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Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques

Predictive modeling with remotely sensed data requires an accurate representation of spatial variability by ground truth data. In this study, we assessed the reliability of the size and location of ground truth data in capturing the landscape spatial variability embedded in the Airborne Visible Infr...

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Autores principales: Salas, Eric Ariel L., Subburayalu, Sakthi Kumaran, Slater, Brian, Dave, Rucha, Parekh, Parshva, Zhao, Kaiguang, Bhattacharya, Bimal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264615/
https://www.ncbi.nlm.nih.gov/pubmed/34278031
http://dx.doi.org/10.1016/j.heliyon.2021.e07439
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author Salas, Eric Ariel L.
Subburayalu, Sakthi Kumaran
Slater, Brian
Dave, Rucha
Parekh, Parshva
Zhao, Kaiguang
Bhattacharya, Bimal
author_facet Salas, Eric Ariel L.
Subburayalu, Sakthi Kumaran
Slater, Brian
Dave, Rucha
Parekh, Parshva
Zhao, Kaiguang
Bhattacharya, Bimal
author_sort Salas, Eric Ariel L.
collection PubMed
description Predictive modeling with remotely sensed data requires an accurate representation of spatial variability by ground truth data. In this study, we assessed the reliability of the size and location of ground truth data in capturing the landscape spatial variability embedded in the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral image in an agricultural region in Anand, India. We derived simulated spectral vegetation and soil indices using Gaussian simulation from AVIRIS-NG image for two point-location datasets, (1) ground truth points from adaptive sampling and (2) points from conditional Latin Hypercube Sampling (cLHS). We compared values of the simulated image indices against the actual image indices (measured) through the analysis of mean absolute errors. Modeling the variogram of the measured indices with the hyperspectral image in high spatial resolution (4m), is an effective way to characterize the spatial heterogeneity at the landscape level. We used geostatistical techniques to analyze the shapes of experimental variograms in order to assess whether or not the ground truth points, when compared against the cLHS-derived points, captured the spatial structures and variability of the studied agricultural area using measured indices. In addition, we explored the capability of the variogram by running tests in different point sample sizes. The ground truth and cLHS datasets were able to derive equivalent values for field spatial variability from image indices, according to our findings. Furthermore, this research presents a methodology for selecting spectral indices and determining the best sample size for efficiently replicating spatial patterns in hyperspectral images.
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spelling pubmed-82646152021-07-16 Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques Salas, Eric Ariel L. Subburayalu, Sakthi Kumaran Slater, Brian Dave, Rucha Parekh, Parshva Zhao, Kaiguang Bhattacharya, Bimal Heliyon Research Article Predictive modeling with remotely sensed data requires an accurate representation of spatial variability by ground truth data. In this study, we assessed the reliability of the size and location of ground truth data in capturing the landscape spatial variability embedded in the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral image in an agricultural region in Anand, India. We derived simulated spectral vegetation and soil indices using Gaussian simulation from AVIRIS-NG image for two point-location datasets, (1) ground truth points from adaptive sampling and (2) points from conditional Latin Hypercube Sampling (cLHS). We compared values of the simulated image indices against the actual image indices (measured) through the analysis of mean absolute errors. Modeling the variogram of the measured indices with the hyperspectral image in high spatial resolution (4m), is an effective way to characterize the spatial heterogeneity at the landscape level. We used geostatistical techniques to analyze the shapes of experimental variograms in order to assess whether or not the ground truth points, when compared against the cLHS-derived points, captured the spatial structures and variability of the studied agricultural area using measured indices. In addition, we explored the capability of the variogram by running tests in different point sample sizes. The ground truth and cLHS datasets were able to derive equivalent values for field spatial variability from image indices, according to our findings. Furthermore, this research presents a methodology for selecting spectral indices and determining the best sample size for efficiently replicating spatial patterns in hyperspectral images. Elsevier 2021-06-29 /pmc/articles/PMC8264615/ /pubmed/34278031 http://dx.doi.org/10.1016/j.heliyon.2021.e07439 Text en © 2021 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Salas, Eric Ariel L.
Subburayalu, Sakthi Kumaran
Slater, Brian
Dave, Rucha
Parekh, Parshva
Zhao, Kaiguang
Bhattacharya, Bimal
Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques
title Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques
title_full Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques
title_fullStr Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques
title_full_unstemmed Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques
title_short Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques
title_sort assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using gaussian simulation and geostatistical techniques
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264615/
https://www.ncbi.nlm.nih.gov/pubmed/34278031
http://dx.doi.org/10.1016/j.heliyon.2021.e07439
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