Vegetation Recovery Using Remote Sensing Image In Yellowstone National Park

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Table of Contents

Literature Review
Overall Recommendations and Conclusion
Works Cited

Literature Review

The Connection between Vegetation Recovery and Burning Severity of Fires

Before analyzing the images produced by means of remote sensing, it is necessary to analyze the aspects and criteria according to which the images can detect various patterns of vegetation recovery after the fire. Specifically, much research has been done on the analysis of connection between biodiversity and remote sensing techniques as well as other methods for types of recovery vegetation.

According to Kennedy, remote sensing contributes greatly to the analysis of vegetation cover and provides sufficient information about atmospheric chemistry (133). In particular, satellite remote sensing techniques can provide exhaustive data on the patterns and criteria necessary for analyzing sophisticated interactions and mechanisms connecting fire density, vegetation cover, atmospheric chemistry, and climate.

The researcher has found that gas emitted into atmosphere as well as shifts occurred to the atmospheric ratio is possible to effectively detect with the help of remote sensing. However, the examination of such dependencies does not provide viable solutions to the analysis of vegetation recovery in relation to temporal scales. Still, there is a possibility to identify the nature of gasses emitted.

More detailed information on this issue is provided by Turner et al. who have managed to provide sufficient justification to remote sensing images and how they can be used to identify various types of forests and vegetation (306). According to the researcher, “…recording numerous densities at different heights throughout the canopy and enables three-dimensional profiles of vegetation structure to be made” (Turner et al. 307).

With the help of this data, it is possible to detect the potential for such techniques as mapping of sub-canopy layers and emergent tree species.

A great contribution to the analysis of distribution patterns and habitat categorizations carried with the help of remote sensing techniques. This examination has been provided by Debinsky, Kindsher, and Jakubauskas (3281). The researchers have also applied to Landsat TM data analysis in order to evaluate various forest and meadow types in Yellowstone Park.

Importantly, the studies also seek to define the relation between vegetations areas and animal species distribution which is quite essential because the foci of birds and animals can be the indicators of dense vegetation.

Particular species can be affiliated to a particular vegetation pattern. Interestingly, the research conducted by Debinski et al, reveals “large differences in species distribution patterns among remotely sensed meadow types” in different temporal dimensions (3283). The same concerns are considered by Gould (1861).

White et al have also been more consistent and pertinent to our research considerations (125). In their studies, they emphasize that aside from vegetations patterns, there are also burning severity patterns resulted in different topographic vegetation. The patterns are received with the help of satellite data that show significant changes in physical characteristics of burnt areas.

The researchers have discovered that it is necessary to be knowledgeable about electromagnetic energy. In this respect, they have also defined that “…more severely burned areas have less vegetation cover and different radiation budgets in post-fire years” (White et al.124).

Such important deductions will be of great relevance to our research because different patterns of burning severity will assist in analyzing the patterns presented in Yellowstone National Park.

With regard to the consideration presented above, it should be emphasized that the vegetation recovery change patterns largely depend on the burning severity of fire. This linkage is revealed through carbon dioxide density, biophysical characteristics of burnt areas, radiation and spectral analysis, and electromagnetic energy.

Spectral Analysis with Regard to Vegetation Recovery Patterns

A possibility to distinguish the changing patterns of vegetation recovery and burning severity cannot be solely relied because such factors as the process of spectral analysis and carbon dioxide density are crucial in providing an accurate and consistent examination of temporal characteristics of vegetation recovery.

In this respect, it is necessary to analyze the connection between carbon dioxide emission, and how they relate to fires and vegetation patterns. It is also imperative to prove why remote sensing, spectral analysis and Landsat TM techniques are crucial in identifying the influences of fire on vegetation recovery.

The research provided by Jakubauskas and Price offer a clear picture of the relations between biotic factors and spectral analysis of forests in the Park (1375). With the help of multiple regression models, the researchers have provided the correlation of digital spectral analysis and biotical factors.

The results have revealed that “tree height and diameter combined to form an index of crown volume, which in turn combined with density for an index of canopy volume” (Jakubauskas and Price 1379). The scholars have also detected other crucial, though less significant, factors and dimensions of spectral analysis such as leaf area index and vegetation index.

Although the research provided by Jakubauskas and Price is of great value for further examination, it can be supported by the studies analyzing vegetation dynamics with regard to temporal scales (1378). In particular, Shannon and Lawrence are more close to the analysis of vegetation recovery patterns in relation to temporal scale (551).

The value of their research consists in presenting change vector analysis with help of 1985 and 1999 images. This analysis is “a rule-based change detection method that examines the angle and magnitude of change between dates in spectra space” (Shannon and Lawrence 551).

The process of change detection has succeed in presenting the changes within herbaceous and shrub land vegetation. The spectral and change vector analyses have detected that “there was a decrease in grass lands and a relative increase in srublands” (Shannon and Lawrence 554). The presented research can greatly assist in the exploration of vegetation recovery patters of change in Yellowstone National Park.

The above-presented research provides consistent information about pattern distributions, but it lacks information about fire factor and its impact on vegetation recovery and accuracy of the research. This gap can be complemented with the explorations provided by Turner, Hargrove, Gardiner, and Romme (731).

In general, spectral analysis plays an important role in identifying the changing patterns of vegetation recovery. It is also significant in defining various species of vegetation and describing pattern distributions on a particular geographic area.

Technical possibilities and Limitations of Remote Sensing Techniques

Remote sensing approaches can differ with regard to various resolutions of remotely sensed images. In order to succeed in researching our objectives, the analysis of advantages and limitations of these techniques is crucial. The studies presented by Wright and Gallant (582), Asner (2), Cohen and Goward (535), and Murtaugh and Philips (99). All scholars provide a comprehensive evaluation of all limitations to using remote sensing tools.

In order to critically assess the technical possibility of remote sensing techniques, Wright and Gallant have provided a historical background of previous researches dedicated to the efficiency assessment (582).

The results show that “remote sensing is the moderate spatial and spectral resolution of multispectral instruments like TM sensor” (Wright and Gallant 584) Therefore, it will be difficult to distinguish forested upland and forested wetland in spectral terms. The application of remote sensing techniques cannot be solely applied, but in combination with ancillary data.

Due to the fact that carbon dioxide is considered to be the indicator of vegetation recovery and burning severity of fire, ancillary technique should also involve carbon mapping as well which will back up the date collected form remotely sensed images (Asner 2).

Such devices are quite relevant and applicable to the temporal analysis of vegetation because carbon spectral patterns of change can also be the signifiers of vegetation recovery stage. In particular, carbon densities can be easily correlated with burning severities, and vegetation recovery, and species analysis. More importantly, the carbon analysis includes the acquisition of maps depicting types of forest, disturbance, and deforestation.

Remote sensing techniques are also applicable to temporal analysis of vegetation patterns. In this regard, Murtaugh and Philips provide a bivariate binary model for evaluating the shifts in land cover with the help of satellite images received at different times (99).

Such classification is aimed at correlating random variables that are dependent on the pixel resolution. Importantly, the researchers have applied to Landsat imaging for pixel classification and its correlation with land cover changes.

Cohen and Goward also emphasize the importance of using remote sensing to assess temporal and spatial characteristics of ecological environment (535). In the particular, they used date obtained from Landsat sensors for constructing biogeochemical cycles and for characterizing vegetation biophysical attributes with regard to biodiversity.

The research find remote sensing valid and reliable for analyzing vegetation and land cover change. In contrast, Ravan and Roy consider it necessary to introduce Geographic information systems for the analysis of various vegetation patterns and obtaining relevant information (129). The combined approach is much more efficient in detecting such characteristics as vegetation shape, size, patch density and porosity.

The research results has revealed significant different between different zones of Madhav National Part of India (Ravan and Roy 130). The structural analysis has provided vegetation recovery also largely dependent of biomass distribution and species diversity. Arising from this research, remote sensing and GIS can be successfully applicable to the temporal analysis of vegetation providing more accurate information.

Innes and Koch state that remote sensing is considered the most efficient tool in assessing vegetation, and other biophysical characteristics such as structural criteria of forest stands, the canopy type and the present of coarse woody debris (397). The researchers emphasize that it is possibly to rely solely on remote sensing when investigating the spatial and temporal characteristics of vegetations.

Interesting discoveries are offered by Turner, Ollinger, and Kimball who also approve remote sensing techniques for evaluating spatial characteristics of vegetation (574). In particular, the researchers resort to remote sensing tools and ecosystem modeling to study the terrestrial carbon cycling.

Pursuant to remote sensing limitation, explain that this device is constantly upgrading and it is possible to select the appropriate resolution of images to analyze the reflectance properties of vegetation and assess biogeochemical processes controlling carbon transformation.

In general, the majority of the above-described researchers prove that remote sensing is one of the most efficient instruments in conducting the assessment of vegetation recovery with regard to its temporal and spatial characteristics. Nevertheless, the analysis will be much more successful if to apply this technique together with GIS approach.

Overall Recommendations and Conclusion

The analysis of image obtained by remote sensing allows to detect various patterns of vegetation recovery with regard to temporal characteristics. The Yellowstone National Park has been analyzed in three various time – 1989, 1999, and 2010. The image obtained from Landsat TM, ISODATA being an ancillary mechanism revealed that there significant changes in vegetation recovery patterns in relation to temporal characteristics.

In addition, classification scheme of vegetation used to shrub land, herbaceous vegetation, sparse vegetation, and bare land has turned out to be flexible and relevant for the research. The presented research proves conducted by Jakubauska and Price (1375)

The results have also show that vegetation recovery patterns are closely connected with burning severity of fire. Importantly, the spectral analysis and Landsat TM show biophysical characteristics of burnt areas. The evaluation has also succeeded in defining the changes of species allocation on the territory of Yellowstone National Park. The technical approach used for the data analysis still had some limitations.

In particular, it was difficult information without geographic information system because some characteristics were impossible to detect, such carbon dioxide cycle. Nevertheless, the classification of species was successfully identified and carefully analyzed with regard to temporal characteristics.

In future, we plan to investigate this area and other territories, but with another combination of techniques either to justify or disapprove the effectiveness of those as compared with the above presented ones. This area is quite wide and, therefore, there is much store for investigation.

Works Cited

Asner, Gregory P. Tropical Forest Carbon Assessment: Integrating Satellite and Airborne Mapping Approaches. Environmental Research Letters 4 (2009):1-11

Cohen, Warren D., and Samuel N. Goward. Landsat’s Role in Ecological Applications of Remote Sensing. BioScience. 54.6 (2004): 535-545.

Debinski, D. M. and Kindscher, K., and Mark Jakubauskas. A Remote Sensing and GIS-based model of habitats and biodiversity in the Greater Yellowstone Ecosysyem. Journal of Remote Sensing. 20.17 (1999): 3281-3291.

Gould, William. Remote Sensing of Vegetation, Plant Species Richness, and Regional Biodiversity Hotspots. Ecological Applications. 10.6 (2000): 1861-1870.

Innes John L., and Barbara Koch. Forest Biodiversity and Its Assessment by Remote Sensing. Global Ecology and Biogeography Letters. 7.6 (1998): 397-419.

Jakubauskas, Mark, and Kevin P. Price. Empirical Relationships between Structural and Spectral Factors of Yellowstone Lodgepole Pine Forests. Photogrammetric Engineering and Remote Sensing. 63.12 (1997, December): 1375-1381

Kennedy, Pam. Biomass Burning Studies: The Use of Remote Sensing. Ecological Bulletins. 15 (1992): 133-148.

Murtaugh, Paul A. and Donald L. Philips. Temporal Correlation of Classification in Remote Sensing. Journal of Agricultural, Biological, and Environmental Statistics. 3.1. (1999, March): 99-110

Ravan, Shirish, A., and P. S. Roy. Satellite Remote Sensing for Ecological Analysis of Forested Landscape. Plant Ecology. 131.2 (1997): 129-141;

Savage, Shannon L., and Rick L. Lawrence. Vegetation Dynamics in Yellostone’s Northern Range: 1985 to 1999. Photogrammetric Engineering & Remote Sensing. 76.5 (2010): 547-556.

Turner, David P., Ollinger Scott V., and John S. Kimball. Integrating Remote Sensing and Ecosystem Process Models for Landscape- to Regional-Scale Analysis of the Carbon Cycle. BioScience. 54.6 (2004, June): 573-584.

Turner, Monica G., Hargrove Willia W., Gardiner Robert H., and William H. Romme. Effects of fire on landscape heterogeneity in Yellowstone National Park, Wyoming. Journal of Vegetation Science. 5 (1994): 731-742.

Turner, Woody, Spector Sasha, Gardiner Ned, Fladeland Matthew, Sterling Eleanor, and Mark Steininger. Remote Sensing for Biodiversity Science and Conservation. Trends in Ecology and Evolution. 18.6. (2003, June): 306-314

White, Joseph D., Ryan, Kevin C., Key, Carl C., and Steven W. Running. Remote Sensing of Forest Fire Severity and Vegetation Recovery. International Journal of Wildland Fire. 6.1 (1996): 125-136.

Wright, Christ and Alisa Gallant. Improved Wetland Remote Sensing in Yellowstone National Park Using Classification Trees to Combine TM imagery and Ancillary Environmental Data. Remote Sensing of Environment. 107 (2007): 582-605.

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