Abstract
To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use change, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which to date have been challenging to map, especially within forested wetlands. The objective of this paper is to investigate the different, but often interacting effects, of evergreen vegetation and inundation on leaf-off bare earth return lidar intensity within mixed deciduous-evergreen forests in the Coastal Plain of Maryland, and to develop an inundation mapping approach that is robust in areas of varying levels of evergreen influence. This was achieved through statistical comparison of field derived metrics, and development of a simple yet robust normalization process, based on first of many, and bare earth lidar intensity returns. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of the normalization process. After normalization, inundated deciduous forest could be distinguished from non-inundated evergreen forest. Inundation was mapped with an overall accuracy between 99.4% and 100%. Inundation maps created using this approach provide insights into physical processes in support of environmental decision-making, and a vital link between fine-scale physical conditions and moderate resolution satellite imagery through enhanced calibration and validation.
Highlights
For effective landscape management under the context of climate change, wetlands must be routinely monitored to assess extent and function
This paper focuses on discrete point return lidar data [10,11] collected using a near-infrared (i.e., 1064 nm) laser, lidar intensity
Within the regions of interest (ROIs) scatter plots (Figure 4) a clear distinction between the evergreen and inundated deciduous classes was present within the first return data
Summary
For effective landscape management under the context of climate change, wetlands must be routinely monitored to assess extent and function. Since in situ monitoring of wetlands at the landscape scale is typically cost and time prohibitive, remotely sensed data are commonly used to assess wetlands at this spatial scale [1]. Optical images, such as aerial photography, in conjunction with field data were used to map wetlands [1]. Great care has been taken in the production of these data, and they are relied upon by numerous managers and scientists, challenges to this type of cartographic process remain [1,2,3] These challenges result in increased uncertainty for certain wetland types, including forested wetlands [2,3]. Forested wetlands are especially difficult to map in areas of low topographic relief, such as the outer Coastal Plain of the Mid-Atlantic U.S As a result, all forested wetlands, but especially smaller, drier wetlands, are often difficult to detect using optical data [1]
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