Publications

A simple mixing model employing reference endmembers (green vegetation, non-photosynthetic vegetation, soil and shade), and using 180 AVIRIS bands, was used to establish an interpretive framework for a forested area in the Pacific Northwest. A regrowth trend, based on changes in the endmember proportions, was defined for conifers that extends from clearcuts to mature forest, and by implication to old growth. Deciduous species within replanted forest plots caused the fractions to be displaced from the main coniferous regrowth trend and to move toward the green vegetation fraction. The results indicate that the spectral information in AVIRIS can be inverted to estimate approximate stand age and relative proportion of deciduous species in the context of the area studied. Using AVIRIS we measured a 3 to 5 percent increase in woody material in old-growth forest, as distinct from other mature forest. This result is consistent with a predicted increase in NPV in old-growth forest, based on field observations. Previous application of the mixing analysis to a TM image of the same area separated old growth based solely on the shade fraction; however the approach required successful removal of shade introduced by topography. Our new results suggest that with the high spectral resolution and high signal-to-noise of AVIRIS images it may be possible to characterize and map old-growth forests in the Northwest using both the NPV fraction and shade.

The El Niño/Southern Oscillation (ENSO) is a global climate phenomenon that impacts human infectious disease risk worldwide through droughts, floods, and other climate extremes. Throughout summer and fall 2014 and winter 2015, El Niño Watch, issued by the US National Oceanic and Atmospheric Administration, assessed likely El Niño development during the Northern Hemisphere fall and winter, persisting into spring 2015.We identified geographic regions where environmental conditions may increase infectious disease transmission if the predicted El Niño occurs using El Niño indicators (Sea Surface Temperature [SST], Outgoing Longwave Radiation [OLR], and rainfall anomalies) and literature review of El Niño-infectious disease associations.SSTs in the equatorial Pacific and western Indian Oceans were anomalously elevated during August-October 2014, consistent with a developing weak El Niño event. Teleconnections with local climate is evident in global precipitation patterns, with positive OLR anomalies (drier than average conditions) across Indonesia and coastal southeast Asia, and negative anomalies across northern China, the western Indian Ocean, central Asia, north-central and northeast Africa, Mexico/Central America, the southwestern United States, and the northeastern and southwestern tropical Pacific. Persistence of these conditions could produce environmental settings conducive to increased transmission of cholera, dengue, malaria, Rift Valley fever, and other infectious diseases in regional hotspots as during previous El Niño events.The current development of weak El Niño conditions may have significant potential implications for global public health in winter 2014-spring 2015. Enhanced surveillance and other preparedness measures in predicted infectious disease hotspots could mitigate health impacts.

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of arthropod-borne diseases. We show that epidemics and epizootics of previously unpredictable Rift Valley fever (RVF) are directly influenced by large-scale flooding associated with the El Niño/Southern Oscillation (ENSO). This flooding affects the ecology of disease transmitting arthropod vectors through vegetation development and other bioclimatic factors. This information is now utilized to monitor, model, and map areas of potential RVF outbreaks and is used as an early warning system for risk reduction of outbreaks to human and animal health, trade, and associated economic impacts. The continuation of such satellite measurements is critical to anticipating, preventing, and managing disease epidemics and epizootics and other climate-related disasters.

SiB2, the second-generation land-surface parameterization developed by Sellers et al., has been incorporated into the Colorado State University general circulation model and tested in multidecade simulation. The control run uses a "bucket" hydrology but employs the same surface albedo and surface roughness distributions as the SiB2 run. Results show that SiB2 leads to a general warming of the continents, as evidenced in the ground temperature, surface air temperature, and boundary-layer-mean potential temperature. The surface sensible heat flux increases and the latent heat flux decreases. This warming occurs virtually everywhere but is most spectacular over Siberia in winter. Precipitation generally decreases over land but increases in the monsoon regions, especially the Amazon basin in January and equatorial Africa and Southeast Asia in July. Evaporation decreases considerably, especially in dry regions such as the Sahara. The excess of precipitation over evaporation increases in the monsoon regions. The precipitable water (vertically integrated water vapor content) generally decreases over land but increases in the monsoon regions. The mixing ratio of the boundary-layer air decreases over newly all continental areas, however, including the monsoon regions. The average (composite) maximum boundary-layer depth over the diurnal cycle increases in the monsoon regions, as does the average PBL turbulence kinetic energy. The average boundary-layer wind speed also increases over most continental regions. Groundwater content generally increases in rainy regions and decreases in dry regions, so that SiB2 has a tendency to increase its spatial variability. SiB2 leas to a general reduction of cloudiness over land. The net surface longwave cooling of the surface increases quite dramatically over land, in accordance with the increased surface temperatures and decreased cloudiness. The solar radiation absorbed at the ground also increases. SiB2 has modest effects on the simulated general circulation of the atmosphere. Its most important impacts on the model are to improve the simulations of surface temperature and snow cover and to enable the simulation of the net rate of terrestrial carbon assimilation

Charcoal is a key energy source for urban households in sub-Saharan Africa and charcoal production is the main cause of forest degradation across the region. We used multitemporal high-resolution remote sensing optical imagery to quantify the extent and intensity of forest degradation associated with charcoal production and its impact on forest carbon stocks for the main supplying area of an African capital. This analysis documents the advance of forest degradation and quantifies its aboveground biomass removals over a seven-year period, registering that, between 2013 and 2016, the average annual area under charcoal production was 103 km 2 and the annual aboveground biomass removals reached 1081 000 (SD = 2461) Mg. Kiln densities in the study area rose to 2 kilns-ha, with an average of 90.7 Mg ha −1 of extracted aboveground biomass. Charcoal production was responsible for the degradation of 55.5% of the mopane woodlands in the study area between 2013 and 2019. We estimated post-disturbance recovery times using an ecosystem model calibrated for the study area. The simulations showed that recovery times could require up to 150 years for current aboveground biomass extraction rates. The results of the remote sensing analysis and the simulations of the ecosystem model corroborate the unsustainability of the present patterns of charcoal production. The detailed characterization of the spatial and temporal patterns of charcoal production was combined with household survey information to quantify the impact of the urban energy demand of the Maputo urban area on forest carbon stocks. The analysis shows that Maputo charcoal demand was responsible for the annual degradation of up to 175.3 km 2 and that the contribution of the study area to this demand fluctuated between 75% and 33% over the study period. The extent, advance pace and distance from urban centers documented in this study support the idea that forest degradation from charcoal production cannot merely be considered a peri-urban process. The intensity of the aboveground biomass (AGB) removals and its contribution to forest carbon stocks changes is significant at the national and regional levels.

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) provides global monthly measurements of both oceanic phytoplankton chlorophyll biomass and light harvesting by land plants. These measurements allowed the comparison of simultaneous ocean and land net primary production (NPP) responses to a major El Niño to La Niña transition. Between September 1997 and August 2000, biospheric NPP varied by 6 petagrams of carbon per year (from 111 to 117 petagrams of carbon per year). Increases in ocean NPP were pronounced in tropical regions where El Niño–Southern Oscillation (ENSO) impacts on upwelling and nutrient availability were greatest. Globally, land NPP did not exhibit a clear ENSO response, although regional changes were substantial.

Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Terrestrial ecosystems absorb about 2.8 Gt C yr, which is estimated to be about a quarter of the carbon emitted from fossil fuel combustion. However, the uncertainties of this sink are large, in the order of  40%, with spatial and temporal variations largely unknown. One of the largest factors contributing to the uncertainty is photosynthesis, the process by which plants absorb carbon from the atmosphere. Currently, photosynthesis, or gross ecosystem productivity (GEP), can only be inferred from flux‐towers by measuring the exchange of CO2 in the surrounding air column. Consequently, carbon models suffer from a lack of spatial coverage of accurate GEP observations. Here, we show that photosynthetic light‐ use efficiency (e), hence photosynthesis can be directly inferred from spaceborne measurements of reflectance. We demonstrate that the differential between reflectance measurements in bands associated with the vegetation xanthophyll cycle and estimates of canopy shading obtained from multi‐ angular satellite observations (using the CHRIS/Proba sensor) permits us to infer plant photosynthetic efficiency, independently of vegetation type and structure (r=0.68, compared to flux measurements). This is a significant advance over previous approaches seeking to model global scale photosynthesis indirectly from a combination of growth limiting factors, most notably pressure deficit and temperature. When combined with modeled global scale photosynthesis, satellite inferred e can improve model estimates through data assimilation. We anticipate that our findings will guide the development of new spaceborne approaches to observe vegetation carbon uptake and improve current predictions of global CO2 budgets and future climate scenarios by providing regularly timed calibration points for modeling plant photosynthesis consistently at a global scale.