Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods

Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall veget...

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Autores principales:	Xiaolei Yu, Xulin Guo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fractional vegetation cover SamplePoint image classification OBIA image analysis Northern Mixed Grasslands Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/37e60c0e20bf4b518adc091a1f0fb462
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spelling	oai:doaj.org-article:37e60c0e20bf4b518adc091a1f0fb4622021-11-11T19:15:46ZExtracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods10.3390/s212173101424-8220https://doaj.org/article/37e60c0e20bf4b518adc091a1f0fb4622021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7310https://doaj.org/toc/1424-8220Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall vegetation cover, ignoring variation in live and dead fractions. In the arid regions of the Canadian prairies, grass cover is typically a mixture of green and senescent plant material, and it is essential to monitor both green and senescent vegetation fractional cover. In this study, we designed and built a camera stand to acquire the close-range photographs of rangeland fractional vegetation cover. Photographs were processed by four approaches: SamplePoint software, object-based image analysis (OBIA), unsupervised and supervised classifications to estimate the fractional cover of green vegetation, senescent vegetation, and background substrate. These estimates were compared to in situ surveys. Our results showed that the SamplePoint software is an effective alternative to field measurements, while the unsupervised classification lacked accuracy and consistency. The Object-based image classification performed better than other image classification methods. Overall, SamplePoint and OBIA produced mean values equivalent to those produced by in situ assessment. These findings suggest an unbiased, consistent, and expedient alternative to in situ grassland vegetation fractional cover estimation, which provides a permanent image record.Xiaolei YuXulin GuoMDPI AGarticlefractional vegetation coverSamplePointimage classificationOBIAimage analysisNorthern Mixed GrasslandsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7310, p 7310 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fractional vegetation cover SamplePoint image classification OBIA image analysis Northern Mixed Grasslands Chemical technology TP1-1185
spellingShingle	fractional vegetation cover SamplePoint image classification OBIA image analysis Northern Mixed Grasslands Chemical technology TP1-1185 Xiaolei Yu Xulin Guo Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods
description	Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall vegetation cover, ignoring variation in live and dead fractions. In the arid regions of the Canadian prairies, grass cover is typically a mixture of green and senescent plant material, and it is essential to monitor both green and senescent vegetation fractional cover. In this study, we designed and built a camera stand to acquire the close-range photographs of rangeland fractional vegetation cover. Photographs were processed by four approaches: SamplePoint software, object-based image analysis (OBIA), unsupervised and supervised classifications to estimate the fractional cover of green vegetation, senescent vegetation, and background substrate. These estimates were compared to in situ surveys. Our results showed that the SamplePoint software is an effective alternative to field measurements, while the unsupervised classification lacked accuracy and consistency. The Object-based image classification performed better than other image classification methods. Overall, SamplePoint and OBIA produced mean values equivalent to those produced by in situ assessment. These findings suggest an unbiased, consistent, and expedient alternative to in situ grassland vegetation fractional cover estimation, which provides a permanent image record.
format	article
author	Xiaolei Yu Xulin Guo
author_facet	Xiaolei Yu Xulin Guo
author_sort	Xiaolei Yu
title	Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods
title_short	Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods
title_full	Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods
title_fullStr	Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods
title_full_unstemmed	Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods
title_sort	extracting fractional vegetation cover from digital photographs: a comparison of in situ, samplepoint, and image classification methods
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/37e60c0e20bf4b518adc091a1f0fb462
work_keys_str_mv	AT xiaoleiyu extractingfractionalvegetationcoverfromdigitalphotographsacomparisonofinsitusamplepointandimageclassificationmethods AT xulinguo extractingfractionalvegetationcoverfromdigitalphotographsacomparisonofinsitusamplepointandimageclassificationmethods
_version_	1718431577242861568

Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods

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