Extraction of tidal channel networks from airborne scanning laser altimetry and aerial photography

The study of the morphodynamics of tidal channel networks is important because of their role in tidal propagation and the evolution of salt-marshes and tidal flats. Channel dimensions range from tens of metres wide and metres deep near the low water mark to only 20-30cm wide and 20cm deep for the smallest channels on the marshes. The conventional method of measuring the networks is cumbersome, involving manual digitising of aerial photographs. This paper describes a semi-automatic knowledge-based network extraction method that is being implemented to work using airborne scanning laser altimetry (and later aerial photography). The channels exhibit a width variation of several orders of magnitude, making an approach based on multi-scale line detection difficult. The processing therefore uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels using a distance-with-destination transform. Breaks in the networks are repaired by extending channel ends in the direction of their ends to join with nearby channels, using domain knowledge that flow paths should proceed downhill and that any network fragment should be joined to a nearby fragment so as to connect eventually to the open sea.

Generalized Bayesian cloud detection for satellite imagery. part 1: technique and validation for night-time imagery over land and sea

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud-masks. Here, this is done over both land and ocean using night-time (infrared) imagery. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 87% and 48% for ocean and land, respectively using the Bayesian technique, compared to 74% and 39%, respectively for the threshold-based techniques associated with the validation dataset.

Generalised Bayesian cloud detection for satellite imagery. part 2: technique and validation for day-time imagery

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud masks. Here, the technique is shown to be suitable for daytime applications over land and sea, using visible and near-infrared imagery, in addition to thermal infrared. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 89% and 73% for ocean and land, respectively using the Bayesian technique, compared to 90% and 70%, respectively for the threshold-based techniques associated with the validation dataset.

Hybrid image segmentation using fuzzy clustering algorithms satellite imagery searching and retrieval

Satellite image processing is a complex task that has received considerable attention from many researchers. In this paper, an interactive image query system for satellite imagery searching and retrieval is proposed. Like most image retrieval systems, extraction of image features is the most important step that has a great impact on the retrieval performance. Thus, a new technique that fuses color and texture features for segmentation is introduced. Applicability of the proposed technique is assessed using a database containing multispectral satellite imagery. The experiments demonstrate that the proposed segmentation technique is able to improve quality of the segmentation results as well as the retrieval performance.

Yaringa and French Island Marine National Park habitat mapping

Deakin University and the Department of Primary Industries were commissioned by ParksVictoria (PV) to create two updated habitat maps for Yaringa and French Island MarineNational Parks. The team obtained a ground-truth data set using in situ video and still photographs. This dataset was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from World-View-2 imagery atmospherically corrected by CSIRO and LiDAR (Light Detection and Ranging) bathymetry. In addition, the team applied an unsupervised classification approach to an aerial photograph to assess the differences between the two remote sensors. This report describes the results of the mapping as well as the methodology used to produce these habitat maps.This study has provided mapping of intertidal and subtidal habitats of Yaringa and FrenchIsland MNPs at a 2 m resolution with fair to good accuracies (Kappa 0.40-0.75). These were combined with mangrove and saltmarsh habitats recently mapped by Boon et al. (2011) to provide compete-coverage habitat maps of Yaringa and French Island MNPs.The mapping showed that Yaringa MNP was dominated by mangroves, wet saltmarsh and dense Zostereaceae, covering 33%, 29% and 19%, respectively. Similarly, intertidalvegetation and subtidal vegetation (dominated by Zosteraceae) covered 26% and 25% ofFrench Island MNP. However, as a result of turbidity and missing satellite imagery 27% ofFrench Island MNP remains unmapped.The coupling of WV-2 and LiDAR reduced potential artefacts (e.g. sun glint causing whiteand black pixels known as the “salt and pepper effect”). The satellite classification appeared to provide better results than the aerial photography classification. However, since there is a two-year difference between the capture of the aerial photography and the collection of the ground-truth data this comparison is potentially temporally confounded. It must also be noted that there are differences in costs of the data,the spatial resolution between the two datasets (i.e. WV-2 = 2 m and the Aerial = 0.5 m) and the amount spectral information contained in the data (i.e. WV-2 = 8 bands and the aerial = 4 bands), which may ultimately determine its utility for a particular project.The spatial assessment using FRAGSTATs of habitat patches within Yaringa MNP provides a viable and cost effect way to assess habitat condition (i.e. shape, size and arrangement).This spatial assessment determined that dense Zosteraceae and NVSG habitat classeswere generally larger in patch size and continuity than the medium/sparse Zosteraceaehabitat. The application spatial techniques to time-series mapping may provide a way toremotely monitor the change in the spatial characteristics of marine habitats.This work was successful in providing new baseline habitat maps using a repeatable method meaning that any future changes in intertidal and shallow water marine habitats may be assessed in a consistent way with quantitative error assessments. In wider use, these maps should also allow improved conservation planning, fisheries and catchment management, and contribute toward infrastructure planning to limit impacts on Western Port.

Quickbird satellite imagery for riparian management : characterizing riparian filter strips and detecting concentrated flow in an agricultural watershed

Riparian ecology plays an important part in the filtration of sediments from upland agricultural lands. The focus of this work makes use of multispectral high spatial resolution remote sensing imagery (Quickbird by Digital Globe) and geographic information systems (GIS) to characterize significant riparian attributes in the USDA’s experimental watershed, Goodwin Creek, located in northern Mississippi. Significant riparian filter characteristics include the width of the strip, vegetation properties, soil properties, topography, and upland land use practices. The land use and vegetation classes are extracted from the remotely sensed image with a supervised maximum likelihood classification algorithm. Accuracy assessments resulted in an acceptable overall accuracy of 84 percent. In addition to sensing riparian vegetation characteristics, this work addresses the issue of concentrated flow bypassing a riparian filter. Results indicate that Quickbird multispectral remote sensing and GIS data are capable of determining riparian impact on filtering sediment. Quickbird imagery is a practical solution for land managers to monitor the effectiveness of riparian filtration in an agricultural watershed.

Time-series of seagrass and land cover maps from 1972 to 2010, in South East Queensland, Australia, with links to GeoTIFFs

Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972-2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

Preliminary applications of Landsat images and aerial photography for determining land-use, geologic, and hydrologic characteristics : Yampa River basin, Colorado and Wyoming /

Mode of access: Internet.

Panoramic aerial photography for detection of oak decline and mortality in central Texas /

"3400"--P. [1].

The 2008 Terrestrial Vegetation of Biscayne National Parko FL, USA Derived From Aerial Photography, NDVI, and LiDAR

Established as a National Park in 1980, Biscayne National Park (BISC) comprises an area of nearly 700 km2 , of which most is under water. The terrestrial portions of BISC include a coastal strip on the south Florida mainland and a set of Key Largo limestone barrier islands which parallel the mainland several kilometers offshore and define the eastern rim of Biscayne Bay. The upland vegetation component of BISC is embedded within an extensive coastal wetland network, including an archipelago of 42 mangrove-dominated islands with extensive areas of tropical hardwood forests or hammocks. Several databases and vegetation maps describe these terrestrial communities. However, these sources are, for the most part, outdated, incomplete, incompatible, or/and inaccurate. For example, the current, Welch et al. (1999), vegetation map of BISC is nearly 10 years old and represents the conditions of Biscayne National Park shortly after Hurricane Andrew (August 24, 1992). As a result, a new terrestrial vegetation map was commissioned by The National Park Service Inventory and Monitoring Program South Florida / Caribbean Network.

Evaluating High-Resolution Aerial Photography Acquired by Unmanned Aerial Systems for Use in Mapping Everglades Wetland Plant Associations

Mapping of vegetation patterns over large extents using remote sensing methods requires field sample collections for two different purposes: (1) the establishment of plant association classification systems from samples of relative abundance estimates; and (2) training for supervised image classification and accuracy assessment of satellite data derived maps. One challenge for both procedures is the establishment of confidence in results and the analysis across multiple spatial scales. Continuous data sets that enable cross-scale studies are very time consuming and expensive to acquire and such extensive field sampling can be invasive. The use of high resolution aerial photography (hrAP) offers an alternative to extensive, invasive, field sampling and can provide large volume, spatially continuous, reference information that can meet the challenges of confidence building and multi-scale analysis.