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Abstract
Negative impacts of climate change on ecosystems have been increasing, and both the intensification and the mitigation of these impacts are strongly linked with human activities. Management and reduction of human-induced disturbances on ecosystems can mitigate the effects of climate change and enhance the ecosystem recovery process. Here, we investigate coupled human and climate effects on the wetland ecosystem of the lower Helmand basin from 1977 to 2014. Using time series climate-variable data and land-use changes from Landsat time series imagery, we compared changes in ecosystem status between the upstream and downstream regions. Results show that despite a strong and prolonged drought in the region, the upstream region of the lower Helmand basin remained dominated by agriculture, causing severe water stress on the Hamoun wetlands downstream. The loss of available water in wetlands was followed by large-scale land abandonment in rural areas, migration to the cities, and increasing unemployment and economic hardship. Our results suggest that unsustainable land-use policies in the upstream region, combined with synergistic effects of human activities and climate in lower Helmand basin, have exacerbated the effects of water stress on local inhabitants in the downstream region.

Mapping forest aboveground biomass (AGB) using satellite data is an important task, particularly for reporting of carbon stocks and changes under climate change legislation. It is known that AGB can be mapped using synthetic aperture radar (SAR), but relationships between AGB and radar backscatter may be confounded by variations in biophysical forest structure (density, height or cover fraction) and differences in the resolution of satellite and ground data. Here, we attempt to quantify the effect of these factors by relating L-band ALOS PALSAR HV backscatter and unique country-wide LiDAR-derived maps of vegetation penetrability, height and AGB over Denmark at different spatial scales (50 m to 500 m). Trends in the relations indicate that, first, AGB retrieval accuracy from SAR improves most in mapping at 100-m scale instead of 50 m, and improvements are negligible beyond 250 m. Relative errors (bias and root mean squared error) decrease particularly for high AGB values (>110 Mg ha -1 ) at coarse scales, and hence, coarse-scale mapping (≥150 m) may be most suited for areas with high AGB. Second, SAR backscatter and a LiDAR-derived measure of fractional forest cover were found to have a strong linear relation (R 2 = 0.79 at 250-m scale). In areas of high fractional forest cover, there is a slight decline in backscatter as AGB increases, indicating signal attenuation. The two results demonstrate that accounting for spatial scale and variations in forest structure, such as cover fraction, will greatly benefit establishing adequate plot-sizes for SAR calibration and the accuracy of derived AGB maps.

The forests of Kalimantan are under severe pressure from extensive land use activities dominated by logging, palm oil plantations, and peatland fires. To implement the forest moratorium for mitigating greenhouse gas emissions, Indonesia's government requires information on the carbon stored in forests, including intact, degraded, secondary, and peat swamp forests. We developed a hybrid approach of producing a wall-to-wall map of the aboveground biomass (AGB) of intact and degraded forests of Kalimantan at 1 ha grid cells by combining field inventory plots, airborne lidar samples, and satellite radar and optical imagery. More than 110 000 ha of lidar data were acquired to systematically capture variations of forest structure and more than 104 field plots to develop lidar-biomass models. The lidar measurements were converted into biomass using models developed for 66 439 ha of drylands and 44 250 ha of wetland forests. By combining the AGB map with the national land cover map, we found that 22.3 Mha (106 ha) of forest remain on drylands ranging in biomass from 357.2 ± 12.3 Mgha−1 in relatively intact forests to 134.2 ± 6.1 Mgha−1 in severely degraded forests. The remaining peat swamp forests are heterogeneous in coverage and degradation level, extending over 3.62 Mha and having an average AGB of 211.8 ± 12.7 Mgha−1. Emission factors calculated from aboveground biomass only suggest that the carbon storage potential of more than 15 Mha of degraded and secondary dryland forests will be about 1.1 PgC.

National forest inventories in tropical regions are sparse and have large uncertainty in capturing the physiographical variations of forest carbon across landscapes. Here, we produce for the first time the spatial patterns of carbon stored in forests of Democratic Republic of Congo (DRC) by using airborne LiDAR inventory of more than 432,000 ha of forests based on a designed probability sampling methodology. The LiDAR mean top canopy height measurements were trained to develop an unbiased carbon estimator by using 92 1-ha ground plots distributed across key forest types in DRC. LiDAR samples provided estimates of mean and uncertainty of aboveground carbon density at provincial scales and were combined with optical and radar satellite imagery in a machine learning algorithm to map forest height and carbon density over the entire country. By using the forest definition of DRC, we found a total of 23.3 ± 1.6 GtC carbon with a mean carbon density of 140 ± 9 MgC ha-1 in the aboveground and belowground live trees. The probability based LiDAR samples capture variations of structure and carbon across edaphic and climate conditions, and provide an alternative approach to national ground inventory for efficient and precise assessment of forest carbon resources for emission reduction (ER) programs. ; SCOPUS: ar.j ; info:eu-repo/semantics/published

Live woody vegetation is the largest reservoir of biomass carbon, with its restoration considered one of the most effective natural climate solutions. However, terrestrial carbon fluxes remain the largest uncertainty in the global carbon cycle. Here, we develop spatially explicit estimates of carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show that live biomass has removed 4.9 to 5.5 PgC year −1 from the atmosphere, offsetting 4.6 ± 0.1 PgC year −1 of gross emissions from disturbances and adding substantially (0.23 to 0.88 PgC year −1 ) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink ; This study was funded by NASA Interdisciplinary Science Program (NNH16ZDA001N-IDS). M.L. and Y. Yang have been supported by the NASA Postdoctoral Program, administered by Universities Space Research Association under contract with NASA.G.-J.N. was supported by the European Union H2020-VERIFY project (776810).

Although the incidence of human monkeypox has greatly increased in Central Africa over the last decade, resources for surveillance remain extremely limited. We conducted a geospatial analysis using existing data to better inform future surveillance efforts. Using active surveillance data collected between 2005 and 2007, we identified locations in Sankuru district, Democratic Republic of Congo (DRC) where there have been one or more cases of human monkeypox. To assess what taxa constitute the main reservoirs of monkeypox, we tested whether human cases were associated with (i) rope squirrels (Funisciurus sp.), which were implicated in monkeypox outbreaks elsewhere in the DRC in the 1980s, or (ii) terrestrial rodents in the genera Cricetomys and Graphiurus, which are believed to be monkeypox reservoirs in West Africa. Results suggest that the best predictors of human monkeypox cases are proximity to dense forests and associated habitat preferred by rope squirrels. The risk of contracting monkeypox is significantly greater near sites predicted to be habitable for squirrels (OR=1.32; 95% CI 1.08-1.63). We recommend that semi-deciduous rainforests with oil-palm, the rope squirrel's main food source, be prioritized for monitoring.

Although the incidence of human monkeypox has greatly increased in Central Africa over the last decade, resources for surveillance remain extremely limited. We conducted a geospatial analysis using existing data to better inform future surveillance efforts. Using active surveillance data collected between 2005 and 2007, we identified locations in Sankuru district, Democratic Republic of Congo (DRC) where there have been one or more cases of human monkeypox. To assess what taxa constitute the main reservoirs of monkeypox, we tested whether human cases were associated with (i) rope squirrels (Funisciurus sp.), which were implicated in monkeypox outbreaks elsewhere in the DRC in the 1980s, or (ii) terrestrial rodents in the genera Cricetomys and Graphiurus, which are believed to be monkeypox reservoirs in West Africa. Results suggest that the best predictors of human monkeypox cases are proximity to dense forests and associated habitat preferred by rope squirrels. The risk of contracting monkeypox is significantly greater near sites predicted to be habitable for squirrels (OR = 1.32; 95% CI 1.08–1.63). We recommend that semi-deciduous rainforests with oil-palm, the rope squirrel's main food source, be prioritized for monitoring.