Land-use and land-cover change (LULCC) is the main cause of the global biodiversity crisis and protected areas are critical to prevent habitat loss. Rapid changes in institutional and socio-economic conditions, such as the collapse of the former Soviet Union in 1991, often trigger widespread LULCC. Yet, it is unclear how effective protected areas are in safeguarding habitat within them during such periods of rapid LULCC. Our goal here was to map changes in forest cover and agricultural lands from 1984 to 2010 in order to assess the effectiveness of two strictly protected areas, Oksky and Mordovsky State Nature Reserves, in temperate European Russia. We analyzed dense time series of Landsat images for three Landsat footprints and applied a support vector machine classification and trajectory-based change detection to map forest disturbance. We then used matching statistics to quantify the effectiveness of the protected areas. Our analyses highlighted considerable post-Soviet LULCC in European Russia. The LULCC maps revealed disturbances on 5.02% of the total forest area, with strongly declining disturbance rates in post-Soviet times. We also found that 39.89% of the agricultural land used in 1988 was abandoned after 1991, leading to widespread forest regrowth. Oksky and Mordovsky State Nature Reserves had a significantly lower probability of forest disturbance (− 0.1 to − 3.5% lower) in comparison to their surrounding areas. This suggests that protected areas were relatively effective in limiting human-induced forest disturbance in European Russia, despite lower levels of control and an eroding infrastructure for nature protection. Moreover, we found drastic land-cover changes, particularly forest regrowth, in the surroundings of these protected areas, highlighting conservation opportunities. Protected areas can play a key role in biodiversity conservation during periods of rapid LULCC, and remote sensing coupled with matching statistics provide important tools for monitoring the success and failure of conservation efforts.
Remote Sensing of Environment – Elsevier
Published: Jun 15, 2013
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