/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments. Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.

Remote sensing imagery in vegetation mapping: a review

https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.4319/lo.1989.34.4.0790

Coastal environments: An introduction to the physical, ecological and cultural systems of coastlines

https://academic.uprm.edu/~jchinea/proyectos/ndvi/rocchini_e2010.pdf

Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges

The robust estimation of geodetic datum transformation is discussed. The basic principle of robust estimation is introduced. The error influence functions of the robust estimators, together with those of least-squares estimators, are given. Particular attention is given to the robust initial estimates of the transformation parameters, which should have a high breakdown point in order to provide reliable residuals for the following estimation. The median method is applied to solve for robust initial estimates of transformation parameters since it has the highest breakdown point. A smooth weight function is then used to improve the efficiency of the parameter estimates in successive iterative computations. A numerical example is given on a datum transformation between a global positioning system network and the corresponding geodetic network in China. The results show that when the coordinates are contaminated by outliers, the proposed method can still give reasonable results.

Robust estimation of geodetic datum transformation

Image classification of historical aerial photographs is very useful for the study of medium‐to‐long term (10–50 years) vegetation changes. To determine the quality of information derived from the classification process, accuracy assessment of the classification is implemented. Error matrix, which is primarily used in remote sensing for accuracy assessment, is typically based on an evaluation of the derived classification against some ‘ground truth’ or reference dataset. Regrettably ‘ground truths’ for some old historical photographs are rarely available. To solve this problem we formulate, in this Part I, methods of classification of aerial photographs and computation of accuracy assessment parameters of classification products in the absence of ground data, using the fuzzy classification technique. In addition, since point estimates of these accuracy parameters require associated standard errors, in order to be useful for statistical analysis, the method of computation of standard errors of accuracy mea...

/pdf/methods-for-fuzzy-classification-and-accuracy-assessment-of-3cvorwywpl.pdf

Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analyses. Part I: Algorithm development

Curvilinear geodetic datum transformations

Linear mixture model approach for selecting fuzzy exponent value in fuzzy c-means algorithm

A key problem of contemporary static and kinematic positioning is the problem of transformation of conformai coordinates of universal Mercator projection (UMP) type from a local datum (regional, national) to a global datum, for instance, the World Geodetic System 1984 (WGS 84) with reference to Boyle (1987) Such a problem is met if we use WGS 84 GPS‐derived ellipsoidal coordinates of a point for localization in a local chart of UMP type In this article we derive and test the equations of a curvilinear datum transformation of ellipsoidal GPS coordinates in a global datum to conformai coordinates of UMP type in a local datum The curvilinear datum transformation includes three parameters for translation, three parameters for rotation, one scale parameter, and two form parameters which account for a change in the semimajor axis and in the relative eccentricity of the reference ellipsoid

Transformation of conformai coordinates of type Mercator from a global datum (WGS 84) to a local datum (Regional, national)

Huge vegetal losses caused by unsustainable quarrying practices have limited the role played by vegetation cover in mitigating the global impact of climate change. There is a need for a holistic study that will employ remotely sensed data in GIS domain to determine the extent of the effect of quarrying activities on vegetation cover in the study area. The need for extraordinary details with high accuracy and enhanced imagery has necessitated the use of Geo-Eye-1 satellite imagery with 1-m resolution for 2015. The study was carried out in Ebonyi State where 27 quarry sites were studied in six local government areas where mining activities were operated. The study employed geographical information system, Garmin eTrex GPS receiver and global positioning system. Geographical information was processed using ArcGIS to map the patterns and extent of land use. The findings from the study showed that the visual interpretation of the satellite image confirmed that white patches on the imagery were areas affected by intensified quarrying activities. The very dark green colours were interpreted as forest with no observed human activities. The supervised classification of land use also showed that quarrying activities occupied 0.1% of the study area with a total of 402.855 ha of green cover lost in Ebonyi State due to quarrying operations. Quarrying practices have destroyed arable lands, economic trees and forests in the area. This implies that there is a need for policies to be enforced with strict adherence to sustainable quarrying guidelines and consistent monitoring.

Francis Okeke

Papers

Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analyses. Part I: Algorithm development

Curvilinear geodetic datum transformations

Linear mixture model approach for selecting fuzzy exponent value in fuzzy c-means algorithm

Transformation of conformai coordinates of type Mercator from a global datum (WGS 84) to a local datum (Regional, national)

Quarrying and its effect on vegetation cover for a sustainable development using high-resolution satellite image and GIS