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.

Carbon loss and removal due to forest disturbance and regeneration in the Amazon.

The forest carbon flux is the difference between the total carbon loss from deforestation, forest degradation, and natural disturbance and removal of atmospheric CO2 due to photosynthetic activity. The Amazon rainforest accounts for approximately a quarter of global emissions from land use change, due in part to its' immense size, carbon storage, and recent history of land use change. Large area estimates of carbon exchange in forests are highly uncertain, however, which reflects the pervasive challenges in estimating carbon flux parameters, such as disturbance area and forest carbon pools. In this study, we use a new dataset with characterized uncertainty on deforestation, degradation, and natural disturbances in the Amazon Ecoregion to estimate carbon loss from disturbance and removals from regeneration at biennial intervals from 1996 to 2017. Using the gain-loss approach to estimating carbon flux in a Monte Carlo analysis we found that carbon loss from degradation and deforestation averaged 0.23 (±0.09) Pg C biennium-1 and 0.34 (±0.16) Pg C biennium-1, respectively. While deforestation contributed the most to carbon loss overall, there were two biennial periods in which degradation and natural disturbance resulted in more carbon loss. Regeneration partially offset these emissions, but our results show that loss is occurring much more rapidly than removal, resulting in a total net carbon loss of 4.86 to 5.32 Pg C over the study period. With the compounding effect of drought and fires in addition to continued deforestation it appears certain that forest disturbance in the Amazon will continue to be a significant factor in the terrestrial carbon cycle.

Ongoing forest disturbance in Guatemala's protected areas.

Protected areas in Guatemala provide habitat for diverse tropical ecosystems, contain ancient archeological sites, sequester carbon, and support economic activity through eco-tourism. However, many of the forests in these protected areas have been converted to other uses or degraded by human activity, and therefore are considered "paper parks". In this study, we analyzed time series of satellite data to monitor deforestation, degradation, and natural disturbance throughout Guatemala from 2000 to 2017. A recently developed methodology, Continuous Degradation Detection (CODED), was used to detect forest disturbances of varying size and magnitude. Through sample-based statistical inference, we estimated that 854 137 ha (± 83 133 ha) were deforested and 1 012 947 ha (±139 512 ha) of forest was disturbed but not converted during our study period. Forest disturbance in protected areas ranged from under 1% of a park's area to over 95%. Our estimate of the extent of deforestation is similar to previous studies, however, degradation and natural disturbance affect a larger area. These results suggest that the total amount of forest disturbance can be significantly underestimated if degradation and natural disturbance are not taken into account. As a consequence, we found that the protected areas of Guatemala are more affected by disturbance than previously realized.

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.

Extensive land cover change across Arctic-Boreal Northwestern North America from disturbance and climate forcing.

A multitude of disturbance agents, such as wildfires, land use, and climate-driven expansion of woody shrubs, is transforming the distribution of plant functional types across Arctic-Boreal ecosystems, which has significant implications for interactions and feedbacks between terrestrial ecosystems and climate in the northern high-latitude. However, because the spatial resolution of existing land cover datasets is too coarse, large-scale land cover changes in the Arctic-Boreal region (ABR) have been poorly characterized. Here, we use 31 years (1984-2014) of moderate spatial resolution (30 m) satellite imagery over a region spanning 4.7 × 106  km2 in Alaska and northwestern Canada to characterize regional-scale ABR land cover changes. We find that 13.6 ± 1.3% of the domain has changed, primarily via two major modes of transformation: (a) simultaneous disturbance-driven decreases in Evergreen Forest area (-14.7 ± 3.0% relative to 1984) and increases in Deciduous Forest area (+14.8 ± 5.2%) in the Boreal biome; and (b) climate-driven expansion of Herbaceous and Shrub vegetation (+7.4 ± 2.0%) in the Arctic biome. By using time series of 30 m imagery, we characterize dynamics in forest and shrub cover occurring at relatively short spatial scales (hundreds of meters) due to fires, harvest, and climate-induced growth that are not observable in coarse spatial resolution (e.g., 500 m or greater pixel size) imagery. Wildfires caused most of Evergreen Forest Loss and Evergreen Forest Gain and substantial areas of Deciduous Forest Gain. Extensive shifts in the distribution of plant functional types at multiple spatial scales are consistent with observations of increased atmospheric CO2 seasonality and ecosystem productivity at northern high-latitudes and signal continental-scale shifts in the structure and function of northern high-latitude ecosystems in response to climate change.

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.