Mustard John F.

No Thumbnail Available
Last Name
Mustard
First Name
John F.
ORCID

Filters

2010 - 2012 2
2
Reset filters

Settings
Type: Article × Start date: 2010 × End date: 2020 × Subject: Remote sensing ×

Search Results

Now showing 1 - 2 of 2
  • Article
    The Amazon frontier of land-use change : croplands and consequences for greenhouse gas emissions
    (American Meteorological Society, 2010-10-28) Galford, Gillian L. ; Melillo, Jerry M. ; Mustard, John F. ; Cerri, Carlos E. P. ; Cerri, Carlos C.
    The Brazilian Amazon is one of the most rapidly developing agricultural frontiers in the world. The authors assess changes in cropland area and the intensification of cropping in the Brazilian agricultural frontier state of Mato Grosso using remote sensing and develop a greenhouse gas emissions budget. The most common type of intensification in this region is a shift from single- to double-cropping patterns and associated changes in management, including increased fertilization. Using the enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, the authors created a green-leaf phenology for 2001–06 that was temporally smoothed with a wavelet filter. The wavelet-smoothed green-leaf phenology was analyzed to detect cropland areas and their cropping patterns. The authors document cropland extensification and double-cropping intensification validated with field data with 85% accuracy for detecting croplands and 64% and 89% accuracy for detecting single- and double-cropping patterns, respectively. The results show that croplands more than doubled from 2001 to 2006 to cover about 100 000 km2 and that new double-cropping intensification occurred on over 20% of croplands. Variations are seen in the annual rates of extensification and double-cropping intensification. Greenhouse gas emissions are estimated for the period 2001–06 due to conversion of natural vegetation and pastures to row-crop agriculture in Mato Grosso averaged 179 Tg CO2-e yr−1, over half the typical fossil fuel emissions for the country in recent years.
  • Article
    Regional-scale phenology modeling based on meteorological records and remote sensing observations
    (American Geophysical Union, 2012-09-14) Yang, Xi ; Mustard, John F. ; Tang, Jianwu ; Hong, Xu
    Changes of vegetation phenology in response to climate change in the temperate forests have been well documented recently and have important implications on the regional and global carbon and water cycles. Predicting the impact of changing phenology on terrestrial ecosystems requires an accurate phenology model. Although species-level phenology models have been tested using a small number of vegetation species, they are rarely examined at the regional level. In this study, we used remotely sensed phenology and meteorological data to parameterize the species-level phenology models. We used a remotely sensed vegetation index (Two-band Enhanced Vegetation Index, EVI2) derived from the Moderate Resolution Spectroradiometer (MODIS) 8-day reflectance product from 2000 to 2010 of New England, United States to calculate remotely sensed vegetation phenology (start/end of season, or SOS/EOS). The SOS/EOS and the daily mean air temperature data from weather stations were used to parameterize three budburst models and one senescence model. We compared the relative strengths of the models to predict vegetation phenology and selected the best model to reconstruct the “landscape phenology” in New England from year 1960 to 2010. Of the three budburst models tested, the spring warming model showed the best performance with an averaged Root Mean Square Deviation (RMSD) of 4.59 days. The Akaike Information Criterion supported the spring warming model in all the weather stations. For senescence modeling, the Delpierre model was better than a null model (the averaged phenology of each weather station, averaged model efficiency = 0.33) and has a RMSD of 8.05 days. A retrospective analysis using the spring warming model suggests a statistically significant advance of SOS in New England from 1960 to 2010 averaged as 0.143 days per year (p = 0.015). EOS calculated using the Delpierre model and growing season length showed no statistically significant advance or delay between 1960 and 2010 in this region. These results suggest the applicability of species-level phenology models at the regional level (and potentially terrestrial biosphere models) and the feasibility of using these models in reconstructing and predicting vegetation phenology.