Norm Campbell

Bi-directional reflectance distribution function approaches to radiometric calibration of Landsat ETM+ imagery

There are many remote sensing studies where it is desirable to have a radiometrically matched time series of images or image mosaics., This paper examines the use of bi-directional reflectance distribution function (BRDF) models for radiometric correction of Landsat 7 ETM+ imagery. It describes a simple radiometric correction method which combines: a top-of-atmosphere reflectance adjustment with an empirical BRDF model. The model parameters were derived from an overlapping sequence of Landsat 7 images.

Empirical Models for Radiometric Calibration of Digital Aerial Frame Mosaics

The advent of routine collection of high-quality digital photography provides for traditional uses, as well as “remote sensing” uses such as the monitoring of environmental indicators. A well-devised monitoring system, based on consistent data and methods, provides the opportunity to track and communicate changes in features of interest in a way that has not previously been possible. Data that are geometrically and radiometrically consistent are fundamental to establishing systems for monitoring. In this paper, we focus on models for the radiometric calibration of mosaics consisting of thousands of images. We apply the models to the data acquired by the Australian Commonwealth Scientific and Industrial Research Organisation and its partners as part of regular systematic acquisitions over the city of Perth for a project known as Urban Monitor. One goal of the project, and hence the model development, is to produce annually updated mosaics calibrated to reflectance at 0.2-m ground sample distance for an area of approximately 9600 km2. This equates to terabytes of data and, for frame-based instruments, tens of thousands of images. For the experiments considered in this paper, this requires mosaicking estimates derived from 3000 digital photographic frames, and the methods will shortly be expanded to 30 000+ frames. A key part of the processing is the removal of spectral variation due to the viewing geometry, typically attributed to the bidirectional reflectance distribution function (BRDF) of the land surface. A variety of techniques based on semiempirical BRDF kernels have been proposed in the literature for correcting the BRDF effect in single frames, but mosaics with many frames provide unique challenges. This paper presents and illuminates a complete empirical radiometric calibration method for digital aerial frame mosaics, based on a combined model that uses kernel-based techniques for BRDF correction and incorporates additive and multiplicative terms for correcting other effects, such as variations due to the sensor and atmosphere. Using ground truth, which consists of laboratory-measured white, gray, and black targets that were placed in the field at the time of acquisition, we calculate the fundamental limitations of each model, leading to an optimal result for each model type. We demonstrate estimates of ground reflectance that are accurate to approximately 10%, 5%, and 3% absolute reflectances for ground targets having reflectances of 90%, 40%, and 4%, respectively.

The decorrelation stretch transformation

Abstract The three-stage decorrelation stretch analysis—transformation to principal component scores; contrast-stretching of the scores to equalise the variances; and reversal of the initial transformation—is shown to produce linear combinations of the original bands which are uncorrelated and have unit variances. As such, the effectiveness of the decorrelation stretch analysis in producing enhanced displays depends fortuitously on the particular contrasts which result. For some examples, setting the first scaled principal component to zero before reversing the initial transformation has little effect on the decorrelation stretch coefficients. In other examples, a small change in the eigenvector defining the first principal component results in only a small change in the decorrelation stretch coefficients and yet produces a distinctly different decorrelation stretch image. A more general formulation of the decorrelation stretch analysis involving simplification and/or exclusion of some of the first-stage ...

Techniques for BRDF Correction of Hyperspectral Mosaics

The need to correct view- and sun-angle-dependent intensity gradients in aerial and satellite images is well established, with bidirectional reflectance distribution function (BRDF) kernel-based techniques receiving much of the recent attention in the literature. In these methods, a plausible (physical or empirical) model of the BRDF is fitted to the data and then used to normalize the image to standard solar and view angles. As yet, very little attention has been paid to the case where the images are hyperspectral (i.e., there are many contiguous bands captured simultaneously), beyond using known techniques on each band. This can lead to loss of spectral integrity and poor agreement on overlapping regions unless careful attention is paid to these factors. In this paper, a range of techniques that can be employed for the purpose of hyperspectral mosaicking is presented.

Spectral discrimination and mapping of waterlogged cereal crops in Western Australia

Abstract Abstract. A study was conducted in Western Australia to determine whether remotely-sensed spectral data can be used to detect and map areas in cereal crops where growth has been affected by waterlogging, Spectral discrimination was established between waterlogged and non-waterlogged crop using either 13-band airborne MSS data or Landsat-TM data. Near infrared and thermal channels were found to be important in providing the observed discrimination. Classification procedures incorporating measures of confidence of class membership were applied, and produced classification maps which agreed closely with ground information, It is concluded that timely Landsal-TM data, together with ground calibration information, can be used for mapping and monitoring waterlogged cereal crops.

A BRDF-corrected Landsat 7 mosaic of the Australian continent

Landsat TM imagery is being used to monitor landcover change in a number of operational projects in Australia. Most recently, the Australian Greenhouse Office (AGO) has undertaken a project to provide historical monitoring of changes in woody vegetation across the continent. In order to map and monitor landcover changes in a consistent and comparable manner across broad areas, it is highly desirable to have a consistently calibrated numerical base. To this end, the AGO has supported the creation of a rectification and calibration base for Australia, using Landsat TM data; 369 Landsat 7 images from the period July 1999 to September 2000 were purchased from the Australian Centre for Remote Sensing (ACRES). This paper describes the processing and production of the calibration base from these images.

Mapping and monitoring land use and condition change in the southwest of Western Australia using remote sensing and other data

In the south-west of Western Australia, the clearing of land for agricultural production has lead to rising saline ground water, resulting in the loss of previously productive land to salinity; damage to buildings, roads and other infrastructure; the decline in pockets of remnant vegetation and biodiversity; and the reduction in water quality. The region in question comprises some 24 million hectares of land. This has resulted in a wide variety of stakeholders requesting quantitative information regarding historical, present and future trends in land condition and use. Historically, two methods have been widely used to obtain information: (1) surveys requesting land managers to provide estimates of land use and condition; and (2) human interpretation of aerial photography. Data obtained from the first approach has in the past been incomplete, inaccurate and non-spatial. The second approach is relatively expensive and as a result is incomplete and is not regularly updated.In this paper, we describe an approach to land use/condition monitoring using remotely sensed and other data such as digital elevation models (DEMs). We outline our methodology and give examples of mapping and monitoring change in woody vegetation and salinity.

Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Sets

As a result of extensive farmland clearing over the last hundred years or so, dry-land salinity is a major problem in Western Australia. In fact, in some parts of the state, over 20 percent of Agricultural land is no longer productive. Prior to the work to be described in this chapter, no reliable large scale estimates of the extent or progression of salinity were available. This chapter describes a methodology for monitoring the historical extent of salinity, using a time series of satellite imagery, landform information derived from digital elevation models and ground truth data collected by experts with local knowledge. This work has served to highlight the salinity problem to decision makers in government and to provide input into the process of developing and applying remedial measures to arrest the spread of salinity.

Some observations on crop profile modelling

Abstract LANDSAT data are typically available for a number of overpasses during a growing season. There is currently considerable interest in modelling the so-called crop profile for such multitemporal data by a non-linear profile function of some (spectral) index for the spectral bands. The derived coefficients are used in a subsequent allocation procedure. This paper outlines some results obtained from an evaluation of the approach for crop data from the wheatbelt of Western Australia. Specifically, the degree of separation of crop classes from pasture classes, as measured by the discriminant root, is compared for analyses based on the original bands, on various spectral indices and on fitted coefficients from a crop profile function describing the temporal change in these indices. For the data considered, a marked loss of discrimination is found for analyses based on various spectral indices, when compared with those based directly on the corresponding discriminant functions (where the linear combinati...

Quantifying the discriminatory power of remote sensing technologies for benthic habitat mapping

ABSTRACT When mapping benthic habitats using remotely sensed data, the ability to discriminate between pairs of habitats is a key measure of the usefulness of a set of one or more input covariates. In the case where some input data is already available, but a superior map is sought, map-makers would like to know which additional remote sensing data would make the greatest improvement to the quality of their maps. Depending on the purpose of the map, this could be measured by the extent to which a selected pair of habitats is discriminated. This study exploits an existing data-rich study site in order to provide guidance for the use of remote sensing technology in regions where such data do not exist already. LiDAR (light detection and ranging) reflectivity, multibeam backscatter, World View 2 (WV2) bands 1–4, multibeam bathymetry, and depth-derived variables are analysed to determine the extent to which they enable benthic habitats of interest to be discriminated from one another in a statistical sense. Ground truth is employed in the form of towed video. Quantitative results are tabulated for each of the six pairs of four key habitat classes: macroalgae, seagrass, sand, and reef. The technique of Canonical Variate Analysis (CVA) is used to calculate ratios of between-class to within-class variation and cross-validated error rate estimates are calculated for the best combination of N variables, where N varies from 1 to 8. It is found that: Reef and Macroalgae classes cannot be statistically distinguished with the technologies and training methods studied here; WV2 augmented with depth provides good discrimination between the separable classes; multibeam echosounder depth and backscatter data both provide good information for mapping cover types, but in general are not as useful as optical data if it is available. LiDAR reflectivity is a very useful covariate, which has comparable discriminatory power to any one of the first three WV2 bands, with the added potential to penetrate to greater depths than the passive satellite sensors.