Physical and Environmental Geography Commons^™

How Much Variation In Land Surface Phenology Can Climate Oscillation Modes Explain At The Scale Of Mountain Pastures In Kyrgyzstan?, Monika A. Tomaszewska, Geoffrey M. Henebry

NASA Land-Cover Land-Use Change Data Sets

Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics …

Land Surface Phenology In The Highland Pastures Of Montane Central Asia: Interactions With Snow Cover Seasonality And Terrain Characteristics, Monika A. Tomaszewska, Lan H. Nguyen, Geoffrey Henebry

NASA Land-Cover Land-Use Change Data Sets

Many studies have shown that high elevation environments are among very sensitive to climatic changes and where impacts are exacerbated. Across Central Asia, which is especially vulnerable to climate change due to aridity, the ability of global climate projections to capture the complex dynamics of mountainous environments is particularly limited. Over montane Central Asia, agropastoralism constitutes a major portion of the rural economy. Extensive herbaceous vegetation forms the basis of rural economies in Kyrgyzstan. Here we focus on snow cover seasonality and the effects of terrain on phenology in highland pastures using remote sensing data for 2001–2017. First, we describe …

Investigating Smoke Aerosol Emission Coefficients Using Modis Active Fire And Aerosol Products – A Case Study In The Conus And Indonesia, Xiaoman Lu, Xiaoyang Zhang, Fangjun Li, Mark Cochrane

Global Land Surface Season Data Sets

This data set is in relation to the paper of the same title, which has been submitted to the Journal of Geophysical Research: Biogeosciences.

Instructions for viewing the data in “Readme.txt”

Changing Snow Seasonality In The Highlands Of Kyrgyzstan, Monika Tomaszewska, Geoffrey Henebry

NASA Land-Cover Land-Use Change Data Sets

Few studies have examined changing snow seasonality in Central Asia. Here, we analyzed changes in the seasonality of snow cover across Kyrgyzstan (KGZ) over 14 years from 2002/03–2015/16 using the most recent version (v006) of MODIS Terra and Aqua 8 day snow cover composites (MOD10A2/MYD10A2).We focused on three metrics of snow seasonality—first date of snow, last date of snow, and duration of snow season—and used nonparametric trends tests to assess the significance and direction of trends. We evaluated trends at three administration scales and across elevation. We used two techniques to assure that our identification of significant trends was not …

Investigation Of The Fire Radiative Energy Biomass Combustion Coefficient - A Comparison Of Polar And Geostationary Satellite Retrievals Over The Conterminous United States, Fangjun Li, Xiaoyang Zhang

Global Land Surface Season Data Sets

The data is for the article "Investigation of the Fire Radiative Energy Biomass Combustion Coefficient - A Comparison of Polar and Geostationary Satellite Retrievals Over the Conterminous United States", which has been submitted to the Journal of Geophysical Research: Biogeosciences.

The data file contains a total of 20 files in 10 folders that are associated to the figures and tables in the article. General instruction for viewing output data for the paper can be found in the "Readme" text file.

Multi-Year Modis Active Fire Type Classification Over The Brazilian Tropical Moist Forest Biome, David P. Roy, S. S. Kumar

GSCE Faculty Publications

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km² and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity …

Sensitivity Analysis Of The Gems Soil Organic Carbon Model To Land Cover Land Use Classification Uncertainties Under Different Climate Scenarios In Senegal, Amadou M. Dieye, David P. Roy, N. P. Hanan, S. Lui, M. Hansen, A. Toure

GSCE Faculty Publications

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat …

Radiative Forcing Over The Conterminous United States Due To Contemporary Land Cover Use Change And Sensitivity To Snow And Interannual Albedo Variability, Christoper A. Barnes, David P. Roy

GSCE Faculty Publications

Satellite‐derived land cover land use (LCLU), snow and albedo data, and incoming surface solar radiation reanalysis data were used to study the impact of LCLU change from 1973 to 2000 on surface albedo and radiative forcing for 58 ecoregions covering 69% of the conterminous United States. A net positive surface radiative forcing (i.e., warming) of 0.029 Wm−2 due to LCLU albedo change from 1973 to 2000 was estimated. The forcings for individual ecoregions were similar in magnitude to current global forcing estimates, with the most negative forcing (as low as −0.367 Wm−2) due to the transition to forest and the …