Abstract: A system for analyzing remotely sensed photos of a forest or other areas of interest uses a computer system to increase the variation in NIR data having values that represent items of interest. In one embodiment, a computer system applies a stretching function to the NIR data to increase their variation. The objective spectral stretched NIR data is used to differentiate different types of vegetation in the remotely sensed image. Objective-based Vegetation Index (OVI) values are calculated from the objective spectral stretched NIR data that allow different types of vegetation to be distinguished. In one embodiment, the OVI values are used to differentiate hardwoods from conifers in a digital aerial photo of a forest.

Abstract: A computer system is programmed to analyze data from aerial images and LiDAR data obtained from a region of interest in order to determine whether a group of LiDAR data points representing an individual item of vegetation (i.e. a blob) is a particular type of vegetation. Infrared data from aerial images of the region of interest is stretched and divided by red spectral data to compute an objective-stretched vegetation index value (OVI) for a pixel. The mean LiDAR intensity and the mean OVI for the LiDAR data points and the pixels in the area of a blob are used to predict what type of vegetation is represented in the area of the blob.

Abstract: A computer system for creating artifact-free aerial images of a region of interest. A computer system receives one or more input aerial images that are obtained at different times. A false-color image is created by applying two or more of the input aerial images to different color channel inputs of the false-color image. Based on the color of the pixels in the false-color image, pixels in the two or more input aerial images are classified as representing clear, cloud or shadow areas. An output image is created by combining pixels that are classified as representing clear areas from two or more of the input aerial images.

Abstract: A computer system is programmed to analyze data from aerial images and LiDAR data obtained from a region of interest in order to determine whether a group of LiDAR data points representing an individual item of vegetation (i.e. a blob) is a particular type of vegetation. Infrared data from aerial images of the region of interest is stretched and divided by red spectral data to compute an objective-stretched vegetation index value (OVI) for a pixel. The mean LiDAR intensity and the mean OVI for the LiDAR data points and the pixels in the area of a blob are used to predict what type of vegetation is represented in the area of the blob.

Abstract: A processor-based apparatus for computing stream vectors for an area of interest divides the area of interest into a number of hydrologic unit code (HUC) areas. The processor calculates stream vectors from lower resolution digital elevation map (DEM) data. For those HUC areas where higher resolution DEM data is available, the processor merges the lower resolution and higher resolution DEM data. Stream vectors are calculated from the merged DEM data and used to replace those stream vectors calculated from the lower resolution DEM data. Attributes of stream vectors that receive upstream flow from an adjacent HUC area are corrected.

Abstract: A system for analyzing remotely sensed photos of a forest or other areas of interest uses a computer system to increase the variation in NIR data having values that represent items of interest. In one embodiment, a computer system applies a stretching function to the NIR data to increase their variation. The objective spectral stretched NIR data is used to differentiate different types of vegetation in the remotely sensed image. Objective-based Vegetation Index (OVI) values are calculated from the objective spectral stretched NIR data that allow different types of vegetation to be distinguished. In one embodiment, the OVI values are used to differentiate hardwoods from conifers in a digital aerial photo of a forest.

Abstract: A programmed computer system estimates the age of trees from a number of remotely sensed images of an area of interest. Vegetation Index (V.I.) values are determined for pixel locations in the number of images. The V.I. values are analyzed to find a V.I. value that correlates with a known age of a tree. Once the date of the image that produced the V.I. value is known, the current age of the trees that correspond to the pixel location is determined.

Abstract: A programmed computer system estimates the age of trees from a number of remotely sensed images of an area of interest. Vegetation Index (V.I.) values are determined for pixel locations in the number of images. The V.I. values are analyzed to find a V.I. value that correlates with a known age of a tree. Once the date of the image that produced the V.I. value is known, the current age of the trees that correspond to the pixel location is determined.

Abstract: A system for filling in and/or replacing pixel data in a target image uses pixel data from a source image. In one embodiment, the pixel data in the source image are classified and boundaries of local class areas or groups of similarly classified pixels are determined. The pixel data in the local class areas are compared to determine one or more scaling factors. The missing pixel data or data to be replaced in the target image is obtained from the source image and scaled with the one or more scaling factors.

Abstract: A processor-based apparatus for computing stream vectors for an area of interest divides the area of interest into a number of hydrologic unit code (HUC) areas. The processor calculates stream vectors from lower resolution digital elevation map (DEM) data. For those HUC areas where higher resolution DEM data is available, the processor merges the lower resolution and higher resolution DEM data. Stream vectors are calculated from the merged DEM data and used to replace those stream vectors calculated from the lower resolution DEM data. Attributes of stream vectors that receive upstream flow from an adjacent HUC area are corrected.

Abstract: A method and apparatus for identifying individual trees and its canopy shape in LiDAR data determines if the view of each LiDAR data point is blocked by one or more neighboring LiDAR data points. LiDAR data points that do not have neighboring LiDAR data points that block the view are considered to be a central part of a tree canopy. In one embodiment, those LiDAR data points that are central part of a canopy are added to an output file that stores clusters of data points for each canopy detected. The central part of the canopy area can be analyzed to predict one or more characteristics of the tree.

Abstract: A method and apparatus for identifying individual trees in LiDAR data based on the view of a LiDAR data point. In one embodiment, those LiDAR data points that do not have neighboring LiDAR data points that block the local view at a defined angle are considered to be in a central part of a tree canopy. In one embodiment, those LiDAR data points that are in a central part of a tree canopy are added to an output file that stores clusters of data points for each tree canopy detected. The central part of the tree canopy area can be analyzed to predict one or more characteristics of the tree.

Abstract: A computer system for creating artifact-free aerial images of a region of interest. A computer system receives one or more input aerial images that are obtained at different times. A false-color image is created by applying two or more of the input aerial images to different color channel inputs of the false-color image. Based on the color of the pixels in the false-color image, pixels in the two or more input aerial images are classified as representing clear, cloud or shadow areas. An output image is created by combining pixels that are classified as representing clear areas from two or more of the input aerial images.

Abstract: A computer system creates and stores a library of LiDAR models for standard trees that have measured characteristics. A point cloud of LiDAR data from an unidentified tree in a forest is compared against a point cloud defined by a LiDAR model of a standard tree in the library to find a match. If a match is found, one or more characteristics of the matching standard tree are associated with the unidentified tree.

Abstract: A false color composite image is created by assigning mid infrared data from three time-spaced images of an area of interest to corresponding RGB color components for the false color composite image. The RGB color components for the false color composite image are then converted into color space data and classified into a number of color classes. An age is assigned to the color classes to create a classified image of age classes of the area of interest.

Abstract: A system for identifying forest stands within an area of interest that are exhibiting abnormal growth determines a relationship between vegetation index (VI) values determined from a first and a second image of the area of interest. From the relationship, an expected or predicted VI value for each forest stand is determined and compared with the actual VI value computed for the forest stand from the first image. Those forest stands with a difference between the actual and predicted VI values that exceed a threshold are identified as exhibiting abnormal growth.

Abstract: A false color composite image is created by assigning mid infrared data from three time-spaced images of an area of interest to corresponding RGB color components for the false color composite image. The RGB color components for the false color composite image are then converted into color space data and classified into a number of color classes. An age is assigned to the color classes to create a classified image of age classes of the area of interest.

Abstract: A false color composite image is created by assigning mid infrared data from three time-spaced images of an area of interest to corresponding RGB color components for the false color composite image. The RGB color components for the false color composite image are then converted into color space data and classified into a number of color classes. An age is assigned to the color classes to create a classified image of age classes of the area of interest.

Abstract: A method and apparatus for identifying individual trees in LiDAR data based on the view of a LiDAR data point. In one embodiment, those LiDAR data points that do not have neighboring LiDAR data points that block the local view at a defined angle are considered to be in a central part of a tree canopy. In one embodiment, those LiDAR data points that are in a central part of a tree canopy are added to an output file that stores clusters of data points for each tree canopy detected. The central part of the tree canopy area can be analyzed to predict one or more characteristics of the tree.

Abstract: A method and apparatus for identifying individual trees and its canopy shape in LiDAR data determines if the view of each LiDAR data point is blocked by one or more neighboring LiDAR data points. LiDAR data points that do not have neighboring LiDAR data points that block the view are considered to be a central part of a tree canopy. In one embodiment, those LiDAR data points that are central part of a canopy are added to an output file that stores clusters of data points for each canopy detected. The central part of the canopy area can be analyzed to predict one or more characteristics of the tree.