Use of remote sensing to determine the relationship of early vigour to grain yield in canola (Brassica napus L.) germplasm

2014 ◽  
Vol 65 (12) ◽  
pp. 1288 ◽  
Author(s):  
R. B. Cowley ◽  
D. J. Luckett ◽  
J. S. Moroni ◽  
S. Diffey
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Vegetation Index ◽  
Field Experiments ◽  
Biomass Accumulation ◽  
Brassica Napus L ◽  
Sensing Technology ◽  
Normalised Difference Vegetation Index ◽  
Small Plot ◽  
Relationship Of

Early crop vigour in canola, as in other crops, is likely to result in greater competition with weeds, more rapid canopy closure, improved nutrient acquisition, improved water-use efficiency, and, potentially, greater final grain yield. Laborious measurements of crop biomass over time can be replaced with newer remote-sensing technology to aid data acquisition. Normalised difference vegetation index (NDVI) is a surrogate for biomass accumulation that can be recorded rapidly and repeatedly with inexpensive equipment. In seven small-plot field experiments conducted over a 4-year period with diverse canola germplasm (n = 105), we have shown that NDVI measures are well correlated with final grain yield. We found NDVI values were most correlated with yield (r >0.7) if readings were taken when the crop had received 210–320 growing degree-days (usually the mid-vegetative phase of growth). It is suggested that canola breeders may use NDVI to objectively select for vigorous genotypes that are more likely to have higher grain yields.

Making the invisible visible: using UAS-based high-resolution color-infrared imagery to identify buried medieval monastery walls

2015 ◽  
Vol 3 (2) ◽  
pp. 58-67 ◽  
Author(s):  
Jan Rudolf Karl Lehmann ◽  
Keturah Zoe Smithson ◽  
Torsten Prinz
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Resolution ◽  
Vegetation Index ◽  
Archaeological Site ◽  
Unmanned Aerial Systems ◽  
Surrounding Water ◽  
Normalised Difference Vegetation Index ◽  
Remote Sensing Techniques ◽  
Aerial Systems

Remote sensing techniques have become an increasingly important tool for surveying archaeological sites. However, budgeting issues in archaeological research often limit the application of satellite or airborne imagery. Unmanned aerial systems (UAS) provide a flexible, quick, and more economical alternative to commonly used remote sensing techniques. In this study, the buried features of the archaeological site of the Kleinburlo monastery, near Münster, Germany, were identified using high-resolution color–infrared (CIR) images collected from a UAS platform. Based on these CIR images, a modified normalised difference vegetation index (NDVIblue) was calculated, showing reflectance spectra of vegetation anomalies caused by water stress. In the presented study, the vegetation growing on top of the buried walls was better nourished than the surrounding plants because very wet conditions over the days previous to data collection caused higher levels of water stress in the surrounding water-drenched land. This difference in water stress was a good indicator for detecting archaeological remains.

Effect of fertiliser phosphorus and nitrogen on the concentrations of oil and protein in grain and the grain yield of canola (Brassica napus L.) grown in south-western Australia

2007 ◽  
Vol 47 (8) ◽  
pp. 984 ◽  
Author(s):  
R. F. Brennan ◽  
M. D. A. Bolland
Keyword(s):  
Brassica Napus ◽  
Grain Yield ◽  
Western Australia ◽  
Field Experiments ◽  
Grain Production ◽  
Brassica Napus L ◽  
Single Superphosphate ◽  
P Deficiency ◽  
Soil P ◽  
Soil P Testing

The effect of fertiliser phosphorus (P) and nitrogen (N) on seed (grain) yield and concentration of oil and protein in grain of canola (oil-seed rape; Brassica napus L.) was measured in two field experiments undertaken at eight sites from 1993–2005 in south-western Australia, on soils deficient in P and N. Six rates of P (0–40 kg P/ha as single superphosphate) and four rates of N (0–138 kg N/ha as urea) were applied. Significant grain yield increases (responses) to applied P occurred in both experiments and these responses increased as rates of applied N increased. For grain production, the P × N interaction was significant in all eight years and locations of the two experiments. Application of P had no effect on concentration of oil and protein in grain. Application of N always decreased the concentration of oil and increased the concentration of protein in grain. For canola grain production in the region, responses to applied N always occur whereas responses to applied P are rare, but if soil P testing indicates likely P deficiency, both P and N fertiliser need to be applied.

scholarly journals Separation of soil and canopy reflectance signatures of Mid German agricultural soils

2011 ◽  
Vol 51 (No, 7) ◽  
pp. 296-303 ◽  
Author(s):  
T. Behrens ◽  
K. Gregor ◽  
W. Diepenbrock
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Vegetation Index ◽  
Near Infrared ◽  
Field Experiments ◽  
Agricultural Soils ◽  
Soil Reflectance

Remote sensing can provide visual indications of crop growth during production season. In past, spectral optical estimations were well performed in the ability to be correlated with crop and soil properties but were not consistent within the whole production season. To better quantify vegetation properties gathered via remote sensing, models of soil reflectance under changing moisture conditions are needed. Signatures of reflected radiation were acquired for several Mid German agricultural soils in laboratory and field experiments. Results were evaluated at near-infrared spectral region at the wavelength of 850 nm. The selected soils represented different soil colors and brightness values reflecting a broad range of soil properties. At the wavelength of 850 nm soil reflectance ranged between 10% (black peat) and 74% (white quartz sand). The reflectance of topsoils varied from 21% to 32%. An interrelation was found between soil brightness rating values and spectral optical reflectance values in form of a linear regression. Increases of soil water content from 0% to 25% decreased signatures of soil reflectance at 850 nm of two different soil types about 40%. The interrelation of soil reflectance and soil moisture revealed a non-linear exponential function. Using knowledge of the individual signature of soil reflectance as well as the soil water content at the measurement, soil reflectance could be predicted. As a result, a clear separation is established between soil reflectance and reflectance of the vegetation cover if the vegetation index is known.

Use of normalised difference vegetation index, nitrogen concentration, and total nitrogen content of whole maize plant and plant fractions to estimate yield and nutritive value of hybrid forage maize

2011 ◽  
Vol 62 (5) ◽  
pp. 374 ◽  
Author(s):  
M. R. Islam ◽  
S. C. (Yani) Garcia ◽  
D. Henry
Keyword(s):  
Grain Yield ◽  
Multiple Regression ◽  
Vegetation Index ◽  
Nutritive Value ◽  
Biomass Yield ◽  
Maize Plant ◽  
Forage Maize ◽  
Leaf Stage ◽  
Whole Plant ◽  
Normalised Difference Vegetation Index

This study was conducted to investigate the potentials of normalised difference vegetation index (NDVI), nitrogen (N) concentration (%), and N content (g/plant) of whole maize plant to estimate yield and nutritive value of hybrid forage maize. Hybrid forage maize was grown with two rates of pre-sowing fertiliser N (0, 135 kg/ha) and three rates of post-sowing fertiliser N (0, 79, 158 kg N/ha) applied at the six-leaf stage. Data on the NDVI and N (% and g/plant) of maize were collected at 2-, 3-, 6-, 8-, 12-, 16-, 18-leaf stages and at harvest. Metabolisable energy (ME) content of the whole maize plant at harvest was estimated from in vitro digestibility. Simple, polynomial, and multiple regression analyses were conducted and only the best-fit models were selected. The 8-leaf stage was found to be the most effective stage for use of the NDVI in predicting biomass yield (R2 = 0.81), grain yield (R2 = 0.72), and N (%) (R2 = 0.92) of forage maize. Nitrogen (%) at the 8-leaf stage was also best related to biomass yield (R2 = 0.88). Multiple regressions at the 3-leaf stage increased the coefficient of determination for both biomass yield and grain yield (R2 = 0.77) over the relationships obtained from N (%) of the whole plant at 2- or 3-leaf stage. The NDVI and N (%) of the whole plant at 8-leaf stage were the best predictors of yield, but failed to predict ME content of the hybrid forage maize. Multiple regression models at the 3-leaf stage were almost as effective as the NDVI and N (%) of whole maize plant at the 8-leaf stage in predicting biomass and grain yield of forage maize.

scholarly journals Determining nitrogen deficiencies for maize using various remote sensing indices

2022 ◽  
Author(s):  
Brayden W. Burns ◽  
V. Steven Green ◽  
Ahmed A. Hashem ◽  
Joseph H. Massey ◽  
Aaron M. Shew ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Nitrogen Fertilizer ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nitrogen Deficiency ◽  
Maize Grain Yield ◽  
Nitrogen Inputs ◽  
Chlorophyll Index ◽  
Maize Grain

AbstractDetermining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

scholarly journals RELATIONSHIP OF SPECTRAL REFLECTANCE AND NDVI TO SOME SOIL PROPERTIES OF BRICKS FACTORIES SOILS IN NAHRAWAN AREA,BAGHDAD IRAQ

2019 ◽  
Vol 50 (3) ◽  
Author(s):  
R. K. Abdullatiff
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Spectral Reflectivity ◽  
Tigris River ◽  
The Difference ◽  
Remote Sensing Techniques ◽  
The One ◽  
Relationship Of

A study was conducted to investigate the effect of the brick industry on the environmental system of these project soils of the brick factories in Alnahrawan district. Remote sensing techniques was used to study the relationship between the spectral reflectivity and the vegetative index on the one hand and some surface soil characters of the project and to determine the variation in vegetation cover for the same area and for two different periods.Ten sites were selected to study spectral reflectivity under similar geomorphological conditions near the brickworks project in the Anahrawan district with an area of 10,000 hectares. Soil samples were taken from the surface and at a depth of 0-30 cm. Some chemical and physical characters of research soil were analyzed in the soil department laboratories, college of Agriculture, Baghdad University.Several satellite images taken from the satellite Land sat (ETM) 2013 and another from same satellite in 1990 T.M to determining the change between the two periods. After obtaining remote sensing data (reflectivity and vegetation index).the correlation analysis was carried out between these data. It was observed that the soil salinity values were decreased due to the drainage that the area was confined between the Tigris River and the Diyala tributary which leads to good natural drainage.The attached tables indicate that thedigital numbers of the soil sampling sites in 2013 are highly significant correlated, While some of the characters did not show the use of this region industrially. After calculating the difference between the two images to determine the change. A 100% change was observed and the vegetation cover was sharply reduced between the two images. as well as the extension of the land of empty land, although these lands are still suitable for agriculture.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau

2019 ◽  
Vol 11 (2) ◽  
pp. 417 ◽  
Author(s):  
Qingqing Ma ◽  
Linrong Chai ◽  
Fujiang Hou ◽  
Shenghua Chang ◽  
Yushou Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Field Experiments ◽  
Grazing Intensity ◽  
Remote Sensing Data ◽  
Gansu Province ◽  
Livestock Grazing ◽  
Scientific Management ◽  
Sensing Data

Remote sensing data have been widely used in the study of large-scale vegetation activities, which have important significance in estimating grassland yields, determining grassland carrying capacity, and strengthening the scientific management of grasslands. Remote sensing data are also used for estimating grazing intensity. Unfortunately, the spatial distribution of grazing-induced degradation remains undocumented by field observation, and most previous studies on grazing intensity have been qualitative. In our study, we tried to quantify grazing intensity using remote sensing techniques. To achieve this goal, we conducted field experiments at Gansu Province, China, which included a meadow steppe and a semi-arid region. The correlation between a vegetation index and grazing intensity was simulated, and the results demonstrated that there was a significant negative correlation between NDVI and relative grazing intensity (p < 0.05). The relative grazing intensity increased with a decrease in NDVI, and when the relative grazing intensity reached a certain level, the response of NDVI to relative grazing intensity was no longer sensitive. This study shows that the NDVI model can illustrate the feasibility of using a vegetation index to monitor the grazing intensity of livestock in free-grazing mode. Notably, it is feasible to use the remote sensing vegetation index to obtain the thresholds of livestock grazing intensity.

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