Remote Sensing of Coniferous Forest Structure in Grand Teton National Park

Satellite remotely sensed multispectral data provides a systematic, synoptic means for broad-area spatially-explicit estimation of biophysically important variables. By using ground measurements of biotic properties to calibrate remotely sensed multispectral data, vegetation properties measured at sample points can be extrapolated across a large geographic region (Graetz, 1990). Biophysical variables derived by this empirical method may include the successional state of the vegetation, or an intrinsic property of the vegetation, such as biomass, leaf area index, cover, or moisture content (Jensen, 1983; Waring et al., 1986; Spanner et al., 1984; Graetz, 1990). Spatially-explicit estimation of forest biophysical factors at landscape to regional scales has applications in forest management and ecology, including insect infestation susceptability, forest fire behavior, and estimating plant and animal species habitat and diveristy. Our previous research examined relationships -- between forest structure, successional state, and spectral reflectance characteristics. Results indicated that decreases in visible and middle-infrared spectral reflectance are related to the age and development of a coniferous forest stand. Spectral reflectance changes are rapid during the initial stages of stand regeneration, but the rate of change slows as the stand progresses into later successional stages (Blodgett and Jakubauskas, 1996; Jakubauskas, 1996). Our ongoing objectives are to develop methods for estimation of forest biophysical parameters from satellite remotely sensed data and to compare Yellowstone and Teton coniferous forests in terms of forest structure and successional pattern. Forest stands sampled in 1995 in Grand Teton National Park are 500 - 1000 meters lower in elevation than the Yellowstone sites (sampled 1992-1994), and subject to different temperature and precipitation regimes. Sampling in 1995 was directed at increasing our database of lodgepole-dominated forest stands in the Greater Yellowstone Area.

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Stratifying Forest Overstory for Improving Effective LAI Estimation Based on Aerial Imagery and Discrete Laser Scanning Data

Remote Sensing ◽

10.3390/rs12132126 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2126 ◽

Cited By ~ 1

Author(s):

Zhaoshang Xu ◽

Guang Zheng ◽

L. Monika Moskal

Keyword(s):

Forest Structure ◽

Laser Scanning ◽

Structural Information ◽

Canopy Cover ◽

Remotely Sensed ◽

Background Information ◽

Estimation Accuracy ◽

Forest Stands ◽

Remotely Sensed Data ◽

Area Index

Accurately mapping forest effective leaf area index (LAIe) at the landscape level is a crucial step to better simulate various ecological and physiological processes such as photosynthesis, respiration, transpiration, and precipitation interception. The LAIe products obtained from two-dimensional (2-D) remotely sensed optical imageries are usually biased due to their inability to identify the vertical forest structure and eliminate the effects of forest background (i.e., shrubs, grass, snow, and bare earth). In this study, we first stratified the forest overstory and background layers and generated a forest background mask layer based on the structural information implicitly contained within the aerial laser scanning (ALS) data. We improved the retrieval accuracy of LAIe by combining light detection and ranging (Lidar)-based three dimensional (3-D) structural and 2-D spectral information. Then, we obtained the improved final LAIe estimation result by masking the forest background pixels from the optical remotely sensed imageries. Our results showed that: (1) Removing forest background information could effectively (R2 increase from 20% to 30%) improve the estimation accuracy of optical-based forest LAIe depending on forest structure characteristics. (2) The forest background in the forest stands with low canopy cover showed more apparent effects on LAIe estimation compared with the forest stands with a high canopy cover. (3) The combination of ALS and optical remotely sensed data could produce the best LAIe retrieval result effectively by removing the forest background information.

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Mushrooms of Grand Teton National Park

The UW National Parks Service Research Station Annual Reports ◽

10.13001/uwnpsrc.1982.2327 ◽

1982 ◽

Vol 6 ◽

pp. 69-80

Author(s):

Kent McKnight ◽

Kimbal Harper ◽

Karl McKnight

Keyword(s):

Soil Moisture ◽

National Park ◽

Forest Stands ◽

Mushroom Species ◽

Grand Teton National Park ◽

Number Of Species ◽

Floristic Study ◽

Early Autumn ◽

Made In

The recent checklist of macrofungi of Teton and Yellowstone Parks (McKnight 1982) provides a foundation on which to develop a more extensive floristic study. Collections of fungi and measurements of soil moisture were made in and around the Parks during the summer and early autumn of 1982 directed toward (1) adding to the number of species known to occur there; (2) comparing the mushroom species fruiting on 11 different forest stands over a 12-week period; and (3) elucidating possible correlations with mushroom fruiting and soil moisture.

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The dynamics of changes in the structure and species composition of primary forest stands in selected areas of Pieniny National Park

Forest Research Papers ◽

10.2478/v10111-011-0034-5 ◽

2011 ◽

Vol 72 (4) ◽

pp. 339-356 ◽

Cited By ~ 3

Author(s):

Andrzej Jaworski ◽

Dorota Jakubowska

Keyword(s):

Species Composition ◽

National Park ◽

Primary Forest ◽

Forest Stands

Dynamika zmian budowy, struktury i składu gatunkowego drzewostanów o charakterze pierwotnym na wybranych powierzchniach w Pienińskim Parku Narodowym

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Economic analysis of alternative bison and elk management practices on the National Elk Refuge and Grand Teton National Park: A comparison of visitor and household responses

Open-File Report ◽

10.3133/ofr20041305 ◽

2004 ◽

Author(s):

John B. Loomis ◽

Lynne Caughlan

Keyword(s):

Economic Analysis ◽

National Park ◽

Management Practices ◽

Grand Teton National Park ◽

Household Responses

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Deglaciation and postglacial environmental changes in the Teton Mountain Range recorded at Jenny Lake, Grand Teton National Park, WY

Quaternary Science Reviews ◽

10.1016/j.quascirev.2016.02.024 ◽

2016 ◽

Vol 138 ◽

pp. 62-75 ◽

Cited By ~ 13

Author(s):

Darren J. Larsen ◽

Matthew S. Finkenbinder ◽

Mark B. Abbott ◽

Adam R. Ofstun

Keyword(s):

National Park ◽

Environmental Changes ◽

Mountain Range ◽

Grand Teton National Park

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Spatially explicit statistical modeling of random and systematic land cover transitions in the main grassland plain of Nech Sar National Park, Ethiopia

Ecological Processes ◽

10.1186/s13717-019-0199-z ◽

2019 ◽

Vol 8 (1) ◽

Cited By ~ 2

Author(s):

Kiros Tsegay Deribew

Keyword(s):

Land Cover ◽

Land Cover Change ◽

Statistical Modeling ◽

National Park ◽

Spatial Extent ◽

Aerial Photographs ◽

Woody Vegetation ◽

Spatially Explicit ◽

Strong Signal ◽

The Past

AbstractThe main grassland plain of Nech Sar National Park (NSNP) is a federally managed protected area in Ethiopia designated to protect endemic and endangered species. However, like other national parks in Ethiopia, the park has experienced significant land cover change over the past few decades. Indeed, the livelihoods of local populations in such developing countries are entirely dependent upon natural resources and, as a result, both direct and indirect anthropogenic pressures have been placed on natural parks. While previous research has looked at land cover change in the region, these studies have not been spatially explicit and, as a result, knowledge gaps in identifying systematic transitions continue to exist. This study seeks to quantify the spatial extent and land cover change trends in NSNP, identify the strong signal transitions, and identify and quantify the location of determinants of change. To this end, the author classifies panchromatic aerial photographs in 1986, multispectral SPOT imagery in 2005, and Sentinel imagery in 2019. The spatial extent and trends of land cover change analysis between these time periods were conducted. The strong signal transitions were systematically identified and quantified. Then, the basic driving forces of the change were identified. The locations of these transitions were also identified and quantified using the spatially explicit statistical model. The analysis revealed that over the past three decades (1986–2019), nearly 52% of the study area experienced clear landscape change, out of which the net change and swap change attributed to 39% and 13%, respectively. The conversion of woody vegetation to grassland (~ 5%), subsequently grassland-to-open-overgrazed land (28.26%), and restoration of woody vegetation (0.76%) and grassland (0.72%) from riverine forest and open-overgrazed land, respectively, were found to be the fully systematic transitions whereas the rest transitions were recorded either partly systematic or random transitions. The location of these most systematic land cover transitions identified through the spatially explicit statistical modeling showed drivers due to biophysical conditions, accessibility, and urban/market expansions, coupled with successive government policies for biodiversity management, geo-politics, demographic, and socioeconomic factors. These findings provide important insights into biodiversity loss, land degradation, and ecosystem disruption. Therefore, the model for predicted probability generally suggests a 0.75 km and 0.72 km buffers which are likely to protect forest and grassland from conversion to grassland and open-overgrazed land, respectively.

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