An Investigation Of The Relationship Between Tree Water Potential And Dielectric Constant

McDonald, K. C.; Zimmerman, R.; Way, J.

doi:10.1109/igarss.1992.576758

Cited by 13 publications

(13 citation statements)

References 7 publications

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“…Because trees attacked by bark beetles become progressively more water stressed due to xylem disruption [68], a possible reduction in the radar backscatter of attacked areas resulting from a decreased dielectric constant of infested trees was expected. This assumption was based on the observations of McDonald et al [70], who monitored ten trees over several weeks and analyzed the relationship between tree water availability, dielectric constant and radar backscatter. In contrast, current results displayed higher backscatter values in areas with bark beetle attack.…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data

Ortiz¹,

Breidenbach²,

Kändler³

2013

Remote Sensing

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Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management decisions to prevent larger damages. For this reason, a field-based bark beetle monitoring program is currently implemented in Germany. The combination of remote sensing and field data may help minimizing the reaction time and reducing costs of monitoring programs covering large forested areas. In this case study, RapidEye and TerraSAR-X data were analyzed separately and in combination to detect bark beetle green attack. The remote sensing data were acquired in May 2009 for a study site in south-west Germany. In order to distinguish healthy areas and areas affected by bark beetle green attack, three statistical approaches were compared: generalized linear models (GLM), maximum entropy (ME) and random forest (RF). The spatial scale (minimum mapping unit) was 78.5 m 2 .TerraSAR-X data resulted in fair classification accuracy with a cross-validated Cohen's Kappa Coefficient (kappa) of 0.23. RapidEye data resulted in moderate classification accuracy with a kappa of 0.51. The highest classification accuracy was obtained by combining the TerraSAR-X and RapidEye data, resulting in a kappa of 0.74. The accuracy of ME models was considerably higher than the accuracy of GLM and RF models. OPEN ACCESSRemote Sens. 2013, 5 1913

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Section: Discussionmentioning

confidence: 99%

Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data

Ortiz¹,

Breidenbach²,

Kändler³

2013

Remote Sensing

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“…Remote sensing methods can be classified under multiple criteria and, in this work, they have been categorized according to the part of the plant subjected to measurement. In this way, the different sensors used in the available literature can be applied to the soil [9][10][11][12][13][14][15][16][17], directly to the canopy or to different organs of individual plants, such as leaves or trunks and stems [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing for Plant Water Content Monitoring: A Review

et al. 2021

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This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

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“…Considerable additional work was done in the 1990s, when it was hypothesized that vegetation dielectric properties are likely to be related to the vegetation water status. McDonald et al (1992) suggested that strong correlations may exist between dielectric constant of trees and xylem water potential, as evaporation of water occurs through the leaves. Measurements under controlled conditions showed that the xylem dielectric constant responds directly to changes in xylem water potential.…”

Section: Relation Between Plant Dynamics and Dielectric Prop-ertiesmentioning

confidence: 99%