Linking imaging spectroscopy and LiDAR with floristic composition and forest structure in Panama

Higgins, Mark; Asner, Gregory P.; Martin, Robert E.; Knapp, David; Anderson, Christopher B.; Kennedy-Bowdoin, Ty; Saenz, Roni; Aguilar, Antonio Gutiérrez; Wright, S. Joseph

doi:10.1016/j.rse.2013.09.032

Cited by 21 publications

(12 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, at smaller spatial scales, we find that LiDAR‐derived variables, especially those like height variance in LiDAR returns that exploit the sensor's high precision, are significant (and, in fact, prominent) in detecting the primary remotely sensible environmental gradients driving compositional variation (Hill and Thomson , Higgins et al. ).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing

Hakkenberg

Peet

Urban

et al. 2017

Ecological Applications

View full text Add to dashboard Cite

Abstract. In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling.

show abstract

Section: Discussionmentioning

confidence: 99%

“…, Higgins et al. ). And when combined, LiDAR‐hyperspectral systems have been found to outperform either when used on its own (Hill and Thomson , Leutner et al.…”

Section: Introductionmentioning

confidence: 99%

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing

Hakkenberg

Peet

Urban

et al. 2017

Ecological Applications

View full text Add to dashboard Cite

show abstract

“…The majority of work at the landscape scale has thus focused on general description of forest physiognomy, relatively small spatial domains, subsets of common species, or family-level taxonomy (e.g. Higgins et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Somers

Asner

Martin

et al. 2015

Remote Sensing of Environment

Self Cite

View full text Add to dashboard Cite

“…The combination of both technologies is considered the most promising strategy to provide highly accurate estimates of forest structural attributes [66,114,123] and other plant traits [124,125]. Future research attempts are needed to properly evaluate the capability of combined approaches and to tackle various technical issues such as the temporal and geometric integration of these complementary observational approaches.…”

Section: Strategies For Improved Simultaneous Retrieval Of Biochemicamentioning

confidence: 99%

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

et al. 2015

View full text Add to dashboard Cite

Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R 2 = 0.81, rRMSE = 10%), basal area (R 2 = 0.68, rRMSE = 20%), and timber volume (R 2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information.

show abstract

Linking imaging spectroscopy and LiDAR with floristic composition and forest structure in Panama

Cited by 21 publications

References 48 publications

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

Contact Info

Product

Resources

About