Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results

Rocchini, D.; Andreo, V.; Förster, M.; Garzon Lopez, C.X.; Gutierrez, A.P.; Gillespie, T.W.; Hauffe, H.C.; He, K.S.; Kleinschmit, B.; Mairota, P.; Marcantonio, M.; Metz, M.; Nagendra, H.; Pareeth, S.; Ponti, L.; Ricotta, C.; Rizzoli, A.; Schaab, G.; Zebisch, M.; Zorer, R.; Neteler, M.G. (2015). Potential of remote sensing to predict species invasions: a modelling perspective. PROGRESS IN PHYSICAL GEOGRAPHY, 39 (3): 283-309. doi: 10.1177/0309133315574659 handle: http://hdl.handle.net/10449/24798

Potential of remote sensing to predict species invasions: a modelling perspective

Rocchini, Duccio;Garzon Lopez, Carol Ximena;Hauffe, Heidi Christine;Marcantonio, Matteo;Metz, Markus;Pareeth, Sajid;Rizzoli, Annapaola;Zorer, Roberto;Neteler, Markus Georg
2015-01-01

Abstract

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results
Biological invasion
Ecological niche models
Physiologically-based demographic models
Satellite imagery
Species distribution modelling
Species diversity
Uncertainty
Modelli di distribuzione delle specie
Telerilevamento
Settore BIO/07 - ECOLOGIA
2015
Rocchini, D.; Andreo, V.; Förster, M.; Garzon Lopez, C.X.; Gutierrez, A.P.; Gillespie, T.W.; Hauffe, H.C.; He, K.S.; Kleinschmit, B.; Mairota, P.; Marcantonio, M.; Metz, M.; Nagendra, H.; Pareeth, S.; Ponti, L.; Ricotta, C.; Rizzoli, A.; Schaab, G.; Zebisch, M.; Zorer, R.; Neteler, M.G. (2015). Potential of remote sensing to predict species invasions: a modelling perspective. PROGRESS IN PHYSICAL GEOGRAPHY, 39 (3): 283-309. doi: 10.1177/0309133315574659 handle: http://hdl.handle.net/10449/24798
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