Analysis of Igneada and its surrounding vegetation dynamics using normalized difference vegetation index data from 1987-2000.(Report)

ABSTRACT

Ozyavuz, M. 2010. Analysis of Igneada and its surrounding vegetation dynamics using normalized difference vegetation index data from 1987-2000. Journal of Coastal Research, 26(6), 1001-1006. West Palm Beach (Florida), ISSN 0749-0208.

Igneada area, which is studied in this GEF-II Project, includes different kinds of ecosystems and a wide range of biodiversity. These characteristics make it one of the most important areas in Turkey. The forests in Igneada and the surrounding areas have unique characteristics. In other parts of Turkey and Europe, these types of forests have been damaged due to anthropogenic effects. The surface area of these forests is approximately 3000 ha. Igneada lagoons are located in the northeastern part of Trakya (Thrace) along the Black Sea littoral. Alluvial forests with associated aquatic and coastal ecosystems include sand dunes, seawater, lagoons, swamp, forest, and riparian ecosystems. The area was declared a national park in 2007, but cultural pressures have gradually increased. In this study, Landsat satellite images in the years 1987 and 2000 were normalized difference vegetation index (NDVI) classified. Based on this classification, healthy vegetation structure increased by 6.5% between 1987 and 2000, water surface increased by 1%, and other areas (agriculture, settlement, dune, and unvalued) decreased at the rate of 6.7%.

ADDITIONAL INDEX WORDS: Igneada, Landsat, NDVI, unsupervised classification.

INTRODUCTION

Remote sensing is a very useful tool with which to identify greening period and phenology on a large spatial scale (hundreds of kilometers) (Prasad, Anuradha, and Badinath, 2005; Yu et al. 2003). To monitor vegetation productivity over a long period and on a large spatial scale, the use of satellite imagery is the only viable option (Symeonakis and Drake, 2004).

Vegetation typically shows seasonal and annual dynamics. The daily temporal resolution and globe coverage of satellite sensors (advanced very high resolution radiometer [AVHRR], onboard National Oceanic and Atmospheric Administration satellites) makes it possible to monitor vegetation at different spatial and temporal resolutions globally (Ma and Frank, 2005).

Vegetation indices (VI) and derived metrics have been extensively used for monitoring and detecting vegetation and land cover change (De Fries, Hansen, and Townshend, 1995). Vegetation indices are produced by calculating the ratios of different wavebands of reflected radiation and are related to the abundance and activity of radiation absorbers such as water and plant chlorophyll. These indices enable the estimation of biomass, percentage cover, absorbed photosynthetically active radiation (PAR) and leaf area index. Vegetation dynamics are related to climate (Bonan, 2002; Jackson et al., 2001; Justice, Holben, and Gwynne, 1986) mainly temperature and precipitation variations (Churkina and Running, 1998; Prasad, Anuradha, and Badinath, 2005).

Phenology of vegetation is clearly coupled to annual and seasonal cycles of temperature and precipitation. Extreme climatic conditions on a regional scale may influence the length of the greening period and ecosystem capacity for C[O.sub.2] sequestration (Hill and Donald, 2003). The development of VIs is based on differential absorption, transmittance, and reflectance of energy by the vegetation in the red and near-infrared regions of the electromagnetic spectrum. Vegetation indices are especially advantageous with multidate data sets. Various multidate VIs are clustered to classify broad areas (usually at continental scale) according to the seasonalities of their green up-senescence sequences.

Several studies have characterized vegetation phenology at continental scales using time-series of VIs obtained from satellite data. Many phonological indicators with varying range of complexity have been proposed in various studies based on: fitting logistic curves (Badhwar, 1984; Zhang et al., 2003), moving averages (Reed et al., 1994), normalized …