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Tree Density Estimations Using a Distance Method in Mali Savanna

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The biological characteristics of trees in tropical dry savannas make it difficult to conduct inventories of tree density, and this has aroused interest in distance-based methods. This study proposes a distance-based tree density estimator using Matérn point processes, generating clustered spatial patterns. It was defined as the maximum likelihood estimator of the density, based on an approximate distribution of the distance from a random point to the pth nearest tree. It was compared with seven estimators identified in the literature as the most efficient. The estimators were compared on a benchmark of 10 point processes, with six being adjusted to observed tree patterns in six Mali savannas (West Africa). The proposed estimator was generally the most efficient. However, this result ignores that (i) all estimators do not require the same effort on the field, (ii) the point-processes benchmark was restricted to Matérn processes, and (iii) all estimators are not equivalent with respect to measurement errors. FOR. SCI. 51(1):7–18.

Keywords: Distance sampling; Matérn process; density-adapted sampling; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; spatial pattern; tropical dry forest

Document Type: Regular Article

Affiliations: 1: Cirad-forêt BP 1813 Bamako Mali Phone: (+223) 224 64 28;, Fax: (+223) 221 87 17, Email: 2: Institut d'Économie Rurale BP 258 Bamako Mali Phone: (+223) 224 64 28, Email: 3: Cirad-forêt, Campus International de Baillarguet TA 10/D, 34398 Montpellier Cedex 5 France Phone: (+33) 467 615 800 ext.4209, Email:

Publication date: 2005-02-01

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
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