Sensitivity of boreal forest regional water flux and net primary production simulations to sub-grid-scale land cover complexity

We use a general ecosystem process model (BIOME-BGC) coupled with remote sensing information to evaluate the sensitivity of boreal forest regional evapotranspiration (ET) and net primary production (NPP) to land cover spatial scale. Simulations were conducted over a 3 year period (1994–1996) at spatial scales ranging from 30 to 50 km within the BOREAS southern modeling subarea. Simulated fluxes were spatially complex, ranging from 0.1 to 3.9 Mg C ha−1 yr−1 and from 18 to 29 cm yr−1. Biomass and leaf area index heterogeneity predominantly controlled this complexity, while biophysical differences between deciduous and coniferous vegetation were of secondary importance. Spatial aggregation of land cover characteristics resulted in mean monthly NPP estimation bias from 25 to 48% (0.11–0.20 g C m−2 d−1) and annual estimation errors from 2 to 14% (0.04–0.31 Mg C ha−1 yr−1). Error was reduced at longer time intervals because coarse scale overestimation errors during spring were partially offset by underestimation of fine scale results during summer and winter. ET was relatively insensitive to land cover spatial scale with an average bias of less than 5% (0.04 kg m−2 d−1). Factors responsible for differences in scaling behavior between ET and NPP included compensating errors for ET calculations and boreal forest spatial and temporal NPP complexity. Careful consideration of landscape spatial and temporal heterogeneity is necessary to identify and mitigate potential error sources when using plot scale information to understand regional scale patterns. Remote sensing data integrated within an ecological process model framework provides an efficient mechanism to evaluate scaling behavior, interpret patterns in coarse resolution data, and identify appropriate scales of operation for various processes

Epidemiological aspects of cutaneous leishmaniasis in Iran

Background: The prevalence of Cutaneous Leishmaniasis (CL) has been reported as 1.8 to 37.9 in different provinces of Iran. However, enough knowledge about epidemiological aspects of CL disease is needed to launch a proper program to plan control and preventive strategies about the disease. Objectives: The present study aimed to determine the epidemiological aspect of CL in Iran during the first 6 months of 2014. Patients and Methods: In this cross-sectional study, all cases of CL reported to centers for disease control and prevention (CDC) by state health departments from March 2014 to September 2014 were included. Descriptive statistics including frequency tables, measures of central value, and measures of dispersion to describe the study variables were used to analyze data. Area maps were created using ArcView GIS v. 3.3. Results: Most of the CL cases were observed in eastern, central, and southern provinces. Two thousand thirty-one cases (55.13) were male and 2,306 (62.6) were living in urban areas. The mean age of the patients was 27 ± 18 years old. More than 31 of them were under 14 years of age. Also, 3570 individuals (96.91) were new cases. more wounds were seen so that 62.75 of the wounds were on the hands, 24.8 in the head and neck, and 2.7 in the body. Conclusions: According to the epidemiological features of CL in Iran, Providing a uniform mechanism for control and prevention of this disease is not possible. Thus, initial actions such as staff training, screening in endemic areas, and treatment of patients with urban leishmaniasis as a reservoir for the disease can be useful, according to the geographical position and carrier. © 2015, Infectious Diseases and Tropical Medicine Research Center

Abiotic controls on macroscale variations of humid tropical forest height

Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004–2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics.The research was funded by Gabon National Park (ANPN) under the contract of 011-ANPN/2012/SE-LJTW at UCLA. We thank IIASA, FAO, USGS, NASA, Worldclim science teams for making their data available. (011-ANPN/2012/SE-LJTW - Gabon National Park (ANPN) at UCLA

Trends in high northern latitude soil freeze and thaw cycles from 1988 to 2002

In boreal and tundra ecosystems the freeze state of soils limits rates of photosynthesis and respiration. Here we develop a technique to identify the timing of freeze and thaw transitions of high northern latitude land areas using satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I). Our results indicate that in Eurasia there was a trend toward earlier thaw dates in tundra (−3.3 ± 1.8 days/decade) and larch biomes (−4.5 ± 1.8 days/decade) over the period 1988–2002. In North America there was a trend toward later freeze dates in evergreen conifer forests by 3.1 ± 1.2 days/decade that led, in part, to a lengthening of the growing season by 5.1 ± 2.9 days/decade. The growing season length in North American tundra increased by 5.4 ± 3.1 days/decade. Despite the trend toward earlier thaw dates in Eurasian larch forests, the growing season length did not increase because of parallel changes in timing of the fall freeze (−5.4 ± 2.1 days/decade), which led to a forward shift of the growing season. Thaw timing was negatively correlated with surface air temperatures in the spring, whereas freeze timing was positively correlated with surface air temperatures in the fall, suggesting that surface air temperature is one of several factors that determines the timing of soil thaw and freeze. The high spatial resolution, frequent temporal coverage, and duration of the SMMR and SSM/I satellite records makes them suitable for rigorous time series analysis and change detection in northern terrestrial ecosystems

Parametric and non-parametric forest biomass estimation from PolInSAR data

Biomass estimation performance from model-based polarimetric SAR interferometry (PolInSAR) using generic parametric and non-parametric regression methods is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data is decomposed into ground and volume contributions, estimating vertical forest structure, and using a set of obtained parameters for biomass regression. The considered estimation methods include multiple linear regression, support vector machine and random forest. The biomass estimation performance is evaluated on DLR's airborne SAR data at L- and P-bands over Krycklan Catchment, a boreal forest test site in Northern Sweden. The combination of polarimetric indicators and estimated structure information has improved the root mean square error (RMSE) of biomass estimation up to 28% at L-band and up to 46% at P-band. The cross-validated biomass RMSE was reduced to 20 tons/ha

Synergism of optical and radar data for forest structure and biomassSinergismo entre dados ópticos e de radar da estrutura da floresta e biomassa

AbstractThe structure of forests, the three-dimensional arrangement of individual trees, has a profound effect on how ecosystems function and carbon cycle, water and nutrients. Repeated optical satellite observations of vegetation patterns in two-dimensions have made significant contributions to our understanding of the state and dynamics of the global biosphere. Recent advances in Remote Sensing technology allow us to view the biosphere in three-dimensions and provide us with refined measurements of horizontal as well as vertical structure of forests. This paper provides an overview of the recent advances in fusion of optical and radar imagery in assessing terrestrial ecosystem structure and aboveground biomass. In particular, the paper will focus on radar and LIDAR sensors from recent and planned spaceborne missions and provide theoretical and practical applications of the measurements. Finally, the relevance of these measurements for reducing the uncertainties of terrestrial carbon cycle and the response of ecosystems to future climate will be discussed in details. ResumoA estrutura de florestas, o arranjo tridimensional de árvores individuais, tem um efeito profundo sobre o funcionamento dos ecossistemas e do ciclo do carbono, água e nutrientes. Repetidas observações de satélite óptico de padrões de vegetação em duas dimensões trouxeram contribuições significativas para a nossa compreensão do estado e da dinâmica da biosfera global. Recentes avanços na tecnologia de Sensoriamento Remoto nos permitem ver a biosfera em três dimensões e nos fornecer medições apuradas da estrutura horizontal, bem como a vertical das florestas. Esse artigo fornece uma visão geral dos recentes avanços na fusão de imagens ópticas e de radar para avaliar a estrutura do ecossistema terrestre e biomassa. Em particular, o trabalho concentra-se em sensores radar e LIDAR de recentes missões espaciais planejadas e fornece aplicações teóricas e praticas das medições. Por fim, a relevância dessas medidas para reduzir as incertezas do ciclo de carbono terrestre e de resposta dos ecossistemas ao clima no futuro será discutida em detalhes

Post-drought decline of the Amazon carbon sink

Amazon forests have experienced frequent and severe droughts in the past two decades. However, little is known about the large-scale legacy of droughts on carbon stocks and dynamics of forests. Using systematic sampling of forest structure measured by LiDAR waveforms from 2003 to 2008, here we show a significant loss of carbon over the entire Amazon basin at a rate of 0.3 ± 0.2 (95% CI) PgC yr−1 after the 2005 mega-drought, which continued persistently over the next 3 years (2005–2008). The changes in forest structure, captured by average LiDAR forest height and converted to above ground biomass carbon density, show an average loss of 2.35 ± 1.80 MgC ha−1 a year after (2006) in the epicenter of the drought. With more frequent droughts expected in future, forests of Amazon may lose their role as a robust sink of carbon, leading to a significant positive climate feedback and exacerbating warming trends.The research was partially supported by NASA Terrestrial Ecology grant at the Jet Propulsion Laboratory, California Institute of Technology and partial funding to the UCLA Institute of Environment and Sustainability from previous National Aeronautics and Space Administration and National Science Foundation grants. The authors thank NSIDC, BYU, USGS, and NASA Land Processes Distributed Active Archive Center (LP DAAC) for making their data available. (NASA Terrestrial Ecology grant at the Jet Propulsion Laboratory, California Institute of Technology)Published versio

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr−1, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr−1) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake