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Title: Advanced earth observation approach for multiscale forest ecosystem services modelling and mapping (MIMOSE)
Authors: Chirici, Gherardo
Sallustio, Lorenzo
Vizzarri, Matteo
Marchetti, Marco
Barbati, Anna
Corona, Piermaria
Travaglini, Davide
Cullotta, Sebastiano
Lafortezza, Raffaele
Lombardi, Fabio
Keywords: Ecosystem services;Earth observation;Remote sensing;MIMOSE Project;Forests;Mapping
Issue Date: 2014
Publisher: Department of Environmental Biology - University La Sapienza of Rome
Source: Chirici, G. et al. 2014. Advanced earth observation approach for multiscale forest ecosystem services modelling and mapping (MIMOSE). "Annali di Botanica" 4: 27–34
In the last decade ecosystem services (ES) have been proposed as a method for quantifying the multifunctional role of forest ecosystems.
Their spatial distribution on large areas is frequently limited by the lack of information, because field data collection with traditional methods
requires much effort in terms of time and cost. In this contribution we propose a methodology (namely, Multiscale Mapping of ecoSystem
services - MiMoSe) based on the integration of remotely sensed images and field observation to produce a wall-to-wall geodatabase of forest parcels
accompanied with several information useful as a basis for future trade-off analysis of different eS. Here, we present the application of the MiMoSe
approach to a study area of 443,758 hectares coincident with the administrative Molise region in Central italy. The procedure is based on a local
high resolution forest types map integrated with information on the main forest management approaches. through the non-parametric k-nearest
neighbors techniques, we produced a growing stock volume map integrating a local forest inventory with a multispectral satellite IRS LISS III
imagery. With the growing stock volume map we derived a forest age map for even-aged forest types. Later these information were used to
automatically create a vector forest parcels map by multidimensional image segmentation that were finally populated with a number of information
useful for ES spatial estimation. The contribution briefly introduces to the MIMOSE methodology presenting the preliminary results we achieved which
constitute the basis for a future implementation of ES modeling
ISSN: 2239-3129
DOI: 10.4462/annbotrm-11810
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