pulse returns to the sensor after hitting features on the surface of the Earth. Figure 13
shows the display of Laser Vegetation Imaging Sensor (LVIS) [55] waveform Lidar
which was acquired over the Patuxent Watershed, Maryland (USA), in 2003 and
2004. Overhead view displayed as a point cloud (a). When viewing the data for a
specific point, it displays as one wavelength—(b) represents a pulse that hit a road
(a low, flat surface), and (c) represents a pulse for a forested area (several vegetation
layers at different elevations).
Because of Lidar’s ability to show differing heights, it is useful for threedimensional modeling, for distinguishing between different tree species, to see
into areas shadowed from nearby taller features/objects, and for providing finescale delineation between features (the latter is dependent upon the density of the
point cloud). Some examples of applications of Lidar for land use or land cover
identification include:
In coastal mapping for Camp Lejeune, North Carolina (USA), researchers fused
elevations extracted from Lidar with IKONOS imagery to classify roads, water,
marshes, roofs, trees, and sand [56]. Investigators opined that fine-scale classification was needed to distinguish features with similar spectral characteristics. They
found that using Lidar surface elevations along with the multispectral imagery
increased their accuracy for these classifications.
In applications to distinguish features within shadowed areas, researchers have
used Lidar data with aerial images to extract land use for rural Spain [57]. These
researchers found that the combination of these two types of data allowed extraction
of land uses within shadowed areas. In a second study, researchers successfully
used aerial photos and Lidar to identify land use in shadows within an urban area—
the City of Alcala ´, Madrid, Spain [58].
For another urban study, researchers used the combination of Lidar and aerial
photos to enhance urban land use analysis for Austin, Texas (USA) [59]. Most
specifically, they used a building detection algorithm to identify buildings from
Lidar data and then used seven spatial characteristics of these buildings to help
classify varying residential land uses.
In a watershed study, for the Garonne and Allier River watersheds in France,
researchers used airborne Lidar and SPOT images for land cover classification
[60]. Nine separate land cover types were classified—five different types of riparian
forests, along with gravel, low vegetation, water, and bare earth.
6.2 Hyperspectral Imagery
Hyperspectral remote sensing is the collection of spectral data forming images with
hundreds of bands, each band no more than a few nanometers wide. Hyperspectral
data does not necessarily cover a broad region of the electromagnetic spectrum, but
divides the spectrum into smaller segments. As an example, Fig. 14 shows differences in bandwidths and total wavelength coverage between images acquired with
Landsats 7 and 8—multispectral sensors and Airborne Visible/Infrared Imaging
24
T.E. Parece and J.B. Campbell
shows the display of Laser Vegetation Imaging Sensor (LVIS) [55] waveform Lidar
which was acquired over the Patuxent Watershed, Maryland (USA), in 2003 and
2004. Overhead view displayed as a point cloud (a). When viewing the data for a
specific point, it displays as one wavelength—(b) represents a pulse that hit a road
(a low, flat surface), and (c) represents a pulse for a forested area (several vegetation
layers at different elevations).
Because of Lidar’s ability to show differing heights, it is useful for threedimensional modeling, for distinguishing between different tree species, to see
into areas shadowed from nearby taller features/objects, and for providing finescale delineation between features (the latter is dependent upon the density of the
point cloud). Some examples of applications of Lidar for land use or land cover
identification include:
In coastal mapping for Camp Lejeune, North Carolina (USA), researchers fused
elevations extracted from Lidar with IKONOS imagery to classify roads, water,
marshes, roofs, trees, and sand [56]. Investigators opined that fine-scale classification was needed to distinguish features with similar spectral characteristics. They
found that using Lidar surface elevations along with the multispectral imagery
increased their accuracy for these classifications.
In applications to distinguish features within shadowed areas, researchers have
used Lidar data with aerial images to extract land use for rural Spain [57]. These
researchers found that the combination of these two types of data allowed extraction
of land uses within shadowed areas. In a second study, researchers successfully
used aerial photos and Lidar to identify land use in shadows within an urban area—
the City of Alcala ´, Madrid, Spain [58].
For another urban study, researchers used the combination of Lidar and aerial
photos to enhance urban land use analysis for Austin, Texas (USA) [59]. Most
specifically, they used a building detection algorithm to identify buildings from
Lidar data and then used seven spatial characteristics of these buildings to help
classify varying residential land uses.
In a watershed study, for the Garonne and Allier River watersheds in France,
researchers used airborne Lidar and SPOT images for land cover classification
[60]. Nine separate land cover types were classified—five different types of riparian
forests, along with gravel, low vegetation, water, and bare earth.
6.2 Hyperspectral Imagery
Hyperspectral remote sensing is the collection of spectral data forming images with
hundreds of bands, each band no more than a few nanometers wide. Hyperspectral
data does not necessarily cover a broad region of the electromagnetic spectrum, but
divides the spectrum into smaller segments. As an example, Fig. 14 shows differences in bandwidths and total wavelength coverage between images acquired with
Landsats 7 and 8—multispectral sensors and Airborne Visible/Infrared Imaging
24
T.E. Parece and J.B. Campbell