The use of Landsat imagery to assess large-scale forest cover changes in space and time,minimizing false-positive changes |
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| Institution: | 1. Centro de Capacitación y Experimentación Forestal, C/Vadillo-Castril s/n, 23470 Cazorla, Jaén, Spain;2. Universidad Pública de Navarra, Campus de Arrosadia, s/n, 31006 Pamplona, Navarra, Spain.;3. Departamento de Geografía, Universidad de las Islas Baleares, Carr. de Valldemossa, km 7,5, 07122 Palma, Islas Baleares, Spain.;4. Irstea, Research Unit on Forest Ecosystems (EFNO), Domaine des Barres, 45290 Nogent-Sur-Vernisson, France.;5. Departamento de Ciencia y Tecnología Agroforestal y Genética, Escuela Técnica Superior de Ingenieros Agrónomos y de Montes, University of Castilla-La Mancha, Campus Universitario s/n, 02071 Albacete, Spain.;1. Andalusian Institute of Earth Sciences, CSIC-University of Granada, Avda. de las Palmeras 4, 18100 Armilla, Granada, Spain;2. Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Granada, Campus of Cartuja s/n, 18071 Granada, Spain;3. Aguas Termales de Graena (S.A.), Cortes y Graena, Granada, Spain;1. Edenor S.A., Ciudad Autónoma de Buenos Aires, Argentina;2. Departamento de Electrotecnia, Facultad de Ingeniería, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina;3. Secretaría de Investigación y Doctorado, Facultad de Ingeniería, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina |
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| Abstract: | A new approach, based on the application of multi-spectral remote sensing data of Landsat imagery, is introduced to determine large-scale spatiotemporal variations of forest cover changes quantitatively and with a high degree of precision. The test area covers about 837,330.5 ha of a mountainous region in Central Italy. The approach employs several multi-temporal Landsat acquisitions to account for forest cover changes larger than 0.5 ha for the period from March 2002 to July 2011. In contrast to automated approaches that strongly curtail mapping time, the approach introduced here allowed us to map only the real forest cover change, based on a robust validation and rectification of the detected forest change. Derived high spatial resolution data of forest change estimates indicate that about 5.7% (47,670.5 ha) of the observed forest area was subject to human-induced change between 2002 and 2011. Moreover, the detected forest cover changes, most of which are identifiable as timber harvesting, are considerably larger than those reported in the official statistics and often fall within the perimeter of restricted areas (i.e., national parks and natural reserves). |
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| Keywords: | High resolution remotely sensed images Image differencing change detection Forest monitoring Coppice forest Clear-cut mapping Central Italy |
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