Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S.

Abstract

Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon products, gross primary productivity (GPP), and net ecosystem exchange (NEE) to quantify their spatial and temporal variabilities for selected terrestrial ecosystems across Texas during the 2015–2018 study period. These SMAP carbon products are available at 9 km spatial resolution on a daily basis. The ten selected SMAP grids are located in seven climate zones and dominated by five major land uses (developed, crop, forest, pasture, and shrub). Results showed CO2 emissions and uptake were affected by land-use and climatic conditions across Texas. It was also observed that climatic conditions had more impact on CO2 emissions and uptake than land-use in this state. On average, South Central Plains and East Central Texas Plains ecoregions of East Texas and Western Gulf Coastal Plain ecoregion of Upper Coast climate zones showed higher GPP flux and potential carbon emissions and uptake than other climate zones across the state, whereas shrubland on the Trans Pecos climate zone showed lower GPP flux and carbon emissions/uptake. Comparison of GPP and NEE distribution maps between 2015 and 2018 confirmed substantial changes in carbon emissions and uptake across Texas. These results suggest that SMAP carbon products can be used to study the terrestrial carbon cycle at regional to global scales. Overall, this study helps to understand the impacts of climate, land-use, and ecosystem dynamics on the terrestrial carbon cycle.

Characteristics of Land-Use Carbon Emissions and Carbon Balance Zoning in the Economic Belt on the Northern Slope of Tianshan

“…With the help of this method, the attribute values between two neighboring spatial units would be calculated and the spatial correlation could be further analyzed. In this way, the agglomeration features for each spatial unit could be analyzed and evaluated [63]. This approach branched into global and local spatial autocorrelation models, and several calculating procedures were proposed, such as Moran's I, Geary's C, Getis, and Join count.…”

Estimation of Particulate Matter Less Than 10 Microns Volume Through Various Formats of Spatial Interpolation Methods

“…There are 2 major causes of air pollution including: human actions, for example, demands on energy for consuming in households, industries, and agriculture as well as air pollution caused by cars, ships, and planes that causes CO2, NO2, and hydrocarbons giving bad effects to human's health directly (Canha et al, 2021;Thai et al, 2021). Combustion of these types of fuel also increase the problems on air pollution every year; natural actions, for example, volcanic eruption causing high amount of dust and ash blowing in the air (Perera, 2018;Mperejekumana et al, 2021), wildfire causing smoke that is dangerous for respiratory system, decomposition of humus and carcass that may cause CO2, CH4, and NH3 in case of chemical reaction (Ray et al, 2019;Junpen et al, 2020); and dust caused by broken objects that would be distributed in the air when they are blown by the wind.…”