Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Abstract

© iForest -Biogeosciences and Forestry IntroductionAssessment of natural forest expansion represents a crucial issue to elucidate several processes, including biogeochemical cycles, atmospheric composition related to climate change, and forest carbon uptake, as well as socio-economic processes and issues. Anthropogenic and naturally induced land cover changes affect spatial and temporal distribution and availability of environmental resources, and alter ecosystem composition and productivity. Globally, these processes can be considered the primary catalysts for change in biogeochemical cycling, atmospheric composition, and climate (Pielke 2005, Metz et al. 2007, Turner et al. 2007). Forest land-use and land-cover change (LU-LCC) were recognised as key issues in greenhouse gas removal/emission processes as specified by the Good Practices Guidance for Land Use, Land Use Change, and Forestry (GPG-LULUCF) during the Intergovernmental Panel on Climate Change (IPCC) established at the Kyoto Protocol (Penman et al. 2003). Observation and assessment of forest cover changes are crucial to elucidate the complexities inherent in feedback processes between forest distribution and human activities in sustainable forest development, natural resource management, biodiversity conservation, ecosystem functioning, and biogeochemical cycling (IPCC 2007). In Mediterranean regions, natural forest expansion is primarily related to the abandonment of agricultural practices and cattle-raising in marginal areas representing the principal change in Italy's Mediterranean rural landscape over the past five decades (Piussi 2005). These processes generally vary in terms of the vegetation successional series and time scale, however the expansion dynamics are shared, beginning with an initial phase of spontaneous shrub dominance, followed by tree colonisation (Biondi et al. 2006).In recent decades, satellite-based high-resolution observations with multispectral scanners provided the scientific community with consistent data to implement detailed thematic mapping for local and regional scale land classification (Friedl et al. 2002, 2010, Lu & Weng 2007, Giri 2012. The near infrared wavebands on the Landsat Thematic Mapper (TM) facilitates advanced land classification analyses based on differences in spectral reflectance of different land cover types. In particular, specific foliar reflectance, pigment absorptions, and foliar moisture wavelength ranges represent the basis of vegetation class analyses. Furthermore, the availability of such land cover data at different spatial and temporal scales promotes the development and implementation of vegetation change detection techniques, which furthers our understanding on vegetation and ecosystem dynamics (Cohen & Fiorella 1998, Coppin et al. 2004, Lu et al. 2004, Martinez & Gilabert 2009).In forest ecosystems, land cover change dynamic detection based on visual and statistical approaches represents a challenge to the scientific community due to the difficulties in remotely sensed image acquisition err...

Land cover changes and forest landscape evolution (1985–2009) in a typical Mediterranean agroforestry system (high Agri Valley)

“…istat.it/); the increase in forest areas reported by the Regione Basilicata (2009) that delineates a strong expansion of forests in the period 1960-2000 through an estimation based on the Corine Land Cover 2000 (Büttner et al, 2002) and the 1960 Land Use Map of Italy (Vitelli, 2007); and the positive evolutionary tendency of the forest covers in the time span 1984-2010 highlighted by Mancino et al (2014).…”

“…tion (30 m), reasonable revisiting frequency (16 days), and availability of long time series (since July 1972) to carry out multitemporal analyses (Collins and Woodcock, 1996;Borrelli et al, 2014;Mancino et al, 2014).…”

Monitoring the Secondary Forest Succession and Land Cover/Use Changes of the Błędów Desert (Poland) Using Geospatial Analyses

“…can now replace the manual work of an operator (photointerpretation) in determining the borders of the LULC classes and monitoring process of the secondary forest succession. The geodata and modern technologies provide accurate information on the spatial and temporal distribution of LULC classes and deliver indicators that show the dynamic process of the landscape changes (especially process of the secondary forest succession) including the spatial range and structure of vegetation (Bergen, Dronova 2007, Falkowski et al 2009, Mancino et al 2014, Suzanchi, Kaur 2011. The automation of the processing using GIS analysis and GEOBIA tools, allows for the obtainment of very accurate borders of the land cover classes compared to the traditionally applied photo-interpretation and on-screen vectorization methods, but in a faster, more cost-effective, objective and efficient way (Moskal, Jakubauskas 2013, Szostak et al 2014, Wężyk, de Kok 2005.…”

Analysis of the combined and single effects of LULC and climate change on the streamflow of the Upper Blue Nile River Basin (UBNRB): Using statistical trend tests, remote sensing landcover maps and the SWAT model

“…5. The resulting NDVI images for the time periods were subtracted to assess (Hu et al, 2004;Jensen, 1996;Lu et al, 2004;Mancino et al, 2014;Singh, 1989) as cited by Mancino et al (2014). To be more conservative n=1 was selected for this study to narrow the ranges of the threshold for reliable classification.…”

“…Once the landcover classification of the year 2010 Landsat image is completed and its accuracy is checked, the NDVI differencing technique (Mancino et al, 2014) was applied to classify the images of 1973, 1985 and 1995. This technique was chosen to increase the accuracy of classification as it is hard to find an accurately classified digital or analogue LULC map of the study area during the period of 1973, 1985 and 1995 and also the information obtained from the elders are more 25 subjective and its reliability is questionable. In order to increase the accuracy, we first calculated the NDVI from the Landsat MSS (1973) and three pre-processed Landsat TM images (1985,1995,2010) following the general normalized difference between band TM4 and band TM3 images eqn.…”