The future of evapotranspiration : global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources

The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space-based perspective, necessary to advance them

Spatial distribution of forest aboveground biomass estimated from remote sensing and forest inventory data in New England, USA.

Abstract We combined satellite (Landsat 7 and Moderate Resolution Imaging Spectrometer) and U.S. Department of Agriculture forest inventory and analysis (FIA) data to estimate forest aboveground biomass (AGB) across New England, USA. This is practical for large-scale carbon studies and may reduce uncertainty of AGB estimates. We estimate that total regional forest AGB was 1,867 teragram (1012, dry weight) in 2001, with a mean AGB density of 120 Mg/ha (Standard deviation = 54 Mg/ha) ranging from 15 to 240 Mg/ha within a 95% percentile. The majority of regional AGB density was in the range of 80 to 160 Mg/ha (58.2%). High AGB densities were observed along the Appalachian Mountains from northwestern Connecticut to the Green Mountains in Vermont and White Mountains in New Hampshire, while low AGB densities were concentrated in the Downeast area of Maine (ME) and the Cape Cod area of Massachusetts (MA). At the state level, the averaged difference in mean AGB densities between simulated and FIA (as reference) was -2.0% ranging from 0% to -4.2% with a standard error of 3.2%. Within the 95% confidence interval the differences between FIA and simulated AGB densities ranged from 0 to 6% (absolute value). Our study may provide useful information for regional fuel-loading estimates

Responses of Land Surface Phenology to Wildfire Disturbances in the Western United States Forests

Land surface phenology (LSP) characterizes the seasonal dynamics in the vegetation communities observed for a satellite pixel and it has been widely associated with global climate change. However, LSP and its long-term trend can be influenced by land disturbance events, which could greatly interrupt the LSP responses to climate change. Wildfire is one of the main disturbance agents in the western United States (US) forests, but its impacts on LSP have not been investigated yet. To gain a comprehensive understanding of the LSP responses to wildfires in the western US forests, this dissertation focused on three research objectives: (1) to perform a case study of wildfire impacts on LSP and its trend by comparing the burned and a reference area, (2) to investigate the distribution of wildfire impacts on LSP and identify control factors by analyzing all the wildfires across the western US forests, and (3) to quantify the contributions of land cover composition and other environmental factors to the spatial and interannual variations of LSP in a recently burned landscape. The results reveal that wildfires play a significant role in influencing spatial and interannual variations in LSP across the western US forests. First, the case study showed that the Hayman Fire significantly advanced the start of growing season (SOS) and caused an advancing SOS trend comparing with a delaying trend in the reference area. Second, summarizing \u3e800 wildfires found that the shifts in LSP timing were divergent depending on individual wildfire events and burn severity. Moreover, wildfires showed a stronger impact on the end of growing season (EOS) than SOS. Last, LSP trends were interrupted by wildfires with the degree of impact largely dependent on the wildfire occurrence year. Third, LSP modeling showed that land cover composition, climate, and topography co-determine the LSP variations. Specifically, land cover composition and climate dominate the LSP spatial and interannual variations, respectively. Overall, this research improves the understanding of wildfire impacts on LSP and the underlying mechanism of various factors driving LSP. This research also provides a prototype that can be extended to investigate the impacts on LSP from other disturbances

Review of broad-scale drought monitoring of forests: Toward an integrated data mining approach

Efforts to monitor the broad-scale impacts of drought on forests often come up short. Drought is a direct stressor of forests as well as a driver of secondary disturbance agents, making a full accounting of drought impacts challenging. General impacts can be inferred from moisture deficits quantified using precipitation and temperature measurements. However, derived meteorological indices may not meaningfully capture drought impacts because drought responses can differ substantially among species, sites and regions. Meteorology-based approaches also require the characterization of current moisture conditions relative to some specified time and place, but defining baseline conditions over large, ecologically diverse regions can be as difficult as quantifying the moisture deficit itself. In contrast, remote sensing approaches attempt to observe immediate, secondary, and longer-term changes in vegetation response, yet they too are no panacea. Remote sensing methods integrate responses across entire mixed-vegetation pixels and rarely distinguish the effects of drought on a single species, nor can they disentangle drought effects from those caused by various other disturbance agents. Establishment of suitable baselines from remote sensing may be even more challenging than with meteorological data. Here we review broad-scale drought monitoring methods, and suggest that an integrated data-mining approach may hold the most promise for enhancing our ability to resolve drought impacts on forests. A big-data approach that integrates meteorological and remotely sensed data streams, together with other data sets such as vegetation type, wildfire occurrence and pest activity, can clarify direct drought effects while filtering indirect drought effects and consequences. This strategy leverages the strengths of meteorology-based and remote sensing approaches with the aid of ancillary data, such that they complement each other and lead toward a better understanding of drought impacts

Using eddy covariance, remote sensing, and in situ observations to improve models of springtime phenology in temperate deciduous forests

Phenological events in temperate forests, such as bud burst and senescence, exert strong control over seasonal fluxes of water, energy and carbon. The timing of these transitions is influenced primarily by air temperature and photoperiod, although the exact nature and magnitude of these controls is poorly understood. In this dissertation, I use in situ and remotely sensed observations of phenology in combination with surface meteorological data and measurements of biosphere-atmosphere carbon exchanges to improve understanding and develop models of canopy phenology in temperate forest ecosystems. In the first element of this research I use surface air temperatures and eddy covariance measurements of carbon dioxide fluxes to evaluate and refine widely used approaches for predicting the onset of photosynthesis in spring that account for geographic variation in thermal and photoperiod constraints on phenology. Results from this analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than existing models. A key challenge in developing and testing these models, however, is lack of adequate data sets that characterize phenology over large areas at multi-decadal time scales. To address this need, I develop a new method for estimating long-term average and interannual dynamics in the phenology of temperate forests using time series of Landsat TM/ETM+ images. Results show that estimated spring and autumn transition dates agree closely with in-situ measurements and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. In the final element of this dissertation, I use meteorological data, species composition maps, satellite remote sensing, and ground observations to develop models of springtime leaf onset in temperate deciduous forests that account for geographic differences in how forest communities respond to springtime climate forcing. Results demonstrate important differences in cumulative heating requirements and photoperiod cues among forest types and that regional differences in species composition explain substantial geographic variation in springtime phenology of temperate forests. Together, the results from this dissertation provide an improved basis for observing and modeling springtime phenology in temperate forests

MODIS: Moderate-resolution imaging spectrometer. Earth observing system, volume 2B

The Moderate-Resolution Imaging Spectrometer (MODIS), as presently conceived, is a system of two imaging spectroradiometer components designed for the widest possible applicability to research tasks that require long-term (5 to 10 years), low-resolution (52 channels between 0.4 and 12.0 micrometers) data sets. The system described is preliminary and subject to scientific and technological review and modification, and it is anticipated that both will occur prior to selection of a final system configuration; however, the basic concept outlined is likely to remain unchanged

Assessing Responses of \u3ci\u3eBetula papyrifera\u3c/i\u3e to Climate Variability in a Remnant Population along the Niobrara River Valley in Nebraska U.S. through Dendroecological and Remote Sensing Techniques

Remnant populations of Betula papyrifera have persisted in the Great Plains after the Wisconsin Glaciation along the Niobrara River Valley, Nebraska. Population health has declined in recent years, and has been hypothesized to be due to climate change. We used dendrochronological techniques to assess the response of B. papyrifera to microclimate (1950-2014), and satellite imagery [Landsat 5 TM (1985-2011) and MODIS (2000-2014)] derived NDVI as a proxy for population health. Growing-season streamflow and precipitation were positively correlated with raw and standardized tree-ring widths and basal area increment increase. Increasing winter and spring temperatures were unfavorable for tree growth while increasing summer temperatures were favorable in the absence of drought. The strongest predictor for standardized tree-rings was the Palmer Drought Severity Index, suggesting that B. papyrifera is highly responsive to a combination of temperature and water availability. The NDVI from vegetation community was positively correlated with standardized tree-ring growth, indicating the potential of these techniques to be used as a proxy for ex-situ monitoring of B. papyrifera. These results aid in forecasting the dynamics of the species in the face of climate variability and change in both remnant populations and across its current distribution in northern latitudes of North America

Phenologically-Tuned MODIS NDVI-Based Time Series (2000-2012) For Monitoring Of Vegetation and Climate Change in North-Eastern Punjab, Pakistan

One of the main factors determining the daily variation of the active surface temperature is the state of the vegetation cover It can well be characterized by the Normalized Difference Vegetation Index NDVI The NDVI has the potential ability to signal the vegetation features of different eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles The vegetation phenology is the expression of the seasonal cycles of plant processes and contributes vital current information on vegetation conditions and their connections to climate change The NDVI is computed using near-infrared and red reflectances and thus has both an accuracy and precision A gapless time series of MODIS NDVI MOD13A1 composite raster data from 18th February 2000 to 16th November 2012 with a spatial resolution of 500 m was utilized Time-series terrestrial parameters derived from NDVI have been extensively applied to global climate change since it analyzes each pixel individually without the setting of thresholds to detect change within a time serie