Feasibility of active handheld NDVI sensors for monitoring lichen ground cover

Erlandsson, Rasmus; Arneberg, M.K.; Tømmervik, Hans; Finne, E.A.; Nilsen, Lennart; Bjerke, Jarle W.

doi:10.1016/j.funeco.2023.101233

Cited by 5 publications

(5 citation statements)

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“…Furthermore, this study focuses on physical landscape image acquisition. Meanwhile, the present photographic method of vegetation analysis with normalized difference vegetation index (NDVI) is popular (Moinardeau et al, 2021;Erlandsson et al, 2023;Zhang and Zeng, 2024). The purpose of NDVI approaches is to classify soil more clearly, extract spectral information from a top-down perspective, and then conduct research on vegetation coverage and growth status.…”

Section: Discussionmentioning

confidence: 99%

The correlation between autumnal landscape colors and environmental factors and the analysis of ecological service functions in subtropical urban scenic forests

Wang,

2024

Front. For. Glob. Change

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Background and objectivesUrban scenic forests are a main component of forest parks, and a quantitative study of the landscape color of urban scenic forests can provide a scientific reference for the planning of forest parks and the design of scenic forest management measures. The purpose of the study is to find the quantitative correlation between autumn landscape color and ecological service functions and to obtain the important influencing factors.Materials and methodsThis study focuses on Purple Mountain National Forest Park in Nanjing as a case study area and uses forest resource survey data from Purple Mountain National Forest Park in 2020, autumn landscape color photograph data from Purple Mountain National Forest Park in 2020, and digital elevation model data as the main information sources. The correlation between two ecological functions of above-ground biomass (AGB), tree species diversity (TSD), and influencing factors (including color factors, stand factors, and terrain factors) were both analyzed by Pearson correlation analysis. Then, multiple linear regression (MLR) and random forest (RF) methods were used to perform the quantitative relationship between the functions.ResultsThe results show that, in the established quantitative models of AGB, with TSD as the dependent variable, the correlation coefficients of the MLR model are both above 0.784, while the correlation coefficients of the RF model are all above 0.872. Moreover, the brightness value of the main color (BRI), the number of yellow-green blocks (NYG), and the number of yellow blocks (NY) have important effects on the two ecological service functions.ConclusionIn conclusion, there are complex non-linear relationships between the ecological service functions of AGB, TSD, and influencing factors, and the landscape color can reflect the ecological function of the scenic forest to some extent. In addition, stand factors and color factors have important effects on the ecological function of AGB. Color factors and terrain factors have important effects on the ecological function of TSD. BRI, NYG, and NY have important effects on the two ecological functions. Finally, this quantitative method has universal applicability in the temperate zone, warm temperate zone, and subtropical zone of China.

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Section: Discussionmentioning

confidence: 99%

The correlation between autumnal landscape colors and environmental factors and the analysis of ecological service functions in subtropical urban scenic forests

Wang,

2024

Front. For. Glob. Change

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“…for estimating lichen biomass (e.g., Colpaert et al, 2003, Colpaert & Kumpula, 2012Erlandsson et al, 2023;Silva et al, 2019) or volume (Falldorf et al, 2014). However, more commonly, lichen cover has been mapped using Landsat (e.g., Gilichinsky et al, 2011;Macander et al, 2020;Nelson et al, 2013;Nordberg & Allard, 2014;Theau et al, 2005;Theau & Duguay, 2004) or SPOT-5 (Gilichinsky et al, 2011) data.…”

Section: Lichens As Part Of Animal Habitat Monitoringmentioning

confidence: 99%

“…Approximately half of the published studies have used optical satellite data, one‐fourth of the studies have used airborne (aircraft or helicopter based) data and under 5% of studies have utilized data acquired by sensors on unoccupied aerial vehicles (UAV). Nearly all studies have applied passive optical remote sensing techniques, but there are a couple of exceptions of using active remote sensing technologies where LiDAR (Kaasalainen & Rautiainen, 2005 ; Korpela, 2008 ; Moeslund et al., 2019 ) or an active normalized difference vegetation index (NDVI) sensor (Erlandsson et al., 2023 ) have been employed. Spectral reference data (or close‐range remote sensing data) of lichen samples or entire communities have been collected in laboratory or field conditions in about 30% and 40% of the articles, respectively.…”

Section: Types Of Remote Sensing Data In Lichen‐related Studiesmentioning

confidence: 99%

“…The earliest studies in this specific context were conducted in the 1980s using Landsat‐5 TM images to map areas where lichen cover had changed due to reindeer grazing in northern Fennoscandia (Johansen & Tømmervik, 1990 ; Väre et al., 1996 ). To date, medium resolution satellite data mainly from Landsat sensors (5 TM, 7 ETM+ and 8 OLI) have been used for estimating lichen biomass (e.g., Colpaert et al., 2003 , Colpaert & Kumpula, 2012 ; Erlandsson et al., 2023 ; Silva et al., 2019 ) or volume (Falldorf et al., 2014 ). However, more commonly, lichen cover has been mapped using Landsat (e.g., Gilichinsky et al., 2011 ; Macander et al., 2020 ; Nelson et al., 2013 ; Nordberg & Allard, 2014 ; Theau et al., 2005 ; Theau & Duguay, 2004 ) or SPOT‐5 (Gilichinsky et al., 2011 ) data.…”

Section: Recurring Themes In Studies On Remote Sensing Of Lichensmentioning

confidence: 99%

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Remote sensing and spectroscopy of lichens

Rautiainen,

Kuusinen,

Majasalmi

2024

Ecology and Evolution

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Lichens are combinations of two symbiotic organisms, a green alga or cyanobacterium and a fungus. They grow in nearly all terrestrial ecosystems and survive in habitats, which are very dry or cold, or too poor in nutrients to maintain vegetation growth. Because lichens grow on visible surfaces and exhibit spectral properties, which are clearly different from, for example, vegetation, it is possible to distinguish them in remote sensing data. In this first systematic review article on remote sensing of lichens, we analyze and summarize which lichen species or genera, and in which habitats and geographical regions, have been remotely sensed, and which remote sensing or spectroscopic technologies have been used. We found that laboratory or in situ measured spectra of over 70 lichen species have been reported to date. We show that studies on remote sensing of lichens fall under seven broad themes: (1) collection of lichen spectra for quantification of lichen species or characteristics, (2) pollution monitoring with lichens as ecological indicators, (3) geological and lithological mapping, (4) desert and dryland monitoring, (5) animal habitat monitoring, (6) land cover or vegetation mapping, and (7) surface energy budget modeling.

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“…Es un índice ampliamente utilizado en la agricultura, desde para el seguimiento de los cultivos (Seo et al, 2019), hasta para la predicción de su rendimiento (Ji et al, 2021). El seguimiento de la vegetación y, por lo tanto, del estado ecológico de Predicción de cosechas de setas silvestres en bosques mediterráneos utilizando sensores remotos activos y pasivos Introducción los bosques es una de las aplicaciones directas de uso del NDVI más habituales en el ámbito ambiental y forestal (Prăvălie et al, 2022), aunque también ha demostrado tener utilidad para la cuantificación del carbono orgánico del suelo en pastizales (Li et al, 2023), la estimación de la humedad del suelo (Wang et al, 2004a), la estimación de biomasa (Zhu y Liu, 2015), o incluso para realizar el seguimiento de la cubierta vegetal de líquenes (Erlandsson et al (2023). No se puede olvidar que el NDVI ha demostrado ser en todos los casos un buen estimador de la productividad primaria (Vicente-Serrano et al, 2016), lo que le ha hecho ser el índice de más amplio uso desde los inicios de la teledetección.…”

Section: Predicción De Cosechas De Setas Silvestres En Bosques Medite...unclassified

Predicción de cosechas de setas silvestres en bosques mediterráneos utilizando sensores remotos activos y pasivos

Martínez Rodrigo

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Feasibility of active handheld NDVI sensors for monitoring lichen ground cover

Cited by 5 publications

References 43 publications

The correlation between autumnal landscape colors and environmental factors and the analysis of ecological service functions in subtropical urban scenic forests

The correlation between autumnal landscape colors and environmental factors and the analysis of ecological service functions in subtropical urban scenic forests

Remote sensing and spectroscopy of lichens

Predicción de cosechas de setas silvestres en bosques mediterráneos utilizando sensores remotos activos y pasivos

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