Detection of gradients of forest composition in an urban area using imaging spectroscopy

Gu, Huan; Singh, Aditya; Townsend, Philip A.

doi:10.1016/j.rse.2015.06.010

Cited by 36 publications

(32 citation statements)

References 74 publications

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“…, Gu et al. ). Concurrently, NMDS 3 is positively correlated with the carotenoid reflectance index 2 (CRI2), indicating a greater proportion of orange‐red carotenoid pigments in broadleaf forests undergoing partial leaf senescence (Gitelson et al.…”

Section: Discussionmentioning

confidence: 99%

“…, Gu et al. ). 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%

“…Given a representative group of field plots whose location in ordination space exhibits a statistically significant relationship with remotely sensed environmental and reflectance data, predictive models can be effectively employed to interpolate the mean and variance of new samples or pixels (Gu et al. , Singh et al. ).…”

Section: Discussionmentioning

confidence: 99%

“…Gu et al. () predicted forest composition in Wisconsin, USA with an accuracy of 0.67 and 0.47 for NMDS 1 and 2, respectively. While caution is advised against the direct comparison of predictive accuracies among studies employing disparate methodologies, that accuracies for predicting full stand composition (including undetected understory flora) match those from studies restricted to directly detected upper canopy biota, reaffirms the potential for the full‐stand, predictive modeling approach.…”

Section: Discussionmentioning

confidence: 99%

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

Hakkenberg

Peet

Urban

et al. 2017

Ecological Applications

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Hakkenberg

Peet

Urban

et al. 2017

Ecological Applications

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“…To date, several remote sensing methods have been developed to map tree-community composition using imaging spectroscopy [45][46][47][48][49]. Where imaging spectroscopy (also known as hyperspectral imagery) can characterize variation in tree compositional assemblages, multispectral imageries (e.g., Landsat) have been considered to lack the spectral resolution to effectively demonstrate compositional variation [50,51].…”

Section: Discussionmentioning

confidence: 99%

Large-Scale Mapping of Tree-Community Composition as a Surrogate of Forest Degradation in Bornean Tropical Rain Forests

Fujiki

Aoyagi

Tanaka

et al. 2016

Land

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Assessment of the progress of the Aichi Biodiversity Targets set by the Convention on Biological Diversity (CBD) and the safeguarding of ecosystems from the perverse negative impacts caused by Reducing Emissions from Deforestation and Forest Degradation Plus (REDD+) requires the development of spatiotemporally robust and sensitive indicators of biodiversity and ecosystem health. Recently, it has been proposed that tree-community composition based on count-plot surveys could serve as a robust, sensitive, and cost-effective indicator for forest intactness in Bornean logged-over rain forests. In this study, we developed an algorithm to map tree-community composition across the entire landscape based on Landsat imagery. We targeted six forest management units (FMUs), each of which ranged from 50,000 to 100,000 ha in area, covering a broad geographic range spanning the most area of Borneo. Approximately fifty 20 m-radius circular plots were established in each FMU, and the differences in tree-community composition at a genus level among plots were examined for trees with diameter at breast height ≥10 cm using an ordination with non-metric multidimensional scaling (nMDS). Subsequently, we developed a linear regression model based on Landsat metrics (e.g., reflectance value, vegetation indices and textures) to explain the nMDS axis-1 scores of the plots, and extrapolated the model to the landscape to establish a tree-community composition map in each FMU. The adjusted R 2 values based on a cross-validation approach between the predicted and observed nMDS axis-1 scores indicated a close correlation, ranging from 0.54 to 0.69. Histograms of the frequency distributions of extrapolated nMDS axis-1 scores were derived from each map and used to quantitatively diagnose the forest intactness of the FMUs. Our study indicated that tree-community composition, which was reported as a robust indicator of forest intactness, could be mapped at a landscape level to quantitatively assess the spatial patterns of intactness in Bornean rain forests. Our approach can be used for large-scale assessments of tree diversity and forest intactness to monitor both the progress of Aichi Biodiversity Targets and the effectiveness of REDD+ biodiversity safeguards in production forests in the tropics.

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Imaging Spectroscopy of Urban Environments

et al. 2018

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Future spaceborne imaging spectroscopy data will offer new possibilities for mapping ecosystems globally, including urban environments. The high spectral information content of such data is expected to improve accuracies and thematic detail of maps on urban composition and urban environmental condition. This way, urgently needed information for environmental models will be provided, for example, for microclimate or hydrological models. The diverse vertical structures, highly frequent spatial change and a great variety of materials cause challenges for urban environmental mapping with Earth observation data, especially at the 30 m spatial resolution of data from future spaceborne imaging spectrometers. This paper gives an overview of the state-of-the-art in urban imaging spectroscopy considering decreasing spatial resolution, the related user requirements and existing knowledge gaps, as well as expected future directions for the work with new data sets.

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Detection of gradients of forest composition in an urban area using imaging spectroscopy

Cited by 36 publications

References 74 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

Large-Scale Mapping of Tree-Community Composition as a Surrogate of Forest Degradation in Bornean Tropical Rain Forests

Imaging Spectroscopy of Urban Environments

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