Definiens is hosting a webinar on the topic of OBIA and LiDAR on April 7th. You can register at the link below.
https://www2.gotomeeting.com/register/992960218
Wednesday, March 24, 2010
Webinar: OBIA and LiDAR
Definiens is hosting a webinar on the topic of OBIA and LiDAR on April 7th. You can register at the link below.
https://www2.gotomeeting.com/register/992960218
Tuesday, March 16, 2010
Seeing the trees through the city
From a high-resolution land cover mapping perspective the urban environment is a worst case scenario. 2D and 3D heterogeneity make detecting features of interest extraordinarily difficult. Active sensor technology, particularly LiDAR can help to compliment, and in some cases, take the place of, data from passive sensors.
Below is an example of some graphics I generated for the Million Trees Research Symposium, held March 5th and 6th in New York City. As New York City lacks wall-to-wall LiDAR coverage, I pulled some examples from our work in Baltimore to illustrate the utility of using LiDAR to overcome some of the limitations of high-resolution satellite and aerial imagery. LiDAR generates its own electromagnetic energy, and thus it generally produces consistent height measurements regardless of the natural lighting condition.
As cities such as New York, Boston, and Los Angeles seek to track the impact of their tree planting initiatives over time, they would be wise to consider investing in LiDAR collections every 5-10 years. There is no doubt that imagery has an important roll to play, but in the "urban canyons" it's LiDAR that allows one to see the trees through the city.
Point cloud from downtown Baltimore. The arrows point to street trees in the "urban canyon."
2007 color infrared (CIR) 1m resolution National Agricultural Imagery Program (NAIP) data of the same area. The street trees are obscured by the building shadows.
Normalized digital surface model (nDSM) derived from the LiDAR data. The street trees are clearly visible.
While there has been a lot of talk recently about LiDAR-imagery fusion for automated feature extraction it has been my experience that extracting information from a combination of LiDAR and imagery requires more than a query of height information from the LiDAR and spectral information from the imagery. In the urban canyons it may be more advantageous to use only the LiDAR. Detecting these areas, by the presence of shadows, is of course best done using the imagery. This approach goes beyond simply populating the point cloud with spectral information from the imagery, towards an intelligent system; one that maximizes the capabilities and minimizes limitations.
I would like to thank the City of Baltimore for giving us access to their LiDAR data, particularly our friends in Baltimore City Recreation and Parks.
Below is an example of some graphics I generated for the Million Trees Research Symposium, held March 5th and 6th in New York City. As New York City lacks wall-to-wall LiDAR coverage, I pulled some examples from our work in Baltimore to illustrate the utility of using LiDAR to overcome some of the limitations of high-resolution satellite and aerial imagery. LiDAR generates its own electromagnetic energy, and thus it generally produces consistent height measurements regardless of the natural lighting condition.
As cities such as New York, Boston, and Los Angeles seek to track the impact of their tree planting initiatives over time, they would be wise to consider investing in LiDAR collections every 5-10 years. There is no doubt that imagery has an important roll to play, but in the "urban canyons" it's LiDAR that allows one to see the trees through the city.
Point cloud from downtown Baltimore. The arrows point to street trees in the "urban canyon."
2007 color infrared (CIR) 1m resolution National Agricultural Imagery Program (NAIP) data of the same area. The street trees are obscured by the building shadows.
Normalized digital surface model (nDSM) derived from the LiDAR data. The street trees are clearly visible.While there has been a lot of talk recently about LiDAR-imagery fusion for automated feature extraction it has been my experience that extracting information from a combination of LiDAR and imagery requires more than a query of height information from the LiDAR and spectral information from the imagery. In the urban canyons it may be more advantageous to use only the LiDAR. Detecting these areas, by the presence of shadows, is of course best done using the imagery. This approach goes beyond simply populating the point cloud with spectral information from the imagery, towards an intelligent system; one that maximizes the capabilities and minimizes limitations.
I would like to thank the City of Baltimore for giving us access to their LiDAR data, particularly our friends in Baltimore City Recreation and Parks.
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