Remote sensing estimates of stand-replacement fires in Russia, 2002–2011

The presented study quantifies the proportion of stand-replacement fires in Russian forests through the integrated analysis of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data products. We employed 30 m Landsat Enhanced Thematic Mapper Plus derived tree canopy cover and decadal (2001–2012) forest cover loss (Hansen et al 2013 High-resolution global maps of 21st-century forest cover change Science 342 850–53) to identify forest extent and disturbance. These data were overlaid with 1 km MODIS active fire (earthdata.nasa.gov/data/near-real-time-data/firms) and 500 m regional burned area data (Loboda et al 2007 Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data Remote Sens. Environ. 109 429–42 and Loboda et al 2011 Mapping burned area in Alaska using MODIS data: a data limitations-driven modification to the regional burned area algorithm Int. J. Wildl. Fire 20 487–96) to differentiate stand-replacement disturbances due to fire versus other causes. Total stand replacement forest fire area within the Russian Federation from 2002 to 2011 was estimated to be 17.6 million ha (Mha). The smallest stand-replacement fire loss occurred in 2004 (0.4 Mha) and the largest annual loss in 2003 (3.3 Mha). Of total burned area within forests, 33.6% resulted in stand-replacement. Light conifer stands comprised 65% of all non-stand-replacement and 79% of all stand-replacement fire in Russia. Stand-replacement area for the study period is estimated to be two times higher than the reported logging area. Results of this analysis can be used with historical fire regime estimations to develop effective fire management policy, increase accuracy of carbon calculations, and improve fire behavior and climate change modeling efforts

Forest Cover Change within the Russian European North after the Breakdown of Soviet Union (1990–2005)

Forest cover dynamics (defined as tree canopy cover change without regard to forest land use) within the Russian European North have been analyzed from 1990 to 2005 using a combination of results from two Landsat-based forest cover monitoring projects: 1990–2000 and 2000–2005. Results of the forest cover dynamics analysis highlighted several trends in forest cover change since the breakdown of the Soviet planned economy. While total logging area decreased from the 1990–2000 to the 2000–2005 interval, logging and other forms of anthropogenically-induced clearing increased within the Central and Western parts of the region. The most populated regions of European Russia featured the highest rates of net forest cover loss. Our results also revealed intensive gross forest cover loss due to forest felling close to the Russian-Finland border. The annual burned forest area almost doubled between the two time intervals. The 2000–2005 gross forest cover gain results suggest that tree encroachment on abandoned agriculture land is a wide-spread process over the region. The analysis demonstrates the value of regional-scale Landsat-based forest cover and change quantification. Our results supplemented official data by providing independently derived spatial information that could be used for assessing on-going trends and serve as a baseline for future forest cover monitoring

Effect of novel porphyrazine photosensitizers on normal and tumor brain cells

Photodynamic therapy (PDT) is a clinically approved procedure for targeting tumor cells. Though several different photosensitizers have been developed, there is still much demand for novel photosensitizers with improved properties. In this study we aim to characterize the accumulation, localization and dark cytotoxicity of the novel photosensitizers developed in-house derivatives of porphyrazines (pz I-IV) in primary murine neuronal cells, as well as to identify the concentrations at which pz still effectively induces death in glioma cells yet is nontoxic to nontransformed cells. The study shows that incubation of primary neuronal and glioma cells with pz I-IV leads to their accumulation in both types of cells, but their rates of internalization, subcellular localization and dark toxicity differ significantly. Pz II was the most promising photosensitizer. It efficiently killed glioma cells while remaining nontoxic to primary neuronal cells. This opens up the possibility of evaluating pz II for experimental PDT for glioma

Tree canopy extent and height change in Europe, 2001-2021, quantified using Landsat data archive

European forests are among the most extensively studied ecosystems in the world, yet there are still debates about their recent dynamics. We modeled the changes in tree canopy height across Europe from 2001 to 2021 using the multidecadal spectral data from the Landsat archive and calibration data from Airborne Laser Scanning (ALS) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidars. Annual tree canopy height was modeled using regression tree ensembles and integrated with annual tree canopy removal maps to produce harmonized tree height map time series. From these time series, we derived annual tree canopy extent maps using a >= 5 m tree height threshold. The root-mean-square error (RMSE) for both ALS-calibrated and GEDI-calibrated tree canopy height maps was = 94% for the tree canopy extent maps and >= 80% for the annual tree canopy removal maps. Analyzing the map time series, we found that the European tree canopy extent area increased by nearly 1% overall during the past two decades, with the largest increase observed in Eastern Europe, Southern Europe, and the British Isles. However, after the year 2016, the tree canopy extent in Europe declined. Some regions reduced their tree canopy extent between 2001 and 2021, with the highest reduction observed in Fennoscandia (3.5% net decrease). The continental extent of tall tree canopy forests (>= 15 m height) decreased by 3% from 2001 to 2021. The recent decline in tree canopy extent agrees with the FAO statistics on timber harvesting intensification and with the increasing extent and severity of natural disturbances. The observed decreasing tree canopy height indicates a reduction in forest carbon storage capacity in Europe

Global Trends of Forest Loss Due to Fire From 2001 to 2019

Forest fires contribute to global greenhouse gas emissions and can negatively affect public health, economic activity, and provision of ecosystem services. In boreal forests, fires are a part of the ecosystem dynamics, while in the humid tropics, fires are largely human-induced and lead to forest degradation. Studies have shown changing fire dynamics across the globe due to both climate and land use change. However, global trends in fire-related forest loss remain uncertain due to the lack of a globally consistent methodology applied to high spatial resolution data. Here, we create the first global 30-m resolution satellite-based map of annual forest loss due to fire. When producing this map, we match the mapped area of forest loss due to fire to the reference area obtained using a sample-based unbiased estimator, thus enabling map-based area reporting and trend analysis. We find an increasing global trend in forest loss due to fire from 2001 to 2019, driven by near-uniform increases across the tropics, subtropical, and temperate Australia, and boreal Eurasia. The results quantify the increasing threat of fires to remaining forests globally and may improve modeling of future forest fire loss rates under various climate change and development scenarios

The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results

Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system

cover change within the Russian European north after the breakdown of soviet union

Forest cover dynamics (defined as tree canopy cover change without regard to forest land use) within the Russian European North have been analyzed from 1990 to 2005 using a combination of results from two Landsat-based forest cover monitoring projects: 1990-2000 and 2000-2005. Results of the forest cover dynamics analysis highlighted several trends in forest cover change since the breakdown of the Soviet planned economy. While total logging area decreased from the 1990-2000 to the 2000-2005 interval, logging and other forms of anthropogenically-induced clearing increased within the Central and Western parts of the region. The most populated regions of European Russia featured the highest rates of net forest cover loss. Our results also revealed intensive gross forest cover loss due to forest felling close to the Russian-Finland border. The annual burned forest area almost doubled between the two time intervals. The 2000-2005 gross forest cover gain results suggest that tree encroachment on abandoned agriculture land is a wide-spread process over the region. The analysis demonstrates the value of regional-scale Landsat-based forest cover and change quantification. Our results supplemented official data by providing independently derived spatial information that could be used for assessing on-going trends and serve as a baseline for future forest cover monitoring

Agricultural fires in European Russia, Belarus, and Lithuania and their impact on air quality, 2002 – 2012. in land-cover and land-use changes in Eastern Europe after the collapse of Soviet Union in 1991

This chapter describes the first research to quantify air pollution emissions at a moderate to coarse scale from agricultural burning in Belarus, Lithuania, and European Russia using MODIS and Landsat-based estimates of fire, land-cover and land-use. Agricultural burning in Belarus, Lithuania, and European Russia showed a strong and consistent seasonal geographic pattern from 2002 to 2012, with the majority of fires occurring from March to June and a smaller peak in July and August. Over this 11-year period, there was a decrease in both cropland and pasture burning throughout the region. For Smolensk Oblast, a Russian administrative region with comparable agro-environmental conditions to Belarus and Lithuania, a detailed analysis of Landsat-based burned area estimations for croplands, pastures and field data collected in summer 2014 showed that the agricultural burning area can be up to 10 times larger than the 1 km MODIS active fire estimates. Using the annual MODIS and Landsat-based burned area estimations, we identified 25 carbon, particulate matter, volatile organic carbon (VOCs), and harmful air pollutants (HAPs) emissions for all agricultural burning, including both croplands and pastures. In general, European Russia is the main source of agricultural burning emissions. Lithuania and Belarus have relatively minor contributions. Indeed, emissions from certain agricultural burning air pollutants in European Russia are so large that they are equivalent to 5 % of emissions from all sectors (industry, energy, transportation, all sources of fire) in Lithuania and likely in other neighboring Eastern European countries

Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.https://doi.org/10.3390/rs1203042