PUBLICATIONS
William K. Smith, Matthew P. Dannenberg, et al. (2019)
Remote Sensing of Environment 233: 111401
Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.
Matthew P. Dannenberg, Erika K. Wise, & William K. Smith (2019)
Science Advances 5(10): eaaw0667
Earth’s hydroclimatic variability is increasing, with changes in the frequency of extreme events that may negatively affect forest ecosystems. We examined possible consequences of changing precipitation variability using tree rings in the conterminous United States. While many growth records showed either little evidence of precipitation limitation or linear relationships to precipitation, growth of some species (particularly those in semiarid regions) responded asymmetrically to precipitation such that tree growth reductions during dry years were greater than, and not compensated by, increases during wet years. The U.S. Southwest, in particular, showed a large increase in precipitation variability, coupled with asymmetric responses of growth to precipitation. Simulations suggested roughly a twofold increase in the probability of large negative growth anomalies across the Southwest resulting solely from 20th century increases in variability of cool-season precipitation. Models project continued increases in precipitation variability, portending future growth reductions across semiarid forests of the western United States.
Yulong Zhang, Matthew P. Dannenberg, Taehee Hwang & Conghe Song (2019)
Journal of Geophysical Research: Biogeosciences 124(8): 2419-2431
Terrestrial gross primary production (GPP) is the largest carbon flux entering the biosphere from the atmosphere, which serves as a key driver of global carbon cycle and provides essential matter and energy for life on land. However, terrestrial GPP variability is still poorly understood and difficult to predict, especially at the annual scale. As a major internal climate oscillation, El Niño‐Southern Oscillation (ENSO) influences global climate patterns and thus may strongly alter interannual terrestrial GPP variation. Using a remote sensing‐driven ecosystem model with long‐term satellite and climate data, we comprehensively examined the impacts of ENSO on global GPP dynamics from 1982 to 2016, focusing on lag effects of ENSO and their spatial heterogeneity. We found a clear seasonal lag effect of previous‐year ENSO indices on current‐year global GPP variability. The composite Oceanic Niño Index in the previous‐year's August‐October showed the strongest correlation with global annual GPP (R = −0.51, p < 0.01). Spatially, 20.1% and 11.7% of vegetated land area showed significant negative and positive correlations with the ENSO cycle, respectively. ENSO effects on annual GPP exhibited diverse seasonal evolutions, and the timings of peak ENSO influences were heterogeneous across the globe. Annual GPP from TRENDY land surface model ensemble did not capture the major lag effects of ENSO identified in the satellite‐derived GPP and top‐down‐based land sink. Despite the complexity of the climate system, our efforts linking ENSO with global GPP dynamics provide a simple framework to understand and project climatic influences on the terrestrial carbon cycle.
Erika K. Wise & Matthew P. Dannenberg (2019)
Geophysical Research Letters 46(6): 3408-3416
Paleoclimate data play a critical role in contextualizing recent hydroclimate extremes, but asymmetries in tree‐ring responses to extreme climate conditions pose challenges for reconstruction and interpretation of past climate. Here we establish the extent to which existing tree‐ring records capture precipitation extremes in western North America and evaluate climate factors hypothesized to lead to asymmetric extreme capture, including timing of precipitation, seasonal temperatures, snowpack, and atmospheric river events. We find that while there is dry‐biased asymmetry in one third of western North American tree‐ring records, 45% of sites capture wet extremes as well as or better than dry extremes. Summer extremes are rarely captured at any sites. Latitude and elevation affect site‐level extreme responses, as do seasonal climate conditions, particularly in the autumn and spring. Directly addressing asymmetric extreme value capture in tree‐ring records can aid our interpretation of past climate and help identify alternative avenues for future reconstructions.
Christopher R. Hakkenberg, Matthew P. Dannenberg, Conghe Song & Katherine B. Ensor (2019)
International Journal of Remote Sensing 40(2): 693-718
In 2017, Hurricane Harvey caused substantial loss of life and property in the swiftly urbanizing region of Houston, TX. Now in its wake, researchers are tasked with investigating how to plan for and mitigate the impact of similar events in the future, despite expectations of increased storm intensity and frequency as well as accelerating urbanization trends. Critical to this task is the development of automated workflows for producing accurate and consistent land cover maps of sufficiently fine spatio-temporal resolution over large areas and long timespans. In this study, we developed an innovative automated classification algorithm that overcomes some of the traditional trade-offs between fine spatio-temporal resolution and extent – to produce a multi-scene, 30m annual land cover time series characterizing 21 years of land cover dynamics in the 35,000 km2 Greater Houston area. The ensemble algorithm takes advantage of the synergistic value of employing all acceptable Landsat imagery in a given year, using aggregate votes from the posterior predictive distributions of multiple image composites to mitigate against misclassifications in any one image, and fill gaps due to missing and contaminated data, such as those from clouds and cloud shadows. The procedure is fully automated, combining adaptive signature generalization and spatio-temporal stabilization for consistency across sensors and scenes. The land cover time series is validated using independent, multi-temporal fine-resolution imagery, achieving crisp overall accuracies between 78–86% and fuzzy overall accuracies between 91–94%. Validated maps and corresponding areal cover estimates corroborate what census and economic data from the Greater Houston area likewise indicate: rapid growth from 1997–2017, demonstrated by the conversion of 2,040 km2 (± 400 km2) to developed land cover, 14% of which resulted from the conversion of wetlands. Beyond its implications for urbanization trends in Greater Houston, this study demonstrates the potential for automated approaches to quantifying large extent, fine resolution land cover change, as well as the added value of temporally-dense time series for characterizing higher-order spatio-temporal dynamics of land cover, including periodicity, abrupt transitions, and time lags from underlying demographic and socio-economic trends.
Matthew P. Dannenberg, Erika K. Wise, Mark Janko, Taehee Hwang, & William K. Smith (2018)
Environmental Research Letters 13(3): 034029
We examined seasonal and spatial relationships between land surface phenology and the atmospheric components of five teleconnection patterns over the tropical Pacific, north Pacific, and north Atlantic. We also tested for interactions among these teleconnection patterns and assessed predictability of vegetation activity solely based on knowledge of atmospheric teleconnection indices. Autumn-to-winter composites of the Southern Oscillation Index (SOI) were strongly correlated with start of growing season timing, especially in the Pacific Northwest. The two leading modes of north Pacific variability (the Pacific-North American, PNA, and West Pacific patterns) were significantly correlated with start of growing season timing across much of southern Canada and the upper Great Lakes. These Pacific teleconnections were skillful predictors of spring phenology across an east-west swath of temperate and boreal North America, between 40°N–60°N. While the North Atlantic Oscillation (NAO) was not strongly correlated with start of growing season timing on its own, we found compelling evidence of widespread NAO-SOI and NAO-PNA interaction effects. These results suggest that knowledge of atmospheric conditions over the Pacific and Atlantic Oceans increases the predictability of North American spring phenology. A more robust consideration of the complexity of the atmospheric circulation system, including interactions across multiple ocean basins, is an important step towards accurate forecasts of vegetation activity.
Matthew P. Dannenberg, Christopher R. Hakkenberg & Conghe Song (2018)
Photogrammetric Engineering & Remote Sensing 84(9): 559-568
Land-Cover/Land-Use (LCLU) change is a critical aspect of global environmental change, with profound social and ecological consequences. The southeastern U.S. in particular is changing rapidly, but a long-term, consistent LCLUhistory at fine spatial resolution does not exist for the region. Here, we present a new LCLU history of the southeastern U.S. based on temporal extension of the 2011 National Land Cover Database (NLCD) back to 1986. We used Automatic Adaptive Signature Generalization (AASG) to generate this product from Landsat TM/ETM+ imagery and ancillary topographic information. AASG identifies stable sites between two images and uses these stable sites to generate a new training dataset for updating a classification from one date to the next. Our long-term LCLU classifications are broadly consistent with the NLCD while providing a much longer historical record for characterizing recent changes in the southeastern U.S. and contextualizing their consequences for ecosystem services in the region.
Colin Tucker, Dong Yan, Matthew Dannenberg, Sasha Reed & William Smith (2018)
New Phytologist 218(4): 1318-1320
The precipitation delivered to western North America arrives during winter via midlatitude Pacific storm tracks, and these storm tracks are expected to change under future warming. We examined how past storm track variability has affected water delivery, spring snowpack, vegetation productivity, and wildfire area. We show that northward shifts of the storm track typically resulted in less moisture delivery and smaller snowpacks in the northwestern United States. This reduced moisture supply likely resulted in less vegetation growth and more wildfires. These results suggest that future changes in the position of the Pacific storm track will have important effects on northwestern ecosystems
Matthew P. Dannenberg & Erika K. Wise (2017)
Global Change Biology 23(11): 4896-4906
Moisture delivery to western North America is closely linked to the position and intensity of westerly Pacific storm tracks, which in turn are modified by larger-scale features like the El Niño-Southern Oscillation. Previous studies suggest that midlatitude storm tracks may be intensifying and shifting towards the poles. We used more than three centuries of tree-ring data collected across western North America to reconstruct past variation of Pacific storm track position and intensity. Since 1693 C.E., the storm track has shown high year-to-year and decade-to-decade variability, which was likely influenced by tropical and north Pacific conditions. This long-term record suggests that recent changes in Pacific storm track position and intensity likely reflect a warming-related increase that was amplified by natural decadal variability.
Erika K. Wise & Matthew P. Dannenberg (2017)
Science Advances 3: e1602263
Matthew P. Dannenberg & Erika K. Wise (2016)
Journal of Geophysical Research: Biogeosciences 121: 1178-1189
Projected changes in hydroclimate are likely to have important implications for water resources and terrestrial ecosystems. Here, we examined seasonal drivers of Pinus ponderosa growth in the upper Columbia River Basin of the U.S. Pacific Northwest. All sites and metrics responded positively to warm-season precipitation, but earlywood widths from the lowest elevation sites also reflected cool-season moisture. We also found asymmetric growth responses to extremes: growth responded to low (but not high) precipitation extremes during winter, but to high (but not low) precipitation extremes during summer.
Matthew P. Dannenberg, Christopher R. Hakkenberg & Conghe Song (2016)
Remote Sensing 8(8): 691
Erika K. Wise, Melissa L. Wrzesien, Matthew P. Dannenberg & David L. McGinnis (2015)
Journal of Applied Meteorology and Climatology 54(2): 494-505
Matthew P. Dannenberg, Conghe Song, Taehee Hwang & Erika K. Wise (2015)
Remote Sensing of Environment 159: 167-180
Erika K. Wise & Matthew P. Dannenberg (2014)
Nature Communications 5: 4912
Matthew P. Dannenberg & Erika K. Wise (2013)
Journal of Geophysical Research: Atmospheres 118(17): 9595-9610
Conghe Song, Matthew P. Dannenberg & Taehee Hwang (2013)
Progress in Physical Geography 37(6): 834-854