A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment

Ghiyamat, Azadeh; Shafri, Helmi Zulhaidi Mohd

doi:10.1080/01431160902926681

Cited by 106 publications

(60 citation statements)

References 91 publications

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“…Clark et al (2005) used high spectral and spatial resolution imagery acquired over tropical rain forest canopy of La Selva biological station for automated discrimination of individual tree species. In addition to these, most of the other studies were based on the spectra of leaves or branches alone (Ghiyamat and Shafri 2010). For example, Cochrane (2000) demonstrated the potential for separation of different tropical species based on the foliage reflectance (450-950 nm) of 11 species in southern Pará, Brazil.…”

Section: Fine Resolution Remote Sensing Instruments and Their Applicamentioning

confidence: 99%

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

2010

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Since the launch of the first satellite in 1972, ecologists have been equipped with new tools to address the degradation of tropical forests, previously limited by field-based methods. This article is a review of the state of remote sensing technology in characterizing the degradation of tropical forest. The factors responsible for the structural and functional degradation of the tropical forest and its likely impacts are described in view of generating remote sensing based inputs. In order to assess the degradation and utility of geo-informatics tools, 32 parameters are identified. The research developments at different levels of information extraction from the historic to recent periods are elaborated, and future challenges are predicted. The article concludes that an additional momentum of research is required to answer many unresolved questions of tropical forest degradation.

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Section: Fine Resolution Remote Sensing Instruments and Their Applicamentioning

confidence: 99%

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

2010

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“…Although the chemical composition of turquoise has been studied previously [30], and a formula has been proposed for this mineral (Cu(Al,Fe 3+ ) 6 (PO 4 ) 4 (OH) 8 ·4H 2 O), the relationships between radicals in the turquoise molecule and absorptions at different wavelengths are still vague.…”

Section: Absorption Mechanismmentioning

confidence: 99%

“…Turquoise-hydrous phosphate of aluminum and copper, Cu(Al,Fe 3+ ) 6 (PO 4 ) 4 (OH) 8 ·4H 2 O-is of gemmological interest, with a brilliant history of several thousand years. It was deeply loved by the Chinese, and by people from various countries.…”

Section: Introductionmentioning

confidence: 99%

“…The reflectance property depends on the physical and chemical states of the material, and can be visualized in the form of spectral reflectance curves (or reflectance spectra) as a function of wavelength. Differences in reflectance spectra can be used to classify or distinguish different materials [1][2][3][4][5][6][7]. The development of Hyper-spectral imaging (or imaging spectroscopy) integrates the power of digital imaging and spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

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Reflectance Spectroscopy Characteristics of Turquoise

Qiu

Duan

2016

Minerals

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Abstract:In this study, we determined the reflectance spectra of four types of turquoise with different hardness (porcelain, hard turquoise, soft turquoise, and loose turquoise) using an ASD TM TerraSpec spectrometer (spectral range 350-2500 nm, Visible-Near Infrared, and Short-wave Infrared). Several absorption features, including six narrow absorption peaks at 425 nm, 1480 nm, 2160 nm, 2218 nm, 2253 nm, and 2347 nm, and three wide peaks between 625-756 nm, 756-915 nm, and 1885-2133 nm have been identified. The strength of the absorption of turquoise increased with decreasing hardness. The absorption peaks at 2160 nm, 2218 nm, 2253 nm, 2347 nm, and 1885-2133 nm on some turquoise spectra (porcelain spectra, for example) were relatively weak, while those at 425 nm, 1480 nm, 625-756 nm, and 756-915 nm were always observed on all turquoise spectra, which could be the diagnostic absorption features for turquoise. Additionally, the hyper-spectral imaging (spectral range 1000-2500 nm, Short-wave Infrared) of the four types of turquoise were obtained using a HySpex TM imager. The Spectral Angle Mapper (SAM) method was successfully used to recognize turquoises, suggesting that hyper-spectral imaging may serve as a useful tool for fast turquoise identification and separation, especially for massive turquoise samples.

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“…Hyperspectral imagery has been used to identify tree species occurring within temperate and tropical forests [40][41][42]. Most subtropical and tropical studies to date have relied on high-resolution hyperspectral imagery to identify rainforest and savanna tree species at the individual tree canopy scale [39,[43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

et al. 2015

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An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p < 0.0001) OPEN ACCESSRemote Sens. 2015, 7 5661 of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

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A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment

Cited by 106 publications

References 91 publications

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

Reflectance Spectroscopy Characteristics of Turquoise

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

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