Abstract: A programmed computer or non-transitory computer readable storage media has instructions that are executable by a processor to identify LiDAR data points associated with items of vegetation or other objects. Each LiDAR data point is tested to determine if it lies within the value of a closed surface defined for higher LiDAR points. If so, the LiDAR point is grouped with the LiDAR points associated with a previously identified item of vegetation. If not, a new item of vegetation is identified.

Abstract: A system and method for analyzing a canopy of a forest by analyzing the spatial uniformity of LiDAR data point heights in a number of areas surrounding a tree top, where the areas are smaller than the expected size of the crown of the tree. In one embodiment, the spatial uniformity is quantified as a canopy closure vector based on an analysis of the LiDAR data point heights in a frequency domain. In one particular embodiment, the standard deviation of the frequency components in the cells of a number of rings centered around the average value in an FFT output matrix is used to quantify the spatial uniformity.

Abstract: A method and apparatus for identifying individual trees in LiDAR data based on the view of a LiDAR data point. In one embodiment, the number of trees represented by a LiDAR data point cluster is estimated based on a calculated value related to the exposure to the sky of a LiDAR data point in the LiDAR data point cluster.

Abstract: A programmed computer or non-transitory computer readable storage media has instructions that are executable by a processor to identify LiDAR data points associated with items of vegetation or other objects. Each LiDAR data point is tested to determine if it lies within the value of a closed surface defined for higher LiDAR points. If so, the LiDAR point is grouped with the LiDAR points associated with a previously identified item of vegetation. If not, a new item of vegetation is identified.

Abstract: A method and apparatus for identifying individual trees in LiDAR data based on the view of a LiDAR data point. In one embodiment, the number of trees represented by a LiDAR data point cluster is estimated based on a calculated value related to the exposure to the sky of a LiDAR data point in the LiDAR data point cluster.

Abstract: A system and method for analyzing a canopy of a forest by analyzing the spatial uniformity of LiDAR data point heights in a number of areas surrounding a tree top, where the areas are smaller than the expected size of the crown of the tree. In one embodiment, the spatial uniformity is quantified as a canopy closure vector based on an analysis of the LiDAR data point heights in a frequency domain. In one particular embodiment, the standard deviation of the frequency components in the cells of a number of rings centered around the average value in an FFT output matrix is used to quantify the spatial uniformity.

Abstract: A system for predicting a metric for trees in a forest area analyzes a spatial variation in pixel intensities in or more spectral bands in an image of the trees. The variation in pixel intensities is related to the predicted metric for the trees by a relationship determined from images of trees having ground truth data. In one embodiment, a linear regression determines the relationship between the spatial variation in pixel intensities and the metric. In one embodiment, the spatial variation in the pixel intensities in an image is determined in a frequency domain with a two-dimensional Fourier transform of the pixel intensity values.

Abstract: Aspects of the present invention are directed at using LiDAR data to identify attributes of vegetation. In this regard, a method is provided that identifies the location of individual items of vegetation from raw LiDAR data. In one embodiment, the method includes selecting a coordinate position represented in the LiDAR data that generated a return signal. Then, a determination is made regarding whether the selected coordinate position is inside a geographic area allocated to a previously identified item of vegetation. If the selected coordinate position is not within a geographic area allocated to a previously identified item of vegetation, the method determines that the selected coordinate position is associated with a new item of vegetation. In this instance, a digital representation of the new item of vegetation is generated.

Abstract: Aspects of the present invention are directed at estimating the value of an attribute at a specified geographic location. In one embodiment, a method is provided that estimates the elevation at a principal point using LiDAR data that was collected from spatially related secondary points. More specifically, the method includes identifying secondary points where sample attribute data was obtained that are within a predetermined distance to the principal point where the attribute is being estimated. A secondary point may be selected and allocated a masking zone and a determination made regarding whether one or more distant secondary points are within the area of the masking zone. In this regard, more distant secondary points that are inside a masking zone may be assigned less relevance when estimating the value of the attribute.

Abstract: Aspects of the present invention are directed at using LiDAR data to identify attributes of vegetation. In this regard, a method is provided that allocates points to individual items of vegetation from raw LiDAR data. In one embodiment, the method includes selecting a coordinate position represented in the LiDAR data that generated a return signal. Then, a determination is made regarding whether the selected coordinate position is inside a geographic area allocated to a previously identified item of vegetation. If the selected coordinate position is not within a geographic area allocated to a previously identified item of vegetation, the method determines that the selected coordinate position is associated with a new item of vegetation. In this instance, a digital representation of the new item of vegetation is generated.