A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data

Bradley, Bethany A.; Jacob, Robert W.; Hermance, John F.; Mustard, John F.

doi:10.1016/j.rse.2006.08.002

Cited by 326 publications

(192 citation statements)

References 32 publications

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“…Many methods that take advantage of multi-season and multi-year observations employ time-series spectral profiles, as the large number of data points provides better discrimination of signal from noise, and makes it possible to link vegetation phenology to the spectral trajectory (Kennedy et al, 2014). Curve-fitting (Zhang et al, 2003;Kennedy et al, 2007), harmonics analysis (Cihar et al, 2001;Jakubauskas et al, 2001;Bradley et al, 2007;Geerken, 2009), and wavelet transformation (Sakamoto et al, 2005;Martinez et al, 2009) have all been used to monitor vegetation dynamics and extract phenological markers (e.g. date of greenup, senescence) in forested or agricultural landscapes.…”

Section: Introductionmentioning

confidence: 99%

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

153

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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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Section: Introductionmentioning

confidence: 99%

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

153

View full text Add to dashboard Cite

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“…Generally, for all of the AMSs across the entire study region, the amounts of points scattered closely around the 1:1 line indicated the accuracy of the SBM compared with the FBM. In addition, note that the estimated time derived from the fitting curve had a minimum allowed error of ±10 days because the NDVI data used here was a 10-day composite product without an exact time of the scene acquisition (Bradley et al 2007). As in Fig.…”

Section: Resultsmentioning

confidence: 99%

Dynamic variability of the heading–flowering stages of single rice in China based on field observations and NDVI estimations

et al. 2014

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Although many studies have indicated the consistent impact of warming on the natural ecosystem (e.g., an early flowering and prolonged growing period), our knowledge of the impacts on agricultural systems is still poorly understood. In this study, spatiotemporal variability of the heading-flowering stages of single rice was detected and compared at three different scales using field-based methods (FBMs) and satellite-based methods (SBMs). The headingflowering stages from 2000 to 2009 with a spatial resolution of 1 km were extracted from the SPOT/VGT NDVI time series data using the Savizky-Golay filtering method in the areas in China dominated by single rice of Northeast China (NE), the middle-lower Yangtze River Valley (YZ), the Sichuan Basin (SC), and the Yunnan-Guizhou Plateau (YG). We found that approximately 52.6 and 76.3 % of the estimated heading-flowering stages by a SBM were within ±5 and ±10 days estimation error (a root mean square error (RMSE) of 8.76 days) when compared with those determined by a FBM. Both the FBM data and the SBM data had indicated a similar spatial pattern, with the earliest annual average heading-flowering stages in SC, followed by YG, NE, and YZ, which were inconsistent with the patterns reported in natural ecosystems. Moreover, diverse temporal trends were also detected in the four regions due to different climate conditions and agronomic factors such as cultivar shifts. Nevertheless, there were no significant differences (p>0.05) between the FBM and the SBM in both the regional average value of the phenological stages and the trends, implying the consistency and rationality of the SBM at three scales.

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“…To minimize cloud and atmospheric contamination, the maximum value composite (MVC) (Holben, 1986) and best index slope extraction (BISE) (Viovy et al, 1992) are commonly applied to create weekly, biweekly, or monthly composites. To further reduce noise, time series of VI data are often smoothed using a variety of different methods including Fourier harmonic analysis (Moody and Johnson, 2001), asymmetric Gaussian function-fitting (Jonsson and Eklundh, 2002), piece-wise logistic functions (Zhang et al, 2003), SavitzkyGolay filters (Chen et al, 2004), degree-day based quadratic models (de Beurs and Henebry, 2004), and polynomial curve fitting (Bradley et al, 2007). In mid-and high latitudes, vegetation signals are also contaminated by snow cover during winter.…”

Section: Algorithm Of Phenology Detectionmentioning

confidence: 99%