Cristina Milesi

Assessing the impact of urban land development on net primary productivity in the southeastern United States

The southeastern United States (SE-US) has undergone one of the highest rates of landscape changes in the country due to changing demographics and land use practices over the last few decades. Increasing evidence indicates that these changes have impacted mesoscale weather patterns, biodiversity and water resources. Since the Southeast has one of the highest rates of land productivity in the nation, it is important to monitor the effects of such changes regularly. Here, we propose a remote sensing based methodology to estimate regional impacts of urban land development on ecosystem structure and function. As an indicator of ecosystem functioning, we chose net primary productivity (NPP), which is now routinely estimated from the MODerate resolution Imaging Spectroradiometer (MODIS) data. We used the MODIS data, a 1992 Landsat-based land cover map and nighttime data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) for the years 1992/1993 and 2000 to estimate the extent of urban development and its impact on NPP. The analysis based on the nighttime data indicated that in 1992/1993, urban areas amounted to 4.5% of the total land surface of the region. In the year 2000, the nighttime data showed an increase in urban development for the southeastern United States of 1.9%. Estimates derived from the MODIS data indicated that land cover changes due to urban development that took place during the 1992–2000 period reduced annual NPP of the southeastern United States by 0.4%. Despite the uncertainties in sensor fusion and the coarse resolution of the data used in this study, results show that the combination of MODIS products such as NPP with nighttime data could provide rapid assessment of urban land cover changes and their impacts on regional ecosystem resources. D 2003 Elsevier Science Inc. All rights reserved.

U.S. constructed area approaches the size of Ohio

The construction and maintenance of impervious surfaces—buildings, roads, parking lots, roofs, etc.—constitute a major human alteration of the land surface, changing the local hydrology, climate, and carbon cycling. Three types of national coverage data were used to model the spatial distribution and density of the impervious surface area (ISA) for the conterminous United States. The results (Figure 1) indicate that total ISA of the 48 states and Washington, D.C. is 112,610 km2 (±12,725 km2), which is slightly smaller than the state of Ohio (116,534 km2) and slightly larger than the area of herbaceous wetlands (98,460 km2) of the conterminous United States [Vogelmann et al., 2001].

Variations in atmospheric CO2 growth rates coupled with tropical temperature

Previous studies have highlighted the occurrence and intensity of El Niño–Southern Oscillation as important drivers of the interannual variability of the atmospheric CO2 growth rate, but the underlying biogeophysical mechanisms governing such connections remain unclear. Here we show a strong and persistent coupling (r2 ≈ 0.50) between interannual variations of the CO2 growth rate and tropical land–surface air temperature during 1959 to 2011, with a 1 °C tropical temperature anomaly leading to a 3.5 ± 0.6 Petagrams of carbon per year (PgC/y) CO2 growth-rate anomaly on average. Analysis of simulation results from Dynamic Global Vegetation Models suggests that this temperature–CO2 coupling is contributed mainly by the additive responses of heterotrophic respiration (Rh) and net primary production (NPP) to temperature variations in tropical ecosystems. However, we find a weaker and less consistent (r2 ≈ 0.25) interannual coupling between CO2 growth rate and tropical land precipitation than diagnosed from the Dynamic Global Vegetation Models, likely resulting from the subtractive responses of tropical Rh and NPP to precipitation anomalies that partly offset each other in the net ecosystem exchange (i.e., net ecosystem exchange ≈ Rh − NPP). Variations in other climate variables (e.g., large-scale cloudiness) and natural disturbances (e.g., volcanic eruptions) may induce transient reductions in the temperature–CO2 coupling, but the relationship is robust during the past 50 y and shows full recovery within a few years after any such major variability event. Therefore, it provides an important diagnostic tool for improved understanding of the contemporary and future global carbon cycle.

Green spaces and pregnancy outcomes in Southern California.

Little is known about the impacts of green spaces on pregnancy outcomes. The relationship between green space exposure and preeclampsia has never been studied. We used a hospital-based perinatal database including more than 80,000 births to study the relationships between greenness exposure and three pregnancy outcomes: birth weight in term born infants, preterm deliveries and preeclampsia. Greenness was characterized using the normalized difference vegetation index (NDVI) within circular buffers surrounding maternal homes. Analyses were conducted using generalized estimating equations, adjusted for potential confounders. We observed an increase in birth weight in term born infants and a reduced risk of preterm births associated with an increase in NDVI. No significant association was observed between greenness and preeclampsia. This study provides modest support for beneficial effects of greenness exposure on pregnancy outcomes and calls for confirmation in other study settings.

Decadal variations in NDVI and food production in India

In this study we use long-term satellite, climate, and crop observations to document the spatial distribution of the recent stagnation in food grain production affecting the water-limited tropics (WLT), a region where 1.5 billion people live and depend on local agriculture that is constrained by chronic water shortages. Overall, our analysis shows that the recent stagnation in food production is corroborated by satellite data. The growth rate in annually integrated vegetation greenness, a measure of crop growth, has declined significantly (p < 0.10) in 23% of the WLT cropland area during the last decade, while statistically significant increases in the growth rates account for less than 2%. In

Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data

Algorithms that use remotely-sensed vegetation indices to estimate gross primary production (GPP), a key component of the global carbon cycle, have gained a lot of popularity in the past decade. Yet despite the amount of research on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of different vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) in capturing the seasonal and the annual variability of GPP estimates from an optimal network of 21 FLUXNET forest towers sites. The tested indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation absorbed by plant canopies (FPAR). Our results indicated that single vegetation indices captured 50–80% of the variability of tower-estimated GPP, but no one index performed universally well in all situations. In particular, EVI outperformed the other MODIS products in tracking seasonal variations in tower-estimated GPP, but annual mean MODIS LAI was the best estimator of the spatial distribution of annual flux-tower GPP (GPP = 615 × LAI − 376, where GPP is in g C/m2/year). This simple algorithm rehabilitated earlier approaches linking ground measurements of LAI to flux-tower estimates of GPP and produced annual GPP estimates comparable to the MODIS 17 GPP product. As such, remote sensing-based estimates of GPP continue to offer a useful alternative to estimates from biophysical models, and the choice of the most appropriate approach depends on whether the estimates are required at annual or sub-annual temporal resolution.

Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production

Projected changes in the frequency and severity of droughts as a result of increase in greenhouse gases have a significant impact on the role of vegetation in regulating the global carbon cycle. Drought effect on vegetation Gross Primary Production (GPP) is usually modeled as a function of Vapor Pressure Deficit (VPD) and/or soil moisture. Climate projections suggest a strong likelihood of increasing trend in VPD, while regional changes in precipitation are less certain. This difference in projections between VPD and precipitation can cause considerable discrepancies in the predictions of vegetation behavior depending on how ecosystem models represent the drought effect. In this study, we scrutinized the model responses to drought using the 30-year record of Global Inventory Modeling and Mapping Studies (GIMMS) 3g Normalized Difference Vegetation Index (NDVI) dataset. A diagnostic ecosystem model, Terrestrial Observation and Prediction System (TOPS), was used to estimate global GPP from 1982 to 2009 under nine different experimental simulations. The control run of global GPP increased until 2000, but stayed constant after 2000. Among the simulations with single climate constraint (temperature, VPD, rainfall and solar radiation), only the VPD-driven simulation showed a decrease in 2000s, while the other scenarios simulated an increase in GPP. The diverging responses in 2000s can be attributed to the difference in the representation of the impact of water stress on vegetation in models, i.e., using VPD and/or precipitation. Spatial map of trend in simulated GPP using GIMMS 3g data is consistent with the GPP driven by soil moisture than the GPP driven by VPD, confirming the need for a soil moisture constraint in modeling global GPP.

Artificial outdoor nighttime lights associate with altered sleep behavior in the American general population

STUDY OBJECTIVES Our study aims to explore the associations between outdoor nighttime lights (ONL) and sleep patterns in the human population. METHODS Cross-sectional telephone study of a representative sample of the general US population age 18 y or older. 19,136 noninstitutionalized individuals (participation rate: 83.2%) were interviewed by telephone. The Sleep-EVAL expert system administered questions on life and sleeping habits; health; sleep, mental and organic disorders (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision; International Classification of Sleep Disorders, Second Edition; International Classification of Diseases, 10(th) Edition). Individuals were geolocated by longitude and latitude. Outdoor nighttime light measurements were obtained from the Defense Meteorological Satellite Programs Operational Linescan System (DMSP/OLS), with nighttime passes taking place between 19:30 and 22:30 local time. Light data were correlated precisely to the geolocation of each participant of the general population sample. RESULTS Living in areas with greater ONL was associated with delayed bedtime (P < 0.0001) and wake up time (P < 0.0001), shorter sleep duration (P < 0.01), and increased daytime sleepiness (P < 0.0001). Living in areas with greater ONL also increased the dissatisfaction with sleep quantity and quality (P < 0.0001) and the likelihood of having a diagnostic profile congruent with a circadian rhythm disorder (P < 0.0001). CONCLUSIONS Although they improve the overall safety of people and traffic, nighttime lights in our streets and cities are clearly linked with modifications in human sleep behaviors and also impinge on the daytime functioning of individuals living in areas with greater ONL.

Assessing the environmental impacts of human settlements using satellite data

In the last 50 years, the Mediterranean Basin has experienced a doubling of its population. This demographic growth has been the cause of extensive land use changes that have undermined the ecological stability of large portions of its fragile ecosystems. The population of the Mediterranean countries is expected to grow by another 20 percent in the next 25 years, further increasing the pressure on the natural resources. In this paper, we present a methodology combining photosynthetic activity and human settlements both derived from satellite data for monitoring the effects of human settlements on the environment. We found photosynthesis decreasing as one moves from rural to urban settings in the north and increasing in the south Mediterranean countries. Regional scale assessments using this approach may help policy makers in designing appropriate measures to combat further environmental degradation.

A Semiautomated Probabilistic Framework for Tree-Cover Delineation From 1-m NAIP Imagery Using a High-Performance Computing Architecture

Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps.