Publications

In this paper, we use growing season Normalized Difference Vegetation Index (NDVI) as an indicator of plant growth to quantify the relationships between vegetation production and intra‐annual precipitation patterns for three major temperate biomes in China: grassland, deciduous broadleaf forest, and deciduous coniferous forest. With increased precipitation, NDVI of grassland and deciduous broadleaf forest increased, but that of deciduous coniferous forest decreased. More frequent precipitation significantly increased growth of grassland and deciduous broadleaf forest, but did not alter that of deciduous coniferous forest at low precipitation levels and constrained its growth at high precipitation levels. The relationships between NDVI and average precipitation per event were opposite to those between NDVI and precipitation frequency. Such nonlinear feedback suggests that the responses of vegetation production to changes in precipitation patterns differ by both biome type and precipitation amount.

No abstract is provided for this article.

Los enfoques basados en la tierra para mejorar la productividad de las zonas degradadas pueden ayudar a abordar el triple desafio de nuestro tiempo, con lo que aseguran: un clima estable, seguridad alimentaria y espacio para la naturaleza (Baldwin-Cantello, 2018). La restauracion de bosques y paisajes es un conjunto de estrategias de uso de la tierra, como la agrosilvicultura y la regeneracion natural, que se implementan para lograr los objetivos de gestion relacionados con la conservacion del suelo y la biodiversidad, el agua, la retencion del carbono, la produccion de energia y alimentos y las necesidades de la comunidad (FAO y WRI, 2019). La restauracion de bosques y paisajes puede ayudar a mitigar el cambio climatico, mejorar la salud y la conectividad del paisaje y reducir la presion que se ejerce sobre los bosques naturales mediante la produccion de materias primas como madera, dendrocombustibles y productos forestales no madereros.

Remote sensing of net primary production (NPP) is a critical tool for assessing spatial and temporal patterns of carbon exchange between the atmosphere and biosphere. However, satellite estimates suffer from a lack of large‐scale field data needed for validation, as well as the need to parameterize plant light‐use efficiencies (LUEs). In this study, we estimated cropland NPP with the Carnegie‐Ames‐Stanford‐Approach (CASA), a biogeochemical model driven by satellite observations, and then compared these results with field estimates based on harvest data from United States Department of Agriculture National Agriculture Statistics Service (NASS) county statistics. Observed interannual variations in NPP over a 17‐year period were well modelled by CASA, with exceptions mainly due to occasional difficulties in estimating NPP from harvest yields. The role of environmental stressors in agriculture was investigated by running CASA with and without temperature and moisture down‐regulators, which are used in the model to simulate climate impacts on plant LUE. In most cases, correlations with NASS data were highest with modelled stresses, while the opposite was true for irrigated and temperature resistant crops. Analysis of the spatial variability in computed LUE revealed significantly higher values for corn than for other crops, suggesting a simple parameterization of LUE for future studies based on the fraction of area with corn. Absolute values of LUE were much lower than those reported in field trials, due to uncommonly high yields in most field trials, as well as overestimates of absorbed radiation in CASA attributed to bias from temporal compositing of satellite data. Total NPP for US croplands, excluding Alaska and Hawaii, was estimated as 0.62 Pg C year −1 , representing ∼20% of total US NPP, and exhibited a positive trend of 3.7 Tg C year −2 . These results have several implications for large‐scale carbon cycle research that are discussed, and are especially relevant for studies of the role of agriculture in the global carbon balance.

Terrestrial ecosystems absorb about 2.8 Gt C yr, which is estimated to be about a quarter of the carbon emitted from fossil fuel combustion. However, the uncertainties of this sink are large, in the order of  40%, with spatial and temporal variations largely unknown. One of the largest factors contributing to the uncertainty is photosynthesis, the process by which plants absorb carbon from the atmosphere. Currently, photosynthesis, or gross ecosystem productivity (GEP), can only be inferred from flux‐towers by measuring the exchange of CO2 in the surrounding air column. Consequently, carbon models suffer from a lack of spatial coverage of accurate GEP observations. Here, we show that photosynthetic light‐ use efficiency (e), hence photosynthesis can be directly inferred from spaceborne measurements of reflectance. We demonstrate that the differential between reflectance measurements in bands associated with the vegetation xanthophyll cycle and estimates of canopy shading obtained from multi‐ angular satellite observations (using the CHRIS/Proba sensor) permits us to infer plant photosynthetic efficiency, independently of vegetation type and structure (r=0.68, compared to flux measurements). This is a significant advance over previous approaches seeking to model global scale photosynthesis indirectly from a combination of growth limiting factors, most notably pressure deficit and temperature. When combined with modeled global scale photosynthesis, satellite inferred e can improve model estimates through data assimilation. We anticipate that our findings will guide the development of new spaceborne approaches to observe vegetation carbon uptake and improve current predictions of global CO2 budgets and future climate scenarios by providing regularly timed calibration points for modeling plant photosynthesis consistently at a global scale.

No abstract is provided for this article.

Trees sustain livelihoods and mitigate climate change, but a predominance of trees outside forests and limited resources make it difficult for many developing countries to conduct frequent nation-wide inventories. Here, we propose a rapid and accurate approach to map the carbon stock of each individual tree and shrub at the national scale of Rwanda using aerial imagery and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas, and 15% in plantations. These non-forest trees account for 41% of the national carbon stocks. Natural forests cover 5% of the country and 11% of the total tree count, but comprise 59% of the national carbon stocks. The mapping of all trees facilitates any landscape stratification and is urgently needed for effective planning and monitoring of landscape restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Historical outbreaks of Rift Valley fever (RVF) since the early 1950s have been associated with cyclical patterns of the El Niño/Southern Oscillation (ENSO) phenomenon, which results in elevated and widespread rainfall over the RVF endemic areas of Africa. Using satellite measurements of global and regional elevated sea surface temperatures, elevated rainfall, and satellite derived-normalized difference vegetation index data, we predicted with lead times of 2-4 months areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa, Sudan, and Southern Africa at different time periods from September 2006 to March 2008. Predictions were confirmed by entomological field investigations of virus activity and by reported cases of RVF in human and livestock populations. This represents the first series of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation into the future.