Atmospheric correction of New Zealand Landsat imagery : a thesis presented in partial fulfilment of the requirements for the degree of Master of Philosophy in Earth Science at Massey University

In this study, MODIS data for New Zealand was downloaded and evaluated as input to the 6S atmospheric correction model. Data for one year were downloaded for aerosols, water vapour and ozone and trends of this data were studied. The sensitivity of retrieved reflectance of several targets to changes in the atmospheric components as seen in the MODIS data were also analysed. Several methods were developed for using this data for atmospheric correction and the output compared to a commercial atmospheric correction package (ATCOR 2). In addition, ground measurements were used to confirm the accuracy of the MODIS data. This involved both data obtained from NIWA and readings taken with a hand held MICROTOPS instrument. These readings showed that the MODIS data has some inaccuracies. This can result in a significant error in the retrieved reflectance, especially for darker targets, such as forest. Therefore caution should be exercised when using aerosol values from MODIS in an atmospheric correction. However, the results for water vapour and ozone were reasonably close, giving confidence for using MODIS ozone and water vapour in atmospheric correction. Ground measurements were also taken of targets with a GER 2600 Spectroradiometer and these readings compared to the atmospheric corrections of the same targets. This confirmed the accuracy of the atmospheric correction methods

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets

Remote sensing of tree-grass systems: The Eastern Australian woodlands

About 1.8 million square kilometers of Australia is, or was, covered by woodland vegetation communities, defined here as ecosystems, with widely spaced trees having an overstorey of vertically projected cover of photosynthetic foliage (FPC) of between 10% and 30%. The woodlands of Eastern Australia shown in Figure 9.1 are mostly located within the states of Queensland and New South Wales. This area includes tropical and subtropical Savannas in the north and temperate and Mediterranean woodlands in the southern areas. The woodlands are located between the higher rainfall coastal areas to the east and arid areas to the west. The vegetation types within these woodland areas are quite diverse due to many factors including climate, geology, soils, presence or absence of fire, and land management practices (Lindenmayer et al., 2005). The predominant land use of these woodland areas is grazing of cattle and sheep. In all areas of northern Queensland where precipitation exceeds potential evaporation, vegetation growth is water limited, and trees and grasses compete for rainfall. This has lead to widespread clearing of woody vegetation to increase pasture productivity, and significant areas of woodland have also been cleared for agriculture, particularly those on more fertile clay soils. Some ecosystems have been cleared to the extent that less than 10% of the pre-clearing area remains intact (Fensham et al., 2003). The clearing of woody vegetation has significant biodiversity and greenhouse gas emission implications (Henry et al., 2005)

Alternatives to Landsat-5 Thematic Mapper for operational monitoring of vegetation cover: considerations for natural resource management agencies

There is a serious concern as to whether the Landsat-5 Thematic Mapper (TM) will provide imagery up until the launch of the Landsat Data Continuity Mission (LDCM), which is expected in December 2012. The concern is due to fuel shortages and sporadic satellite and sensor problems. The Landsat-7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) malfunction in 2003 means that ETM+ imagery contains strips of missing data. Consequently there is concern that the highest quality Landsat imagery may not be available for monitoring for the years 2011 and 2012. Natural resource monitoring agencies, therefore, are faced with the serious challenge of determining a suitable alternative to Landsat-5 TM imagery should the need arise. While literature exists on possible alternatives, there is a lack of studies that provide guidelines to monitoring agencies on the issues to be explored when considering an alternative data source. We undertook a study that identified Landsat-7 ETM+, SPOT4 HRVIR, SPOT5 HRG, and IRS-P6 LISS-III as the most suitable alternatives for monitoring vegetation cover in Queensland and New South Wales (NSW) in eastern Australia. None of the sensors were found to be ideal candidates due to a combination of one or more of lower radiometric quality, increased data volumes, additional processing requirements, and higher purchasing costs. We found that the low cost and ease of access to Landsat-7 ETM+ made it a technically and economically viable option for annual monitoring of woody vegetation extent and change in eastern Australia. However, Landsat-7 ETM+ may not be an option in many areas of the world due to high cloud coverage. We used the experiences gained through this work to recommend a process that natural resource management agencies can use to explore the issues related to selecting an alternative to Landsat-5 TM for land cover monitoring

Alternatives to Landsat-5 Thematic Mapper for operational monitoring of vegetation cover: considerations for natural resource management agencies

La possibilite´ que le capteur Thematic Mapper (TM) de Landsat 5 ne puisse fournir des images jusqu'au moment du lancement de la mission LDCM (« Landsat Data Continuity Mission ») pre´vu pour de´cembre 2012 suscite des inquie´tudes. La crainte vient du manque de combustible et des proble`mes sporadiques du satellite et du capteur. Le mauvais fonctionnement du correcteur de lignes de balayage (SLC) du capteur ETM+ (« Enhanced Thematic Mapper ») de Landsat 7 en 2003 fait que les images de ETM+ renferment des bandes de donne´es manquantes. Conse´quemment, on craint que les images de qualite´ supe´rieure de Landsat ne soient pas disponibles pour fins de suivi durant les anne´es 2011 et 2012. Les agences de suivi des ressources naturelles font ainsi face au de´fi conside´rable de de´terminer une alternative ade´quate aux images de TM de Landsat 5 en cas de besoin. Bien qu'il existe une litte´rature sur les alternatives possibles, il y a peu d'e´tudes qui donnent des directives aux agences de suivi sur les proble´matiques a` explorer lorsque l'on conside`re une source de donne´es alternatives. On a entrepris une e´tude qui a permis d'identifier les donne´es de ETM+ de Landsat 7, HRVIR de SPOT 4, HRG de SPOT 5 et de LISS-III de IRS-P6 comme e´tant les alternatives les mieux adapte´es pour le suivi du couvert de ve´ge´tation dans le Queensland et la Nouvelle-Galles du Sud (NSW) dans l'est de l'Australie. On a trouve´ qu'aucun des capteurs n'e´tait ide´al a` cause d'une combinaison de l'un ou de plusieurs des facteurs suivants dont la qualite´ radiome´trique re´duite, les volumes de donne´es accrus, les besoins de traitement supple´mentaires et les couˆ ts d'acquisition plus e´leve´s. On a trouve´ que le faible couˆ t et la facilite´ d'acce`s des donne´es de ETM+ de Landsat 7 rendaient cette option plus viable aux plans technique et e´conomique pour le suivi annuel de l'e´tendue et deschangements de la ve´ge´tation ligneuse dans l'est de l'Australie. Cependant, ETM+ de Landsat 7 ne peut eˆtre conside´re´ comme une option dans plusieurs re´gions du monde duˆ a` l'importance du couvert nuageux. Nous avons utilise´ les expe´riences acquises durant ce travail pour recommander une proce´dure que les agences de suivi des ressources naturelles peuvent mettre de l'avant pour explorer les proble´matiques relie´es au choix d'une alternative aux donne´es de TM de Landsat 5 pour le suivi du couvert

An operational scheme for deriving standardised surface reflectance from landsat TM/ETM+ and SPOT HRG imagery for eastern Australia

Operational monitoring of vegetation and land surface change over large areas can make good use of satellite sensors that measure radiance reflected from the Earth's surface. Monitoring programs use multiple images for complete spatial coverage over time. Accurate retrievals of vegetation cover and vegetation change estimates can be hampered by variation, in both space and time, in the measured radiance, caused by atmospheric conditions, topography, sensor location, and sun elevation. In order to obtain estimates of cover that are comparable between images, and to retrieve accurate estimates of change, these sources of variation must be removed. In this paper we present a preprocessing scheme for minimising atmospheric, topographic and bi-directional reflectance effects on Landsat-5 TM, Landsat-7 ETM+ and SPOT-5 HRG imagery. The approach involves atmospheric correction to compute surface-leaving radiance, and bi-directional reflectance modelling to remove the effects of topography and angular variation in reflectance. The bi-directional reflectance model has been parameterised for eastern Australia, but the general approach is more widely applicable. The result is surface reflectance standardised to a fixed viewing and illumination geometry. The method can be applied to the entire record for these instruments, without intervention, which is of increasing importance with the increased availability of long termimage archives. Validation shows that the corrections improve the estimation of reflectance at any given angular configuration, thus allowing the removal from the reflectance signal of much variation due to factors independent of the land surface. The method has been used to process over 45,000 Landsat-5 TM and Landsat-7 ETM+ scenes and 2,500 SPOT-5 scenes, over eastern Australia, and is now in use in operational monitoring programs