Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies

Pontius, Jennifer; Martin, Mary E.; Plourde, Lucie; Hallett, Richard A.

doi:10.1016/j.rse.2007.12.011

Cited by 122 publications

(91 citation statements)

References 48 publications

(40 reference statements)

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“…Time series of VHR multispectral or panchromatic data have been successful in evaluating pine beetle red attack over time (Wulder et al 2008); QuickBird time series and extracted red-to-green band ratios lead to true positive accuracies of 89-93% for three studied years. Spectral properties of MODIS, AVIRIS, and Landsat data, together with regression analysis, have assessed gypsy moth defoliation (Debeurs and Townsend 2008), decline in emerald ash borer-infested areas (Pontius et al 2008), and mortality of lodgepole pine to bark beetle attack (Coops et al 2009). …”

Section: Regulating Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Section: Regulating Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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“…Hyperspectral data is becoming very important for early stress detection and may be useful for identifying tree-level pre-visual reductions in LAI, chlorophyll (Pontius et al, 2008) and water contents. Early detection means detecting subtle changes in foliage canopy occurring as physiological or biochemical host defence responses to infestation.…”

Section: Forest Vegetation Indicesmentioning

confidence: 99%

Remote Monitoring of Forest Insect Defoliation -A Review-

et al. 2013

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Aim of study: This paper reviews the global research during the last 6 years (2007)(2008)(2009)(2010)(2011)(2012) on the state, trends and potential of remote sensing for detecting, mapping and monitoring forest defoliation caused by insects.Area of study: The review covers research carried out within different countries in Europe and America.Main results: A nation or region wide monitoring system should be scaled in two levels, one using time-series with moderate to coarse resolutions, and the other with fine or high resolution. Thus, MODIS data is increasingly used for early warning detection, whereas Landsat data is predominant in defoliation damage research. Furthermore, ALS data currently stands as the more promising option for operative detection of defoliation.Vegetation indices based on infrared-medium/near-infrared ratios and on moisture content indicators are of great potential for mapping insect pest defoliation, although NDVI is the most widely used and tested.Research highlights: Among most promising methods for insect defoliation monitoring are Spectral Mixture Analysis, best suited for detection due to its sub-pixel recognition enhancing multispectral data, and use of logistic models as function of vegetation index change between two dates, recommended for predicting defoliation.

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“…In the past several years, studies have been conducted to assess the capabilities of commercially available hyperspectral sensors to predict ash tree decline caused by EAB infestation. In Pontius et al (2008), hyperspectral SpecTIR imagery with a spatial resolution of 1m by 1m was used to map ash decline at the landscape level in forests in Michigan and Ohio. It was reported that 71% of the variability in ash decline was accounted for with 6 vegetation indices sensitive to leaf chlorophyll content and water content.…”

Section: Introductionmentioning

confidence: 99%

The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies

Wang

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78% and 19% commission and 23% omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75% overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

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Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies

Cited by 122 publications

References 48 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote Monitoring of Forest Insect Defoliation -A Review-

The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies

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