Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here,  explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-stern coast of Florida, USA.  also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lor resolution (30 m). Both IKO and TM data, collected in October 2009, re preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published.  also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales.
['Ruiliang Pu', 'Susan S. Bell']
I am writing a research paper on a idea Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery
[{"from": "human", "value": "I am writing a research paper on a idea Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here,  explored the capability of IKONOS (IKO) data of high resolution (4\u00a0m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-stern coast of Florida, USA.  also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lor resolution (30\u00a0m). Both IKO and TM data, collected in October 2009, re preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5\u201320% higher than that from other studies published.  also examined the spatial distribution of seagrass over a spatial range of 4\u2013240\u00a0m using the Ripley\u2019s K function [L(d)] and IKO data that represented four different grain sizes [4\u00a0m (one IKO pixel), 8\u00a0m (2\u00a0\u00d7\u00a02 IKO pixels), 12\u00a0m (3\u00a0\u00d7\u00a03 IKO pixels), and 16\u00a0m (4\u00a0\u00d7\u00a04 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley\u2019s K metric repeatedly indicated that seagrass cover representing 4\u00a0m\u00a0\u00d7\u00a04\u00a0m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4\u20138\u00a0m, and a random or slightly clustered pattern of cover over 9\u2013240\u00a0m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2\u00a0\u00d7\u00a024\u00a0m IKO pixels, 3\u00a0\u00d7\u00a034\u00a0m IKO pixels, and 4\u00a0\u00d7\u00a04\u00a0m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4\u201332\u00a0m and a random or slightly clustered pattern across 33\u2013240\u00a0m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4\u00a0m to 240\u00a0m, our novel analyses of moderate-dense SAV cover utilizing Ripley\u2019s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30\u00a0m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales."}]