Remote Detection of Forest Damage

Rock, B. N.; Vogelmann, James E.; Williams, Darrel L.; Vogelmann, Ann F.; Hoshizaki, T.

doi:10.2307/1310339

Cited by 309 publications

(138 citation statements)

References 14 publications

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“…Since the early years of the Landsat program, there has been interest in using the Landsat imagery for monitoring forest disturbances [15][16][17][18][19]. However, previous studies have suggested that it is not possible to estimate gypsy moth defoliation at regional scales using Landsat due to the short window for monitoring and temporal repeat time [29], and aerial sketch maps have remained the operational approach for mapping forest insect disturbances.…”

Section: Toward An Integrated Disturbance Monitoring Systemmentioning

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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Section: Toward An Integrated Disturbance Monitoring Systemmentioning

confidence: 99%

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

Pasquarella

Bradley

Woodcock

2017

Forests

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“…The coefficient of determination (R 2 ) showed a weakly correlated result if R 2 <0.5 [27], [28], [29], [30], [14], [31]. The result of determination value with regression exponential, logarithmic, polynomial and power/geometry got value R 2 <0.5 (value R 2 biggest 0.064).…”

Section: Modeling Estimation Of Total Carbon Mangrove Content (Carbonmentioning

confidence: 99%

Estimation of Mangrove Carbon Stock With Hybrid Method Using Image Sentinel-2

Muhsoni¹

2018

GEOMATE

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ABSTRACT:Field survey data combined with remote sensing data were an ideal and practical method for estimating carbon stocks. The objective of this research was to get an estimation model of mangrove carbon stock with good accuracy. Modeling used hybrid methods, by combining satellite image analysis and field data. The result of this research was to get the mangrove carbon estimation model. Model 1 merging between NNIP vegetation index equation using regression of power/geometry and six variables multiple regression (NDRE or WVVI vegetation index, sediment depth, soil density,% C soil depth 0-15 cm, 15-50 cm and >50 cm). RMSE test resulted 0.4778 t 100 m -2 and % RMSE 16.12%. Model 2 NNIP vegetation index and three variable regression (VIRRE vegetation index, sediment depth, soil density). RMSE test resulted 0.5639 t 100 m -2 and % RMSE 19.03%. Model 3 uses NNIP vegetation index and two variable regression (NDRE vegetation index and sediment depth). RMSE test resulted 0.7295 t 100 m -2 and RMSE % 24.63%. Model 4 incorporation of NNIP vegetation index and multiple regression of 3 variables (VIRRE vegetation index, average sediment depth value 100.63 cm, soil density value 1.02 g cm -3 ). RMSE test resulted 1.0043 t 100 m -2 and % RMSE 33.89%.

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“…widely used in the fields of spectral images (Tsumura et al 1999), laser tuning (Wang and Yao 1992;Wacogne et al 1993), remote sensing (Green et al 1995;Rock et al 1986), astronomy (Hobbs 1993;Miller 19990;Kopp et al 1997), dense wavelength division multiplexing (Juan et al 2004;Luo and Wan 2011) and so on.…”

mentioning

confidence: 99%

A new type Lyot filter insensitive to incident angle

et al. 2012

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The shifting direction of a central wavelength for a quartz birefringent filter is found different when the filter is rotated around the axis parallel and perpendicular to the quartz optical axis in this paper. Based on this result, a new type of two-stage modified-Lyot quartz filter is presented, which show that the central wavelength of this new type Lyot filter is actually insensitive to incident angle through theory analysis when incident angle is less than 18 • , and the maximum transmission decrease only about 66 % compared to normal incidence. Moreover, this conclusion is in good agreements with relative experiment results.

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Remote Detection of Forest Damage

Cited by 309 publications

References 14 publications

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

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

Estimation of Mangrove Carbon Stock With Hybrid Method Using Image Sentinel-2

A new type Lyot filter insensitive to incident angle

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