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Ashish Vivekar (2020) Evaluation of methodology for estimating crop yield from multispectral UAV images : a case study at Lönnstorp, Sweden

Vivekar, Ashish LU (2020) In Lund University GEM thesis series NGEM01 20191
Dept of Physical Geography and Ecosystem Science
Abstract
Adaptation of modern Unmanned Aerial Vehicle (UAV) and multispectral sensor technology in agriculture can enhance the capacity to accurately monitor crops. But these technologies come with its own set of challenges. The major challenge is the understanding of radiometric distortions, which is particularly important while comparing data over different lighting conditions. The study developed and assessed a methodology for extracting radiometrically corrected reflectance values for multi-temporal datasets and to find out if crop yield it can be estimated using it. The empirical line method is used to calibrate the images using spectrally stable panels. The methodology is assessed by studying the association between three vegetation indices... (More)
Adaptation of modern Unmanned Aerial Vehicle (UAV) and multispectral sensor technology in agriculture can enhance the capacity to accurately monitor crops. But these technologies come with its own set of challenges. The major challenge is the understanding of radiometric distortions, which is particularly important while comparing data over different lighting conditions. The study developed and assessed a methodology for extracting radiometrically corrected reflectance values for multi-temporal datasets and to find out if crop yield it can be estimated using it. The empirical line method is used to calibrate the images using spectrally stable panels. The methodology is assessed by studying the association between three vegetation indices namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Difference Vegetation Index (DVI) with corresponding crop yield, crop height and fixed tower sensor data.
The radiometric correction techniques delivered reasonably satisfactory results which was revealed by very strong Pearson correlation (r = 0.87 - 0.95) between fixed tower and UAV sensors. The investigation also identified that the vegetation indices dependency varied positively with (fresh) yield of legume ley, from moderate to high, on the first harvest date (11th May 2019) as well as on the second harvest date (26th July 2019). The Pearson correlation (r) and Spearman rank correlation coefficients (rs) ranged between 0.55 and 0.89 on the first harvest date and between 0.49 and 0.79 on the second harvest. Comparable results were obtained for other crops but only at certain stages of crop development. The analysis revealed moderate to high positive relationship (r = 0.45 – 0.87) between vegetation indices (NDVI and SAVI) and crop height except on 21st August 2019 dataset, where the relationship was rather weak (r = 0.19 and 0.04). DVI showed similar trend (r = 0.62 – 0.81), except in the case of 26th July 2019 and 21st August 2019 datasets, where correlation coefficients were r = 0.19 and r = -0.02 respectively. It was also observed that 6 datasets over the growing period are not enough to clearly see the complete crop phenology, although the comparison of NDVI, SAVI and DVI indicated similar patterns. The inability in reflecting the complete phenology of various crops was mainly due to less frequent data acquisition at various development stages. Nonetheless, very high positive correlation between vegetation indices from UAV sensor and fixed tower sensor validated the capability of UAV sensor to monitor the crops over various stages.
Keywords: Physical Geography and Ecosystem analysis, UAV, Multispectral Sensor, Radiometric Correction, NDVI, SAVI, DVI, Phenology.
Advisor: Lars Eklundh
Master degree project 30 credits in Geo-information Science and Earth Observation for Environmental Modelling and Management (GEM), 2019 Department of Physical Geography and Ecosystem Science, Lund University. Student thesis series INES nr 28 (Less)
Popular Abstract
Adaptation of modern drone and multispectral sensor technology in agriculture can improve the way crops are monitored and crop yields are estimated. But these technologies come with its own set of challenges. The primary challenge is in understanding the effect of changes in illuminance, which is particularly important while comparing data over different lighting conditions. The study developed and assessed a methodology for extracting accurate reflectance values of crops from multi-temporal datasets and to find out if crop yield can be estimated using it. The idea is to find changes in the reflectance only due to change in growth stage of crop while nullifying the effect caused by change in solar illuminance, solar angle, atmospheric... (More)
Adaptation of modern drone and multispectral sensor technology in agriculture can improve the way crops are monitored and crop yields are estimated. But these technologies come with its own set of challenges. The primary challenge is in understanding the effect of changes in illuminance, which is particularly important while comparing data over different lighting conditions. The study developed and assessed a methodology for extracting accurate reflectance values of crops from multi-temporal datasets and to find out if crop yield can be estimated using it. The idea is to find changes in the reflectance only due to change in growth stage of crop while nullifying the effect caused by change in solar illuminance, solar angle, atmospheric condition etc., the absolute reflectance values are used to calculate different vegetation indices which are principally quantitative estimate of vegetation properties. The primary assumption is that the greener crops have higher yield.
The methodology is assessed by studying the association between three vegetation indices namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Difference Vegetation Index (DVI) with corresponding crop yield, crop height and fixed tower sensor data. The reflectance correction technique delivered satisfactory results which were revealed by very strong correlation between fixed tower and on drone sensor. The investigation also identified that the vegetation indices dependency varied positively with (fresh) yield of legume ley, from moderate to high, on the first harvest date as well as on the second harvest date. Comparable results were obtained for other crops but only at certain stage of crop development. The analysis revealed moderate to high positive relationship between vegetation indices and crop height except on one of six datasets.
It was also observed that 6 datasets over the growing period are not enough to clearly see the complete crop phenology, although the comparison of the three vegetation indices indicated similar patterns. The inability to clearly see the complete phenology of various crops was mainly due to less frequent data acquisition in development stages. Nonetheless, very high positive correlation between vegetation indices from drone sensor and fixed tower sensor validated the capability of sensor mounted on unmanned aerial vehicle to monitor the crops over various stages.
Keywords: Physical Geography and Ecosystem analysis, Multispectral Sensor, Radiometric Correction, Reflection calibration, Phenology.
Advisor: Lars Eklundh
Master degree project 30 credits in Geo-information Science and Earth Observation for Environmental Modelling and Management (GEM), 2019 Department of Physical Geography and Ecosystem Science, Lund University. Student thesis series INES nr 28 (Less)
Please use this url to cite or link to this publication:
author
Vivekar, Ashish LU
supervisor
organization
course
NGEM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Physical Geography and Ecosystem analysis, Multispectral Sensor, Radiometric Correction, Reflection calibration, Phenology, GEM
publication/series
Lund University GEM thesis series
report number
28
language
English
id
9003902
date added to LUP
2020-01-31 16:16:48
date last changed
2020-01-31 16:16:48
@misc{9003902,
  abstract     = {Adaptation of modern Unmanned Aerial Vehicle (UAV) and multispectral sensor technology in agriculture can enhance the capacity to accurately monitor crops. But these technologies come with its own set of challenges. The major challenge is the understanding of radiometric distortions, which is particularly important while comparing data over different lighting conditions. The study developed and assessed a methodology for extracting radiometrically corrected reflectance values for multi-temporal datasets and to find out if crop yield it can be estimated using it. The empirical line method is used to calibrate the images using spectrally stable panels. The methodology is assessed by studying the association between three vegetation indices namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Difference Vegetation Index (DVI) with corresponding crop yield, crop height and fixed tower sensor data. 
The radiometric correction techniques delivered reasonably satisfactory results which was revealed by very strong Pearson correlation (r = 0.87 - 0.95) between fixed tower and UAV sensors. The investigation also identified that the vegetation indices dependency varied positively with (fresh) yield of legume ley, from moderate to high, on the first harvest date (11th May 2019) as well as on the second harvest date (26th July 2019). The Pearson correlation (r) and Spearman rank correlation coefficients (rs) ranged between 0.55 and 0.89 on the first harvest date and between 0.49 and 0.79 on the second harvest. Comparable results were obtained for other crops but only at certain stages of crop development. The analysis revealed moderate to high positive relationship (r = 0.45 – 0.87) between vegetation indices (NDVI and SAVI) and crop height except on 21st August 2019 dataset, where the relationship was rather weak (r = 0.19 and 0.04). DVI showed similar trend (r = 0.62 – 0.81), except in the case of 26th July 2019 and 21st August 2019 datasets, where correlation coefficients were r = 0.19 and r = -0.02 respectively. It was also observed that 6 datasets over the growing period are not enough to clearly see the complete crop phenology, although the comparison of NDVI, SAVI and DVI indicated similar patterns. The inability in reflecting the complete phenology of various crops was mainly due to less frequent data acquisition at various development stages. Nonetheless, very high positive correlation between vegetation indices from UAV sensor and fixed tower sensor validated the capability of UAV sensor to monitor the crops over various stages.
Keywords: Physical Geography and Ecosystem analysis, UAV, Multispectral Sensor, Radiometric Correction, NDVI, SAVI, DVI, Phenology. 
Advisor: Lars Eklundh
Master degree project 30 credits in Geo-information Science and Earth Observation for Environmental Modelling and Management (GEM), 2019 Department of Physical Geography and Ecosystem Science, Lund University. Student thesis series INES nr 28},
  author       = {Vivekar, Ashish},
  keyword      = {Physical Geography and Ecosystem analysis,Multispectral Sensor,Radiometric Correction,Reflection calibration,Phenology,GEM},
  language     = {eng},
  note         = {Student Paper},
  series       = {Lund University GEM thesis series},
  title        = {Ashish Vivekar (2020) Evaluation of methodology for estimating crop yield from multispectral UAV images : a case study at Lönnstorp, Sweden},
  year         = {2020},
}