Prospects for quantifying structure, floristic composition and species richness of tropical forests

Gillespie, Thomas W.; Brock, John C.; Wright, C. Wayne

doi:10.1080/01431160310001598917

Cited by 53 publications

(43 citation statements)

References 37 publications

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“…As stand structure plays a key role in deriving biodiversity indicators, LiDAR may be a useful tool in biodiversity management. For example, it appears a quite interesting tool for biodiversity related analysis, such as habitat type classification (Vierling et al 2008, Collin et al 2010, Bassler et al 2011) and species diversity modeling (Gillespie et al 2004, Vierling et al 2008. Concerning with carbon sequestration, forests can sequester and store more carbon than all other terrestrial ecosystems (IPCC 2001) and exchange 90% of total carbon flow between the atmosphere and biosphere (Winjum et al 1992).…”

Section: Introductionmentioning

confidence: 99%

A LiDAR-based approach for a multi-purpose characterization of Alpine forests: an Italian case study

Alberti¹,

Boscutti²,

Pirotti³

et al. 2013

iForest

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Several studies have verified the suitability of LiDAR for the estimation of forest metrics over large areas. In the present study we used LiDAR as support for the characterization of structure, volume, biomass and naturalistic value in mixed-coniferous forests of the Alpine region. Stem density, height and structure in the test plots were derived using a mathematical morphology function applied directly on the LiDAR point cloud. From these data, digital maps describing the horizontal and vertical forest structure were derived. Volume and biomass were then computed using regression models. A strong agreement (accuracy of the map = 97%, Kappa Cohen = 94%) between LiDAR land cover map (i.e., bare soil, forest, shrubs) and ground data was found, while a moderate agreement between coniferous/broadleaf map derived from LiDAR data and ground surveys was detected (accuracy = 73%, Kappa Cohen = 60%). An analysis of the forest structure map derived from LiDAR data revealed a prevalence of even-age stands (66%) in comparison to the multilayered and uneven-aged forests (20%). In particular, the even-age stands, whether adult or mature, were overwhelming (33%). A moderate agreement was then detected between this map and ground data (accuracy = 68%, Kappa Cohen = 58%). Moreover, strong correlations between LiDAR-estimated and ground-measured volume and aboveground carbon stocks were detected. Related observations also showed that stem density can be rightly estimated for adult and mature forests, but not for younger categories, because of the low LiDAR posting density (2.8 points m-2). Regarding environmental issues, this study allowed us to discriminate the different contribution of LiDAR-derived forest structure to biodiversity and ecological stability. In fact, a significant difference in floristic diversity indexes (species richness - R, Shannon index - H’) was found among structural classes, particularly between pole wood (R=15 and H’=2.8; P <0.01) and multilayer forest (R=31 and H’=3.4) or thicket (R=28 and H’=3.4) where both indexes reached their maximum values

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Section: Introductionmentioning

confidence: 99%

A LiDAR-based approach for a multi-purpose characterization of Alpine forests: an Italian case study

Alberti¹,

Boscutti²,

Pirotti³

et al. 2013

iForest

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“…It is important to note that, in tropical forest, tree height measurements from the ground are problematic and cumbersome explaining the enthusiasm aroused by Lidar data (e.g. Gillespie et al 2004). Another important point to improve AGB prediction would be to conduct forest inventories simultaneously to image acquisitions.…”

Section: Requirements For Forest Datamentioning

confidence: 99%

Biomass Prediction in Tropical Forests: The Canopy Grain Approach

Proisy¹,

Barbier²,

Guéroult³

et al. 2012

Remote Sensing of Biomass - Principles and Applications

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“…There has been an increasing effort on the study of vegetation in connection with floristic composition, life forms and biological spectrum in different times (Gillespie 2004, Batalha & Martins 2004, Reddy et al 2011, Theilade et al 2011, Saikia et al 2012, Naveed et al 2012, Burja et al 2013, Aye et al 2014, Thakur 2015, Alemu et al 2015. However, no such study has been conducted hitherto in the Nalbari district of Assam.…”

Section: Introductionmentioning

confidence: 99%

Floristic composition and biological spectrum of weeds in agro-climatic zone of Nalbari district, Assam, India

Bhattacharjya¹,

Sarma²

2016

Trop. Plant Res.

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Present paper deals with the study of floristic composition and biological spectrum of the agro-climatic zone of Nalbari district of Assam. Field observation encompasses a total of 217 weed species from both angiosperms and pteridophytes belonging to 150 genera under 60 families. Asteraceae, being the largest family possesses 19 genera and 22 species. Out of the total species, 183 species (84.33 %) showed the annual life span as against 34 species (15.67 %) showing the perennial life span. One hundred thirty eight species (63.59 %) were recorded to be propagated by seeds/spores; 66 species (30.41 %) were found to opt for both seeds and vegetative propagules and only 13 species (5.99 %) were listed to be propagated exclusively by vegetative propagules. The phytoclimate of the agro-climatic zone can be regarded as thero-cryptophytic since major percentage of the species falls under the life forms Therophytes and Cryptophytes which has been assessed after comparison with the normal world spectrum as proposed by Raunkiaer.

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Prospects for quantifying structure, floristic composition and species richness of tropical forests

Cited by 53 publications

References 37 publications

A LiDAR-based approach for a multi-purpose characterization of Alpine forests: an Italian case study

A LiDAR-based approach for a multi-purpose characterization of Alpine forests: an Italian case study

Biomass Prediction in Tropical Forests: The Canopy Grain Approach

Floristic composition and biological spectrum of weeds in agro-climatic zone of Nalbari district, Assam, India

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