Hyperspectral Remote Sensing of Canopy Chemistry, Physiology, and Biodiversity in Tropical Rainforests

Asner, Gregory P.

doi:10.1201/9781420053432.ch12

Cited by 75 publications

(62 citation statements)

References 80 publications

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“…Immitzer et al, 2016;Gil et al, 2012). Furthermore, observing alien plants as acacia requires data collected from sensors pushing the limits of at least one type of resolution (spatial, temporal or spectral resolution) since the profiles of these species may be quite similar to those of native plants, from a RS perspective (Asner, 2008). For instance, the use of very high spatial resolution in several studies on mapping invasive species has pointed out very encouraging results (e.g.…”

Section: Acacia Spatial Distributionmentioning

confidence: 99%

Mapping invasive alien Acacia dealbata Link using ASTER multispectral imagery: a case study in central-eastern of Portugal

2016

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Aim of the study: Acacia dealbata is an alien invasive species that is widely spread in Portugal. The main goal of this study was to produce an accurate and detailed map for this invasive species using ASTER multispectral imagery.Area of study: The central-eastern zone of Portugal was used as study area. This whole area is represented in an ASTER scene covering about 321.1 x 10 3 ha. Material and methods: ASTER imagery of two dates (flowering season and dry season) were classified by applying three supervised classifiers (Maximum Likelihood, Support Vector Machine and Artificial Neural Networks) to five different land cover classifications (from most generic to most detailed land cover categories). The spectral separability of the land cover categories was analyzed and the accuracy of the 30 produced maps compared.Main results: The highest classification accuracy for acacia mapping was obtained using the flowering season imagery, the Maximum Likelihood classifier and the most detailed land cover classification (overall accuracy of 86%; Kappa statistics of 85%; acacia class Kappa statistics of 100%). As a result, the area occupied by acacia was estimated to be approximated 24,770 ha (i.e. 8% of the study area).Research highlights: The methodology explored proved to be a cost-effective solution for acacia mapping in central-eastern of Portugal. The obtained map enables a more accurate and detailed identification of this species' invaded areas due to its spatial resolution (minimum mapping unit of 0.02 ha) providing a substantial improvement comparably to the existent national land cover maps to support monitoring and control activities.

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Section: Acacia Spatial Distributionmentioning

confidence: 99%

Mapping invasive alien Acacia dealbata Link using ASTER multispectral imagery: a case study in central-eastern of Portugal

2016

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“…Data was tested for normality and determined to require non-parametric statistics. Three correlation-based statistical approaches were conducted: (1) correlation analysis using reflectance, derivatives, and absorbance transformations (Asner 2008;Smith et al 2003); (2) correlation analysis with indices (Im et al 2009); and (3) partial least squares regression (PLSR) with reflectance, derivative, and absorbance values (Asner et al 2011;Martin et al 2008). The purpose of the correlational analyses was to determine any regions of the spectrum (i.e.…”

Section: Indexmentioning

confidence: 99%

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Stein

Thomas

Lorentz

et al. 2014

GIScience & Remote Sensing

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Given the economic importance of loblolly pine (Pinus taeda) in the southeastern US, there is a need to establish efficient methods of detecting potential nutrient deficiencies that may limit productivity. This study evaluated the use of remote sensing for macronutrient assessment in loblolly pine. Reflectance-based models were developed at two spatial scales: (1) a natural nutrient gradient across the species' range, and (2) localized fertilization and genotype treatments in North Carolina and Virginia. Fascicles were collected regionally from 237 samples of 3 flushes at 18 sites, and locally from 72 trees with 2 fertilization treatments and 6 genotypes. Sample spectral reflectance was calculated using a spectroradiometer, and nutrient concentrations were measured with dry combustion and wet chemical digestion. Results were analyzed statistically using nutrient correlations with reflectance and common vegetation indices, and partial least squares regression (PLSR). PLSR performed well at the regional scale, with R 2 values for nitrogen, phosphorus, potassium, calcium, and magnesium of 0. 81, 0.70, 0.68, 0.42, and 0.51, respectively. No model successfully predicted nutrients at local sites for any treatment or canopy stratum. This discrepancy implies that a large nutrient range and/or spatial scale may be necessary to model loblolly pine nutrients with spectral reflectance.

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“…For example, Coops et al [10] examined the utility of Hyperion IS data to predict N in eucalyptus forest canopies while Smith et al [11] and Townsend et al [12] compared the effectiveness of AVIRIS and Hyperion IS data to predict N concentration in temperate forest canopies. Canopy scale analysis to predict N from airborne IS data has been conducted in temperate [13][14][15][16] and tropical [17][18][19][20] forest ecosystems. Work in the boreal forest has been more limited.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

et al. 2015

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Information on foliar macronutrients is required in order to understand plant physiological and ecosystem processes such as photosynthesis, nutrient cycling, respiration and cell wall formation. The ability to measure, model and map foliar macronutrients (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)) at the forest canopy level provides information on the spatial patterns of ecosystem processes (e.g., carbon exchange) and provides insight on forest condition and stress. Imaging spectroscopy (IS) has been used particularly for modeling N, using airborne and satellite imagery mostly in temperate and tropical forests. However, there has been very little research conducted at these scales to model P, K, Ca, and Mg and few studies have focused on boreal forests. We report results of a study of macronutrient modeling using spaceborne IS and airborne light OPEN ACCESSRemote Sens. 2015, 7 9046 detection and ranging (LiDAR) data for a mixedwood boreal forest canopy in northern Ontario, Canada. Models incorporating Hyperion data explained approximately 90% of the variation in canopy concentrations of N, P, and Mg; whereas the inclusion of LiDAR data significantly improved the prediction of canopy concentration of Ca (R 2 = 0.80). The combined used of IS and LiDAR data significantly improved the prediction accuracy of canopy Ca and K concentration but decreased the prediction accuracy of canopy P concentration. The results indicate that the variability of macronutrient concentration due to interspecific and functional type differences at the site provides the basis for the relationship observed between the remote sensing measurements (i.e., IS and LiDAR) and macronutrient concentration. Crown closure and canopy height are the structural metrics that establish the connection between macronutrient concentration and IS and LiDAR data, respectively. The spatial distribution of macronutrient concentration at the canopy scale mimics functional type distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using only IS, only LiDAR or their combination demonstrates the excellent potential for mapping these macronutrients at canopy scales across larger geographic areas into the next decade with the launch of new IS satellite missions and by using spaceborne LiDAR data.

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Hyperspectral Remote Sensing of Canopy Chemistry, Physiology, and Biodiversity in Tropical Rainforests

Cited by 75 publications

References 80 publications

Mapping invasive alien Acacia dealbata Link using ASTER multispectral imagery: a case study in central-eastern of Portugal

Mapping invasive alien Acacia dealbata Link using ASTER multispectral imagery: a case study in central-eastern of Portugal

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

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