CCEs. Furthermore, the large variety of information available with CCEs such that crop variety, crop condition, water sources, stress conditions etc., can be used as input parameters to train any model to predict better results.</p>

scholarly journals Use of remote methods for monitoring formation of yield of spring barley

2020 ◽  
pp. 58-61
Author(s):  
Irina Gennadyevna Storchak ◽  
Fedor Vladimirovich Eroshenko
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Agricultural Sector ◽  
Crop Yields ◽  
Spring Barley ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Yield Data ◽  
Sensing Data ◽  
The Earth

When cultivating barley, there is a need to monitor the condition of crops and forecast yields using objective and inexpensive methods. Remote sensing data of the Earth is used to solve various problems in the agricultural sector related to monitoring vegetation, including monitoring the condition of agricultural crops throughout the growing season. The main advantages of this observation are: efficiency, objectivity, multi-scale and cost-effective. The question of the possibility of predicting crop yields in the scientific literature has not yet been adequately reflected. Therefore, the purpose of the research was to identify the relationship between the data of remote sensing of the Earth and the yield of spring barley for the conditions of the Stavropol Territory. The studies used data from the VEGA IKI RAS service (averaged NDVI values of spring crops in the Stavropol Territory) and the statistical office of the Stavropol Territory. In the analysis of materials, NDVI values were tied to the stages of organogenesis. It was found that the closest correlation between (0.64) NDVI and spring barley yield corresponds to the phase of the formation of the caryopsis. When analyzing yield data and values of the NDVI vegetation index on fixed calendar dates (weeks) of the year, it was shown that a statistically significant correlation appears between the 13th and 26th calendar weeks of the year. Therefore, the Stavropol Territory is characterized by the dependence of barley productivity on NDVI values of spring crops. The closest it is observed in the phase of the formation of the seed. Thus, for the conditions of the Stavropol Territory, it is possible to predict the yield of spring barley according to remote sensing data of the Earth.

scholarly journals Estimating Crop Yields at the Field Level Using Landsat and Modis Products

2018 ◽  
Vol 66 (5) ◽  
pp. 1141-1150 ◽  
Author(s):  
František Jurečka ◽  
Vojtěch Lukas ◽  
Petr Hlavinka ◽  
Daniela Semerádová ◽  
Zdeněk Žalud ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Spring Barley ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Yield Data ◽  
Field Level

Remote sensing can be used for yield estimation prior to harvest at the field level to provide helpful information for agricultural decision making. This study was undertaken in Polkovice, located at low elevations in the Czech Republic. From 2014–2016, two datasets of satellite imagery were used: the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 datasets. Satellite data were compared with yields and other observations at the level of land blocks. Winter oilseed rape, winter wheat and spring barley yield data, representing the crops planted over the analyzed period, were used for comparison. In 2016, a more detailed analysis was conducted. We tested a relationship between remote sensing data and the spatial yield variability measured by a yield monitor from a combine harvester. Correlations varied from approximately r = 0.4 to r = 0.7 with the highest correlation (r = 0.74) between yield and the Green Normalized Difference Vegetation Index collected from a drone. Vegetation indices from both Landsat 8 and the MODIS showed a positive relationship with yields for the compared period. The highest correlation was between yield and the Enhanced Vegetation Index (r = 0.8) while the lowest was between yield and the Normalized Difference Vegetation Index from MODIS (r = 0.1).

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals Assessing migration patterns in Passerina ciris using the world’s bird collections as an aggregated resource

2015 ◽  
Author(s):  
Ethan Linck ◽  
Eli S Bridge ◽  
Jonah M Duckles ◽  
Adolfo G Navarro-Sigüenza ◽  
Sievert Rohwer
Keyword(s):  
Remote Sensing ◽  
Behavioral Ecology ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Abundance Index ◽  
Population Movements ◽  
Relative Population ◽  
History Museum ◽  
Abundance Indices ◽  
Passerina Ciris

Natural history museum collections (NHCs) represent a rich and largely untapped source of data on demography and population movements. NHC specimen records can be corrected to a crude measure of collecting effort and reflect relative population densities with a method known as abundance indices. We plot abundance index values from georeferenced NHC data in a 12-month series for the new world migratory passerine Passerina ciris across its molting and wintering range in Mexico and Central America. We illustrate a statistically significant change in abundance index values across regions and months that suggests a quasi-circular movement around its non-breeding range, and use enhanced vegetation index (EVI) analysis of remote sensing plots to demonstrate non-random association of specimen record density with areas of high primary productivity. We demonstrate how abundance indices from NHC specimen records can be applied to infer previously unknown migratory behavior, and be integrated with remote sensing data to allow for a deeper understanding of demography and behavioral ecology across space and time.

scholarly journals Inherent Reflectance Variability of Vegetation

Agriculture ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 246 ◽  
Author(s):  
Baabak Mamaghani ◽  
M. Grady Saunders ◽  
Carl Salvaggio
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Time Of Day ◽  
Unmanned Aircraft ◽  
P Value ◽  
Ground Sample ◽  
Significant Difference

With the inception of small unmanned aircraft systems (sUAS), remotely sensed images have been captured much closer to the ground, which has meant better resolution and smaller ground sample distances (GSDs). This has provided the precision agriculture community with the ability to analyze individual plants, and in certain cases, individual leaves on those plants. This has also allowed for a dramatic increase in data acquisition for agricultural analysis. Because satellite and manned aircraft remote sensing data collections had larger GSDs, self-shadowing was not seen as an issue for agricultural remote sensing. However, sUAS are able to image these shadows which can cause issues in data analysis. This paper investigates the inherent reflectance variability of vegetation by analyzing six Coneflower plants, as a surrogate for other cash crops, across different variables. These plants were measured under different forecasts (cloudy and sunny), at different times (08:00 a.m., 09:00 a.m., 10:00 a.m., 11:00 a.m. and 12:00 p.m.), and at different GSDs (2, 4 and 8 cm) using a field portable spectroradiometer (ASD Field Spec). In addition, a leafclip spectrometer was utilized to measure individual leaves on each plant in a controlled lab environment. These spectra were analyzed to determine if there was any significant difference in the health of the various plants measured. Finally, a MicaSense RedEdge-3 multispectral camera was utilized to capture images of the plants every hour to analyze the variability produced by a sensor designed for agricultural remote sensing. The RedEdge-3 was held stationary at 1.5 m above the plants while collecting all images, which produced a GSD of 0.1 cm/pixel. To produce 2, 4, and 8 cm GSD, the MicaSense RedEdge-3 would need to be at an altitude of 30.5 m, 61 m and 122 m respectively. This study did not take background effects into consideration for either the ASD or MicaSense. Results showed that GSD produced a statistically significant difference (p < 0.001) in Normalized Difference Vegetation Index (NDVI, a commonly used metric to determine vegetation health), R 2 values demonstrated a low correlation between time of day and NDVI, and a one-way ANOVA test showed no statistically significant difference in the NDVI computed from the leafclip probe (p-value of 0.018). Ultimately, it was determined that the best condition for measuring vegetation reflectance was on cloudy days near noon. Sunny days produced self-shadowing on the plants which increased the variability of the measured reflectance values (higher standard deviations in all five RedEdge-3 channels), and the shadowing of the plants decreased as time approached noon. This high reflectance variability in the coneflower plants made it difficult to accurately measure the NDVI.

scholarly journals Statistical assessment of vegetation dynamics within protected areas using remote sensing data

2013 ◽  
Vol 43 (4) ◽  
pp. 5
Author(s):  
Maria Elena Menconi ◽  
David Grohmann
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Photosynthetic Activity ◽  
Remote Sensing Data ◽  
Ratio Method ◽  
Vegetation Development ◽  
Sensing Data ◽  
Landscape Factors

This study aimed to test the effectiveness of protected areas to preserve vegetation. The first step was to identify vegetation suitable areas, designed as areas with optimal morphological terrain features for a good photosynthetic activity. These areas were defined according to the following landscape factors: slope, altitude, aspect and land use. Enhanced vegetation index (EVI) was chosen as vegetation dynamics indicator. This method is based on a statistical approach using remote sensing data in a geographic information system (GIS) environment. The correlation between EVI and landscape factor was evaluated using the frequency ratio method. Classes of landscape factors that show good correlation with a high EVI were combined to obtain vegetation suitable areas. Once identified, these areas and their vegetation dynamics were analysed by comparing the results obtained whenever these areas are included or not included in protected areas. A second EVI dataset was used to verify the accuracy in identifying vegetation suitable areas and the influence of each landscape factor considered in their identification. This validation process showed that vegetation suitable areas are significant in identifying areas with good photosynthetic activity. The effects analysis showed a positive influence of all landscape factors in determining suitability. This methodology, applied to central regions of Italy, shows that the vegetation suitable areas located inside protected areas are <em>greener</em> than those outside protected areas. This suggests that the protective measures established by the institution of the parks have proved to be effective, at least as far as the status of vegetation development is concerned.

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