Kulawardhana, Ranjani W (2013-12). Quantification of Salt Marsh Carbon Stocks: Integration of Remote Sensing Data and Techniques with Field Measurements. Doctoral Dissertation. Thesis uri icon

abstract

  • Recent climatic change projections have increased scientific and public attention on the issues relating to carbon cycling patterns, its controls, and the importance of ecosystems in the cycling and sequestration process. Global carbon studies, however, primarily have focused on dry land ecosystems that extend over large areas and have not accounted for the relatively small and scattered, though highly carbon rich, ecosystems such as mangrove swamps and salt marshes. Using data from a Spartina alterniflora dominated salt marsh in Galveston, Texas this study integrates remote sensing data (multispectral and Light Detection and Ranging - lidar) with field measurements for the quantification of carbon pools in salt marsh ecosystems. Findings in this study show the capability of remote sensing data for the characterization of salt marsh terrain and vegetation heights and the estimation of above-ground biomass quantities. The best biomass prediction models using lidar heights reported considerably low errors, i.e. the percent root square errors (% RSEs) are close to 20%, which is the recommended error threshold for remote sensing based forest biomass prediction models. Our findings also demonstrate that lidar as compared to spectral data can provide better estimates of above-ground biomass and carbon, even in the herbaceous and low-relief context of a salt marsh. A clear zonation of terrain, vegetation characteristics and the distribution of biomass quantities within the marsh extent was also observed. Distribution of biomass quantities revealed linkages with the elevation. Variations in soil properties (i.e. carbon and bulk density) in the soil profile were linked to the temporal changes in soil carbon accumulations on the marsh surface, relative sea level history and resulting vegetation transitions as corroborated by historical aerial images. In general, the amounts of soil carbon stored in recently established Spartina alterniflora intertidal marshes were significantly lower than those that have remained in situ for a longer period of time. These findings indicate that, even though salt marshes can respond to relative sea level rise by migrating landward, their status as a carbon sink varies as a function of both space and time. Thus, in order to predict carbon in a wetland, researchers need to know not only the elevation, the relative sea level rise rate, and the accretion rate - but also the history of land cover change and vegetation transition. Findings of this study contribute to carbon quantification efforts in these vulnerable ecosystems. Further, these findings will also contribute to the increased understanding of the capabilities of remote sensing datasets and techniques for the quantification of these important carbon stocks.
  • Recent climatic change projections have increased scientific and public attention on the issues relating to carbon cycling patterns, its controls, and the importance of ecosystems in the cycling and sequestration process. Global carbon studies, however, primarily have focused on dry land ecosystems that extend over large areas and have not accounted for the relatively small and scattered, though highly carbon rich, ecosystems such as mangrove swamps and salt marshes. Using data from a Spartina alterniflora dominated salt marsh in Galveston, Texas this study integrates remote sensing data (multispectral and Light Detection and Ranging - lidar) with field measurements for the quantification of carbon pools in salt marsh ecosystems.

    Findings in this study show the capability of remote sensing data for the characterization of salt marsh terrain and vegetation heights and the estimation of above-ground biomass quantities. The best biomass prediction models using lidar heights reported considerably low errors, i.e. the percent root square errors (% RSEs) are close to 20%, which is the recommended error threshold for remote sensing based forest biomass prediction models. Our findings also demonstrate that lidar as compared to spectral data can provide better estimates of above-ground biomass and carbon, even in the herbaceous and low-relief context of a salt marsh.

    A clear zonation of terrain, vegetation characteristics and the distribution of biomass quantities within the marsh extent was also observed. Distribution of biomass quantities revealed linkages with the elevation. Variations in soil properties (i.e. carbon and bulk density) in the soil profile were linked to the temporal changes in soil carbon accumulations on the marsh surface, relative sea level history and resulting vegetation transitions as corroborated by historical aerial images. In general, the amounts of soil carbon stored in recently established Spartina alterniflora intertidal marshes were significantly lower than those that have remained in situ for a longer period of time. These findings indicate that, even though salt marshes can respond to relative sea level rise by migrating landward, their status as a carbon sink varies as a function of both space and time. Thus, in order to predict carbon in a wetland, researchers need to know not only the elevation, the relative sea level rise rate, and the accretion rate - but also the history of land cover change and vegetation transition.

    Findings of this study contribute to carbon quantification efforts in these vulnerable ecosystems. Further, these findings will also contribute to the increased understanding of the capabilities of remote sensing datasets and techniques for the quantification of these important carbon stocks.

publication date

  • December 2013