2006
DOI: 10.2458/azu_jrm_v59i1_reeves
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Applying Improved Estimates of MODIS Productivity to Characterize Grassland Vegetation Dynamics

Abstract: Prescribed fire was used in two semiarid grasslands to reduce shrub cover, promote grass production, and reduce erosional loss that represents a potential non-point-source of sediment to degrade water quality. This study measured transported soil sediment, dynamics in soil surface microtopography, cover of the woody shrub, grass, and bare ground cover classes, and soil fertility measured by nitrogen-mineralization potentials for the respective cover classes over a 9-year period during which 2 fires occurred. I… Show more

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Cited by 4 publications
(7 citation statements)
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References 15 publications
(20 reference statements)
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“…The temporal lag we quantified between the points of maximum greenness and maximum aboveground biomass (figures 1 and 2) is an important consideration for remote sensing-driven estimates of carbon dynamics in grassland ecosystems. As in the annual grassland communities we studied, Reeves et al (2006) found that the highest degree of association between NDVI and biomass occurred near peak (maximum) greenness. While this period may also be the period of maximum photosynthesis, as commonly assumed (Reeves et al 2006, S. Los, personal communication, 11 May 2007, Inoue et al 2008, photosynthesis continues for some time afterwards and contributes, along with some reallocation from belowground components of plants, to increasing canopy biomass, as evident in this study.…”
Section: Discussionmentioning
confidence: 93%
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“…The temporal lag we quantified between the points of maximum greenness and maximum aboveground biomass (figures 1 and 2) is an important consideration for remote sensing-driven estimates of carbon dynamics in grassland ecosystems. As in the annual grassland communities we studied, Reeves et al (2006) found that the highest degree of association between NDVI and biomass occurred near peak (maximum) greenness. While this period may also be the period of maximum photosynthesis, as commonly assumed (Reeves et al 2006, S. Los, personal communication, 11 May 2007, Inoue et al 2008, photosynthesis continues for some time afterwards and contributes, along with some reallocation from belowground components of plants, to increasing canopy biomass, as evident in this study.…”
Section: Discussionmentioning
confidence: 93%
“…As in the annual grassland communities we studied, Reeves et al (2006) found that the highest degree of association between NDVI and biomass occurred near peak (maximum) greenness. While this period may also be the period of maximum photosynthesis, as commonly assumed (Reeves et al 2006, S. Los, personal communication, 11 May 2007, Inoue et al 2008, photosynthesis continues for some time afterwards and contributes, along with some reallocation from belowground components of plants, to increasing canopy biomass, as evident in this study. Thus, if one wishes to estimate total biomass production or maximum standing biomass in grasslands, there will necessarily be elevated uncertainty in NDVI-based estimates for the period after maximum greenness.…”
Section: Discussionmentioning
confidence: 93%
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“…Quantitative estimates of biomass are now available as an operational product from the Moderate Resolution Imaging Spectroradiometer (MODIS). These estimates offer a reliable measure of the vegetation dynamics in space and time, with the finest resolutions being 250 m, and daily coverage (Reeves et al, 2006). There are, however, issues associated with assuming that remotely sensed vegetation indices are sufficient for quantifying the dynamics of carbon cycling when compared to field measurements (Scurlock et al, 2002).…”
Section: Introductionmentioning
confidence: 99%
“…In the LOOCV method, all data items, except 1, are iteratively used to build the predictive equation. A single one is held out during each iteration and is then used as validation data (Reeves et al 2006). The standard procedure for LOOCV involves the use of a single observation from the original sample as the validation data and the remaining observations as the training data.…”
Section: Grassland Dry Biomass Cover Monitoring Models and Compositionmentioning
confidence: 99%