Landscape ecology is based on the premise that there are strong links between ecological pattern and ecological function and process. Ecological systems are spatially heterogeneous, exhibiting considerable complexity and variability in time and space. This variability is typically represented by categorical maps or by a collection of samples taken at specific spatial locations (point data). Categorical maps quantize variability by identifying patches that are relatively homogeneous and that exhibit a relatively abrupt transition to adjacent areas. Alternatively, point-data analysis (geostatistics) assumes that the system property is spatially continuous, making fewer assumptions about the nature of spatial structure. Each data model provides capabilities that the other does not, and they should be considered complementary. Although the concept of patches is intuitive and consistent with much of ecological theory, point-data analysis can answer two of the most critical questions in spatial pattern analysis: what is the appropriate scale to conduct the analysis, and what is the nature of the spatial structure? I review the techniques to evaluate categorical maps and spatial point data, and make observations about the interpretation of spatial pattern indices and the appropriate application of the techniques. Pattern analysis techniques are most useful when applied and interpreted in the context of the organism(s) and ecological processes of interest, and at appropriate scales, although some may be useful as coarse-filter indicators of ecosystem function. I suggest several important needs for future research, including continued investigation of scaling issues, development of indices that measure specific components of spatial pattern, and efforts to make point-data analysis more compatible with ecological theory.

https://www.umass.edu/landeco/teaching/landscape_ecology/references/Gustafson_1998.pdf

Quantifying Landscape Spatial Pattern: What Is the State of the Art?

Preface 1. Spatial concepts and notions 2. Ecological and spatial processes 3. Points, lines and graphs 4. Spatial analysis of complete point location data 5. Contiguous units analysis 6. Spatial analysis of sample data 7. Spatial relationship and multiscale analysis 8. Spatial autocorrelation and inferential tests 9. Spatial partitioning: spatial clusters and boundary detection 10. Spatial diversity analysis 11. Spatio-temporal analysis 12. Closing comments and future directions References Index.

Spatial Analysis A Guide for Ecologists

Landscape Ecology in Theory and Practice

Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images Image texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods. Early evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice. This review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging. • Tumor spatial heterogeneity is an important prognostic factor. • Image texture analysis is an approach of quantifying heterogeneity. • Different methods can be applied, including statistical-, model-, and transform-based methods. • Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.

https://link.springer.com/content/pdf/10.1007%2Fs13244-012-0196-6.pdf

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Preface 1. Concepts of spatial pattern 2. Sampling 3. Basic methods for one dimension and one species 4. Spatial pattern of two species 5. Multispecies pattern 6. Two-dimensional analysis of spatial pattern 7. Point patterns 8. Pattern on an environmental gradient 9. Conclusions and future directions Bibliography Glossary of abbreviations List of plant species Index.

/pdf/spatial-pattern-analysis-in-plant-ecology-4vmx3s8bh4.pdf

Spatial Pattern Analysis in Plant Ecology

Lacunarity analysis is a multiscaled method for describing patterns of spatial dispersion. It can be used with both binary and quantitative data in one, two, and three dimensions. Although originally developed for fractal objects, the method is more general and can be readily used to describe nonfractal and multifractal patterns. Lacunarity analysis is broadly applicable to many data sets used in the natural sciences; we illustrate its application to both geological and ecological data. {copyright} {ital 1996 The American Physical Society.}

https://www.geobabble.org/~hnw/plotnick.pdf

Lacunarity analysis: A general technique for the analysis of spatial patterns

Hemispheric Asymmetry of the Marine Stratigraphic Record: Conceptual Proof of a Unipolar Ice Cap

Predictable Variations in the Marine Stratigraphic Record of the Northern and Southern Hemispheres and Reservoir Potential

Several large, TCF-size gas fields have been discovered in the Surma Basin, Bangladesh. Detailed sequence stratigraphy was performed on log and seismic data to study these fields and future potential of the area. The prospective section is Upper Miocene sands caught up in a series of younger compressional fault-related folds caused by the Indian Plate colliding with S.E. Asia in the late Tertiary. World-class gas/water contacts are observed on the seismic data over the fields. Sequence stratigraphic techniques reveal an ordered, predictable stratigraphic architecture of sandy highstands and transgressions, and muddy aggraded prograding complexes with deep incisions at each sequence boundary. This serves as a framework to understand the hydrocarbon accumulations in the area. Cyclostratigraphy is used to understand the unusual lithology distributions in the basin.

Martin A. Perlmutter

Papers

Lacunarity analysis: A general technique for the analysis of spatial patterns

Hemispheric Asymmetry of the Marine Stratigraphic Record: Conceptual Proof of a Unipolar Ice Cap

Predictable Variations in the Marine Stratigraphic Record of the Northern and Southern Hemispheres and Reservoir Potential

Upside-down Sequence Stratigraphy, Sandy Highstands, And Muddy Prograding Complexes In the Surma Basin, Bangladesh