Environmental risk in Northeast Brazil: estimation of burning areas in Coreaú River Basin, Ceará, Brazil.

This work aims to estimate the burned areas in the hydrographic basin of the Coreaú River, State of Ceará, north of Northeast Brazil, which has an area of 10,633.67 km2, through the NOAA/AVHRR satellite, between the years from 2010 and 2017. The data were acquired at the base of INPE, where they were tabulated and generated a vector file of points. A density map of the fire sources was elaborated, from which the burned areas were estimated in the watershed studied over the defined period of years. There were 1786 fire outbreaks, totaling an estimated accumulated area of 1187.66 km2 of fires, which corresponds to 11.17% of the entire length of the hydrographic basin. The municipality of Mucambo presented a ratio of 40% of its territory comprised by the mapped fires. In relation to the conservation units, they mapped 795 hot spots in their perimeters.

An integrated pan-tropical biomass map using multiple reference datasets.

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.

Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites.

AIM: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. LOCATION: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1. METHODS: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. RESULTS: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%. MAIN CONCLUSIONS: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery.

This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. This dataset was produced through a citizen science project called "Power to the People", which mapped rural homes for electrical infrastructure planning and computer-vision-based mapping. Additional details on this work are presented in "Power to the People: Applying citizen science to home-level mapping for rural energy access" [1]. 578,010 home annotations were made on approximately 1,267 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health.

Recent advances in satellite mapping of global air quality: evidences during COVID-19 pandemic.

There was a significant decline in air pollution in different parts of the world due to enforcement of lockdown by many countries to check the spread of the coronavirus (COVID-19) pandemic. In particular, commercial and industrial activities had been limited globally with restricted air and surface traffic movements in response to social distancing and isolation. Both satellite remote sensing and ground-based monitoring were used to measure the change in the air quality. There was momentous decline in the averaged concentrations of nitrogen dioxide (NO2), carbon dioxide (CO2), sulphur dioxide (SO2), methane (CH4) and aerosols. Many cities across India, China and several major cities in Europe observed strong reductions in nitrogen dioxide levels dropping by around 40-50% owing to lockdowns. Similarly, concentrations of SO2 in polluted areas in India, especially around large coal-fired power plants and industrial areas decreased by around 40% as evidenced by the comparative satellite mapping during April 2019 and April 2020. Recent advances in sensors on board various satellites played a significant role in real-time monitoring of emission regimes over various parts of the world. The satellite data is relying upon single scene profusion for real-time air quality measurements, and also using averaged dataset over certain time-period. The daily global-scale remote sensing data of NO2, as measured through the Copernicus Sentinel-5 Precursor Tropospheric Monitoring Instrument (S5p/TROPOMI) of European Space Agency (ESA), indicated exceptional decreases in tropospheric NO2 pollution in urban areas. Similarly, Greenhouse gases Observing Satellite (GOSAT) of Japan Aerospace Exploration Agency, with a repeat cycle of three days helped in assessing the sources and sinks of CO2 and CH4 on a sub-continental scale. Supplementary Information: The online version contains supplementary material available at 10.1007/s42398-021-00166-w.

Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013-2017.

Satellite-based mapping has been proven to be an effective method to reveal the spatiotemporal variations of PM2.5 distributions. However, most satellite AOD (aerosol optical depth) statistical models suffer from unstable accuracy over long time spans. This study thus aims to propose an accurate and stable method for PM2.5 concentration estimations in time series. Specifically, a three-step residual variance constraint method (RVCM) is developed to simulate PM2.5 concentrations from January 2013 to December 2017 with the aid of AODs and other auxiliary data. Results show that the five-year fitting R2 and cross-validation R2 of RVCMs improved from 0.77 to 0.88 and 0.71 to 0.84, respectively, compared to those models without residual variance constraint (WO-RVCM). Additionally, RVCM demonstrated more stable performance on time series simulation of PM2.5 concentrations than WO-RVCM, with the yearly fitting R2 of 0.89, 0.88, 0.85, 0.87 and 0.88, and corresponding cross validation R2 of 0.85, 0.84, 0.80, 0.82 and 0.83, respectively. Furthermore, accuracy verification of removed outliers in residual variance constraint modeling confirmed the credibility of RVCM in outliers' simulation compared to WO-RVCM models. Finally, RVCM-aided estimations of time series PM2.5 concentrations and associated premature deaths in the study area (east and southeast mainland China) revealed their total decrease rates were 35.21% and 21.57%, and excellent air quality days increased from 7% to 35%. These findings suggest that residual variance constraint is effective and could be a reliable solution to providing time series AOD-PM2.5 modeling with stable accuracy over long time spans.