Peter Caccetta

Joint Processing of Landsat and ALOS-PALSAR Data for Forest Mapping and Monitoring

Recent technological advances in the field of radar remote sensing have allowed the deployment of an increasing number of new satellite sensors. These provide an important source of Earth observation data, which add to the currently existing optical data sets. In parallel, the development of robust methods for global forest monitoring and mapping is becoming increasingly important. As a consequence, there is significant interest in the development of global monitoring systems that are able to take advantage of the potential synergies and complementary nature of optical and radar data. This paper proposes an approach for the combined processing of Landsat and ALOS-PALSAR data for the purpose of forest mapping and monitoring. This is achieved by incorporating the PALSAR data into an existing operational Landsat-based processing system. Using a directed discriminant technique, a probability map of forest presence/absence is first generated from the PALSAR imagery. This SAR classification data is then combined with a time series of similar Landsat-based maps within a Bayesian multitemporal processing framework, leading to the production of a time series of joint radar-optical maps of forest extents. This approach is applied and evaluated over a pilot study area in northeastern Tasmania, Australia. Experimental outcomes of the proposed joint processing framework are provided, demonstrating its potential for the integration of different types of remote sensing data for forest monitoring purposes.

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.

A methodology to estimate the future extent of dryland salinity in the southwest of Western Australia.

In the southwestern agricultural region of Western Australia, the clearing of the original perennial vegetation for annual vegetation-based dryland agriculture has lead to rising saline groundwater levels. This has had effects such as reduced productivity of agricultural land, death of native vegetation, reduced stream water quality and infrastructure damage. These effects have been observed at many locations within the 18 million ha of cleared land. This has lead to efforts to quantify, in a spatially explicit way, the historical and likely future extent of the area affected, with the view to informing management decisions. This study was conducted to determine whether the likely future extent of the area affected by dryland salinity could be estimated by means of developing spatially explicit maps for use in management and planning. We derived catchment-related variables from digital elevation models and perennial vegetation presence/absence maps. We then used these variables to predict the salinity hazard extent by applying a combination of decision tree classification and morphological image processing algorithms. Sufficient objective data such as groundwater depth, its rate of rise, and its concentration of dissolved salts were generally not available, so we used regional expert opinion (derived from the limited existing studies on salinity hazard extent) as training and validation data. We obtained an 87% agreement in the salinity hazard extent estimated by this method compared with the validation data, and conclude that the maps are sufficient for planning. We estimate that the salinity hazard extent is 29.7% of the agricultural land.

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.

Multi-temporal land-cover classification and change analysis with conditional probability networks: The case of Lesvos Island (Greece)

This study uses a series of Landsat images to map the main land-cover types on the Mediterranean island of Lesvos, Greece. We compare a single-year maximum likelihood classification (MLC) with a multi-temporal maximum likelihood classification (MTMLC) approach, with time-series class labels modelled using a first-order hidden Markov model comprising continuous and discrete variables. A rigorous validation scheme shows statistically significant higher accuracy figures for the multi-temporal approach. Land-cover change accuracies were also greatly improved by the proposed methodology: from 46% to 70%. The results show that when only two dates are used, the mapping of land use/cover is unreliable and a large number of the changes identified are due to the individual-year commission and omission errors.

Image Fusion with Conditional Probability Networks for Monitoring the Salinization of Farmland

We show how a series of satellite images can be used in conjunction with data derived from a digital terrain model to monitor salinity in farmland. A conditional probability network (CPN) is constructed to produce salinity maps by combining uncertain information in images with uncertain knowledge or rules, where the rules are of a temporal and spatial nature. A specific model for extending conditional probability networks to handle the case of spatial context is given. To implement this model requires minor modifications to existing code for handling nonspatial CPNs.

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.

BRDF and illumination calibration for very high resolution imaging sensors

The advent of very high resolution airborne and spaceborne imaging systems provides opportunities for quantitative mapping and monitoring applications using data from such sensors. Radiometric calibration is an essential step for carrying out quantitative analysis. In this paper, a radiometric calibration approach for high resolution image data is presented, which includes Bi-directional Reflectance Distribution Function (BRDF) calibration, terrain illumination correction for removal of illumination effects caused by undulating surfaces, and detection of shadow and occlusion using high resolution Digital Surface Models (DSM). These methods are showcased in an urban environment monitoring project named Urban Monitor. This project employs photogrammetric sensors to acquire high resolution panchromatic and multi-spectral data. Some radiometric calibration results from the Urban Monitor project are presented. Particular issues such as several shadows cast by above ground objects are addressed and possible solutions are explored. Future work inspired by the recent advances from scene perception research is discussed.

Wall-to-wall mapping of forest extent and change in Tasmania using ALOS PALSAR data

Consistent estimation of carbon stocks at national level requires the integration of wall-to-wall, time-series satellite and in situ data of forest area, type and change. In this paper we demonstrate a consistent approach to the generation of wall-to-wall time-series mosaics using ALOS PALSAR data acquired over 2007 to 2009 for Tasmania, Australia. The project is part of a series of National Demonstrators initiated by the Group on Earth Observations (GEO) Forest Carbon Tracking (FCT) task that emphasize the contribution and operational use of satellite measurements for forest monitoring and national carbon accounting. Interoperability between optical and Synthetic Aperture Radar (SAR) derived forest measurements will also be demonstrated. The project will deliver a series of forest monitoring products and technical documentation, which will be made available as a guide to GEO member countries with a desire to develop their own national carbon accounting systems.

Evaluation of CBERS Image Data: Geometric and Radiometric Aspects

In Australia, Landsat imagery is currently used in a number of regional and national monitoring projects. However, the future of Landsat imagery is not assured. Both Landsat 5 and Landsat 7 are estimated to run out of fuel around 2010. With the looming gap in Landsat data continuity it is timely to consider the issues involved in using data from other sensors to continue these monitoring programs. In the context of the Australian Greenhouse Office (AGO) Land Cover Change Program (LCCP) (http://www.climatechange.gov.au/ncas), this paper describes the issues on CBERS geometric and radiometric aspects, and quantifies the effects of using CBERS images to produce forest cover maps. Other alternatives to Landsat data that are being considered by AGO are SPOT 4 and Landsat 7 SLC-off images (Furby and Wu 2006a) and the Indian Remote Sensing satellites (IRS) (Furbyand Wu 2006b).