A global land cover training dataset from 1984 to 2020.

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

The impact of conflict-driven cropland abandonment on food insecurity in South Sudan revealed using satellite remote sensing.

Armed conflicts often hinder food security through cropland abandonment and restrict the collection of on-the-ground information required for targeted relief distribution. Satellite remote sensing provides a means for gathering information about disruptions during armed conflicts and assessing the food security status in conflict zones. Using ~7,500 multisource satellite images, we implemented a data-driven approach that showed a reduction in cultivated croplands in war-ravaged South Sudan by 16% from 2016 to 2018. Propensity score matching revealed a statistical relationship between cropland abandonment and armed conflicts that contributed to drastic decreases in food supply. Our analysis shows that the abandoned croplands could have supported at least a quarter of the population in the southern states of South Sudan and demonstrates that remote sensing can play a crucial role in the assessment of cropland abandonment in food-insecure regions, thereby improving the basis for timely aid provision.

Spatiotemporal tracking of carbon emissions and uptake using time series analysis of Landsat data: A spatially explicit carbon bookkeeping model.

Reducing terrestrial carbon emissions to the atmosphere requires accurate measuring, reporting and verification of land surface activities that emit or sequester carbon. Many carbon accounting practices in use today provide only regionally aggregated estimates and neglect the spatial variation of pre-disturbance forest conditions and post-disturbance land cover dynamics. Here, we present a spatially explicit carbon bookkeeping model that utilizes a high-resolution map of aboveground biomass and land cover dynamics derived from time series analysis of Landsat data. The model produces estimates of carbon emissions/uptake with model uncertainty at Landsat resolution. In a case study of the Colombian Amazon, an area of 47 million ha, the model estimated total emissions of 3.97 ± 0.71 Tg C yr-1 and uptake by regenerating forests of 1.11 ± 0.23 Tg C yr-1 2001-2015, with an additional 45.1 ± 7.99 Tg of carbon remaining in the form of woody products, decomposing slash and charcoal at the end of 2015 (estimates provided with ±95% confidence intervals). Total emissions attributed to the study period (including carbon not yet released) is 6.97 ± 1.24 Tg C yr-1. The presented model is based on recent technological advancements in the field of remote sensing to achieve spatially explicit modeling of carbon emissions and uptake associated with land surface changes and post-disturbance landscapes that is compliant with international reporting criteria.

Satellite-based estimates reveal widespread forest degradation in the Amazon.

Anthropogenic and natural forest disturbance cause ecological damage and carbon emissions. Forest disturbance in the Amazon occurs in the form of deforestation (conversion of forest to non-forest land covers), degradation from the extraction of forest resources, and destruction from natural events. The crucial role of the Amazon rainforest in the hydrologic cycle has even led to the speculation of a disturbance "tipping point" leading to a collapse of the tropical ecosystem. Here we use time series analysis of Landsat data to map deforestation, degradation, and natural disturbance in the Amazon Ecoregion from 1995 to 2017. The map was used to stratify the study area for selection of sample units that were assigned reference labels based on their land cover and disturbance history. An unbiased statistical estimator was applied to the sample of reference observations to obtain estimates of area and uncertainty at biennial time intervals. We show that degradation and natural disturbance, largely during periods of severe drought, have affected as much of the forest area in the Amazon Ecoregion as deforestation from 1995 to 2017. Consequently, an estimated 17% (1,036,800 ± 24,800 km2 , 95% confidence interval) of the original forest area has been disturbed as of 2017. Our results suggest that the area of disturbed forest in the Amazon is 44%-60% more than previously realized, indicating an unaccounted for source of carbon emissions and pervasive damage to forest ecosystems.

The Pathogenesis of Glenohumeral Deformity and Contracture Formation in Obstetric Brachial Plexus Palsy-A Review.

Contractures of the shoulder joint and glenohumeral joint dysplasia are well known complications to obstetrical brachial plexus palsy. Despite extensive description of these sequelae, the exact pathogenesis remains unknown. The prevailing theory to explain the contractures and glenohumeral joint dysplasia states that upper trunk injury leads to nonuniform muscle recovery and thus imbalance between internal and external rotators of the shoulder. More recently, another explanation has been proposed, hypothesizing that denervation leads to reduced growth of developing muscles and that reinnervation might suppress contracture formation. An understanding of the pathogenesis is desirable for development of effective prophylactic treatment. This article aims to describe the current state of knowledge regarding these important complications.

Forest loss in New England: A projection of recent trends.

New England has lost more than 350,000 ha of forest cover since 1985, marking a reversal of a two-hundred-year trend of forest expansion. We a cellular land-cover change model to project a continuation of recent trends (1990-2010) in forest loss across six New England states from 2010 to 2060. Recent trends were estimated using a continuous change detection algorithm applied to twenty years of Landsat images. We addressed three questions: (1) What would be the consequences of a continuation of the recent trends in terms of changes to New England's forest cover mosaic? (2) What social and biophysical attributes are most strongly associated with recent trends in forest loss, and how do these vary geographically? (3) How sensitive are projections of forest loss to the reference period-i.e. how do projections based on the period spanning 1990-to-2000 differ from 2000-to-2010, or from the full period, 1990-to-2010? Over the full reference period, 8201 ha yr-1 and 468 ha yr-1 of forest were lost to low- and high-density development, respectively. Forest loss was concentrated in suburban areas, particularly near Boston. Of the variables considered, 'distance to developed land' was the strongest predictor of forest loss. The next most important predictor varied geographically: 'distance to roads' ranked second in the more developed regions in the south and 'population density' ranked second in the less developed north. The importance and geographical variation in predictor variables were relatively stable between reference periods. In contrast, there was 55% more forest loss during the 1990-to-2000 reference period compared to the 2000-to-2010 period, highlighting the importance of understanding the variation in reference periods when projecting land cover change. The projection of recent trends is an important baseline scenario with implications for the management of forest ecosystems and the services they provide.

Assessing the global warming potential of human settlement expansion in a mesic temperate landscape from 2005 to 2050.

Expansion of human settlements is an important driver of global environmental change that causes land use and land cover change (LULCC) and alters the biophysical nature of the landscape and climate. We use the state of Massachusetts, United States (U.S.) to present a novel approach to quantifying the effects of projected expansion of human settlements on the biophysical nature of the landscape. We integrate nationally available datasets with the U.S. Environmental Protection Agency's Integrated Climate and Land Use Scenarios model to model albedo and C storage and uptake by forests and vegetation within human settlements. Our results indicate a 4.4 to 14% decline in forest cover and a 35 to 40% increase in developed land between 2005 and 2050, with large spatial variability. LULCC is projected to reduce rates of forest C sequestration, but our results suggest that vegetation within human settlements has the potential to offset a substantial proportion of the decline in the forest C sink and may comprise up to 35% of the terrestrial C sink by 2050. Changes in albedo and terrestrial C fluxes are expected to result in a global warming potential (GWP) of +0.13 Mg CO2-C-equivalence ha(-1)year(-1) under the baseline trajectory, which is equivalent to 17% of the projected increase in fossil fuel emissions. Changes in terrestrial C fluxes are generally the most important driver of the increase in GWP, but albedo change becomes an increasingly important component where housing densities are higher. Expansion of human settlements is the new face of LULCC and our results indicate that when quantifying the biophysical response it is essential to consider C uptake by vegetation within human settlements and the spatial variability in the influence of C fluxes and albedo on changes in GWP.

Implications of land use change on the national terrestrial carbon budget of Georgia.

BACKGROUND: Globally, the loss of forests now contributes almost 20% of carbon dioxide emissions to the atmosphere. There is an immediate need to reduce the current rates of forest loss, and the associated release of carbon dioxide, but for many areas of the world these rates are largely unknown. The Soviet Union contained a substantial part of the world's forests and the fate of those forests and their effect on carbon dynamics remain unknown for many areas of the former Eastern Bloc. For Georgia, the political and economic transitions following independence in 1991 have been dramatic. In this paper we quantify rates of land use changes and their effect on the terrestrial carbon budget for Georgia. A carbon book-keeping model traces changes in carbon stocks using historical and current rates of land use change. Landsat satellite images acquired circa 1990 and 2000 were analyzed to detect changes in forest cover since 1990. RESULTS: The remote sensing analysis showed that a modest forest loss occurred, with approximately 0.8% of the forest cover having disappeared after 1990. Nevertheless, growth of Georgian forests still contribute a current national sink of about 0.3 Tg of carbon per year, which corresponds to 31% of the country anthropogenic carbon emissions. CONCLUSIONS: We assume that the observed forest loss is mainly a result of illegal logging, but we have not found any evidence of large-scale clear-cutting. Instead local harvesting of timber for household use is likely to be the underlying driver of the observed logging. The Georgian forests are a currently a carbon sink and will remain as such until about 2040 if the current rate of deforestation persists. Forest protection efforts, combined with economic growth, are essential for reducing the rate of deforestation and protecting the carbon sink provided by Georgian forests.