Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland

Eitel, Jan U. H.; Vierling, Lee A.; Litvak, M. E.; Long, Dan S.; Schulthess, Urs; Ager, Alan A.; Krofcheck, D. J.; Stoscheck, Leo

doi:10.1016/j.rse.2011.09.002

Cited by 215 publications

(162 citation statements)

References 41 publications

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“…These two vegetation indices are known to be well correlated with chlorophyll and carotenoid concentrations [65]. The vegetation indices based on the combination of the red and red-edge bands have been shown to be useful for the estimation of chlorophyll a and b concentrations at leaf and canopy levels because they minimize the effects of the soil reflectance [8] and are less influenced by leaf biomass than the NDVI [7,65].…”

Section: Discussionmentioning

confidence: 99%

“…However, recently launched optical satellites include the red-edge band that allows the identification of changes in the health of green vegetation in early phases [7,8]. The green attack is followed by red attack in which the identification of infested trees is easier due to the discoloration of the foliage in the visual spectrum [9].…”

Section: Introductionmentioning

confidence: 99%

“…The identification of green attack was less satisfactory due to small differences in the spectral response between healthy and attacked trees. Eitel et al [7] also used RapidEye data to analyze the capabilities of the red-edge band for early detection of stress induced by girdling of conifers in New Mexico. They concluded that RapidEye data may be useful for detecting bark beetle green attack.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ortiz¹,

Breidenbach²,

Kändler³

2013

Remote Sensing

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“…Thanks to its state-of-the-art specifications, Sentinel-2 [12,13] was designed for a variety of land monitoring applications such as water detection [14], mapping built-up areas [15] and crop type and tree species identification [16]. In addition to its spatial resolution, its payload offers thirteen spectral bands from Blue to SWIR, including Red-edge bands which have already proved to be useful for forest stress monitoring [17], land use and land cover mapping [18,19] and biophysical variable retrieval [20][21][22][23]. In fragmented landscapes, the components of the ecological networks are generally small, i.e., sub-pixel targets undetected by conventional multi-spectral classification methods [24,25].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

et al. 2016

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Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.

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“…Brosinsky et al [18] investigated the spectral response of ash trees (Fraxinus excelsior L.) to physiological stress from flooding over a 3-month period, whereas Buddenbaum et al [27] modelled the photosynthesis rate of young European beech trees under drought stress following a two-year water stress treatment by using close-range hyperspectral visible, near infrared and thermal sensors. Another common method to induce water stress is the girdling of trees with differing degrees of intensity [65,66].…”

Section: Trends In Close-range Rs Approaches For Assessing Fhmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland

Cited by 215 publications

References 41 publications

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

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

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

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