Remote sensing and probabilistic sampling based forest inventory method
View Patent ↗A remote sensing and probabilistic sampling based forest inventory method can correlate aerial data, such as LiDAR, CIR, and/or Hyperspectral data with actual sampled and measured ground data to facilitate obtainment, e.g., prediction, of a more accurate forest inventory. The resulting inventory can represent an empirical description of the height, DBH and species of every tree within the sample area. The use of probabilistic sampling methods can greatly improve the accuracy and reliability of the forest inventory.
1. A computer-implemented forest inventory method comprising:
a. processing remote sensing data indicative of tree attribute information for said forest using a computer system, said remote sensing data comprising at least one of LiDAR data and digital images;
b. defining a sampling frame within said remote sensing data using said computer system;
c. determining a field plot corresponding to said sampling frame and collecting field plot data therefrom using said computer system, said field plot data comprising actual tree attribute information;
d. generating a correlated model using said computer system by combining said field plot data with said remote sensing data corresponding to said sample frame;
e. applying said correlated model using said computer system to all said remote sensing data to produce a probabilistic forest inventory;
f. wherein generating said correlated model further comprising using said computer system for automatic field tree matching to create a table in which measured field tree records are merged with tree polygon objects based upon geographic proximity, wherein said tree polygon objects are derived from said remote sensing data; and
g. using said computer system to manually adjust said tree matching based upon interpreter estimate that a field tree is either contributing some pixels of a tree polygon that was created, or is not visible from the air because of a larger tree that contributed some or all pixels of said tree polygon.
2. A computer-implemented forest inventory method comprising:
a. processing imagery data using a computer system, said imagery data indicative of tree attribute information for said forest;
b. using said computer system, classifying tree polygons within said imagery data to derive said tree attribute information;
c. correlating field data using said computer system, said field data comprising at least one of actual tree attribute information and plot center location;
d. using said computer system, generating a correlated model utilizing said tree attribute information derived from said imagery data and said actual tree attribute information;
e. generating a probabilistic forest inventory by applying said correlated model to all said imagery data using said computer system;
f. wherein said imagery data further comprises at least one of digital images, LiDAR data, and property boundary information;
g. wherein said digital images further comprise color infrared photography, and said imagery data further comprises at least one of stand shapes and tree crown polygon shapes;
h. wherein said digital image processing further comprises color infrared processing and LiDAR processing using said computer system; and
i. wherein said LiDAR processing comprises:
i. calculating Digital Elevation Model (DEM);
ii. selecting highest pixel and subtracting DEM;
iii. mapping digital surface value; and
iv. converting data to gray-scale.
3. A computer-implemented forest inventory method comprising:
a. processing imagery data using a computer system, said imagery data indicative of tree attribute information for said forest;
b. using said computer system, classifying tree polygons within said imagery data to derive said tree attribute information;
c. correlating field data using said computer system, said field data comprising at least one of actual tree attribute information and plot center location;
d. using said computer system, generating a correlated model utilizing said tree attribute information derived from said imagery data and said actual tree attribute information;
e. generating a probabilistic forest inventory by applying said correlated model to all said imagery data using said computer system;
f. wherein correlating said field data further comprises:
i. capturing actual tree attribute information indicative of at least one of tree height and location; and
ii. creating match data correlating said actual tree attributes with said tree attributes derived from said imagery data;
g. using said computer system for automatic field tree matching to create a table in which measured field tree records are merged with tree polygon objects based upon geographic proximity; and
h. using said computer system to manually adjust said tree matching based upon interpreter estimate that a field tree is either contributing some pixels of the tree polygon that was created, or is not visible from the air because of a larger tree that contributed some or all pixels of the tree polygon.