Large-Scale Urban Impervous Surfaces Estimation Through Incorporating Temporal and Spatial Information into Spectral Mixture Analysis

With rapid urbanization, impervious surfaces, a major component of urbanized areas, have increased concurrently. As a key indicator of environmental quality and urbanization intensity, an accurate estimation of impervious surfaces becomes essential. Numerous automated estimation approaches have been developed during the past decades. Among them, spectral mixture analysis (SMA) has been recognized as a powerful and widely employed technique. While SMA has proven valuable in impervious surface estimation, effects of temporal and spectral variability have not been successfully addressed. In particular, impervious surface estimation is likely to be sensitive to seasonal changes, majorly due to the shadowing effects of vegetation canopy in summer and the confusion between impervious surfaces and soil in winter. Moreover, endmember variability and multi-collinearity have adversely impacted the accurate estimation of impervious surface distribution with coarse resolution remote sensing imagery. Therefore, the main goal of this research is to incorporate temporal and spatial information, as well as geostatistical approaches, into SMA for improving large-scale urban impervious surface estimation. Specifically, three new approaches have been developed in this dissertation to improve the accuracy of large-scale impervious surface estimation. First, a phenology based temporal mixture analysis was developed to address seasonal sensitivity and spectral confusion issues with the multi-temporal MODIS NDVI data. Second, land use land cover information assisted temporal mixture analysis was proposed to handle the issue of endmember class variability through analyzing the spatial relationship between endmembers and surrounding environmental and socio-economic factors in support of the selection of an appropriate number and types of endmember classes. Third, a geostatistical temporal mixture analysis was developed to address endmember spectral variability by generating per-pixel spatial varied endmember spectra. Analysis results suggest that, first, with the proposed phenology based temporal mixture analysis, a significant phenophase differences between impervious surfaces and soil can be extracted and employed in unmxing analysis, which can facilitate their discrimination and successfully address the issue of seasonal sensitivity and spectral confusion. Second, with the analyzed spatial distribution relationship between endmembers and environmental and socio-economic factors, endmember classes can be identified with clear physical meanings throughout the whole study area, which can effectively improve the unmixing analysis results. Third, the use of the spatially varying per-pixel endmember generated from the geostatistical approach can effectively consider the endmember spectra spatial variability, overcome the endmember within-class variability issue, and improve the accuracy of impervious surface estimates. Major contributions of this research can be summarized as follows. First, instead of Landsat Thematic Mapper (TM) images, MODIS imageries with large geographic coverage and high temporal resolution have been successfully employed in this research, thus making timely and regional estimation of impervious surfaces possible. Second, this research proves that the incorporation of geographic knowledge (e.g. phonological knowledge, spatial interaction, and geostatistics) can effectively improve the spectral mixture analysis model, and therefore improve the estimation accuracy of urban impervious surfaces

Spectral Mixture Analysis for Monitoring and Mapping Desertification Processes in Semi-arid Areas in North Kordofan State, Sudan

Multi-temporal remotely sensed data (MSS, TM and ETM+)were used for monitoring and mapping the desertification processes in North Kordofan State, Sudan.A liear mixture model (LMM) was adopted to analyse and the desertification proccesses by using the image endmembers. interpretation of ancillary data and field observation was adopted to verfiy the role of human impacts in the temporal changes in the study area. The findings of the study proved the powerfull of remotely sensed data in monitoring and mapping the desertification processes and come out with valuable recommendations which could contribute positively in reducing desert encroachment in the area

Spectral Mixture Analysis for Monitoring and Mapping Desertification Processes in Semi-arid Areas in North Kordofan State, Sudan

Multi-temporal remotely sensed data (MSS, TM and ETM+)were used for monitoring and mapping the desertification processes in North Kordofan State, Sudan.A liear mixture model (LMM) was adopted to analyse and the desertification proccesses by using the image endmembers. interpretation of ancillary data and field observation was adopted to verfiy the role of human impacts in the temporal changes in the study area. The findings of the study proved the powerfull of remotely sensed data in monitoring and mapping the desertification processes and come out with valuable recommendations which could contribute positively in reducing desert encroachment in the area

Mapping urban surface materials with imaging spectroscopy data on different spatial scales

This work focuses on the development of methods for mapping urban surface materials by means of imaging spectroscopy data with different spatial resolution. General findings from this work represent a sensor- and site-independent framework for the automated extraction of spectrally pure pixels using an urban image spectral library while coping with its potential incompleteness. The extraction of spectrally pure pixels serves as a basic prerequisite for the subsequent use of image analysis methods to obtain detailed urban surface material maps. These material maps enabled the determination of gradual material transitions that were finally related to complex spectral mixtures resulting from 30 m spatial resolution imaging spectroscopy data to analyse typical material compositions within certain administrative units. The findings demonstrate the great potential of using upcoming spaceborne imaging spectroscopy data for a regular area-wide mapping of surface materials in urban areas. Im Fokus dieser Arbeit stand die Entwicklung von Methoden zur Kartierung urbaner Oberflächenmaterialien mittels abbildender Spektroskopiedaten unterschiedlicher räumlicher Auflösung. Das vorgestellte Konzept zur automatisierten sensor- und ortsunabhängigen Extraktion spektral reiner Pixel aus flugzeuggetragenen Fernerkundungsdaten berücksichtigt dabei die mögliche Unvollständigkeit einer urbanen Bildspektralbibliothek. Die Extraktion spektral reiner Pixel dient als Grundvoraussetzung für den späteren Einsatz von Bildanalyseverfahren zur Gewinnung detaillierter Kartierungen urbaner Oberflächenmaterialien. Aus diesen sind Materialgradienten ableitbar, die mit den komplexen Spektralmischungen aus Hyperspektraldaten mit 30 m räumlicher Auflösung in Verbindung gebracht wurden. Die Analyse typischer Materialzusammensetzungen innerhalb städtischer Verwaltungseinheiten zeigt das enorme Potential zukünftiger Hyperspektralsatelliten für die Erfassung des Materialvorkommens von Städten

Quantifying the spatio-temporal dynamics of woody plant encroachment using an integrative remote sensing, GIS, and spatial modeling approach.

Despite a longstanding universal concern about and intensive research into woody plant encroachment (WPE)---the replacement of grasslands by shrub- and woodlands---our accumulated understanding of the process has either not been translated into sustainable rangeland management strategies or with only limited success. In order to increase our scientific insights into WPE, move us one step closer toward the sustainable management of rangelands affected by or vulnerable to the process, and identify needs for a future global research agenda, this dissertation presents an unprecedented critical, qualitative and quantitative assessment of the existing literature on the topic and evaluates the utility of an integrative remote sensing, GIS, and spatial modeling approach for quantifying the spatio-temporal dynamics of WPE.In sum, this dissertation demonstrates that integrative remote sensing, GIS, and spatial modeling approaches have enormous potential for addressing questions relevant to both rangelands research and management. However, it also suggests that much work remains to be done before we can translate our understanding of WPE into sustainable rangeland management strategies. In particular, we need to more fully explore the limitations and potentials of currently available data and techniques for quantifying WPE; build structures for data sharing and integration; develop a set of relevant standards; more actively engage in collaborative research efforts; and foster cross-cutting dialogues among researchers, managers, and communities.Specifically, this research demonstrates that the application of cutting-edge remote sensing techniques (Multiple Endmember Spectral Mixture Analysis, fuzzy logic-based change detection) to conventional medium spatial and spectral resolution imagery (Landsat Thematic Mapper, Landsat Enhanced Thematic Mapper Plus, ASTER) can be used to generate spatially explicit estimates of temporal changes in the abundance of woody plants and other surface materials. The research also shows that spatial models (Geographically Weighted Regression, Weights of Evidence, Weighted Logistic Regression) integrating this timely remotely sensed information with readily available GIS data can yield reasonably accurate estimates of an area's relative vulnerability to WPE and of the importance of anthropogenic and geoecological variables influencing the process. Such models may also be used for the testing of existing and generation of new scientific hypotheses about WPE, for evaluating the impact of natural or human-induced modifications of a landscape on the landscape's vulnerability to WPE, and for identifying target areas for conservation, restoration, or other management objectives.Findings from this research suggest that gaps in our current understanding of WPE and difficulties in devising sustainable rangeland management strategies are in part due to the complex spatio-temporal web of interactions between geoecological and anthropogenic variables involved in the process as well as limitations of presently available data and techniques. However, an in-depth analysis of the published literature also reveals that aforementioned problems are caused by two further crucial factors: the absence of information acquisition and reporting standards and the relative lack of long-term, large-scale, multi-disciplinary research efforts. The methodological framework proposed in this dissertation yields data that are easily standardized according to various criteria and facilitates the integration of spatially explicit data generated by a variety of studies. This framework may thus provide one common ground for scientists from a diversity of fields. Also, it has utility for both research and management

A multiscale remote sensing assessment of subtropical indigenous forests along the wild coast, South Africa

The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level

An investigation into using textural analysis and change detection techniques on medium and high spatial resolution imagery for monitoring plantation forestry operations.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2006.Plantation forestry involves the management of man-made industrial forests for the purpose of producing raw materials for the pulp and paper, saw milling and other related wood products industries. Management of these forests is based on the cycle of planting, tending and felling of forest stands such that a sustainable operation is maintained. The monitoring and reporting of these forestry operations is critical to the successful management of the forestry industry. The aim of this study was to test whether the forestry operations of clear-felling, re-establishment and weed control could be qualitatively and quantitatively monitored through the application of classification and change detection techniques to multi-temporal medium (15-30 m) and a combination of textural analysis and change detection techniques on high resolution (0.6-2.4 m) satellite imagery. For the medium resolution imagery, four Landsat 7 multi-spectral images covering the period from March 2002 to April 2003 were obtained over the midlands of KwaZulu-Natal, South Africa, and a supervised classification, based on the Maximum Likelihood classifier, as well as two unsupervised classification routines were applied to each of these images. The supervised classification routine used 12 classes identified from ground-truthing data, while the unsupervised classification was done using 10 and 4 classes. NDVI was also calculated and used to estimate vegetation status. Three change detection techniques were applied to the unsupervised classification images, in order to determine where clear-felling, planting and weed control operations had occurred. An Assisted "Classified" Image change detection technique was applied to the Ten-Class Unsupervised Classification images, while an Assisted "Quantified Classified" change detection technique was applied to the Four-Class Unsupervised Classification images. An Image differencing technique was applied to the NDVI images. For the high resolution imagery, a series of QuickBird images of a plantation forestry site were used and a combination of textural analysis and change detection techniques was tested to quantify weed development in replanted forest stands less than 24 months old. This was achieved by doing an unsupervised classification on the multi-spectral bands, and an edge-enhancement on the panchromatic band. Both the resultant datasets were then vectorised, unioned and a matrix derived to determine areas of high weed. It was found that clear-felling operations could be identified with accuracy in excess of 95%. However, using medium resolution imagery, newly planted areas and the weed status of forest stands were not definitively identified as the spatial resolution was too coarse to separate weed growth from tree stands. Planted stands younger than one year tended to be classified in the same class as bare ground or ground covered with dead branches and leaves, even if weeds were present. Stands older than one year tended to be classified together in the same class as weedy stands, even where weeds were not present. The NDVI results indicated that further research into this aspect could provide more useful information regarding the identification of weed status in forest stands. Using the multi-spectral bands of the high resolution imagery it was possible to identify areas of strong vegetation, while crop rows were identifiable on the panchromatic band. By combining these two attributes, areas of high weed growth could be identified. By applying a post-classification change detection technique on the high weed growth classes, it was possible to identify and quantify areas of weed increase or decrease between consecutive images. A theoretical canopy model was also derived to test whether it could identify thresholds from which weed infestations could be determined. The conclusions of this study indicated that medium resolution imagery was successful in accurately identifying clear-felled stands, but the high resolution imagery was required to identify replanted stands, and the weed status of those stands. However, in addition to identifying the status of these stands, it was also possible to quantify the level of weed infestation. Only wattle (Acacia mearnsii) stands were tested in this manner but it was recommended that in addition to applying these procedures to wattle stands, they also are tested in Eucalyptus and Pinus stands. The combination of textural analysis on the panchromatic band and classification of multi-spectral bands was found to be a suitable process to achieve the aims of this study, and as such were recommended as standard procedures that could be applied in an operational plantation forest monitoring environment

Visible-to-Near-Infrared Spectral Variability of Hydrated Sulfates and Candidate Mars Landing Sites: Implications for the Mastcam-Z Investigation on NASA’s Mars-2020 Rover Mission

The Mars-2020 rover mission will explore an astrobiologically relevant ancient environment on Mars, establish geologic context of the region, assess past habitability, and cache rocks for a future sample return. The Mastcam-Z instrument is a stereoscopic, zoom-enabled multispectral imager that will be critical to these objectives. As one of the mission’s primary reconnaissance tools, Mastcam-Z’s two cameras will acquire red-green-blue (RGB) true-color images and visible-to-near-infrared (VNIR) images with 11 narrowband filters from ~400-1100 nm. Mastcam-Z’s new ~975 nm spectral filter will help characterize the ~950-1000 nm absorption band in hydrated minerals, which has not been resolvable by previous rover multispectral imagers. We hypothesize that this filter will allow Mastcam-Z to better characterize VNIR hydration bands in hydrated sulfates, although mineral mixtures, iron-oxide dust contamination, and varying grain size will affect band depths. At the time of this writing, three candidate landing sites are being considered for the Mars-2020 mission, each bearing mineralogical units extensively investigated by high-resolution orbital SWIR (short-wave-infrared) imagery. We also hypothesize that Mastcam-Z VNIR spectral parameters can distinguish the prominent geologic units characterized by orbital SWIR at each landing site, although iron-oxide dust distribution is the suspected primary control on VNIR spectral variability on Mars. Synthetic magnesium and calcium sulfate samples were measured with a laboratory spectrometer and acquired spectra were convolved to expected Mastcam-Z resolution to identify the spectral filter combinations most sensitive to signatures of hydration. Sulfate samples were subject to bimodal mineral mixing, grain size separation, and contamination with a martian dust simulant to quantify the spectral effects these properties have on the ~950-1000 nm hydration band. SWIR imagery from the Mars 2020 candidate landing sites were also convolved to expected Mastcam-Z resolution. Spectral parameters were then developed at expected Mastcam-Z resolution that would corroborate mineral detections made by extensively-tested SWIR spectral parameters and SWIR spectra of prominent units. Mastcam-Z-simulated hydrated sulfate spectra reveal that Mastcam-Z can detect hydration in bimodal mixtures of hydrated Mg-sulfates but bimodal mixtures Ca-sulfates may present challenge unless significantly gypsum-rich. Iron-oxide dust contamination significantly shallows the ~950-1000 nm hydration band in hydrated sulfates, whereas band depth generally increases with increasing grain size. Mastcam-Z-simulated orbital imagery reveals distinct Mastcam-Z VNIR spectral parameters distinguishing prominent geologic units for each landing site, although modified versions of Pancam parameters are recommended for the Columbia Hills. Seasonal changes in spectral variability at the Columbia Hills and regional variation in spectral parameter effectiveness at NE Syrtis indicate VNIR spectral properties on Mars are likely controlled by surface dust distribution. These results provide specific operational recommendations for Mastcam-Z and insights into the nature of VNIR spectra on Mars

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems