Relationship of a Landsat cumulative disturbance index to canopy nitrogen and forest structure

Deel, Lindsay N.; McNeil, Brenden E.; Curtis, Philip G.; Serbin, Shawn P.; Singh, Aditya; Eshleman, Keith N.; Townsend, Philip A.

doi:10.1016/j.rse.2011.10.026

Cited by 18 publications

(13 citation statements)

References 64 publications

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“…We employed partial least-squares regression, PLSR (Wold et al 1984, Geladi and Kowalski 1986, Wolter et al 2008, to predict canopy traits from imaging spectroscopy, as done in many previous studies (Coops et al 2003, Townsend et al 2003, Deel et al 2012). In practice, PLSR iteratively transforms predictor and response variables to find latent vectors and subsequently produce calibration factors and a linear model.…”

Section: Discussionmentioning

confidence: 99%

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

Singh

Serbin

McNeil

et al. 2015

Ecological Applications

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229

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A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M(area)), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ15N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both M(area) and %N PLSR models had a R2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R2 > 0.65 and carbon and cellulose with R2 > 0.45. Although R2 of %C and cellulose were lower than M(area) and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ15N was the lowest (R2 = 0.48, RMSE = 0.95 per thousand), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.

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Section: Discussionmentioning

confidence: 99%

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

Singh

Serbin

McNeil

et al. 2015

Ecological Applications

Self Cite

229

237

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“…Deel et al [38] previously proposed a method for monitoring defoliation using a cloud-free image composite created by combining pixel values with the lowest disturbance [39] from annual images. However, this approach results in a base image with pixel values derived from different dates or years and may represent the canopy in different phenological states.…”

Section: Advantages Of the Lts Synthetic Image Approachmentioning

confidence: 99%

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

Pasquarella

Bradley

Woodcock

2017

Forests

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Introduced insects and pathogens impact millions of acres of forested land in the UnitedStates each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar) outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS) products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators.

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“…To date, LTS analysis techniques have been developed mainly for disturbance analysis and mapping, with few exceptions such as the threshold age mapping algorithm (TAMA), which is an automated procedure for mapping forest age using LTS data (Helmer et al 2009). Techniques used for disturbance analysis have also been proven useful in other applications, such as characterizing current forest structure (Deel et al 2012;Pflugmacher et al 2012). Changes in forested ecosystems can be categorized into 3 types:…”

Section: Approaches For Analyzing Ltsmentioning

confidence: 99%