Applications and limitations of remote sensing for threat analysis in protected areas_ inferring anthropic pressure from habitat and land cover impacts

Carmela MARANGI, Harini NAGENDRA, Paola MAIROTA, Dino TORRI, Richard LUCAS, Panayotis DIMOPOULOS, João Pradinho HONRADO, Madhura NIPHADKAR, Sander MÜCHER, Valeria TOMASELLI, Ioannis MANAKOS, Palma BLONDA., 2013, Applications and limitations of remote sensing for threat analysis in protected areas_ inferring anthropic pressure from habitat and land cover impacts, Changing European Landscapes. Landscape ecology, local to global. IALE 2013 European Congress, Manchester, UK, 9-12/09/2013,
URL: http://www.cnr.it/prodotto/i/272674

Protected areas are continuously subjected to ecological change due to anthropic pressures. Analyses of changes in the extent and intensity of pressures over time are essential for adaptive management, yet such analyses are rarely conceptualized or performed in a well-defined, standardized way, with a frequent lack of clarity in development, definition and measurement. Over-time remote sensing data has great potential for mapping spatial pattern of pressures and their impacts. Some pressures can be mapped directly (e.g. land use dynamics, some invasive species), whereas for others the nature, intensity and spatial pattern of impacts can be used to infer on pressures. This paper develops a framework within which remote sensing datasets in combination with GIS and ecological modeling may be used to identify potential pressure growth through either direct detection or indirect monitoring of impacts on landscapes, land cover/habitat types, communities and species through multi-temporal remote sensing image series. The use of this framework is illustrated through categorization of pressure-derived impacts on protected areas in six countries - Greece, Italy, Portugal, Wales, The Netherlands, and India - located in diverse biogeographic, environmental and social-ecological contexts, and facing a different range of pressures. The framework is conceptually robust, geographically invariant, scalable and spatially-explicit, connecting to the growing data sets from remote sources, and we urge it to be tested over a wide range of pressures and social-ecological settings.

Data from https://intranet.cnr.it/people/