Statistics for Spatial Data.

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Modern Applied Statistics With S

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

Studies from a variety of disciplines documentrecentchange in the northern high-latitude environment.Prompted by predictions of an amplified response oftheArctic to enhanced greenhouse forcing, we present asynthesis of these observations. Pronounced winter andspring warming over northern continents since about 1970ispartly compensated by cooling over the northern NorthAtlantic. Warming is also evident over the centralArcticOcean. There is a downward tendency in sea ice extent,attended by warming and increased areal extent of theArctic Ocean's Atlantic layer. Negative snow coveranomalies have dominated over both continents sincethelate 1980s and terrestrial precipitation has increasedsince 1900. Small Arctic glaciers have exhibitedgenerally negative mass balances. While permafrost haswarmed in Alaska and Russia, it has cooled in easternCanada. There is evidence of increased plant growth,attended by greater shrub abundance and northwardmigration of the tree line. Evidence also suggeststhatthe tundra has changed from a net sink to a net sourceofatmospheric carbon dioxide.Taken together, these results paint a reasonablycoherent picture of change, but their interpretationassignals of enhanced greenhouse warming is open todebate.Many of the environmental records are either short,areof uncertain quality, or provide limited spatialcoverage. The recent high-latitude warming is also nolarger than the interdecadal temperature range duringthis century. Nevertheless, the general patterns ofchange broadly agree with model predictions. Roughlyhalfof the pronounced recent rise in Northern Hemispherewinter temperatures reflects shifts in atmosphericcirculation. However, such changes are notinconsistentwith anthropogenic forcing and include generallypositive phases of the North Atlantic and ArcticOscillations and extratropical responses to theEl-NinoSouthern Oscillation. An anthropogenic effect is alsosuggested from interpretation of the paleoclimaterecord,which indicates that the 20th century Arctic is thewarmest of the past 400 years.

/pdf/observational-evidence-of-recent-change-in-the-northern-high-30d1vjk72n.pdf

Observational evidence of recent change in the northern high-latitude environment

Several recent papers have suggested replacing the terminology of fire intensity and fire severity. Part of the problem with fire intensity is that it is sometimes used incorrectly to describe fire effects, when in fact it is justifiably restricted to measures of energy output. Increasingly, the term has created confusion because some authors have restricted its usage to a single measure of energy output referred to as fireline intensity. This metric is most useful in understanding fire behavior in forests, but is too narrow to fully capture the multitude of ways fire energy affects ecosystems. Fire intensity represents the energy released during various phases of a fire, and different metrics such as reaction intensity, fireline intensity, temperature, heating duration and radiant energy are useful for different purposes. Fire severity, and the related term burn severity, have created considerable confusion because of recent changes in their usage. Some authors have justified this by contending that fire severity is defined broadly as ecosystem impacts from fire and thus is open to individual interpretation. However, empirical studies have defined fire severity operationally as the loss of or change in organic matter aboveground and belowground, although the precise metric varies with management needs. Confusion arises because fire or burn severity is sometimes defined so that it also includes ecosystem responses. Ecosystem responses include soil erosion, vegetation regeneration, restoration of community structure, faunal recolonization, and a plethora of related response variables. Although some ecosystem responses are correlated with measures of fire or burn severity, many important ecosystem processes have either not been demonstrated to be predicted by severity indices or have been shown in some vegetation types to be unrelated to severity. This is a critical issue because fire or burn severity are readily measurable parameters, both on the ground and with remote sensing, yet ecosystem responses are of most interest to resource managers.

/pdf/fire-intensity-fire-severity-and-burn-severity-a-brief-3f34ow4qed.pdf

Fire intensity, fire severity and burn severity: a brief review and suggested usage

Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression

USGS Digital Spectral Library splib06a

[1] Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision-making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2002JE001847

Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies

/pdf/usgs-spectral-library-version-7-1lht85c117.pdf

Raymond F. Kokaly

Papers

Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression

USGS Digital Spectral Library splib06a

Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies

USGS Spectral Library Version 7