Using the Landsat data archive to assess long-term regional forest dynamics assessment in Eastern Europe, 1985-2012

Abstract. Dramatic political and economic changes in Eastern European countries following the dissolution of the “Eastern Bloc” and the collapse of the Soviet Union greatly affected land-cover and land-use trends. In particular, changes in forest cover dynamics may be attributed to the collapse of the planned economy, agricultural land abandonment, economy liberalization, and market conditions. However, changes in forest cover are hard to quantify given inconsistent forest statistics collected by different countries over the last 30 years. The objective of our research was to consistently quantify forest cover change across Eastern Europe from 1985 until 2012 using the complete Landsat data archive. We developed an algorithm for processing imagery from different Landsat platforms and sensors (TM and ETM+), aggregating these images into a common set of multi-temporal metrics, and mapping annual gross forest cover loss and decadal gross forest cover gain. Our results show that forest cover area increased from 1985 to 2012 by 4.7% across the region. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Most forest disturbance recovered fast, with only 12% of the areas of forest loss prior to 1995 not being recovered by 2012. Timber harvesting was the main cause of forest loss. Logging area declined after the collapse of socialism in the late 1980s, increased in the early 2000s, and decreased in most countries after 2007 due to the global economic crisis. By 2012, Central and Baltic Eastern European countries showed higher logging rates compared to their Western neighbours. Comparing our results with official forest cover and change estimates showed agreement in total forest area for year 2010, but with substantial disagreement between Landsat-based and official net forest cover area change. Landsat-based logging areas exhibit strong relationship with reported roundwood production at national scale. Our results allow national and sub-national level analysis of forest cover extent, change, and logging intensity and are available on-line as a baseline for further analyses of forest dynamics and its drivers

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests\u27 absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. Google Earth™ time series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user\u27s accuracy of 78% and a producer\u27s accuracy of 68%. Excluding errors of adjacency, user\u27s and producer\u27s accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (Google Earth™) classification; however, user\u27s (42%) and producer\u27s (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user\u27s and producer\u27s accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user\u27s and producer\u27s accuracies) and urban gain (72% and 18% for respective user\u27s and producer\u27s accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs. gov/CONUS_5Y_LandCover/)

Mapping pervasive selective logging in the south-west Brazilian Amazon 2000–2019

Tropical forests harbour the highest biodiversity on the planet and are essential to human livelihoods and the global economy. However, continued loss and degradation of forested landscapes, coupled with a rapidly rising global population is placing incredible pressure on forests globally. The United Nations has developed the Reducing Emissions from Deforestation and forest Degradation (REDD+) programme in response to the challenges facing tropical forests and in recognition of the role they can play in climate mitigation. REDD+ requires consistent and reliable monitoring of forests, however, national-level methodologies for measuring degradation are often bespoke and, because of an inability to track degradation effectively, the majority of countries combine reporting for deforestation and forest degradation into a single value. Here, we extend a recent analysis that enabled the detection of selective logging at the scale of a logging concession to a regional-scale estimation of selective logging activities. We utilized logging records from across Brazil to train a supervised classification algorithm for detecting logged pixels in Landsat imagery then predicted the extent of logging over a 20 year period throughout Rondônia, Brazil. Approximately one-quarter of the forested lands in Rondônia were cleared between 2000 and 2019. We estimate that 11.0% of the forest area present in 2000 had been selectively logged by 2019, comprising >11,500 km2 of forest. In general, rates of selective logging were twice as high in the first decade relative to the last decade of the period. Our approach is a considerable advance in developing an operationalized selective logging monitoring system capable of detecting subtle forest disturbances over large spatial scales

Overexpression of Brain- and Glial Cell Line-Derived Neurotrophic Factors Is Neuroprotective in an Animal Model of Acute Hypobaric Hypoxia.

peer reviewedCurrently, the role of the neurotrophic factors BDNF and GDNF in maintaining the brain's resistance to the damaging effects of hypoxia and functional recovery of neural networks after exposure to damaging factors are actively studied. The assessment of the effect of an increase in the level of these neurotrophic factors in brain tissues using genetic engineering methods on the resistance of laboratory animals to hypoxia may pave the way for the future clinical use of neurotrophic factors BDNF and GDNF in the treatment of hypoxic damage. This study aimed to evaluate the antihypoxic and neuroprotective properties of BDNF and GDNF expression level increase using adeno-associated viral vectors in modeling hypoxia in vivo. To achieve overexpression of neurotrophic factors in the central nervous system's cells, viral constructs were injected into the brain ventricles of newborn male C57Bl6 (P0) mice. Acute hypobaric hypoxia was modeled on the 30th day after the injection of viral vectors. Survival, cognitive, and mnestic functions in the late post-hypoxic period were tested. Evaluation of growth and weight characteristics and the neurological status of animals showed that the overexpression of neurotrophic factors does not affect the development of mice. It was found that the use of adeno-associated viral vectors increased the survival rate of male mice under hypoxic conditions. The present study indicates that the neurotrophic factors' overexpression, induced by the specially developed viral constructs carrying the BDNF and GDNF genes, is a prospective neuroprotection method, increasing the survival rate of animals after hypoxic injury

Will REDD+ safeguards mitigate corruption? Qualitative evidence from Southeast Asia

High levels of faith and finance are being invested in REDD+ as a promising global climate change mitigation policy. Since its inception in 2007, corruption has been viewed as a potential impediment to the achievement of REDD+ goals, partly motivating ‘safeguards’ rolled out as part of national REDD+ readiness activities. We compare corruption mitigation measures adopted as part of REDD+ safeguards, drawing on qualitative case evidence from three Southeast Asian countries that have recently piloted the scheme: Indonesia, the Philippines, and Vietnam. We find that while REDD+ safeguards adopt a conventional principal-agent approach to tackling corruption in the schemes, our case evidence confirms our theoretical expectation that REDD+ corruption risks are perceived to arise not only from principal-agent type problems: they are also linked to embedded pro-corruption social norms. This implies that REDD+ safeguards are likely to be at best partially effective against corruption, and at worst will not mitigate corruption at all

FOREST DYNAMICS IN EASTERN EUROPE (1985–2012) USING LANDSAT DATA ARCHIVE

The collapse of the “Eastern Bloc” and the breakdown of the Soviet Union in the late 1980’s – early 1990’s led to dramatic political and economic changes in Eastern European countries. Reduction of crop area, changes in the forest legislation, land privatization, markets change and other social and economic factors have caused changes in forested area within these countries. Statistical data on forest extent provided by the countries are not always comparable due to the use of different methods and definitions. Long-term archive of Landsat satellite data allows independent evaluation of the extent and change of forest area. We have developed an algorithm for the automatic processing of images obtained by different sensors (TM and ETM+) and compilation of a consistent image time series to map the total forest area, its loss and gain. The algorithm was used to estimate the forest area change in Eastern Europe from 1985 to 2012. The results showed that the forested area increased by 4,7 % in 2012 compared to 1985. The average annual forest loss was 0,41 % of the total forest area, and disturbance area increased from 1985 to 2012. Forest cover is quickly restored after the disturbance, and only 12 % of forest disturbance area occurred before 1995 was not recovered by 2012. The main factor of the forest disturbance was timber harvesting. The annual logging area declined after the collapse of the planned economy in the late 80’s, increased by the year 2000, and then decreased again due to the economic crisis of 2007–2009. Our results and regional maps are available online at http://glad.umd.edu/europe/ and may be used to analyze changes in forest area at national and sub-national levels

Using the Landsat data archive to assess long-term regional forest dynamics assessment in Eastern Europe, 1985-2012

Dramatic political and economic changes in Eastern European countries following the dissolution of the “Eastern Bloc” and the collapse of the Soviet Union greatly affected land-cover and land-use trends. In particular, changes in forest cover dynamics may be attributed to the collapse of the planned economy, agricultural land abandonment, economy liberalization, and market conditions. However, changes in forest cover are hard to quantify given inconsistent forest statistics collected by different countries over the last 30 years. The objective of our research was to consistently quantify forest cover change across Eastern Europe from 1985 until 2012 using the complete Landsat data archive. We developed an algorithm for processing imagery from different Landsat platforms and sensors (TM and ETM+), aggregating these images into a common set of multi-temporal metrics, and mapping annual gross forest cover loss and decadal gross forest cover gain. Our results show that forest cover area increased from 1985 to 2012 by 4.7% across the region. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Most forest disturbance recovered fast, with only 12% of the areas of forest loss prior to 1995 not being recovered by 2012. Timber harvesting was the main cause of forest loss. Logging area declined after the collapse of socialism in the late 1980s, increased in the early 2000s, and decreased in most countries after 2007 due to the global economic crisis. By 2012, Central and Baltic Eastern European countries showed higher logging rates compared to their Western neighbours. Comparing our results with official forest cover and change estimates showed agreement in total forest area for year 2010, but with substantial disagreement between Landsat-based and official net forest cover area change. Landsat-based logging areas exhibit strong relationship with reported roundwood production at national scale. Our results allow national and sub-national level analysis of forest cover extent, change, and logging intensity and are available on-line as a baseline for further analyses of forest dynamics and its drivers