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Aha! Volume Number 1

Fall 2007 | Download Pdf |

Key articles:
  1. Remote Sensing for Forest Inventory, Fact or Fiction? A Case Study
  2. Carbon Capture: The Unexpected Value from Forests!
  3. ImageTree awarded a Patent, Mines Key Inventory Metrics
  4. ImageTree Adds Change Management to ForestSense Platform
  5. ForestSense Flourishes on Four Fronts

Aha! Volume Number 2

Spring 2008 | Download Pdf |

COVER STORY
Capital Markets Could Save Tropical Rainforests: Going REDD for Green
Click Here to Read the Article

This edition of ImageTree's Aha! also includes a break-down of ImageTree's ForestSense product and introduces the Idea Leadership Series.

Biometrician Jim Flewelling's Indepedent Research

Forest Inventory Predictions from Individual Tree Crowns: Regression Modeling within a Sample Framework written by James W. Flewelling

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Abstract. Remotely sensed data can be used to make digital maps showing individual tree crowns (ITC) for entire forests. Attributes of the ITCs may include area, shape, height and color. The crown map is sampled in a way that provides an unbiased linkage between ITCs and identifiable trees measured on the ground. Methods of avoiding edge bias are given. In an example from a forest of young southern pine, the forest is delineated into several thousand stands. Forty stands are sampled, each with two 0.12 acre plots. The resultant estimator of a volume surrogate, tree basal area times height summed over all trees, is unbiased and has a 90 percent confidence interval of ± 4.1 percent. The root mean square errors for basal area and the volume surrogate at the stand level are estimated at 9.7 percent and 12.8 percent respectively. That precision in basal area for individual stands is approximately the same as would have been achieved by ground sampling with ten 0.12 acre plots in each stand, making no use of the remotely sensed data.