Sea level affecting marshes model and remotely sensed and geo-spatial datasets for a large area

Abstract

To understand the variation of coastal wetlands, Park initiated the sea level affecting marshes model (SLAMM) in 1986. One can use the model to simulate changes of land-use and land-cover types in coastal zones with ground measurement-based model inputs. Parameterization of the model is not easy because of the requirement of extensive field measurements that can be prohibitive in labor and cost for the study of a large spatial extent. Thus, an alternative approach to provide the model's inputs must be developed. Remotely sensed and geo-spatial datasets were explored to deliver the inputs. Using the datasets, we derived as many categories of the land-use and land-cover types determined and needed by the SLAMM as possible. Then, for categories required by the SLAMM and existing in the study area, but not derivable from the remote sensing and geo-spatial datasets, we simply recoded the categories into categories obtained from the remote sensing and geo-spatial datasets spatially. We compared the SLAMM outputs before and after the recoding. At the test site of the state of Washington, USA, promising results were obtained.

Keywords:

• sea level affecting marshes model (SLAMM)
• coastal area
• remote sensing
• geo-spatial data

Acknowledgement

This work was supported by a grant from National Natural Science Foundation of China (No. 41021061). The ALOS PALSAR and AVNIR-2 data used in this article were provided by the Japan Aerospace Exploration Agency (JAXA) through a contract to the East Carolina University, NC, USA.