Review of Satellite Remote Sensing Use in Forest Health Studies~!2010-01-27~!2010-04-05~!2010-06-29~!

Wang, Junming; Sammis, T. W.; Gutschick, Vincent P.; Gebremichael, Mekonnen; Dennis, Sam; Harrison, R. W.

doi:10.2174/1874923201003010028

Cited by 48 publications

(39 citation statements)

References 43 publications

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“…Besides the tree to stand-level factors, the risk of bark beetle infestation is increased by large-scale drivers, which include climatic variables and/or landscape connectivity [28]. Most of these latter changes are well reflected in spectral indices and can be documented using multi-temporal remote sensing [29]. Free access to the Landsat data archive by the US Geological Survey (USGS) in 2008 accelerated the use of these data for multi-temporal change detection [30].…”

Section: Introductionmentioning

confidence: 99%

Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

et al. 2016

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Tree mortality caused by bark beetle infestation has significant effects on the ecology and value of both natural and commercial forests. Therefore, prediction of bark beetle infestations is critical in forest management. Existing predictive models, however, rarely consider the influence of long-term stressors on forest susceptibility to bark beetle infestation. In this study we introduce pre-disturbance spectral trajectories from Landsat Thematic Mapper (TM) imagery as an indicator of long-term stress into models of bark beetle infestation together with commonly used environmental predictors. Observations for this study come from forests in the central part of the Šumava Mountains, in the border region between the Czech Republic and Germany, Central Europe. The areas of bark beetle-infested forest were delineated from aerial photographs taken in 1991 and in every year from 1994 to 2000. The environmental predictors represent forest stand attributes (e.g., tree density and distance to the infested forest from previous year) and common abiotic factors, such as topography, climate, geology, and soil. Pre-disturbance spectral trajectories were defined by the linear regression slope of Tasseled Cap components (Wetness, Brightness and Greenness) calculated from a time series of 16 Landsat TM images across years from 1984 until one year before the bark beetle infestation. Using logistic regression and multimodel inference, we calculated predictive models separately for each single year from 1994 to 2000 to account for a possible shift in importance of individual predictors during disturbance. Inclusion of two pre-disturbance spectral trajectories (Wetness slope and Brightness slope) significantly improved predictive ability of bark beetle infestation models. Wetness slope had the greatest predictive power, even relative to environmental predictors, and was relatively stable in its power over the years. Brightness slope improved the model only in the middle of the disturbance period (1996). Importantly, these pre-disturbance predictors were not correlated with other predictors, and therefore bring additional explanatory power to the model. Generally, the predictive power of most fitted model decreases as time progresses and models describing the initial phase of bark beetle outbreaks appear more reliable for conducting near-future predictions. The pre-disturbance spectral trajectories are valuable not only for assessing the risk of bark beetle infestation, but also for detection of long-term gradual changes even in non-forest ecosystems.

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Section: Introductionmentioning

confidence: 99%

Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

et al. 2016

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“…New satellite remote-sensing technologies, particularly hyperspectral imaging spectroscopy and full polarimetric SAR data promise to extend the database of forest observations with new potential for forest ecosystem assessment (see Section 3) [8]. Over the last four decades, fundamental research on the applicability of satellite remote sensing observations for forest parameter estimation have been conducted within the framework of numerous case studies [9,10].…”

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confidence: 99%

In Situ/Remote Sensing Integration to Assess Forest Health—A Review

et al. 2016

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For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.

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“…This indicated interspecific competition of L. camara and S. robusta for the resources in mixed patches which largely affect the health of particular species. Forest vigor and health can be evaluated by using satellite-derived vegetation indices such as NDVI that can monitor large remote areas with an effective database (26) . Higher index value (9) was showing pure patches while lower index values (< 8) were showing mixed patches (Fig.…”

Section: Philippensis L Camara Accasia Catechu Bauhinia Variegatamentioning

confidence: 99%

Species type and forest health assessment via hyperspectral remote sensing in the part of Himalayan range, India

Singh

Lekshmi

et al. 2014

Dhaka Univ. J. Biol. Sci

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Ground spectra using analytical spectral device were collected for dominant species in Rajpur and Mussoorie hills of Lesser Himalayas of India for mapping dominant plant species. Hyperspectral remote sensing data (EO-1) was used for classification of temperate species such as Quercus leucotrichophora, Cedrus deodara, Thuja orientalis, subtropical species like Pinus roxburghii, and tropical species such as Shorea robusta, Lantana camara, etc. using Spectral Angle Mapper. A total of 14 species were mapped along with the 4 other land use/cover classes (Agriculture, settlement, main river course, barren land). Map accuracy was 77% assessed on the basis of 66 ground truth ground control point. The exotic species, Lantana camara was mapped in the area which has been found to be distributed from tropical to lower temperate regions and was showing impact on the health of the neighboring species which was derived from the Hyperion data. Large impact has been observed in the Shorea robusta species and its health distribution map showed 48% healthy and 52% less healthy part.

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Review of Satellite Remote Sensing Use in Forest Health Studies~!2010-01-27~!2010-04-05~!2010-06-29~!

Cited by 48 publications

References 43 publications

Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

In Situ/Remote Sensing Integration to Assess Forest Health—A Review

Species type and forest health assessment via hyperspectral remote sensing in the part of Himalayan range, India

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