Changes in Atmospheric Constituents and in Radiative Forcing

http://www.geog.ucl.ac.uk/~mdisney/teaching/PPRS/papers/foody.pdf

Status of land cover classification accuracy assessment

. 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)

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

/pdf/change-detection-techniques-4g34rgajio.pdf

Change detection techniques

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

Global land cover mapping from MODIS: algorithms and early results

Error modelling for the map overlay operation modelling error in overlaid categorical maps user considerations in landscape characterization knowledge-based approaches to determining and correcting areas of unreliability in geographical databases observations and comments on the generation and treatment of error in digital GIS data developing confidence limits on errors of suitability analyses in geographical information systems distance calculations and errors in geographical databases inclusion of accuracy data in a feature based, object- orientated data model accuracy and bias issues in surface representation modelling error in objects and fields frame independence spatial analysis modelling locational uncertainty via hierarchical tesselation minimum cross-entropy convex decompositions of pixel-indexed stochastic matrices - a geographical application of the Ising model the traditional and modern look at Tissot's indicatrix real data and real problems - dealing with large spatial databases the small number problems and the accuracy of spatial databases demand point approximations for location problems modelling realibility on statistical surfaces by polygon filtering scale-independent spatial analysis the effect of data aggregation on a poisson model of Canadian migration statistical methods for inference between incompatible zonal systems statistical effect of spatial data transformations - a proposed general framework learning to live with errors in spatial databases.

The accuracy of spatial databases

The use of fuzzy sets in map accuracy assessment expands the amount of information that can be provided regarding the nature, frequency, magnitude, and source of errors in a thematic map. The need for using fuzzy sets arises from the observation that all map locations do not fit unambiguously in a single map category. Fuzzy sets allow for varying levels of set membership for multiple map categories. A linguistic measurement scale allows the kinds of comments commonly made during map evaluations to be used to quantify map accuracy. Four tables result from the use of fuzzy functions, and when taken together they provide more information than traditional confusion matrices. The use of a hypothetical dataset helps illustrate the benefits of the new methods. It is hoped that the enhanced ability to evaluate maps resulting from the use of fuzzy sets will improve our understanding of uncertainty in maps and facilitate improved error modeling. 40 refs.

Theory and methods for accuracy assessment of thematic maps using fuzzy sets

A prolonged drought in the Lake Tahoe Basin in California has resulted in extensive conifer mortality. This phenomenon can be analyzed using (multitemporal) remote sensing data. Prior research in the same region used more traditional methods of change detection. The present paper introduces a third approach to change detection in remote sensing based on artificial neural networks. The neural network architecture used is a multilayer feedforward network. The results of the study indicate that the artificial neural network (ANN) estimates conifer mortality more accurately than the other approaches. Further, an analysis of its architecture reveals that it uses identifiable scene characteristics-the same as those used by a Gramm-Schmidt transformation. ANN models offer a viable alternative for change detection in remote sensing.

Remote sensing of forest change using artificial neural networks

Traditionally, the classes in thematic maps have been treated as crisp sets, using classical set theory. In this formulation, map classes are assumed to be mutually exclusive and exhaustive. This approach limits the ability of thematic maps to represent the continuum of variation found in most landscapes. Substitution of fuzzy sets allows more flexibility for treatment of map classes in the areas of accuracy assessment and area estimation. Accuracy assessment methods based on fuzzy sets allow consideration of the magnitude of errors and assessment of the frequency of ambiguity in map classes. An example of an accuracy assessment from a vegetation map of the Plumas National Forest illustrates the implementation of these methods. Area estimation based on fuzzy sets and using accuracy assessment data allows estimation of the area of classes as a function of levels of class membership. The fuzzy area estimation methods are an extension of previous methods presented by Card (1982). One interesting result is th...

Sucharita Gopal

Papers

Global land cover mapping from MODIS: algorithms and early results

The accuracy of spatial databases

Theory and methods for accuracy assessment of thematic maps using fuzzy sets

Remote sensing of forest change using artificial neural networks

Fuzzy set theory and thematic maps: accuracy assessment and area estimation