Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

Spruce, Joseph P.; Sader, Steven A.; Ryan, Robert; Smoot, James C.; Kuper, Philip D.; Ross, K. W.; Prados, Donald; Russell, Jeffrey; Gasser, Gerald; McKellip, Rodney; Hargrove, William W.

doi:10.1016/j.rse.2010.09.013

Cited by 141 publications

(105 citation statements)

References 33 publications

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“…Cloud cover has previously been cited as a significant limitation to using Landsat to monitor gypsy moth defoliation due to the ephemeral nature of defoliation events [17,20,21,37]. By using synthetic images, we eliminate issues of compounding cloud cover across multi-date comparisons.…”

Section: Advantages Of the Lts Synthetic Image Approachmentioning

confidence: 99%

“…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. Further, studies comparing the utility of MODIS products for detecting defoliation have found that single-date acquisitions typically outperform multi-date composites [29,37], which suggests that multi-date compositing introduces undesirable temporal uncertainty. In contrast, synthetic images [22] generate a unique predicted image for each day of the year, explicitly accounting for phenological variability for each pixel and allowing for more direct comparison to observed conditions on a given date.…”

Section: Advantages Of the Lts Synthetic Image Approachmentioning

confidence: 99%

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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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Section: Advantages Of the Lts Synthetic Image Approachmentioning

confidence: 99%

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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“…Two operational scales should be considered: a national or regional scale at an early warning level, with coarse resolution satellite-based monitoring for identifying locations of disturbances where they are suspected, and a second local, tactical scale, with finer resolution for assessing the validity and nature of warnings coming from first level, using finer satellites or even overflights and on-the-ground monitoring (e.g. sketch maps produced by the US Forest Service Aerial Detection Surveys (ADS) overflight program for forest disturbances; Spruce et al, 2011). Sketch mapping is to date the most commonly used technique for detection and assessment of forest damage caused by biotic factors (Ciesla et al, 2008), and has been an integral part of forest health protection programs in Canada and the United States since the end of World War II (Ciesla, 2000).…”

Section: Remote Detection Of Forest Defoliationmentioning

confidence: 99%

“…Accurate links between data and image processing require quality data relatively uncontaminated by noise and extraneous effects derived from viewing, light conditions, cloud and atmospheric contamination, etc. Thus, sufficient cloud-free temporal composites across the defoliation period and effective noise reduction of residual atmospheric contamination (Spruce et al, 2011) or background influences, such as soil or understory in open forest stands (Lambin and Linderman, 2006), are required for useable imagery detecting defoliation.…”

Section: Classification Of Damage Degreementioning

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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“…Cloud masks for each MOD09A1 image were generated individually. Cloud-free data are important requirements for the operational monitoring of rice distributions using optical sensors [31]. To fill in the gaps in the EVI and NWDWI time series caused by clouds, the conditional temporal interpolation method was used in this study [32].…”

Section: Reconstruction Of the Spectral Index Profilementioning

confidence: 99%

Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images

Shi

Huang

2015

Remote Sensing

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Abstract:A large-area map of the spatial distribution of rice is important for grain yield estimations, water management and an understanding of the biogeochemical cycling of carbon and nitrogen. In this paper, we developed the Normalized Weighted Difference Water Index (NWDWI) for identifying the unique characteristics of rice during the flooding and transplanting period. With the aid of the ASTER Global Digital Elevation Model and the phenological data observed at agrometeorological stations, the spatial distributions of single cropping rice and double cropping early and late rice in the Yangtze River Delta region were generated using the NWDWI and time-series Enhanced Vegetation Index data derived from MODIS/Terra data during the 2000-2010 period. The accuracy of the MODIS-derived rice planting area was validated against agricultural census data at the county level. The spatial accuracy was also tested based on a land use map and Landsat ETM+ data. The decision coefficients for county-level early and late rice were 0.560 and 0.619, respectively. The MODIS-derived area of late rice exhibited higher consistency with the census data during the 2000-2010 period. The algorithm could detect and monitor rice fields with different cropping patterns at the same site and is useful for generating spatial datasets of rice on a regional scale.

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Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

Cited by 141 publications

References 33 publications

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

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

Remote Monitoring of Forest Insect Defoliation -A Review-

Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images

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