An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data

Tan, Bin; Morisette, Jeffrey T.; Wolfe, Robert E.; Gao, Feng; Ederer, G.; Nightingale, Joanne; Pedelty, J. A.

doi:10.1109/jstars.2010.2075916

Cited by 197 publications

(146 citation statements)

References 19 publications

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“…For example, the low value of TI (an index of accumulated annual gross productivity) and GS duration recorded in year 2000 resulted in the lack of a statistically significant decreasing trend between 2000 and 2010, but such a trend was clear between 2001 and 2010. Year 2000 was the wettest year in the last three decades (considering historical regional precipitation mean) and consequently was highly cloudy, which may have affected data on vegetation reflectance and EVI (Tan et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Lake Fluctuations, Plant Productivity, and Long-Term Variability in High-Elevation Tropical Andean Ecosystems

Carilla

Grau

Paolini

et al. 2013

Arctic, Antarctic, and Alpine Research

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Section: Discussionmentioning

confidence: 99%

Lake Fluctuations, Plant Productivity, and Long-Term Variability in High-Elevation Tropical Andean Ecosystems

Carilla

Grau

Paolini

et al. 2013

Arctic, Antarctic, and Alpine Research

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“…Because raw MODIS 250 m data are noisy and have a large number of missing observations, we temporally smoothed the data using a modified asymmetric Gaussian filter within an augmented version of TIMESAT (Jonsson & Elklunh, 2002), and then fit a curve to the data that approximates the phenological pattern to fill data gaps (Gao et al, 2008;Tan et al, 2011). The result is a high-quality dataset shown to be suitable for both classification and direct assessment of EVI values (Tan et al, 2011). We selected the MODIS EVI data for the growing season in each tile (23 observations), and then computed the maximum EVI from these values for each year, 2001-2010.…”

Section: Remot E Sensing Dat Amentioning

confidence: 99%

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

151

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Abstract. Urbanization is one of the most important components of global environmentalchange, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a new methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify ca 2010 urban extent; and (3) mapping newly built areas (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250 m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East-Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socicoeconomic/political and ecological/climatic conditions with good accuracy (70-91% overall accuracy by country, 69-89% by biome). The 250 m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of ii the error in the change detection is related to human decision making or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as provide a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.

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“…When insufficient observations are available to apply a "Full Inversion" or "Magnitude Inversion", a combined spatial and temporal gap filling technique [Tan et al, 2011] was used to obtain a low-quality TLEU result. The technique employs autoregressive forecasting and backcasting [Von Storch and Zwiers, 2001] to extrapolate residual gaps in the time series and compute a 14-day moving average for each grid cell and emphasizing the middle date of observations.…”

Section: Appendix C: Quality Assessment and Uncertainty Metricsmentioning

confidence: 99%

Holidays in lights: Tracking cultural patterns in demand for energy services

2015

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Successful climate change mitigation will involve not only technological innovation, but also innovation in how we understand the societal and individual behaviors that shape the demand for energy services. Traditionally, individual energy behaviors have been described as a function of utility optimization and behavioral economics, with price restructuring as the dominant policy lever. Previous research at the macro-level has identified economic activity, power generation and technology, and economic role as significant factors that shape energy use. However, most demand models lack basic contextual information on how dominant social phenomenon, the changing demographics of cities, and the sociocultural setting within which people operate, affect energy decisions and use patterns. Here we use high-quality Suomi-NPP VIIRS nighttime environmental products to: (1) observe aggregate human behavior through variations in energy service demand patterns during the Christmas and New Year's season and the Holy Month of Ramadan and (2) demonstrate that patterns in energy behaviors closely track sociocultural boundaries at the country, city, and district level. These findings indicate that energy decision making and demand is a sociocultural process as well as an economic process, often involving a combination of individual price-based incentives and societal-level factors. While nighttime satellite imagery has been used to map regional energy infrastructure distribution, tracking daily dynamic lighting demand at three major scales of urbanization is novel. This methodology can enrich research on the relative importance of drivers of energy demand and conservation behaviors at fine scales. Our initial results demonstrate the importance of seating energy demand frameworks in a social context.

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An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data

Cited by 197 publications

References 19 publications

Lake Fluctuations, Plant Productivity, and Long-Term Variability in High-Elevation Tropical Andean Ecosystems

Lake Fluctuations, Plant Productivity, and Long-Term Variability in High-Elevation Tropical Andean Ecosystems

Detecting change in urban areas at continental scales with MODIS data

Holidays in lights: Tracking cultural patterns in demand for energy services

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