Application of advanced techniques for the remote detection, modelling and spatial analysis of mesquite (prosopis spp.) invasion in Western Australia

Invasive plants pose serious threats to economic, social and environmental interests throughout the world. Developing strategies for their management requires a range of information that is often impractical to collect from ground based surveys. In other cases, such as retrospective analyses of historical invasion rates and patterns, data is rarely, if ever, available from such surveys. Instead, historical archives of remotely sensed imagery provides one of the only existing records, and are used in this research to determine invasion rates and reconstruct invasion patterns of a ca 70 year old exotic mesquite population (Leguminoseae: Prosopis spp.) in the Pilbara Region of Western Australia, thereby helping to identify ways to reduce spread and infill. A model was then developed using this, and other, information to predict which parts of the Pilbara are most a risk. This information can assist in identifying areas requiring the most vigilant intervention and pre-emptive measures. Precise information of the location and areal extent of an invasive species is also crucial for land managers and policy makers for crafting management strategies aimed at control, confinement or eradication of some or all of the population. Therefore, the third component of this research was to develop and test high spectral and spatial resolution airborne imagery as a potential monitoring tool for tracking changes at various intervals and quantifying the effectiveness of management strategies adopted. To this end, high spatial resolution digital multispectral imagery (4 channels, 1 m spatial resolution) and hyperspectral imagery (126 channels, 3 m spatial resolution) was acquired and compared for its potential for distinguishing mesquite from coexisting species and land covers.These three modules of research are summarised hereafter. To examine the rates and patterns of mesquite invasion through space and time, canopies were extracted from a temporal series of panchromatic aerial photography over an area of 450 ha using unsupervised classification. Non-mesquite trees and shrubs were not discernible from mesquite using this imagery (or technique) and so were masked out using an image acquired prior to invasion. The accuracy of the mesquite extractions were corroborated in the field and found to be high (R2 = 0.98, P36 m2 (66-94%) with both approaches and image types. However, both approaches used on the hyperspectral imagery were more reliable at capturing patches >36 m2 than the DMSI using either approach. The lowest omission and commission rates were obtained using pairwise separation on the hyperspectral imagery, which was significantly more accurate than DMSI using an overall separation approach (Z=2.78, P36 m2. However, hyperspectral imagery processed using pairwise separation appears to be superior, even though not statistically different to hyperspectral imagery processed using overall separation or DMSI processed using pairwise separation at the 95% confidence level. Mapping smaller patches may require the use of very high spatial resolution imagery, such as that achievable from unmanned airborne vehicles, coupled with a hyperspectral instrument. Alternatively, management may continue to rely on visual airborne surveys flown at low altitude and speed, which have proven to be capable at mapping small and isolated mesquite shrubs in the study area used in this research

A Probabilistic Assessment of Soil Erosion Susceptibility in a Head Catchment of the Jemma Basin, Ethiopian Highlands

Soil erosion represents one of the most important global issues with serious effects on agriculture and water quality, especially in developing countries, such as Ethiopia, where rapid population growth and climatic changes affect widely mountainous areas. The Meskay catchment is a head catchment of the Jemma Basin draining into the Blue Nile (Central Ethiopia) and is characterized by high relief energy. Thus, it is exposed to high degradation dynamics, especially in the lower parts of the catchment. In this study, we aim at the geomorphological assessment of soil erosion susceptibilities. First, a geomorphological map was generated based on remote sensing observations. In particular, we mapped three categories of landforms related to (i) sheet erosion, (ii) gully erosion, and (iii) badlands using a high-resolution digital elevation model (DEM). The map was validated by a detailed field survey. Subsequently, we used the three categories as dependent variables in a probabilistic modelling approach to derive the spatial distribution of the specific process susceptibilities. In this study we applied the maximum entropy model (MaxEnt). The independent variables were derived from a set of spatial attributes describing the lithology, terrain, and land cover based on remote sensing data and DEMs. As a result, we produced three separate susceptibility maps for sheet and gully erosion as well as badlands. The resulting susceptibility maps showed good to excellent prediction performance. Moreover, to explore the mutual overlap of the three susceptibility maps, we generated a combined map as a color composite where each color represents one component of water erosion. The latter map yields useful information for land-use managers and planning purposes

Reconciling the contribution of environmental and stochastic structuring of tropical forest diversity through the lens of imaging spectroscopy.

Both niche and stochastic dispersal processes structure the extraordinary diversity of tropical plants, but determining their relative contributions has proven challenging. We address this question using airborne imaging spectroscopy to estimate canopy β-diversity for an extensive region of a Bornean rainforest and challenge these data with models incorporating niches and dispersal. We show that remotely sensed and field-derived estimates of pairwise dissimilarity in community composition are closely matched, proving the applicability of imaging spectroscopy to provide β-diversity data for entire landscapes of over 1000 ha containing contrasting forest types. Our model reproduces the empirical data well and shows that the ecological processes maintaining tropical forest diversity are scale dependent. Patterns of β-diversity are shaped by stochastic dispersal processes acting locally whilst environmental processes act over a wider range of scales

Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms

A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hot spot regions i n the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re - densification mechanisms. Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms

Earth Resources: A continuing bibliography, issue 28

This bibliography lists 436 reports, articles, and other documents introduced into the NASA scientific and technical information system between October 1, 1980 and December 31, 1980. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems instrumentation and sensors, and economic analysis

An integrative approach using remote sensing and social analysis to identify different settlement types and the specific living conditions of its inhabitants

Someday in 2007, the world population reached a historical landmark: for the first time in human history, more than half of the world´s population was urban. A stagnation of this urbanization process is not in sight, so that by 2050, already 70 percent of humankind is projected to live in urban settlements. Over the last few decades, enormous migrations from rural hinterlands to steadily growing cities could be witnessed coming along with a dramatic growth of the world’s urban population. The speed and the scale of this growth, particularly in the so called less developed regions, are posing tremendous challenges to the countries concerned as well as to the world community. Within mega cities the strongest trends and the most extreme dimensions of the urbanization process can be observed. Their rapid growth results in uncontrolled processes of fragmentation which is often associated with pronounced poverty, social inequality, socio-spatial and political fragmentation, environmental degradation as well as population demands that outstrip environmental service capacity. For the majority of the mega cities a tremendous increase of informal structures and processes has to be observed. Consequentially informal settlements are growing, which represent those characteristic municipal areas being subject to particularly high population density, dynamics as well as marginalization. They have quickly become the most visible expression of urban poverty in developing world cities. Due to the extreme dynamics, the high complexity and huge spatial dimension of mega cities, urban administrations often only have an obsolete or not even existing data basis available to be at all informed about developments, trends and dimensions of urban growth and change. The knowledge about the living conditions of the residents is correspondingly very limited, incomplete and not up to date. Traditional methods such as statistical and regional analyses or fieldwork are no longer capable to capture such urban process. New data sources and monitoring methodologies are required in order to provide an up to date information basis as well as planning strate¬gies to enable sustainable developments and to simplify planning processes in complex urban structures. This research shall seize the described problem and aims to make a contribution to the requirements of monitoring fast developing mega cities. Against this background a methodology is developed to compensate the lack of socio-economic data and to deduce meaningful information on the living conditions of the inhabitants of mega cities. Neither social science methods alone nor the exclusive analysis of remote sensing data can solve the problem of the poor quality and outdated data base. Conventional social science methods cannot cope with the enormous developments and the tremendous growth as they are too labor-, as well as too time- and too cost-intensive. On the other hand, the physical discipline of remote sensing does not allow for direct conclusions on social parameters out of remote sensing images. The prime objective of this research is therefore the development of an integrative approach − bridging remote sensing and social analysis – in order to derive useful information about the living conditions in this specific case of the mega city Delhi and its inhabitants. Hence, this work is established in the overlapping range of the research topics remote sensing, urban areas and social science. Delhi, as India’s fast growing capital, meanwhile with almost 25 million residents the second largest city of the world, represents a prime example of a mega city. Since the second half of the 20th century, Delhi has been transformed from a modest town with mainly administrative and trade-related functions to a complex metropolis with a steep socio-economic gradient. The quality and amount of administrative and socio-economic data are poor and the knowledge about the circumstances of Delhi’s residents is correspondingly insufficient and outdated. Delhi represents therefore a perfectly suited study area for this research. In order to gather information about the living conditions within the different settlement types a methodology was developed and conducted to analyze the urban environment of the mega city Delhi. To identify different settlement types within the urban area, regarding the complex and heterogeneous appearance of the Delhi area, a semi-automated, object-oriented classification approach, based on segmentation derived image objects, was implemented. As the complete conceptual framework of this research, the classification methodology was developed based on a smaller representative training area at first and applied to larger test sites within Delhi afterwards. The object-oriented classification of VHR satellite imagery of the QuickBird sensor allowed for the identification of five different urban land cover classes within the municipal area of Delhi. In the focus of the image analysis is yet the identification of different settlement types and amongst these of informal settlements in particular. The results presented within this study demonstrate, that, based on density classes, the developed methodology is suitable to identify different settlement types and to detect informal settlements which are mega urban risk areas and thus potential residential zones of vulnerable population groups. The remote sensing derived land cover maps form the foundation for the integrative analysis concept and deliver there¬fore the general basis for the derivation of social attributes out of remote sensing data. For this purpose settlement characteristics (e.g., area of the settlement, average building size, and number of houses) are estimated from the classified QuickBird data and used to derive spatial information about the population distribution. In a next step, the derived information is combined with in-situ information on socio-economic conditions (e.g., family size, mean water consumption per capita/family) extracted from georeferenced questionnaires conducted during two field trips in Delhi. This combined data is used to characterize a given settlement type in terms of specific population and water related variables (e.g., population density, total water consumption). With this integrative methodology a catalogue can be compiled, comprising the living conditions of Delhi’s inhabitants living in specific settlement structures – and this in a quick, large-scaled, cost effective, by random or regularly repeatable way with a relatively small required data basis.The combined application of remotely sensed imagery and socio-economic data allows for the mapping, capturing and characterizing the socio-economic structures and dynamics within the mega city of Delhi, as well as it establishes a basis for the monitoring of the mega city of Delhi or certain areas within the city respectively by remote sensing. The opportunity to capture the condition of a mega city and to monitor its development in general enables the persons in charge to identify unbeneficial trends and to intervene accordingly from an urban planning perspective and to countersteer against a non-adequate supply of the inhabitants of different urban districts, primarily of those of informal settlements. This study is understood to be a first step to the development of methods which will help to identify and understand the different forms, actors and processes of urbanization in mega cities. It could support a more proactive and sustainable urban planning and land management – which in turn will increase the importance of urban remote sensing techniques. In this regard, the most obvious and direct beneficiaries are on the one hand the governmental agencies and urban planners and on the other hand, and which is possibly the most important goal, the inhabitants of the affected areas, whose living conditions can be monitored and improved as required. Only if the urban monitoring is quickly, inexpensively and easily available, it will be accepted and applied by the authorities, which in turn enables for the poorest to get the support they need. All in all, the listed benefits are very convincing and corroborate the combined use of remotely sensed and socio-economic data in mega city research

APPLICATION OF IMAGE ANALYSIS TECHNIQUES TO SATELLITE CLOUD MOTION TRACKING

Cloud motion wind (CMW) determination requires tracking of individual cloud targets. This is achieved by first clustering and then tracking each cloud cluster. Ideally, different cloud clusters correspond to diiferent pressure levels. Two new clustering techniques have been developed for the identification of cloud types in multi-spectral satellite imagery. The first technique is the Global-Local clustering algorithm. It is a cascade of a histogram clustering algorithm and a dynamic clustering algorithm. The histogram clustering algorithm divides the multi-spectral histogram into'non-overlapped regions, and these regions are used to initialise the dynamic clustering algorithm. The dynamic clustering algorithm assumes clusters have a Gaussian distributed probability density function with diiferent population size and variance. The second technique uses graph theory to exploit the spatial information which is often ignored in per-pixel clustering. The algorithm is in two stages: spatial clustering and spectral clustering. The first stage extracts homogeneous objects in the image using a family of algorithms based on stepwise optimization. This family of algorithms can be further divided into two approaches: Top-down and Bottom-up. The second stage groups similar segments into clusters using a statistical hypothesis test on their similarities. The clusters generated are less noisy along class boundaries and are in hierarchical order. A criterion based on mutual information is derived to monitor the spatial clustering process and to suggest an optimal number of segments. An automated cloud motion tracking program has been developed. Three images (each separated by 30 minutes) are used to track cloud motion and the middle image is clustered using Global-Local clustering prior to tracking. Compared with traditional methods based on raw images, it is found that separation of cloud types before cloud tracking can reduce the ambiguity due to multi-layers of cloud moving at different speeds and direction. Three matching techniques are used and their reliability compared. Target sizes ranging from 4 x 4 to 32 x 32 are tested and their errors compared. The optimum target size for first generation METEOSAT images has also been found.Meteorological Office, Bracknel

Characterizing Human-Environment Interactions in the Galápagos Islands: A Case Study of Land Use/Land Cover Dynamics in Isabela Island

This dissertation examines contemporary land use and land cover (LULC) change in the communities and protected areas of Isabela Island to provide insights into human-environment interactions in the Galápagos Islands of Ecuador. The growing human presence in Galápagos over the last four decades has been accompanied by significant changes in LULC on inhabited islands in the archipelago. Local stakeholders and decision-makers have recently called for a more integrative approach to understanding interactions between people and the environment in the archipelago. This study is guided by two complementary bodies of work situated within the human-environment tradition of Geography - land change science and landscape ecology. First, support Vector Machine (SVM) and Object Based Image Analysis (OBIA) classifiers are evaluated for mapping LULC from high spatial resolution satellite images. The results show that thematic LULC classifications produced by OBIA are more accurate overall than those generated by SVM. However, important tradeoffs exist between improvements in classification accuracy and processing requirements. The composition and spatial configuration of LULC change are then mapped and quantified from a time series of QuickBird and WorldView-2 satellite images from 2003 to 2010. The pattern metric and change detection analyses reveal that land use change is extensive within the communities due to the expansion and consolidation of built-up areas, and fragmentation of and declines in agriculture. The Galápagos National Park is primarily transformed by exotic plant invasion, forests expansion, and shrinking coastal lagoons. Patterns of agricultural land abandonment, plant invasion, and forest expansion over the same period are described from pattern metric and overlay analyses. Potential drivers of these LULC transitions are identified from logistic regression models, descriptive statistics of agricultural surveys and population censuses, and interviews with landowners. The results reveal that agricultural abandonment is widespread throughout Isabela, and many abandoned fields are invaded by introduced plants, such as guava. Biophysical and geographic factors, such as topography and distance to roads, do not significantly explain patterns of agricultural land abandonment or associated land cover transitions at the pixel level. However, rural-urban migration, declines in the profitability of agriculture, and small labor pools appear to influence agricultural abandonment.Doctor of Philosoph

Urban Growth and Its Impact on Urban Heat Sink and Island Formation in the Desert City of Dubai.

The rapid pace of urban growth in Dubai has attracted the attention of economists, environmentalists and urban planners. This thesis quantifies the extent of urbanisation within the Emirate since the discovery of oil and investigates the impacts of such growth on urban temperatures. The study used remotely-sensed imagery in the absence of publicly available data on city growth and microclimate. The study used a hybrid classification method and landscape metrics to capture historical urban forms, rates and engines of growth in the Emirate. Stepwise multiple regression analysis techniques were subsequently used to investigate the relationship between the rate and form of urbanisation and the intensity of the urban heat sink between 1990 and 2011. Local Climate Zones were then developed to specifically investigate the impacts of urban geometry variables and proximity to water on both urban heat sinks during the day-time and urban heat islands during the night. The study revealed a significant increase in urban area over time (1972-2011) with accelerated phases of growth, linked to local and global economic conditions, occurring during specific periods. Physical urban growth has now outpaced population growth, indicating urban sprawl. This growth has occurred at the expense of sand and has included a significant increase in vegetation and water bodies unlike other desert cities in the Gulf region. The results demonstrated that urban growth has promoted a heat sink effect during daytime and that all urban land use types contributed to this effect. Urban albedo was not responsible for the daytime urban heat sink; other factors including the specific heat capacity of urban materials, urban geometry and proximity to the Gulf were mainly responsible. Furthermore, increases in vegetation cover and impervious surface cover over time have contributed to the daytime (morning) urban heat sink. At night-time, urban geometry and proximity to the Gulf were the major influences upon the formation of urban heat islands. This research contributes to better understanding of urbanisation in desert cities as demonstrated through Dubai, revealing previously unknown spatiotemporal variations in urban areas across the city through the use of a time-series of satellite images. The findings provide new insights into the impacts of land cover, land use, proximity to water and urban geometry on the formation of urban heat sinks and urban heat islands in the desert environment