Changes in Atmospheric Constituents and in Radiative Forcing

. Reactive gases and aerosols are produced by terrestrial ecosystems, processed within plant canopies, and can then be emitted into the above-canopy atmosphere. Estimates of the above-canopy fluxes are needed for quantitative earth system studies and assessments of past, present and future air quality and climate. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is described and used to quantify net terrestrial biosphere emission of isoprene into the atmosphere. MEGAN is designed for both global and regional emission modeling and has global coverage with ~1 km2 spatial resolution. Field and laboratory investigations of the processes controlling isoprene emission are described and data available for model development and evaluation are summarized. The factors controlling isoprene emissions include biological, physical and chemical driving variables. MEGAN driving variables are derived from models and satellite and ground observations. Tropical broadleaf trees contribute almost half of the estimated global annual isoprene emission due to their relatively high emission factors and because they are often exposed to conditions that are conducive for isoprene emission. The remaining flux is primarily from shrubs which have a widespread distribution. The annual global isoprene emission estimated with MEGAN ranges from about 500 to 750 Tg isoprene (440 to 660 Tg carbon) depending on the driving variables which include temperature, solar radiation, Leaf Area Index, and plant functional type. The global annual isoprene emission estimated using the standard driving variables is ~600 Tg isoprene. Differences in driving variables result in emission estimates that differ by more than a factor of three for specific times and locations. It is difficult to evaluate isoprene emission estimates using the concentration distributions simulated using chemistry and transport models, due to the substantial uncertainties in other model components, but at least some global models produce reasonable results when using isoprene emission distributions similar to MEGAN estimates. In addition, comparison with isoprene emissions estimated from satellite formaldehyde observations indicates reasonable agreement. The sensitivity of isoprene emissions to earth system changes (e.g., climate and land-use) demonstrates the potential for large future changes in emissions. Using temperature distributions simulated by global climate models for year 2100, MEGAN estimates that isoprene emissions increase by more than a factor of two. This is considerably greater than previous estimates and additional observations are needed to evaluate and improve the methods used to predict future isoprene emissions.

/pdf/estimates-of-global-terrestrial-isoprene-emissions-using-2inhk36wkp.pdf

Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

/pdf/a-survey-of-image-classification-methods-and-techniques-for-5bc5ltr9dq.pdf

A survey of image classification methods and techniques for improving classification performance

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

. New burned area datasets and top-down constraints from atmospheric concentration measurements of pyrogenic gases have decreased the large uncertainty in fire emissions estimates. However, significant gaps remain in our understanding of the contribution of deforestation, savanna, forest, agricultural waste, and peat fires to total global fire emissions. Here we used a revised version of the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model and improved satellite-derived estimates of area burned, fire activity, and plant productivity to calculate fire emissions for the 1997–2009 period on a 0.5° spatial resolution with a monthly time step. For November 2000 onwards, estimates were based on burned area, active fire detections, and plant productivity from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor. For the partitioning we focused on the MODIS era. We used maps of burned area derived from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) and Along-Track Scanning Radiometer (ATSR) active fire data prior to MODIS (1997–2000) and estimates of plant productivity derived from Advanced Very High Resolution Radiometer (AVHRR) observations during the same period. Average global fire carbon emissions according to this version 3 of the Global Fire Emissions Database (GFED3) were 2.0 Pg C year−1 with significant interannual variability during 1997–2001 (2.8 Pg C year−1 in 1998 and 1.6 Pg C year−1 in 2001). Globally, emissions during 2002–2007 were relatively constant (around 2.1 Pg C year−1) before declining in 2008 (1.7 Pg C year−1) and 2009 (1.5 Pg C year−1) partly due to lower deforestation fire emissions in South America and tropical Asia. On a regional basis, emissions were highly variable during 2002–2007 (e.g., boreal Asia, South America, and Indonesia), but these regional differences canceled out at a global level. During the MODIS era (2001–2009), most carbon emissions were from fires in grasslands and savannas (44%) with smaller contributions from tropical deforestation and degradation fires (20%), woodland fires (mostly confined to the tropics, 16%), forest fires (mostly in the extratropics, 15%), agricultural waste burning (3%), and tropical peat fires (3%). The contribution from agricultural waste fires was likely a lower bound because our approach for measuring burned area could not detect all of these relatively small fires. Total carbon emissions were on average 13% lower than in our previous (GFED2) work. For reduced trace gases such as CO and CH4, deforestation, degradation, and peat fires were more important contributors because of higher emissions of reduced trace gases per unit carbon combusted compared to savanna fires. Carbon emissions from tropical deforestation, degradation, and peatland fires were on average 0.5 Pg C year−1. The carbon emissions from these fires may not be balanced by regrowth following fire. Our results provide the first global assessment of the contribution of different sources to total global fire emissions for the past decade, and supply the community with an improved 13-year fire emissions time series.

/pdf/global-fire-emissions-and-the-contribution-of-deforestation-r24zw2ke8k.pdf

Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009)

The MODIS global land cover product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP. To generate these maps, a supervised classification methodology is used that exploits a global database of training sites interpreted from high resolution imagery in association with ancillary data. Initial results from MODIS data are very encouraging. At global scales, the distribution of vegetation and land cover types is realistic. As a full year of data is added to the processing stream and the global site database is refined, the quality of the MODIS land cover product will improve accordingly.

Global land cover classification results from MODIS

The invention provides methods and apparatus for optimizing markdown scheduling that group multiple retail sites into bins (or buckets) for purposes of scheduling markdown pricing on an item (or group of related items) sold by those sites. The groupings can be based, for example, on a metric that is a function of the current inventory of the item (or items) and its expected sales at each site. A markdown schedule is generated for the combined grouping of stores in each bucket, rather than for the individual stores that make up that bucket, thereby speeding price optimization determination. A report of those schedules can be used, for example, by pricing managers or other personnel to set prices at the sites. Alternatively, or in an addition, the schedules can be used in conjunction with an inventory control system to set prices for the items and/or on RFID or other electronic shelf displays.

Methods and apparatus for optimizing markdown pricing

Identifying and anticipating the location, size and growth rate of urbanized areas is an important component to understanding, adapting to, and mitigating many aspects of global change. The main objective of this research is to improve understanding of the methodological, scale and validation requirements for mapping urban land cover from coarse resolution remotely sensed MODIS one kilometer data. Defining the extent of urban land is crucial, since knowledge of the size and spatial distribution of cities is important for regional and global environmental modeling as well as resource management and economic development planning. This research relies on the use of a supervised decision tree classifier, a nonparametric algorithm that has been shown to be effective for classifying noisy and incomplete data sets: a technique called boosting improves classification accuracy and provides a means to correct major sources of error using available prior information from the DMSP-OLS radiance calibrated nighttime lights data set. Results for North America indicate that the incorporation of DMSP-OLS data successfully improves urban classification results. Traditional as well as new measures of accuracy assessment demonstrate the effectiveness of the methodology for creating accurate maps of cities over large areas.

Mapping urban areas using coarse resolution remotely sensed data

In recent years, machine learning and data mining methods have become increasingly common in remote sensing applications. One area in which such techniques are particularly useful is classification of remotely sensed data for land cover and vegetation mapping applications. In this paper, we describe new methods to include available information (domain knowledge) in supervised classification of land cover using high dimensional remote sensing observations. specifically, land cover and vegetation classification schemes are generally designed for ecological or land use applications. As a result, the classes of interest are often poorly separable in the multi-spectral or multi-temporal feature space provided by remote sensing. In many cases, ancillary data sources can provide useful information to help distinguish between problematic classes. However, available methods for including ancillary data sources, such as the use of prior probabilities in maximum likelihood classification, are often problematic in practice. This paper presents a method for incorporating prior probabilities in remote sensing-based land cover classification using a supervised decision tree classification algorithm. The method exploits recent theory from the domain of statistics and machine learning that allows robust estimates of class membership to be estimated using a technique known as boosting. This approach allows poorly separable classes to be distinguished based on ancillary information, but does not penalize rare classes.

Integration of domain knowledge in the form of ancillary map data into supervised classification of remotely sensed data

This paper describes research to obtain continental and global scale maps of urban land cover from remotely sensed imagery, specifically utilizing newly available one kilometer data from the MODIS sensor. Defining the extent of urban land is crucial, since knowledge of the size and spatial distribution of cities is important on a number of fronts, from resource management to economic development planning to regional and global climate modeling. The algorithm used for this work is a supervised decision tree classifier, and the technique of boosting is exploited to improve classification accuracy and to provide a means to correct major sources of error using available prior information. First results for North America indicate that the incorporation of. ancillary information successfully improves urban classification results, resolving confusion between the urban and barren classes that normally occurs when only MODIS data is used.

http://ieeexplore.ieee.org/iel5/7695/21049/00977931.pdf

D.K. McIver

Papers

Global land cover classification results from MODIS

Methods and apparatus for optimizing markdown pricing

Mapping urban areas using coarse resolution remotely sensed data

Integration of domain knowledge in the form of ancillary map data into supervised classification of remotely sensed data

Classification of urban areas at continental scales using remotely sensed data