M. A. Friedl

Boston University, Boston, Massachusetts, United States

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Publications (149)271.89 Total impact

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    ABSTRACT: Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO2) concentrations has increased by as much as 50 per cent over the past 50 years. This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation. However, the precise causal mechanisms behind the observed changes in atmospheric CO2 seasonality remain unclear. Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here.
    Nature 11/2014; 515(7527):398-401. · 42.35 Impact Factor
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    ABSTRACT: Time series of multispectral images are widely used to monitor and map land cover. However, high dimensionality and missing data present significant challenges for classification algorithms that use multi-temporal remotely sensed data. Further, generation and assessment of high quality training data, including detection of outliers and changed pixels in training data, is difficult. In this paper we present a new statistical framework that is based on a parametric model that enables a targeted principal component analysis (PCA) to reduce the dimensionality of multi-temporal remote sensing data. In doing so, the model provides a novel basis for land cover classification and evaluating the nature and quality of training data used for supervised classifications. The methodology we describe uses a Kronecker operator to reduce the spectral dimensionality of multi-temporal images while preserving their temporal structure, thereby providing low-dimensional data that is well-suited for classification and outlier detection problems. As part of our framework, we use an expectation–maximization method to impute missing data, and propose new metrics that characterize the representativeness and pixel-to-pixel homogeneity of training sites used for supervised classification. To evaluate our approach, we use data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and extracted more than 200 training sites where the land cover has been characterized from high spatial resolution imagery. The original input data was composed of 196 features (28 dates × 7 bands), and the PCA-based approach we describe captured 91% of the variance, in these 7 bands, in 3 components. Results from maximum likelihood classification show that the retained principal components successfully distinguish land cover classes from one another, with classification results that were comparable to supervised machine learning methods applied to the original MODIS data. Analysis of our site composition metrics show that they successfully characterize the homogeneity (or lack thereof) and representativeness of individual pixels and entire sites relative to other training sites in the same class.
    ISPRS Journal of Photogrammetry and Remote Sensing 11/2014; 97:219–228. · 2.90 Impact Factor
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    ABSTRACT: Rapid urban expansion is a major contributor to environmental change in many parts of the world. This study investigated land cover changes that occurred between 1988 and 2010 in the Calgary–Edmonton corridor in Alberta, a region that has undergone considerable recent urban expansion. We used satellite imagery to develop land cover maps for four different snapshots in time between 1988 and 2010 and used these maps to investigate two principal questions related to urban expansion: (1) How did urban expansion affect other land cover types? and (2) How did urban expansion affect the availability of high-quality agricultural land in the region? Our results show that 60 % of new urban and peri-urban growth between 1988 and 2010 occurred on agricultural land. Nevertheless, total agricultural land increased in the region because of the greater clearing of natural vegetation for agriculture away from the urban core. Urban expansion predominantly occurred on soils that were highly suitable for farming, while new agricultural expansion occurred on soils of poorer quality. As a result, the average soil quality of land used for agriculture has declined in the Calgary–Edmonton corridor, confirming other studies of the food security implications of urbanization.
    Regional Environmental Change 08/2014; 14(4). · 2.26 Impact Factor
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    ABSTRACT: The timing of phenological events exerts a strong control over ecosystem function and leads to multiple feedbacks to the climate system1. Phenology is inherently sensitive to temperature (although the exact sensitivity is disputed2) and recent warming is reported to have led to earlier spring, later autumn3,4 and increased vegetation activity5,6. Such greening could be expected to enhance ecosystem carbon uptake7,8, although reports also suggest decreased uptake for boreal forests4,9. Here we assess changes in phenology of temperate forests over the eastern US during the past two decades, and quantify the resulting changes in forest carbon storage. We combine long-term ground observations of phenology, satellite indices, and ecosystem-scale carbon dioxide flux measurements, along with 18 terrestrial biosphere models. We observe a strong trend of earlier spring and later autumn. In contrast to previous suggestions4,9 we show that carbon uptake through photosynthesis increased considerably more than carbon release through respiration for both an earlier spring and later autumn. The terrestrial biosphere models tested misrepresent the temperature sensitivity of phenology, and thus the e�ect on carbon uptake. Our analysis of the temperature–phenology–carbon coupling suggests a current and possible future enhancement of forest carbon uptake due to changes in phenology. This constitutes a negative feedback to climate change, and is serving to slow the rate of warming.
    Nature Climate Change 07/2014; 4:598-604. · 15.30 Impact Factor
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    Xiaoman Huang, Mark A. Friedl
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    ABSTRACT: More than 12 years of global observations are now available from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). As this time series grows, the MODIS archive provides new opportunities for identification and characterization of land cover at regional to global spatial scales and interannual to decadal temporal scales. In particular, the high temporal frequency of MODIS provides a rich basis for monitoring land cover dynamics. At the same time, the relatively coarse spatial resolution of MODIS (250–500 m) presents significant challenges for land cover change studies. In this paper, we present a distance metric-based change detection method for identifying changed pixels at annual time steps using 500 m MODIS time series data. The approach we describe uses distance metrics to measure (1) the similarity between a pixel's annual time series to annual time series for pixels of the same land cover class and (2) the similarity between annual time series from different years at the same pixel. Pre-processing, including gap-filling, smoothing and temporal subsetting of MODIS 500 m Nadir BRDF-adjusted Reflectance (NBAR) time series is essential to the success of our method. We evaluated our approach using three case studies. We first explored the ability of our method to detect change in temperate and boreal forest training sites in North America and Eurasia. We applied our method to map regional forest change in the Pacific Northwest region of the United States, and in tropical forests of the Xingu River Basin in Mato Grosso, Brazil. Results from these case studies show that the method successfully identified pixels affected by logging and fire disturbance in temperate and boreal forest sites. Change detection results in the Pacific Northwest compared well with a Landsat-based disturbance map, yielding a producer's accuracy of 85%. Assessment of change detection results for the Xingu River Basin demonstrated that detection accuracy improves as the fraction of deforestation within a MODIS pixel increases, but that relatively small changes in forest cover were still detectable from MODIS. Annually, over 80% of pixels with >20% deforested area were correctly identified and the timing of change showed good agreement with reference data. Errors of commission were largely associated with pixels located at the edges of disturbance events and inadequate characterization of land cover changes unrelated to deforestation in the reference data. Although our case studies focused on forests, this method is not specific to detection of forest cover change and has the potential to be applied to other types of land cover change including urban and agricultural expansion and intensification.
    International Journal of Applied Earth Observation and Geoinformation 06/2014; 29:78–92. · 2.54 Impact Factor
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    ABSTRACT: By the end of this century, mean annual temperatures in the Northeastern United States are expected to warm by 3–5 °C, which will have significant impacts on the structure and function of temperate forests in this region. To improve understanding of these impacts, we exploited two recent climate anomalies to explore how the springtime phenology of Northeastern temperate deciduous forests will respond to future climate warming. Specifically, springtime temperatures in 2010 and 2012 were the warmest on record in the Northeastern United States, with temperatures that were roughly equivalent to the lower end of warming scenarios that are projected for this region decades from now. Climate conditions in these two years therefore provide a unique empirical basis, that complements model-based studies, for improving understanding of how northeastern temperate forest phenology will change in the future. To perform our investigation, we analyzed near surface air temperatures from the United States Historical Climatology Network, time series of satellite-derived vegetation indices from NASA's Moderate Resolution Imaging Spectroradiometer, and in situ phenological observations. Our study region encompassed the northern third of the eastern temperate forest ecoregion, extending from Pennsylvania to Canada. Springtime temperatures in 2010 and 2012 were nearly 3 °C warmer than long-term average temperatures from 1971–2000 over the region, leading to median anomalies of more than 100 growing degree days. In response, satellite and ground observations show that leaf emergence occurred up to two weeks earlier than normal, but with significant sensitivity to the specific timing of thermal forcing. These results are important for two reasons. First, they provide an empirical demonstration of the sensitivity of springtime phenology in northeastern temperate forests to future climate change that supports and complements model-based predictions. Second, our results show that subtle differences in the character of thermal forcing can substantially alter the timing of leaf emergence and canopy development. By explicitly comparing and contrasting the timing of thermal forcing and leaf phenology in 2010 and 2012, we show that even though temperatures were warmer in 2012 than in 2010, the nature and timing of thermal forcing in 2010 lead to leaf emergence that was almost a week earlier than 2012.
    Environmental Research Letters 05/2014; 9(5):054006. · 4.09 Impact Factor
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    ABSTRACT: MODIS Collection 5 land cover product (MCD12Q1) provides annually updated global land cover maps since 2001. This time series product has become an essential data source for the generation of many other land surface products and for a variety of regional and global studies. However, classification errors are inherent in the land cover product, which can misrepresent land cover transitions. In particular, land cover transitions are illogical if they contradict ecological rules and are unlikely to be observed. In this study, we evaluated the MODIS land cover product by analyzing the nature and magnitude of its illogical land cover transitions using annual MCD12Q1 land cover maps from 2001 to 2010. Our analysis revealed that illogical transitions exist in the product for all consecutive years, and are distributed most commonly in several regions over the world. To enhance the MODIS land cover product, we applied a spatial–temporal modeling algorithm that incorporates expert knowledge to reduce illogical transitions on five such “hotspot” tiles. The results showed substantial improvements in both accuracy and consistency of the land cover product using the spatial–temporal modeling algorithm. The percentage of illogical transitions in each of the five tiles was significantly reduced among consecutive years and across the entire time series. This study demonstrates the effectiveness of the spatial–temporal modeling algorithm for producing high quality time series of land cover maps, and also highlights the importance of temporal consistency in land cover mapping.
    Remote Sensing of Environment 05/2014; 147:243–255. · 4.77 Impact Factor
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    Biogeosciences 04/2014; · 3.75 Impact Factor
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    Remote Sensing 03/2014; 6(3):2473-2493. · 2.62 Impact Factor
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    ABSTRACT: Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of autumn, can be derived from sensor-based time series data at the near-surface and remote scales, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, as well as to perform digital image analysis for time series-based estimates of phenology dates. We then compare these near-surface results to remote sensing metrics of phenology at the landscape scale, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit, using a generalized sigmoid, to estimate phenology dates. We quantify the statistical uncertainty of phenophase transition dates estimated using this method and show that the generalized sigmoid results in less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than remote sensing metrics of phenology, and that dates derived from the remotely-sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time series estimates for the start of spring are found to closely match visual assessment of leaf out, as well as remote sensing-derived estimates of the start of spring. However late spring and autumn phenology exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.
    Biogeosciences 01/2014; 11(2). · 3.75 Impact Factor
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    ABSTRACT: [1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS.
    Journal of Geophysical Research: Atmospheres. 09/2013; 118(17).
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    ABSTRACT: Background/Question/Methods Impacts of climate change and urbanization on phenology have been documented around the globe, with considerable implications for ecosystem structure and function. Two continental-scale observation networks, Phenocam (http://phenocam.sr.unh.edu) and the USA National Phenology Network (USA-NPN; http://www.usanpn.org) are working with the National Ecological Observatory Network (NEON) to develop phenological monitoring protocols and explore new opportunities for synergistic phenology research. Phenocam, a network of 130+ Internet-linked cameras distributed across North America, uses high frequency canopy monitoring for the development of predictive models of plant phenology and associated ecosystem services. The USA-NPN leverages the efforts of volunteers and professional scientists across the United States to construct a diverse database on plant and animal phenology to support research, management, education and conservation needs. Both organizations are collaborating with NEON, a continental-scale ecological observing system, to enhance and codify phenology data collection, processing and dissemination over its 30-year life span. Members of the Phenocam network are working with NEON to establish protocols for camera operation and develop algorithms to estimate phenophase transition dates from imagery time series. Camera-based monitoring supplements organismal (in-situ, ground-based) field observations collected by both NEON and USA-NPN (according to a common set of standardized protocols). A key element of these protocols is the designation of cross-biome phenophase definitions, allowing seamless integration of NEON phenology data with standardized USA-NPN data sets. Organismal data collection at NEON sites is being designed to describe both interspecific and intraspecific variation in plant phenology, and to facilitate both population- and community-level research. Results/Conclusions The joint efforts of NEON, Phenocam and USA-NPN will bridge two major knowledge gaps in the field of phenology. First, cameras will provide a tool to harmonize phenophase observations of individual plants to the synoptic-scale observations of remote sensing platforms and enhance techniques for validation of satellite derived land surface phenology products. Second, development of multi-scale phenology data sets at NEON eddy covariance sites will facilitate investigation of the feedbacks between ecosystem phenology and carbon/water/energy fluxes between the biosphere and atmosphere. Intermediate products include protocols for linking organisms to cameras, and cameras to satellites. Ultimately, protocols for integrating data across all scales, for a variety of biome types, will contribute to the development of a common, dynamic database of cross-scale phenology datasets, as well as complementary datasets (e.g., climatology, land-cover) for research, education and management applications.
    98th ESA Annual Convention 2013; 08/2013
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    ABSTRACT: Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET "La Thuile" data set, which includes several times more sites (144) and site years (422) than previous efforts have used. Our results show that remotely sensed proxies and modeled GPP are able to capture statistically significant amounts of spatial variation in mean annual GPP in every biome except croplands, but that the total variance explained differed substantially across biomes (R2 ≈ 0.1-0.8). The ability of remotely sensed proxies and models to explain interannual variability GPP was even more limited. Remotely sensed proxies explained 40-60% of interannual variance in annual GPP in moisture-limited biomes including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, and deciduous broadleaf forests. Because important factors that affect year-to-year variation in GPP are not explicitly captured or included in the remote sensing proxies and models we examined (e.g., interactions between biotic and abiotic conditions, and lagged ecosystems responses to environmental process), our results are not surprising. Nevertheless, robust and repeatable characterization of interannual variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. As larger and more comprehensive data sets derived from the FLUXNET community become available, additional systematic assessment and refinement of remote sensing-based methods for monitoring annual GPP is warranted.
    Biogeosciences Discussions 07/2013; 10:11627-11669.
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    Eli K Melaas, Mark A Friedl, Zhe Zhu
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    ABSTRACT: Observations of vegetation phenology provide valuable information regarding ecosystem responses to cli-mate variability and change. Phenology is also a first-order control on terrestrial carbon and energy budgets, and remotely sensed observations of phenology are often used to parameterize seasonal vegetation dynamics in ecosystem models. Current land surface phenology products are only available at moderate spatial resolu-tion and possess considerable uncertainty. Higher resolution products that resolve finer spatial detail are therefore needed. A need also exists for data sets and methods that link ground-based observations of phe-nology to moderate resolution land surface phenology products. Data from the Landsat TM and ETM + sen-sors have the potential to meet these needs, but have been largely unexplored by the phenology research community. In this paper we present a method for characterizing both long-term average and interannual dynamics in the phenology of temperate deciduous broadleaf forests using multi-decadal time series of Landsat TM/ETM+ images. Results show that spring and autumn phenological transition dates estimated from Landsat data agree closely with in-situ measurements of phenology collected at the Harvard Forest in central Massachusetts, 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. Application of this method over larger scales has the potential to provide valuable information related to landscape-scale patterns and long term dynamics in phenology, and for bridging the gap between in-situ phenological mea-surements collected at local scales and land surface phenology metrics derived from moderate spatial reso-lution of instruments such as MODIS and AVHRR.
    Remote Sensing of Environment 05/2013; 132:176-185. · 4.77 Impact Factor
  • Douglas K Bolton, Mark A Friedl
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    ABSTRACT: We used data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) in association with county-level data from the United States Department of Agriculture (USDA) to develop empirical models predicting maize and soybean yield in the Central United States. As part of our analysis we also tested the ability of MODIS to capture inter-annual variability in yields. Our results show that the MODIS two-band Enhanced Vegetation Index (EVI2) provides a better basis for predicting maize yields relative to the widely used Normalized Difference Vegetation Index (NDVI). Inclusion of information related to crop phenology derived from MODIS significantly improved model performance within and across years. Surprisingly, using moderate spatial resolution data from the MODIS Land Cover Type product to identify agricultural areas did not degrade model results relative to using higher-spatial resolution crop-type maps developed by the USDA. Correlations between vegetation indices and yield were highest 65–75 days after greenup for maize and 80 days after greenup for soybeans. EVI2 was the best index for predicting maize yield in non-semi-arid counties (R2 = 0.67), but the Normalized Difference Water Index (NDWI) performed better in semi-arid counties (R2 = 0.69), probably because the NDWI is sensitive to irrigation in semi-arid areas with low-density agriculture. NDVI and EVI2 performed equally well predicting soybean yield (R2 = 0.69 and 0.70, respectively). In addition, EVI2 was best able to capture large negative anomalies in maize yield in 2005 (R2 = 0.73). Overall, our results show that using crop phenology and a combination of EVI2 and NDWI have significant benefit for remote sensing-based maize and soybean yield models.
    Agricultural and Forest Meteorology 05/2013; 173:74 - 84. · 3.89 Impact Factor
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    ABSTRACT: Urban population now exceeds rural population globally, and 60–80% of global energy consumption by households, businesses, transportation, and industry occurs in urban areas. There is growing evidence that built-up infrastructure contributes to carbon emissions inertia, and that investments in infrastructure today have delayed climate cost in the future. Although the United Nations statistics include data on urban population by country and select urban agglomerations, there are no empirical data on built-up infrastructure for a large sample of cities. Here we present the first study to examine changes in the structure of the world's largest cities from 1999 to 2009. Combining data from two space-borne sensors—backscatter power (PR) from NASA's SeaWinds microwave scatterometer, and nighttime lights (NL) from NOAA's defense meteorological satellite program/operational linescan system (DMSP/OLS)—we report large increases in built-up infrastructure stock worldwide and show that cities are expanding both outward and upward. Our results reveal previously undocumented recent and rapid changes in urban areas worldwide that reflect pronounced shifts in the form and structure of cities. Increases in built-up infrastructure are highest in East Asian cities, with Chinese cities rapidly expanding their material infrastructure stock in both height and extent. In contrast, Indian cities are primarily building out and not increasing in verticality. This new dataset will help characterize the structure and form of cities, and ultimately improve our understanding of how cities affect regional-to-global energy use and greenhouse gas emissions.
    Environmental Research Letters 04/2013; 8(2):024004. · 4.09 Impact Factor
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    ABSTRACT: Vegetation phenology is sensitive to climate change and variability, and is a first order control on the carbon budget of forest ecosystems. Robust representation of phenology is therefore needed to support model-based projections of how climate change will affect ecosystem function. A variety of models have been developed to predict species or site-specific phenology of trees. However, extension of these models to other sites or species has proven difficult. Using meteorological and eddy covariance data for 29 forest sites (encompassing 173 site-years), we evaluated the accuracy with which 11 different models were able to simulate, as a function of air temperature and photoperiod, spatial and temporal variability in the onset of spring photosynthetic activity. In parallel, we also evaluated the accuracy with which dynamics in remotely sensed vegetation indices from MODIS captured the timing of spring onset. To do this, we used a subset of sites in the FLUXNET La Thuile database located in evergreen needleleaf and deciduous broadleaf forests with distinct active and dormant seasons and where temperature is the primary driver of seasonality. As part of this analysis we evaluated predictions from refined versions of the 11 original models that include parameterizations for geographic variation in both thermal and photoperiod constraints on phenology. Results from cross-validation analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than the original models. Estimates for the timing of spring onset from MODIS were highly correlated with the onset of photosynthesis derived from flux measurements, but were biased late for needleleaf sites. Our results demonstrate that simple phenology models can be used to predict the timing of spring photosynthetic onset both across sites and across years at individual sites. By extension, these models provide an improved basis for predicting how the phenology and carbon budgets of temperature-limited forest ecosystems may change in the coming decades.
    Agricultural and Forest Meteorology 01/2013; · 3.89 Impact Factor
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    International Journal of Remote Sensing 11/2012; 33:6975-6993. · 1.36 Impact Factor
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    ABSTRACT: A number of land-cover products, both global and regional, have been produced and more are forthcoming. Assessing their accuracy would be greatly facilitated by a global validation database of reference sites that allows for comparative assessments of uncertainty for multiple land-cover data sets. We propose a stratified random sampling design for collecting reference data. Because the global validation database is intended to be applicable to a variety of land-cover products, the stratification should be implemented independently of any specific map to facilitate general utility of the data. The stratification implemented is based on the Köppen climate/vegetation classification and population density. A map of the Köppen classification was manually edited and intersected by two layers of population density and a land water mask. A total of 21 strata were defined and an initial global sample of 500 reference sites was selected, with each site being a 5 × 5 km block. The decision of how to allocate the sample size to strata was informed by examining the distribution of the sample area of land cover for two global products resulting from different sample size allocations to the 21 strata. The initial global sample of 500 sites selected from the Köppen-based stratification indicates that these strata can be used effectively to distribute sample sites among rarer land-cover classes of the two global maps examined, although the strata were not constructed using these maps. This is the first article of two, with the second paper presenting details of how the sampling design can be readily augmented to increase the sample size in targeted strata for the purpose of increasing the sample sizes for rare classes of a particular map being evaluated.
    International Journal of Remote Sensing 09/2012; 33(18):5768-5788. · 1.36 Impact Factor
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    ABSTRACT: Background/Question/Methods The 25-year-old Harvard Forest Summer Research Program in Ecology provides opportunities for > 30 students each summer to conduct mentored, independent research on measuring, understanding, and forecasting the effects of natural disturbances, human activities, and global climatic change on patterns and processes in forest ecosystems. Programmatic goals include: recruiting annual cohorts of diverse students from a range of cultural, ethnic, economic, and demographic backgrounds; providing students with intensive, immersive research experiences; generating reliable data that students and mentors can co-publish in peer-reviewed journals; and encouraging participants to continue their work in science in graduate or professional school and as scientifically-literate citizens. Meeting all of these goals in a single program is challenging and requires new approaches beyond the traditional one student + one mentor research “apprenticeship”. With core support provided through an NSF’s Research Experiences for Undergraduates (REU) Site award and a NASA Innovations in Climate Education (NICE) award, and partnerships with Lincoln University and ESA’s SEEDS program, the Harvard Forest program has pioneered a model in which groups of three students and three or more senior research mentors embark on a multi-disciplinary collaboration focused on a central research question. Each group project includes field work, statistical analysis, computer modeling. Outcomes include blog posts, presentations at a student symposium and national meetings, and peer-reviewed publications with undergraduate coauthors. Results/Conclusions The diverse needs of successful research teams, including computer programming, engineering, and GIS modeling, along with “standard” ecological field skills, has dramatically expanded our pool of potential participants. Since implementing the team research model in 2010, applications have increased 20% (from 491 in 2010 to 605 in 2012), and the number of applications from students self-identifying as being from groups traditionally underrepresented in science has nearly doubled (from 65 in 2010 to 109 in 2012). Three examples of group projects highlight how participating students learn skills germane to individual research projects; engage in peer-to-peer mentorship at the same time that they interact intensively with senior researchers; build networks of friends and future colleagues; and develop practices of collaborative inquiry that are fundamental to modern scientific research. Peer-to-peer mentoring also encourages mentors to mix advanced students with students from schools with limited opportunities for research, and allows students of all levels to see themselves as researchers and teachers. This model of group mentorship engages students from disciplines beyond ecology and biology and encourages ecological literacy across STEM fields and the social sciences.
    97th ESA Annual Convention 2012; 08/2012

Publication Stats

4k Citations
271.89 Total Impact Points


  • 1994–2014
    • Boston University
      • • Department of Earth & Environment
      • • Department of Geography and Environment
      • • Center for Remote Sensing
      Boston, Massachusetts, United States
  • 2013
    • Yale University
      • School of Forestry and Environmental Studies
      New Haven, Connecticut, United States
  • 2008–2009
    • University of Wisconsin, Madison
      • Center for Sustainability and the Global Environment (SAGE)
      Madison, MS, United States
    • Tufts University
      • Department of Computer Science
      Medford, MA, United States
  • 2005
    • University of Massachusetts Boston
      Boston, Massachusetts, United States
  • 1999
    • Beijing Normal University
      • Research Center for Remote Sensing and GIS
      Peping, Beijing, China
  • 1996
    • Purdue University
      • School of Electrical and Computer Engineering
      West Lafayette, IN, United States
  • 1991–1994
    • University of California, Santa Barbara
      • Department of Geography
      Santa Barbara, CA, United States