Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods.

/pdf/digital-change-detection-methods-in-ecosystem-monitoring-a-3qdpdqn88d.pdf

Digital change detection methods in ecosystem monitoring: a review

Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?

https://www.geo.uzh.ch/microsite/rsl-documents/research/publications/peer-reviewed-articles/2007_ReflectanceQuantities_RSE_GS-4044578560/2007_ReflectanceQuantities_RSE_GS.pdf

Reflectance quantities in optical remote sensing - definitions and case studies

The world's forest ecosystems are in a state of permanent flux at a variety of spatial and temporal scales. Monitoring techniques based on multispectral satellite‐acquired data have demonstrated potential as a means to detect, identify, and map changes in forest cover. This paper, which reviews the methods and the results of digital change detection primarily in temperate forest ecosystems, has two major components. First, the different perspectives from which the variability in the change event has been approached are summarized, and the appropriate choice of digital imagery acquisition dates and interval length for change detection are discussed. In the second part, preprocessing routines to establish a more direct linkage between digital remote sensing data and biophysical phenomena, and the actual change detection methods themselves are reviewed and critically assessed. A case study in temperate forests (north‐central U.S.A.) then serves as an illustration of how the different change detectio...

Digital change detection in forest ecosystems with remote sensing imagery

A method for the radiometric correction of wide field-of-view airborne imagery has been developed that accounts for the angular dependence of the path radiance and atmospheric transmittance functions to remove atmospheric and topographic effects. The first part of processing is the parametric geocoding of the scene to obtain a geocoded, orthorectified image and the view geometry (scan and azimuth angles) for each pixel as described in part 1 of this jointly submitted paper. The second part of the processing performs the combined atmospheric/ topographic correction. It uses a database of look-up tables of the atmospheric correction functions (path radiance, atmospheric transmittance, direct and diffuse solar flux) calculated with a radiative transfer code. Additionally, the terrain shape obtained from a digital elevation model is taken into account. The issues of the database size and accuracy requirements are critically discussed. The method supports all common types of imaging airborne optical instrument...

/pdf/geo-atmospheric-processing-of-airborne-imaging-spectrometry-34knvzr8y0.pdf

Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction

The literature contains many examples of image acquisition and analysis which have been inappropriately applied and which have led to empirical results which may not be reproducible, or which are not conclusive. In this paper, we deal with eleven major assumptions which are implicit in the acquisition and in the analysis of passively sensed digital image data. It is hoped that an enumeration of such assumptions might lead to improved rules for image acquisition and analysis.

Assumptions implicit in remote sensing data acquisition and analysis

In this paper, factors controlling the radiance incident on a remote-sensing device and the interaction of the sensor with upwelling radiance are considered. Attention is also given to systematic and random radiance variations across an image: while the former may be corrected for (to an extent) the latter may not, so that it is necessary to be aware of the impact of random variations on target identification, discrimination and quantification. Both the optical reflective and the thermal-infrared parts of the spectrum are considered. Equations are presented to concisely illustrate the processes involved.

Factors limiting the discrimination and quantification of terrestrial features using remotely sensed radiance

Surface reflectance and global irradiance variations across an imaged area determine the minimum signal difference from groups of pixels in different targets needed to discriminate them. The factors affecting such variations and their magnitudes are considered. The sensor output also depends upon the interaction of the spectral response of the sensor and the spectral upwelling radiance from the target. From a consideration of the spectral instrument responses of detectors in the Landsat multispectral scanners (MS), together with measured reflectance factors of a vegetative canopy stressed to different levels of severity, the feasibility of mapping and quantifying disease stress witlh Landsat vegetative indices (VINs) is demonstrated.

The effect of irradiation and reflectance variability on vegetation condition assessment

This communication identifies factors that should be accounted for when using ground radiance measurements to calibrate aircraft or satellite sensors. They include both spatial and spectral instrument characteristics which interact with the heterogeneity, spectral characteristics, and optical anisotropy of ground cover. Unfortunately, since instrument calibration data are not readily available, meaningful intercomparisons are not easily possible.

Relating ground, aircraft and satellite radiance measurements: spectral and spatial considerations

We describe a method of spatial filtering in the frequency domain which enhances edges and boundaries, thus making small urban features such as parks, tree-lined streets and new housing developments, visible on digital images with, for example, 30 m resolution. Hitherto, while satellite imagery has been useful because of its large area and repetitive coverage, the spatial resolution for multiband imagery has been such that it precluded the detailed studies which may now be possible. While spatial frequency filtering, edge enhancement, high-boost and directional filtering have been possible, this has generally involved the use of a convolution matrix whose elements have been defined from general empirical rules. Frequency domain operations offer the advantage of selectively tailoring the filter in order to enhance certain features.

M. J. Duggin

Papers

Assumptions implicit in remote sensing data acquisition and analysis

Factors limiting the discrimination and quantification of terrestrial features using remotely sensed radiance

The effect of irradiation and reflectance variability on vegetation condition assessment

Relating ground, aircraft and satellite radiance measurements: spectral and spatial considerations

The application of spatial filtering methods to urban feature analysis using digital image data