A Comparison of Multispectral and Multitemporal Information in High Spatial Resolution Imagery for Classification of Individual Tree Species in a Temperate Hardwood Forest

Key, Thomas; Warner, Timothy A.; McGraw, James B.; Fajvan, Mary Ann

doi:10.1016/s0034-4257(00)00159-0

Cited by 204 publications

(160 citation statements)

References 29 publications

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“…These results are in line with previous studies such as Key et al (2001) and Hill et al (2010), who used aerial multi-spectral imagery, and Zhu and Liu (2014), who used satellite imagery.…”

Section: Time Windowsupporting

confidence: 92%

“…This time window also matches well with the time windows found in other multi-temporal remote sensing studies for differentiating forest species (Key et al 2001;Hill et al 2010;Zhu and Liu 2014).…”

Section: Acquisition Of Uas Imagerysupporting

confidence: 87%

“…Successful classifications have been carried out using multi-spectral aerial/ satellite imagery (Immitzer et al 2012;Ke et al 2010;Key et al 2001;Korpela et al 2011;Lucas et al 2008;Sesnie et al 2008) and hyperspectral data (Dalponte et al 2014;2012). The scale of analysis varies from tree crown pixel or object (object-based image analysis) to the crown itself.…”

Section: Introductionmentioning

confidence: 99%

“…When available, 3D data (ALS LiDAR, SRTM DEM) can improve the classification accuracy (Dalponte et al 2014;Ke et al 2010;Sesnie et al 2008). Forest species classification has also been studied through the use of multi-temporal remote sensing dataset which allows to highlight phenological differences between forest species (Key et al 2001;Hill et al 2010;Zhu and Liu 2014). These multi-temporal studies were intended to identify discriminant phenological stages (and related date) in order to schedule future imagery acquisition at lower cost within the identified time window.…”

Section: Introductionmentioning

confidence: 99%

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Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

198

179

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Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very-high-resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives.The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management.The comparison of various scales of image analysis identified the smallest object-based image analysis (OBIA) objects (ca. 1 m 2 ) as the most relevant scale. Variables derived from spectral information (bands ratios) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for site 1 and site 2). The classification scenario regarding the health condition of the black alders of the site 1 performed the best (90.6 %).The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multi-spectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metric datasets derived from those dense time series.

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“…These results are in line with previous studies such as Key et al (2001) and Hill et al (2010), who used aerial multi-spectral imagery, and Zhu and Liu (2014), who used satellite imagery.…”

Section: Time Windowsupporting

confidence: 92%

Section: Acquisition Of Uas Imagerysupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

198

179

View full text Add to dashboard Cite

show abstract

“…For instance, Asner et al [34] used imaging spectroscopy (sometimes called 'hyperspectral') data to effectively map the distribution of invasive and native forest tree species in a Hawaiian rainforest. Recent remote sensing techniques have consistently resolved species-level biodiversity [35][36][37][38], even in high-diversity tropical ecosystems. Moreover, Asner & Martin [28,39], in putting forth the 'spectranomics' approach, have shown a strong relationship between remotely sensed chemical variation and phylogeny that also relates to community assembly.…”

Section: (B) Remote Sensing Of Biodiversitymentioning

confidence: 99%

Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

Madritch

Kingdon²,

Singh³

et al. 2014

Phil. Trans. R. Soc. B

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Fine-scale biodiversity is increasingly recognized as important to ecosystemlevel processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

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Remote Sensing of Land Change: A Multifaceted Perspective

Zhu¹,

Qiu²,

Su³

2022

Preprint

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A Comparison of Multispectral and Multitemporal Information in High Spatial Resolution Imagery for Classification of Individual Tree Species in a Temperate Hardwood Forest

Cited by 204 publications

References 29 publications

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

Remote Sensing of Land Change: A Multifaceted Perspective

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