Potential of MODIS EVI and surface temperature for directly estimating per-pixel ecosystem C fluxes

We tested the potential of estimating per-pixel gross primary production (GPP) directly from the MODIS enhanced vegetation index (EVI) and respiration directly from MODIS surface temperature (MOD11). Carbon flux data were obtained from 10 eddy covariance tower sites representing a wide range of North American vegetations. The correlation between across-site tower GPP and EVI was comparable (r = 0.77) to that between tower GPP and MOD17-GPP (r = 0.73), suggesting that EVI could be used to provide reasonably accurate direct estimates of GPP on a truly per-pixel basis. There was also a strong relationship (r2 = 0.67) between respiration and surface temperature of dense vegetation, suggesting that estimation of net ecosystem exchange (NEE) may be possible with relatively simple pixel based models, at least for some vegetation types

Potential of MODIS ocean bands for estimating CO2 flux from terrestrial vegetation: A novel approach

A physiologically-driven spectral index using two ocean-color bands of MODIS satellite sensor showed great potential to track seasonally changing photosynthetic light use efficiency (LUE) and stress-induced reduction in net primary productivity (NPP) of terrestrial vegetation. Based on these findings, we developed a simple ‘‘continuous field’’ model solely based on remotely sensed spectral data that could explain 88% of variability in flux-tower based daily NPP. For the first time, such a procedure is successfully tested at landscape level using satellite imagery. These findings highlight the unexplored potential of narrow-band satellite sensors to improve estimates of spatial and temporal distribution in terrestrial carbon flux

On the use of MODIS EVI to assess gross primary productivity of North American ecosystems

[1] Carbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote‐sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote‐sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote‐sensing data

On the use of MODIS EVI to assess gross primary productivity of North American ecosystems

Sherpa Romeo green journal. Permission to archive final published versionCarbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote-sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote-sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote-sensing dataYe

Real-Time PCR in HIV/Trypanosoma cruzi Coinfection with and without Chagas Disease Reactivation: Association with HIV Viral Load and CD4+ Level

Chagas disease is endemic in Latin America and is caused by the flagellate protozoan T. cruzi. The acute phase is asymptomatic in the majority of the cases and rarely causes inflammation of the heart or the central nervous system. Most infected patients progress to a chronic phase, characterized by cardiac or digestive involvement when not asymptomatic. However, when patients are also exposed to an immunosuppressant (such as chemotherapy), neoplasia, or other infections such as HIV, T. cruzi infection may develop into a severe disease (Chagas disease reactivation) involving the heart and central nervous system. The current microscopic methods for diagnosing Chagas disease reactivation are not sensitive enough to prevent the high rate of death observed in these cases. Therefore, we propose a quantitative method to monitor blood levels of the parasite, which will allow therapy to be administered as early as possible, even if the patient has not yet presented symptoms

Evaluation of simulation-derived data for estimating biogeochemical processes in a secondary forest biome in southern Indiana

There is no abstract available for this thesis.Thesis (M.S.)Department of Natural Resources and Environmental Managemen