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Improving the Precision of Tree Counting by Combining Tree Detection with Crown Delineation and Classification on Homogeneity Guided Smoothed High Resolution (50 cm) Multispectral Airborne Digital Data

2012· article· en· 26 citations· W2033551223 on OpenAlex· 10.3390/rs4051411

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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

The three-model screen

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All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Remote sensing method for counting trees by species from airborne imagery.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

It develops a tree-counting method for forest management, using a method to answer a domain question.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Remote-sensing tree-counting method answers a forestry inventory question.

Abstract

A method of counting the number of coniferous trees by species within forest compartments was developed by combining an individual tree crown delineation technique with a treetop detection technique, using high spatial resolution optical sensor data. When this method was verified against field data from the Shinshu University Campus Forest composed of various cover types, the accuracy for the total number of trees per stand was higher than 84%. This shows improvements over the individual tree crown delineation technique alone which had accuracies lower than 62%, or the treetop detection technique alone which had accuracies lower than 78%. However, the accuracy of the number of trees classified by species was less than 84%. The total number of trees by species per stand was improved with exclusion of the understory species and ranged from 45.2% to 93.8% for Chamaecyparis obtusa and C. pisifera and from 37.9% to 98.1% for broad-leaved trees because many of these were understory species. The better overall results are attributable primarily to the overestimation of Pinus densiflora, Larix kaempferi and broad-leaved trees compensating for the underestimation of C. obtusa and C. pisifera. Practical forest management can be enhanced by registering the output resulting from this technology in a forest geographical information system database. This approach is mostly useful for conifer plantations containing medium to old age trees, which have a higher timber value.

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The record

Venue
Remote Sensing
Topic
Remote Sensing and LiDAR Applications
Field
Environmental Science
Canadian institutions
Natural Resources CanadaCanadian Forest Service
Funders
Japan Society for the Promotion of Science
Keywords
Larix kaempferiForestryRemote sensingLarchUnderstoryCrown (dentistry)ChamaecyparisMultispectral imageEnvironmental scienceCanopyMathematicsGeographyBotanyBiology
Has abstract in OpenAlex
yes