A Landsat composite covering all Amazonia for applications in ecology and conservation

Studies at small spatial extents have shown that local floristic and edaphic patterns within the hyper-diverse Amazonian forests can be identified at a high thematic resolution using Landsat imagery. This suggests that Landsat images have the potential to indicate ecologically relevant environmental and biotic variation in the forests also at the extent of the entire basin. However, the full potential of Landsat data for these purposes has not yet been exploited in ecological and biodiversity research or in conservation applications. This is largely because the artifactual noise that is introduced by atmospheric and directional effects into multi-scene composite images can swamp the subtle spectral differences between different types of primary forest. Here, we present a new Landsat TM/ETM+ image composite for the entire Amazon biome that largely overcomes these problems. It is based on more than 16000 individual image acquisitions from the 10-year period 2000-2009. The images were individually processed to directionally and topographically normalized surface reflectance and combined into 2.5 degree tiles using the medoid compositing criterion. Visual inspection showed that the resulting image composite is radiometrically clearly more consistent than other currently available Landsat composites. We tested the ecological relevance of the new Landsat composite by comparing its reflectance values with edaphic properties measured in more than 300 field sampling localities spread across 2000km of Amazonia. We found a strong correlation between observed and predicted concentration of exchangeable base cations in the surface soil, which indicates that the compositing approach has succeeded in removing most of the artifactual noise. The Landsat composite image should be of great value for a multitude of applications in ecology, biodiversity research and conservation planning that require environmental data layers combining detailed spatial resolution, basin-wide coverage and high radiometric accuracy

Impact of spatial configuration of training data on the performance of Amazonian tree species distribution models

Remote sensing can provide useful explanatory variables for tree species distribution modeling, but only a few studies have explored this potential in Amazonia at local scales. Particularly for tropical forest management it would be useful to be able to predict the potential distribution of important tree taxa in areas where field data is as yet missing. Forest concessions produce valuable census data that cover large areas with high sampling effort and can be used as occurrence data in species distribution models (SDM). Nevertheless, these tree records are often spatially clumped and possibly only provide accurate predictions over areas close to where the training occurrence records are located. Here, we aim at investigating to what degree SDM performance and spatial predictions differ between models that have different spatial configurations of the occurrence data. For this, we divided the available occurrence data from a forest concession census in Peruvian Amazonia into different spatial configurations (narrow, elongated and compact), each of which contained approximately 20% of the full dataset. We then modelled the distributions of five tree taxa using Landsat data and elevation. More elongated configurations of the training data were more representative of the available environmental space, and also produced more robust SDMs. Average model performance (expressed as AUC) was 5% higher and variation in model performance 50% lower when elongated rather than compact configurations of training area were used. This confirms that covering only a small fraction of the environmental variability in the area of interest may lead to misleading SDM predictions, which needs to be taken into account when forest management decisions are based on SDMs.</p

Dating flowering cycles of Amazonian bamboo-dominated forests by supervised Landsat time series segmentation

Bamboo-dominated forests are unusual and interesting because their structure and biomass fluctuate in decades-long cycles corresponding to the flowering and mortality rhythm of the bamboo. In southwestern Amazonia, these forests have been estimated to occupy an area of approximately 160 000 km(2), and a single reproductively synchronized patch can cover up to thousands of square kilometers. Accurate mapping of these forests is challenging, however: the forests are spatially heterogeneous, with bamboo densities varying widely among adjacent sites; much of the area is inaccessible, so field verification of bamboo presence is difficult to obtain and georeferenced records of past flowering events virtually non-existent; and detectability of the bamboo by remote sensing varies considerably during its life cycle. In this study, we develop a supervised time series segmentation approach that allows us to identify both the presence of bamboo forests and the years in which the bamboo flowering and subsequent mortality have occurred. We then apply the method to the entire Landsat TM/ETM+ archive from 1984 to the end of 2018 and validate the classification by visual interpretation of very high resolution imagery. Collecting accurate ground reference data of bamboo presence and bamboo mortality timing is notably difficult in these forests, and we therefore developed a methodology that takes advantage of imperfect reference data obtained from the Landsat time series itself. Our results show that bamboo forests can be differentiated from non-bamboo forests using any of the infrared bands, but band 5 produces the highest classification accuracy. Interestingly, there appears to be a temporal difference in the spectral responses of the three infrared bands to bamboo flowering and mortality: near infrared (band 4) reflectance reacts to the event earlier than shortwave infrared (bands 5 and 7) reflectance. The long Landsat TM/ETM+ archive allows our methodology to detect some areas with two mortality events, with a theoretical maximum interval of 29 years. Analysis of these pixels with repeated mortality confirms that the life cycles of the local bamboo species (Guadua sarcocarpa and G. weberbauerii) last typically 28 years

Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation

Impacts of a large hydroelectric dam on the Madeira River (Brazil) on floodplain avifauna

Hydroelectric dams represent an important threat to seasonally flooded environments in the Amazon basin. We aimed to evaluate how a dam in the Madeira River, one of the largest tributaries of the Amazonas River, affected floodplain avifauna. Bird occurrence was recorded through simultaneous passive acoustic monitoring in early successional vegetation and floodplain forest downstream from the dam and upstream in sites impacted by permanent flooding after dam reservoir filling. Species were identified through manual inspection and semi-automated classification of the recordings. To assess the similarity in vegetation between downstream and upstream sites, we used Landsat TM/ETM+ composite images from before (2009-2011) and after (2016-2018) reservoir filling. Downstream and upstream floodplain forest sites were similar before, but not after dam construction. Early successional vegetation sites were already different before dam construction. We recorded 195 bird species. While species richness did not differ between upstream and downstream sites, species composition differed significantly. Ten species were indicators of early successional vegetation upstream, and four downstream. Ten species were indicators of floodplain forest upstream, and 31 downstream. Seven of 24 floodplain specialist species were detected by the semi-automated classification only upstream. While we found some bird species characteristic of early successional vegetation in the upstream sites, we did not find most species characteristic of tall floodplain forest. Predominantly carnivorous, insectivorous, and nectarivorous species appear to have been replaced by generalist and widely distributed species.</p

Revealing floristic variation and map uncertainties for different plant groups in western Amazonia

Questions: Understanding spatial variation in floristic composition is crucial to quantify the extent, patchiness and connectivity of distinct habitats and their spatial relationships. Broad-scale variation in floristic composition and the degree of uniqueness of different regions remains poorly mapped and understood in several areas across the globe. We here aim to map vegetation heterogeneity in Amazonia. Location Middle Jurua river region, Amazonas State, Brazil.Methods: We mapped four plant groups by applying machine learning to scale up locally observed community composition and using environmental and remotely sensed variables as predictors, which were obtained as GIS layers. To quantify how reliable our predictions were, we made an assessment of model transferability and spatial applicability. We also compared our floristic composition map to the official Brazilian national-level vegetation classification.Results: The overall performance of our floristic models was high for all four plant groups, especially ferns, and the predictions were found to be spatially congruent and highly transferable in space. For some areas, the models were assessed not to be applicable, as the field sampling did not cover the spectral or environmental characteristics of those regions. Our maps show extensive habitat heterogeneity across the region. When compared to the Brazilian vegetation classification, floristic composition was relatively homogeneous within dense forests, while floristic heterogeneity in rainforests classified as open was high. Conclusion: Our maps provide geoecological characterization of the regions and can be used to test biogeographical hypotheses, develop species distribution models and, ultimately, aid science-based conservation and land-use planning.</p

Measurement of differential cross sections for top quark pair production using the lepton plus jets final state in proton-proton collisions at 13 TeV

National Science Foundation (U.S.

Particle-flow reconstruction and global event description with the CMS detector

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions