Mapping relative differences in belowground biomass in wetlands

O’Connell, JL, KB Byrd, and M Kelly. 2015. A Hybrid Model for Mapping Relative Differences in Belowground Biomass and Root:Shoot Ratios Using Spectral Reflectance, Foliar N and Plant Biophysical Data within Coastal Marsh. Remote Sensing 7, 16480-16503

Loadings values of % foliar N from PLS regression of hyperspectral data for Typha spp. Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models.

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Lidar + MODIS to upscale predictions of forest biomass

Li, L., Q. Guo, S. Tao, M. Kelly, and G. Xu. 2015. Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass. ISPRS Journal of Photogrammetry and Remote Sensing. 102: 198–208

Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB.

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20th-century shifts in forest structure in California - evidence from the VTM dataset

McIntyre, P. J., J. H. Thorne, C. R. Dolanc, A. L. Flint, L. E. Flint, M. Kelly and D. D. Ackerly. 2015. Twentieth-century shifts in forest structure in California: Denser forests, smaller trees, and increased dominance of oaks. Proceedings of the National Academy of Sciences 112(5): 1458-1463

change in climate water deficit (left) and change in large trees (right)We document changes in forest structure between historical (1930s) and contemporary (2000s) surveys of California vegetation through comparisons of tree abundance and size across the state and within several ecoregions. Across California, tree density in forested regions increased by 30% between the two time periods, whereas forest biomass in the same regions declined, as indicated by a 19% reduction in basal area. These changes reflect a demographic shift in forest structure: larger trees (>60 cm diameter at breast height) have declined, whereas smaller trees (<30 cm) have increased. Large tree declines were more severe in areas experiencing greater increases in climatic water deficit since the 1930s, based on a hydrologicmodel of water balance for historical climates through the 20th century.

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Lidar-derived volume metrics for aboveground biomass estimation in conifer stands

individual trees in the lidar cloudTao, S., Li, L., Q. Guo, L. Li, B. Xue, M. Kelly, W. Li, G. Xu, and Y. Su. 2014. Airborne Lidar-derived volume metrics for aboveground biomass estimation: A comparative assessment for conifer stands. Agriculture and Forest Management 198–199: 24–3

Estimating aboveground biomass (AGB) is essential to quantify the carbon balance of terrestrial ecosystems, and becomes increasingly important under changing global climate. Volume metrics of individual trees, for example stem volume, have been proven to be strongly correlated to AGB. In this paper, we compared a range of airborne Lidar-derived volume metrics (i.e. stem volume, crown volume under convex hull, and crown volume under Canopy Height Model (CHM)) to estimate AGB. In addition, we evaluated the effect of horizontal crown overlap (which is often neglected in Lidar literature) on the accuracy of AGB estimation by using a hybrid method that combined marker-controlled watershed segmentation and point cloud segmentation algorithms.

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Mapping wetland biomass with three remote sensors

Byrd, K.B., J.L. O'Connell, S. Di Tommaso, and M. Kelly. 2014. Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation. Remote Sensing of Environment 149: 166-180

One of our biomass maps, this one from Mayberry slough

We modeled biomass of emergent vegetation with field spectrometer and satellite data from Landsat, Hyperion and WorldView-2 sensors. Use of narrowbands did not significantly improve biomass predictions over broadbands. Water inundation interacting with plant structure controlled biomass model accuracy. Shortwave infrared bands and multi-temporal datasets improved biomass prediction. These types of maps will track Blue Carbon, sea level rise and land use effects in coastal marshes.

Pdf download. Journal link.

Key words: emergent vegetation, hyperspectral sensor, field spectroscopy, multispectral sensor, water inundation, Blue Carbon, wetland management, error reporting.

Using remote sensing to model biomass accumulation in a wetland plant

Some of the reflectance spectra for S. acutusO’Connell, J.L., K.B. Byrd, M. Kelly. 2014. Remotely-sensed indicators of N-related biomass allocation in Schoenoplectus acutus. PLOS One. 9(3):e90870

Coastal marshes depend on belowground biomass of roots and rhizomes to contribute to peat and soil organic carbon, accrete soil and alleviate flooding as sea level rises. For nutrient-limited plants, eutrophication has either reduced or stimulated belowground biomass depending on plant biomass allocation response to fertilization. Within a freshwater wetland impoundment receiving minimal sediments, we used experimental plots to explore growth models for a common freshwater macrophyte, Schoenoplectus acutus. We used N-addition and control plots (4 each) to test whether remotely-sensed vegetation indices could predict leaf N concentration, root:shoot ratios and belowground biomass of S. acutus. N-addition did not alter whole plant, but reduced belowground biomass 36% and increased aboveground biomass 71%. We correlated leaf N concentration with known N-related spectral regions using all possible normalized difference (ND), simple band ratio (SR) and first order derivative ND (FDN) and SR (FDS) vegetation indices.

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Plant litter influences remote sensing signatures in wetlands

Correlation between fAPAR-hig and two-band vegetation indices usingsimulated Hyperion bands using spectroradiometer data collected at Twitchell IslandSchile, L. K. Byrd, L. Windham-Myers, and M. Kelly. 2013. Accounting for plant litter in remote sensing based estimates of carbon flux in wetlands.  Remote Sensing Letters 4(6):542-551

Monitoring productivity in coastal wetlands is important due to their high carbon sequestration rates and potential role in climate change mitigation. We tested agricultural- and forest-based methods for estimating the fraction of absorbed photosynthetically active radiation (ƒ_APAR), a key parameter for modeling gross primary productivity (GPP), in a restored, managed wetland with a dense litter layer of non-photosynthetic vegetation, and we compared the difference in canopy light transmission between a tidally influenced wetland and the managed wetland.

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