Long-Term Annual Surface Water Change in the Brazilian Amazon Biome: Potential Links with Deforestation, Infrastructure Development and Climate Change

Diversity and epidemiological profile of Pseudomonas aeruginosa from drinking water in Brazil genotyped using multi-locus sequence typing

Pseudomonas aeruginosa is a Gram-negative bacillus associated with waterborne diseases. The objective of this study was to determine whether particular P. aeruginosa sequence types (STs) were associated with drinking water contamination in Brazil. This was achieved by searching the Pseudomonas PubMLST database, which contains the records for 8358 strains collected between 1938 and 2023. The majority (97.2%) had the complete 7-loci multilocus sequence typing profile and were assigned to 3486 STs. After eBURST (an algorithm used to infer patterns of evolutionary descent among clusters), 1219 groups with single-locus variant and 575 groups with double-locus variant were formed. Brazil was the South American country with the most isolates (n = 219, 58.24%), and the Simpson's index was 0.9392. Of the 219 Brazilian isolates, eight were isolated in water and identified as STs 252, 1417, 1605, 2502, 2620, 3078, and 3312. ST252, 1417, and 3078 have already been isolated from clinical cases worldwide. Furthermore, ST1605 and 2620, after the eBURST, they were grouped in the same clonal complex as STs involved in human infections. In conclusion, P. aeruginosa STs involved in human infections were found in bottled drinking water commercialized in Brazil, revealing that these types of drinking waters can be a vehicle of contamination.

Deforestation impacts on dengue incidence in the Brazilian Amazon

The objective of the study is to perform the spatial analysis of the conditioning factors for the increase in the incidence rate of dengue cases in municipalities located in the Amazon biome, in the period from 2016 to 2021. Three statistical approaches were applied: Moran's index, ordinary least squares regression, and geographically weighted regression. The results revealed that the incidence rates of dengue cases cluster in two areas, both located in the south of the Amazon biome, which is associated with the Arc of Deforestation. The variable deforestation influences the increase in dengue incidence rates revealed by the OLS and GWR model. The adjusted R² of the GWR model was 0.70, that is, the model explains about 70% of the total case variation of dengue incidence rates in the Amazon biome. The results of the study evidence the need for public policies aimed at the prevention and combat of deforestation in the Amazon region.

Mapping Three Decades of Changes in the Tropical Andean Glaciers Using Landsat Data Processed in the Earth Engine

The fast retreat of the tropical Andean glaciers (TAGs) is considered an important indicator of climate change impact on the tropics, since the TAGs provide resources to highly vulnerable mountain populations. This study aims to reconstruct the glacier coverage of the TAGs, using Landsat time-series images from 1985 to 2020, by digitally processing and classifying satellite images in the Google Earth Engine platform. We used annual reductions of the Normalized Difference Snow Index (NDSI) and spectral bands to capture the pixels with minimum snow cover. We also implemented temporal and spatial filters to have comparable maps at a multitemporal level and reduce noise and temporal inconsistencies. The results of the multitemporal analysis of this study confirm the recent and dramatic recession of the TAGs in the last three decades, in base to physical and statistical significance. The TAGs reduced from 2429.38 km2 to 1409.11 km2 between 1990 and 2020, representing a loss of 42% of the total glacier area. In addition, the time-series analysis showed more significant losses at altitudes below 5000 masl, and differentiated changes by slope, latitude, and longitude. We found a more significant percentage loss of glacier areas in countries with less coverage. The multiannual validation showed accuracy values of 92.81%, 96.32%, 90.32%, 97.56%, and 88.54% for the metrics F1 score, accuracy, kappa, precision, and recall, respectively. The results are an essential contribution to understanding the TAGs and guiding policies to mitigate climate change and the potential negative impact of freshwater shortage on the inhabitants and food production in the Andean region.

A new species of Proceratophrys Miranda-Ribeiro, 1920 (Anura, Odontophrynidae) from Southern Amazonia, Brazil

Based on concordant differences in morphology, male advertisement call, and 16S mtDNA barcode distance, we describe a new species of Proceratophrys from southern Amazonia, in the states of Mato Grosso and Pará, Brazil. The new species is most similar to P. concavitympanum and P. ararype but differs from these species by its proportionally larger eyes and features of the advertisement call. Additionally, genetic distance between the new species and its congeners is 3.0–10.4% based on a fragment of the 16S rRNA gene, which is greater than the threshold typically characterizing distinct species of anurans. Using an integrative approach (molecular, bioacoustics, and adult morphology), we were able to distinguish the new species from other congeneric species. The new species is known only from the type locality where it is threatened by illegal logging and gold mining as well as hydroelectric dams.

Mapping and Monitoring the Multi-Decadal Dynamics of Australia’s Open Waterbodies Using Landsat

Water detection algorithms are now being routinely applied to continental and global archives of satellite imagery. However, water resource management decisions typically take place at the waterbody rather than pixel scale. Here, we present a workflow for generating polygons of persistent waterbodies from Landsat observations, enabling improved monitoring and management of water assets across Australia. We use Digital Earth Australia’s (DEA) Water Observations from Space (WOfS) product, which provides a water classified output for every available Landsat scene, to determine the spatial locations and extents of waterbodies across Australia. We generated a polygon set of waterbodies that identified 295,906 waterbodies ranging in size from 3125 m2 to 4820 km2. Each polygon was used to generate a time series of WOfS, providing a history of the change in surface area of each waterbody every ~16 days since 1987. We demonstrate the applications of this new dataset, DEA Waterbodies, to understanding local through to national-scale surface water spatio-temporal dynamics. DEA Waterbodies provides new insights into Australia’s water availability and enables the monitoring of important landscape features such as lakes and dams, improving our ability to use earth observation data to make meaningful decisions.

Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

Irrigation systems play an important role in agriculture. Center pivot irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of center pivot irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate center pivot irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected, the area of irrigation in the region was estimated.

Structure and Composition of Terra Firme and Seasonally Flooded Várzea Forests in the Western Brazilian Amazon

Research Highlights: Rare, or sparsely distributed, species drive the floristic diversity of upland, terra firme and seasonally flooded forests in the central Juruá—a remote and hitherto floristically poorly known area in the Brazilian Amazon. Background and Objectives: Floristic inventories are critical for modelling and understanding the role of Amazonian forests in climate regulation, for sustainable management of forest resources and efficient conservation planning. Yet, detailed information about the often complex spatial distributions of many Amazonian woody plants is limited. Here, we provide information about forest structure and species composition from a remote terra firme forest and an adjacent floodplain forest in the western Brazilian Amazon. More specifically, we ask (1) how floristically different are the terra firme and floodplain forests? and (2) how variable is species composition within the same forest type? Materials and Methods: Between September 2016 and October 2017, we inventoried 97 plots (each 0.1 ha; 100 × 10 m) placed at least 800 m apart, with 46 plots in terra firme forest and 51 in seasonally flooded forest. We included all trees, hemi-epiphytes and palms with diameter at breast height (dbh) > 10 cm and woody lianas > 5 cm dbh. We examine forest structure, family- and species-level floristic composition and species diversity within and between forest types using family and species importance values, rarefaction curves and dissimilarity matrices. Results: Terra firme forest and seasonally flooded forest woody plant communities differ both in structure and species composition, which was highly variable within forest types. Many species were shared between terra firme and seasonally flooded forests, but most species were forest type-specific. Whereas species richness was greatest in the terra firme forest, floodplain species richness was among the highest regionally. Conclusions: Floodplain forests are a crucial complement to terra firme forests in terms of Amazonian woody plant diversity.

Spatial and Temporal Changes in Surface Water Area of Sri Lanka over a 30-Year Period

Sri Lanka contains a large number of natural and man-made water bodies, which play an essential role in irrigation and domestic use. The island has recently been identified as a global hotspot of climate change extremes. However, the extent, spatial distribution, and the impact of climate and anthropogenic activities on these water bodies have remained unknown. We investigated the distribution, spatial and temporal changes, and the impacts of climatic and anthropogenic drivers on water dynamics in Dry, Intermediate, and Wet zones of the island. We used Landsat 5 and Landsat 8 images to generate per-pixel seasonal and annual water occurrence frequency maps for the period of 1988–2019. The results of the study demonstrated high inter- and intra-annual variations in water with a rapid increase. Further, results showed strong zonal differences in water dynamics, with most dramatic variations in the Dry zone. Our results revealed that 1607.73 km2 of the land area of the island is covered by water bodies, among this 882.01 km2 (54.86%) is permanent and 725.72 km2 (45.14%) is seasonal water area. Total inland seasonal water increased with a dramatic annual growth rate of 7.06 ± 1.97 km2 compared to that of permanent water (4.47 ± 2.08 km2/year). Sri Lanka has the highest permanent water area during December–February (1045.97 km2), and drops to the lowest in May–September (761.92 km2) when the seasonal water (846.46 km2) is higher than permanent water. The surface water area was positively related to both precipitation and Gross Domestic Product, while negatively related to the temperature. Findings of our study provide important insights into possible spatiotemporal changes in surface water availability in Sri Lanka under certain climate change and anthropogenic activities.

Assessing Land Use and Land Cover Changes in the Direct Influence Zone of the Braço Norte Hydropower Complex, Brazilian Amazonia

Over the decades, hydropower complexes have been built in several hydrographic basins of Brazil including the Amazon region. Therefore, it is important to understand the effects of these constructions on the environment and local communities. This work presents a land use and land cover change temporal analysis considering a 33-year period (1985–2018) in the direct influence zone of the Braço Norte Hydropower Complex, Brazilian Amazonia, using the Collection 4.1 level 3 of the freely available MapBiomas dataset. Additionally, we have assessed the Brazilian Amazon large-scale deforestation process acting as a land use and land cover change driver in the study area. Our findings show that the most impacted land cover was forest formation (from 414 km2 to 287 km2, a reduction of 69%), which primarily shifted into pasturelands (increase of 664%, from 40 km2 to 299 km2). The construction of the hydropower complex also triggered indirect impacts such as the presence of urban areas in 2018 and the consequent increased local demand for crops. Together with the ongoing large-scale Amazonian deforestation process, the construction of the complex has intensified changes in the study area as 56.42% of the pixels were changed between 1985 and 2018. This indicates the importance of accurate economic and environmental impact studies for assessing social and environmental consequences of future construction in this unique region. Our results reveal the need for adopting special policies to minimize the impact of these constructions, for example, the creation of Protected Areas and the definition of locally-adjusted parameters for the ecological-economic zoning considering environmental and social circumstances derived from the local actors that depend on the natural environment to subsist such as indigenous peoples, riverine population, and artisanal fishermen.

Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

Rapidly Accelerating Deforestation in Cambodia’s Mekong River Basin: A Comparative Analysis of Spatial Patterns and Drivers

The Mekong River is a globally important river system, known for its unique flood pulse hydrology, ecological productivity, and biodiversity. Flooded forests provide critical terrestrial nutrient inputs and habitat to support aquatic species. However, the Mekong River is under threat from anthropogenic stressors, including deforestation from land cultivation and urbanization, and dam construction that inundates forests and encourages road development. This study investigated spatio-temporal patterns of deforestation in Cambodia and portions of neighboring Laos and Vietnam that form the Srepok–Sesan–Sekong watershed. A random forest model predicted tree cover change over a 25-year period (1993–2017) using the Landsat satellite archive. Then, a statistical predictive deforestation model was developed using annual-resolution predictors such as land-cover change, hydropower development, forest fragmentation, and socio-economic, topo-edaphic and climatic predictors. The results show that almost 19% of primary forest (nearly 24,000 km2) was lost, with more deforestation in floodplain (31%) than upland (18%) areas. Our results corroborate studies showing extremely high rates of deforestation in Cambodia. Given the rapidly accelerating deforestation rates, even in protected areas and community forests, influenced by a growing population and economy and extreme poverty, our study highlights landscape features indicating an increased risk of future deforestation, supporting a spatial framework for future conservation and mitigation efforts.

Rapid Recent Deforestation Incursion in a Vulnerable Indigenous Land in the Brazilian Amazon and Fire-Driven Emissions of Fine Particulate Aerosol Pollutants

Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km2), which is clearly associated with an increase in the PM2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM2.5 was estimated to be 3594 ton year−1, while the most recent interval of 2017–2019 had an average of 6258 ton year−1. This represented an increase of 58% in the annual average of PM2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within “protected areas” in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential.

Environmental factors influencing the abundance of four species of threatened mammals in degraded habitats in the eastern Brazilian Amazon

On the latest 60 years the degradation and fragmentation of native habitats have been modifying the landscape in the eastern Brazilian Amazon. The adaptive plasticity of an organism has been crucial for its long-term survival and success in these novel ecosystems. In this study, we investigated the response of four endangered species of large terrestrial mammals to the variations in the quality of their original habitats, in a context of high anthropogenic pressure. The distribution of the Myrmecophaga tridactyla (Giant anteater), Priodontes maximus (Giant armadillo), Tapirus terrestris (Lowland tapir) and Tayassu pecari (White-lipped peccary) in all sampled habitats suggests their tolerance to degradation. However, the survival ability of each species in the different habitats was not the same. Among the four species, T. pecari seems to be the one with the least ability to survive in more altered environments. The positive influence of the anthropogenically altered habitats on abundances of three of the four species studied, as observed at the regeneration areas, can be considered as a potential indication of the ecological trap phenomenon. This study reinforces the importance of the forest remnants for the survival of endangered mammal species, in regions of high anthropogenic pressure, as in the eastern Brazilian Amazon.