Dissertations / Theses on the topic 'Landsat and ASTER Data'

Several decades of intensive dryland-farming in the Gadarif Region, located in the Eastern part of Sudan, has led to rapid landuse/landcover (LULC) changes mainly due to agricultural expansion, government policies and environmental calamities such as drought. The study area represents part of the African Sahel. The fundamental goal of the thesis was to assess land degradation and the impact of agriculture expansion on land cover, soil and crops production. To analyse and to monitor the LULC changes, multi-temporal Landsat data of the years 1979, 1989 and 1999 and ASTER data of the year 2009 covering an area of approximately 1200 km² were used. For this a post-classification comparison technique was applied to detect LULC changes from satellite images. Six LULC classes were identified during the classification scheme, namely cultivated land, fallow land, woodland, bare land, settlement and water. For the four dates of satellite images the overall classification accuracy ranged from 86 % to 92 %. During the three decades of the study period an extensive change of LULC patterns occurred. The cultivated areas increased significantly, covering 81 % of the previous woodland in the period 1979 – 2009. Fallow land only increased during the period 1989 – 1999. Over the three decades, urban expansion continuously increased covering an area of 23, 21 and 27 km² for the periods 1979 – 1989, 1989 – 1999 and 1999 – 2009 respectively. The detailed LULC map of the study area was obtained by using a dual polarisation (HH and HV) TerraSAR-X data of the year 2009. The different LULCs of the study area were analysed by employing an object-oriented classification approach. For that purpose, multi-resolution segmentation of the Definiens Software was used for creating the image objects. Using the feature-space optimisation tool the attributes of the TerraSAR-X images were optimised in order to obtain the best separability among classes for the LULC mapping. In addition to the classes that have been obtained by the optical data, the following LULCs resulted from SAR data: harvested land, rock, settlement 1 (local-roof buildings) and settlement 2 (concrete roof buildings). The backscattering coefficients for some classes were different along HH and HV polarisation. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value of 0.82 was resulted from the classification scheme. Accuracy differences among the classes were kept minimal. For more than six decades in the Gadarif Region mechanised dryland farming is practised. As a result, due to continuous conventional tillage, extensive woodcutting and over-grazing, serious soil degradation occurred. To discuss the impact of LULC changes on the selected soil properties, three main LULC types were chosen to be investigated, namely: cultivated land, fallow land and woodland. In addition to the reference soil profiles, soil samples were also collected at two depths from ten sample plots for each of the LULC type. For these soil samples, various soil properties such as texture, bulk density (BD), organic matter (OM), soil pH, electrical conductivity (EC), sodium adsorption ratio (SoAR), phosphorous (P) and potassium (K) were analysed. Laboratory tests proved that soil properties were significantly affected by LULC changes. Within the different LULC types, clay content in the surface layers (0 – 5 and 5 – 15 cm) varied from 59 % to 65 %, whereas silt fractions ranged from 27 % to 37 %. Soil BD, OM and P were significantly different (p < 0·05) across the three LULC types. Soil pH was significantly different between cultivated land and woodland on one side and between fallow land and woodland on the other side. EC and SoAR values of fallow land were found to be significantly different (p < 0·05) from woodland. The dryland vertisol of the Gadarif Region in Sudan produced more than one-third of the national production of sorghum – the main food stuff in the country. Soil compaction has been recognised as one of the major problems in crop production worldwide. Soil strength and infiltration rate are important variables for understanding and predicting the soil processes. The effects of three different landuse systems (cultivated land, fallow land and woodland) on soil compaction and infiltration rate were investigated at two sites of the study area. Site 1 represents the older one of the two. The soil penetration resistance (SPR) was measured in three depths using a manually operated cone penetrometer. Infiltration rate was measured in the field using a double-ring infiltrometer. Following the cone-penetrometer sampling, soil samples were collected to determine the variables that affect SPR and infiltration rate vs. particle size, dry BD, volumetric moisture content (VMC) and organic carbon (OC) content. Field measurements and soil samples were collected for each landuse type. The measured infiltration rate data were inserted into the Kostiakov Model in order to predict the cumulative soil water infiltration. Soil compaction for the cultivated land was 65 % larger in comparison to woodland. Woodland areas showed an increase in the infiltration rate by 87 % and 74 % compared to cultivated and fallow land respectively. Both study sites showed an increase in the dry BD when SPR is increasing, while VMC decreases with increasing SPR. Also, low OC contents were observed to be associated with high SPR values. For Site 1 the average coefficient of determination (R²) for the infiltration data fit to the Kostiakov Model were 0.65, 0.73 and 0.84 for cultivated land, fallow land and woodland respectively. However, for Site 2 they were 0.63, 0.76 and 0.78. In the Gadarif Region agriculture is the main activity and practised in many forms with a variety of environmental effects and consequences. Continuous ploughing of the cultivated land coupled with inproper soil management has contributed to soil deterioration when the landuse changed from woodland to cultivated and fallow land. Therefore, the development of sustainable landuse practises in the dryland-farming of the study area need to be improved in order to reduce the amount of soil degradation in the future
Mehrere Jahrzehnte intensiven Trockenfeldbaus in der Region von Gadarif, welche sich im östlichen Teil des Sudans befindet, führten hauptsächlich aufgrund von landwirtschaftlicher Expansion, politischen Beschlüssen der Regierung und Naturkatastrophen wie Trockenheit zu einer raschen Veränderung der Landnutzung und Landbedeckung. Das wesentliche Ziel dieser Dissertation war es, die Degradation des Landes, sowie die Auswirkungen von landwirtschaftlicher Expansion auf die Landbedeckung, den Boden und den Pflanzenbau im Untersuchungsgebiet, welches Teile der afrikanischen Sahelzone beinhaltet, abzuschätzen. Zur Analyse und Beobachtung der Veränderungen der Landnutzung und Landbedeckung wurden multi-temporale Landsat-Daten der Jahre 1979, 1989 und 1999 sowie ASTER-Daten aus dem Jahr 2009 genutzt, welche eine Fläche von schätzungsweise 1200 km² abdecken. Um Veränderungen von Landnutzung und Landbedeckung aus Satellitenbilddaten zu bestimmen, wurde ein auf Post-Klassifikation basierendes Vergleichsverfahren angewandt. Sechs Landnutzungs- und Landbedeckungsklassen, welche die Namen bewirtschaftetes Land, brach liegendes Land, Waldgebiet, Ödland, besiedeltes Land und Wasserfläche tragen, wurden während des Klassifikationsprozesses bestimmt. Für die vier Aufnahmezeitpunkte der Satellitendaten lag die allgemeine Klassifikationsgenauigkeit zwischen 86 % und 92 %. Während des dreißigjährigen Untersuchungszeitraums fand eine beträchtliche Veränderung der Landnutzungs- und Landbedeckungsstruktur statt. Bewirtschaftete Flächen nahmen in ihrem Anteil signifikant zu und bedeckten innerhalb des Zeitraums von 1979 bis 2009 81 % der früheren Waldgebiete. Der Anteil von brach liegendem Land nahm lediglich während des Zeitraums von 1989 bis 1999 zu. Besiedelte Gebiete breiteten sich über die drei Jahrzehnte kontinuierlich aus und wuchsen innerhalb des Zeitraums von 1979 bis 1989 um eine Fläche von 23 km², sowie um 21 km² zwischen 1989 und 1999 und um 27 km² in dem Zeitabschnitt 1999 – 2009. Eine detaillierte Karte zur Landnutzung und Landbedeckung des Untersuchungsgebiets wurde mittels der Nutzung dual polarisierter (HH und HV) TerraSAR-X Daten aus dem Jahr 2009 erzeugt. Die verschiedenen Landnutzungen und Landbedeckungen im Beobachtungsgelände wurden durch die Anwendung eines objektorientierten Klassifikationsansatzes analysiert. Um Bildobjekte zu erzeugen, wurde für diesen Zweck die auf einer mehrfachen Auflösung basierende Segmentierung der Software Definiens genutzt. Das Werkzeug Feature Space Optimisation wurde für die Optimierung der Attribute der TerraSAR-X Bilder angewandt, damit eine ideale Unterscheidungsfähigkeit entlang der Klassen für die Kartierung der Landnutzungen und Landbedeckungen erreicht werden kann. Zusätzlich zu jenen Klassen, welche mittels optischer Daten abgeleitet wurden, ergaben sich aus SAR-Daten noch die nachfolgenden Landnutzungen und Landbedeckungen: Abgeerntetes Land, Fels, Besiedlung 1 (Gebäude mit landestypischer Bedachung) und Besiedlung 2 (Gebäude mit Betondach). Die Koeffizienten der Rückstreuung entlang der Polarisationen HH und HV waren für einige Klassen unterschiedlich. Der günstigste Trennungsabstand der getesteten spektralen, formgebenden und texturalen Features ergab verschiedene Abweichungen zwischen den bestimmten Klassen der Landnutzung und Landbedeckung. Die Klassifikationsmaßnahmen ergaben eine Gesamtgenauigkeit von 84 % mit einem Kappa-Wert von 0.82. Genauigkeitsunterschiede entlang der Klassen wurden minimal gehalten. Seit über sechs Jahrzehnten wird in der Region Gadarif maschinenbetriebener Trockenfeldbau ausgeübt. In Folge dessen fand eine beträchtliche Abholzung und Überweidung sowie eine schwerwiegende Bodendegradation aufgrund des stetigen konventionellen Feldbaus statt. Um die Auswirkungen der Veränderung von Landnutzung und Landbedeckung auf die ausgewählten Bodenbeschaffenheiten auszuwerten, wurden drei Haupttypen der Landnutzung und Landbedeckung für die weitere Untersuchung ausgewählt: Bewirtschaftetes Land, brach liegendes Land, und Waldgebiet. Zusätzlich zu den Referenzbodenprofilen wurden außerdem für jeden Landnutzungs- und Landbedeckungstyp auf je zehn Probeflächen Bodenproben in zwei Tiefen entnommen. Bei diesen Bodenproben wurden zahlreiche Bodeneigenschaften analysiert, wie etwa Textur, Bodendichte (BD), organischer Materialgehalt (OM), pH-Wert des Bodens, elektrische Leitfähigkeit (EC), Adsorptionsgeschwindigkeit von Natrium (SoAR), Phosphorgehalt (P) sowie Kaliumgehalt (K). Labortests ergaben, dass die Bodeneigenschaften signifikant durch die Veränderungen der Landnutzung und Landbedeckung beeinflusst werden. Innerhalb der verschiedenen Landnutzungs- und Landbedeckungstypen variierte der Tongehalt in den Deckschichten (0 – 5 cm und 5 – 15 cm) zwischen 59 % und 65 %, wohin gegen sich die Lehmanteile von 27 % bis 37 % bewegten. Bodendichte, organischer Materialgehalt und Phosphorgehalt zeigten signifikant unterschiedliche Werte bei den drei Typen der Landnutzung und Landbedeckung (p < 0.05). Der pH-Wert des Bodens war signifikant verschieden zwischen bewirtschaftetem Land und Waldgebiet zum einen, und zwischen brach liegendem Land und Waldgebiet zum anderen. Die Werte der elektrischen Leitfähigkeit und der Adsorptionsgeschwindigkeit von Natrium bei brach liegendem Land erwiesen sich als maßgeblich verschieden zu jenen von Waldgebieten (p < 0.05). Auf dem Trockenland-Vertisolboden der Region Gadarif im Sudan wurde mehr als ein Drittel der nationalen Hirseproduktion erwirtschaftet – dem Haupternährungserzeugnis des Landes. Bodenverdichtung erwies sich als eines der weltweiten Hauptprobleme für den Pflanzenbau. Bodenfestigkeit und Versickerungsrate sind wichtige Variabeln, um Bodenprozesse verstehen und vorhersagen zu können. Die Auswirkungen der drei verschiedenen Landnutzungssysteme (bewirtschaftetes Land, brach liegendes Land und Waldgebiet) auf die Bodenverdichtung und Versickerungsrate wurden an zwei Standorten im Beobachtungsgebiet untersucht. Standort 1 ist der ältere der beiden. Der Widerstand der Bodenpenetration (SPR) wurde in drei Tiefen durch eine manuell angewandte Rammsonde gemessen. Mittels der Nutzung eines Doppelring-Infiltrometers ist die Versickerungsrate im Feld gemessen worden. Im Anschluss an die Probenentnahme mittels Rammsonden wurden Bodenproben gesammelt, um jene Variabeln bestimmen zu können, welche den Widerstand der Bodenpenetration sowie der Versickerungsrate im Vergleich zur Partikelgröße, zur trockenen Bodendichte, zum volumetrischen Feuchtigkeitsgehalt (VMC) und zum organischen Karbongehalt (OC) beeinflussen. Für jeden Landnutzungstypen wurden Feldmessungen durchgeführt und Bodenproben entnommen. Die gemessenen Daten der Versickerungsrate wurden in das Kostiakov-Modell eingespeist, um die gesamte Bodenwasserversickerung vorhersagen zu können. Die Bodenverdichtung bei bewirtschaftetem Land war 65 % stärker als bei Waldgebiet. Für Waldgebietsflächen wurde eine Zunahme der Versickerungsrate um 87 % verglichen mit bewirtschaftetem Land und um 74 % im Vergleich zu brach liegendem Land aufgezeigt. Beide Untersuchungsstandorte zeigten eine Zunahme in der trockenen Bodendichte für den Fall, dass der Widerstand der Bodenpenetration zunimmt, während der volumetrische Feuchtigkeitsgehalt mit zunehmendem Bodenpenetrationswiderstand abnimmt. Ebenso wurde beobachtet, dass ein geringer organischer Karbongehalt in Verbindung zu hohen Widerstandswerten der Bodenpenetration steht. Bei Standort 1 passte der durchschnittliche Bestimmungskoeffizient (R²) der Versickerungsrate zum Kostiakov-Modell mit den Werten 0.65 für bewirtschaftetes Land, 0.73 für brach liegendes Land und 0.84 für Waldgebiet. Für Standort 2 indessen ergaben die Werte 0.63, 0.76 und 0.78. Landwirtschaft, die in vielen Formen ausgeübt wird, ist die Haupttätigkeit in der Region Gadarif, und geht mit verschiedenartigsten Umweltauswirkungen und Konsequenzen einher. Kontinuierliche Feldbestellung des bewirtschafteten Landes, verbunden mit ungeeigneter Bodenbewirtschaftung, hat sich seit jenem Zeitpunkt, als sich die Landnutzung von Waldgebiet zu bewirtschaftetem und brach liegendem Land änderte, zu Bodenschädigung geführt. Daher muss die Entwicklung nachhaltiger Landnutzungspraktiken beim Trockenfeldbau im Untersuchungsgebiet verbessert werden, damit in Zukunft der Umfang der Bodendegradation verringert werden kann

Die Zuwanderung nach Tirana führte im Verlauf der 1990er Jahre zu einem enormen Flächenverbrauch auf Kosten landwirtschaftlicher Flächen im Umland der albanischen Hauptstadt. Im Rahmen der vorliegenden Arbeit wird die Entwicklung des rasanten Flächenverbrauchs mit computergestützten Methoden dokumentiert. Grundlage der Untersuchung bilden zwei zu unterschiedlichen Zeitpunkten (1988 und 2000) aufgenommene Satellitenszenen, mit Hilfe derer eine Änderungsanalyse durchgeführt wird. Ziel der Änderungsanalyse ist es, den Flächennutzungswandel zu analysieren, Daten zu generieren und die Ergebnisse in geeigneter Weise zu visualisieren. Zu den protagonistischen Verfahren der Änderungsanalyse zählen sowohl die Maximum-Likelihood Klassifikation sowie ein wissensbasierter Klassifizierungsansatz. Die Ergebnisse der Änderungsanalyse werden in Änderungskarten dargestellt und mittels einer GIS-Software statistisch ausgewertet.

Publicación a texto completo no autorizada por el autor
Con el propósito de complementar las diferentes técnicas de prospección geofísica aplicada a la exploración del agua subterránea se propone el uso de imágenes satelitales Landsat y Aster. El área de estudio es la Región Lambayeque que está ubicada en la costa norte del Perú, entre las coordenadas geográficas 5º 28' 36'' y 7º 14' 37'' de latitud Sur y 79º 41' 30'' y 80º 37' 23'' de longitud Oeste. Los datos utilizados son las imágenes registradas por el sensor ETM+ del satélite Landsat-7, ortorectificadas y con un porcentaje de nubes inferior al 10% de fecha 31-10- 2000 y las imágenes del modelo de elevación digital ASTER GDEM. Se realiza un análisis visual y estadístico de los datos imágenes usando diferentes técnicas como el ajuste de histogramas, composición de colores, componentes principales, filtros y fusiones de imágenes de diferentes resoluciones espaciales, para identificar unidades hidrogeológicas y el patrón de drenaje natural del área de estudio. Con los datos imágenes ETM+ de las bandas 3 y 4 se calcula el índice de vegetación diferencial normalizado (NDVI) y con los datos imágenes ETM+ de la banda 6 se estima la temperatura de la superficie terrestre del área de estudio. Los resultados del análisis estadístico de las imágenes ETM+ y ASTER GDEM se usan para clasificar mediante un árbol de decisiones las zonas potencialmente con agua subterránea, el resultado es un mapa temático con zonas potencialmente con agua subterránea. El mapa temático es validado parcialmente con el inventario de pozos de agua subterránea realizado por INRENA en la parte media baja de la cuenca ChancayLambayeque (INRENA, 2001) observando que el 73% de los pozos están ubicados dentro de las zonas referidas como potencialmente con agua subterránea. El resultado obtenido no es determinante en la existencia de acuíferos libres, se requieren de técnicas in situ para la determinación precisa de éstos. Sin embargo, el método proporciona información a nivel regional de zonas potencialmente con agua subterránea de la Región Lambayeque.
Tesis

Städtische Gebiete unterscheiden sich von Flächen mit ruraler Prägung, im Ergebnis sind sie stark modifiziert bezüglich ihrer Strahlungs- und Energiebilanz. In der vorliegenden Arbeit werden die Oberflächentemperaturen der Metropole Berlin im Mesomaßstab, unter Verwendung von Satellitenaufnahmen der ASTER- und Landsat-5- und 7-Sensoren untersucht sowie durch zusätzliche Messungen mit einer Thermalbildkamera über einen Zeitraum von 17 Monaten im Mikromaßstab erweitert. Diese Daten kombiniert mit GIS-basierten Informationen über die Landnutzungs- und Strukturtypen werden für die Analyse der räumlichen und zeitlichen Verteilung der Oberflächentemperaturen genutzt ebenso wie zur Ermittlung funktioneller Beziehungen zwischen dem thermischen Verhalten der Oberflächen und der zugehörigen Stadtstruktur. Bei der Auswertung geht es vorrangig um physikalische Prozesse und Eigenschaften, die einen Einfluss auf energetische Flüsse und ihre Interaktion mit städtischen Oberflächen haben. Eine thermische Charakteristik einzelner Bezirke über verschiedene Nutzungsklassen bis hin zu einzelnen Materialien wird erstellt. Dabei sind Temperaturdifferenzen von mehreren zehntel Kelvin zwischen den typischen städtischen Oberflächen Dächern und Rasenflächen zu beobachten. Die Resultate zeigen, dass die Verteilung der LST sehr verschieden ist und stark korreliert mit den Landbedeckungen. Es wird dargestellt, welche Stadtstrukturen besonders thermisch belastet sind, welche individuelle thermische Bedeutung einzelne Materialien haben. Besonderes Augenmerk wird auf Möglichkeiten der Beeinflussung durch Abschattung gelegt. Durch Verschattung können Differenzen der Oberflächentemperaturen von mehr als 10 Kelvin erreicht werden. Abschließend werden Modellierungen zur Verifizierung der Kameramessungen sowie zum Aufzeigen des Einflusses minimaler Änderungen in kleinräumigen Klimaten genutzt.
Urban areas differ from surfaces of rural character. They are very modified in their radiation- and energy balance. In this study land-surface temperatures of the city of Berlin are analyzed with the help of satellite pictures of the ASTER- and Landsat-5 and -7 sensors in mesoscale and then extended by extra measurements of an infrared camera in microscale over the course of 17 months. This data combined with GIS based information on different land use and -structures are used for the analysis of spatial and time distribution, as well as for the determination of functional relations between thermal behaviour of surfaces and the related urban structures. The evaluation mainly deals with physical processes and properties that have an influence on energetic flows and their interactions with urban surfaces. A thermal characteristic of individual districts, from different land use classes to specific materials is being created. In this there are differences in temperature of several tenths Kelvin between the typical urban surfaces of roofs and grass areas visible. The results show that the distribution of the LST varies immensely and correlates with the land coverage. It is shown, what urban structures are most thermic burdened, what individual significance specific materials have. Special attention is paid to the different possibilities of the influence through shadow. It is possible to reach a difference of surface temperatures of more than 10 Kelvin by shadow. At the end models with the 3-dimensional ENVImet are used to verify the camera measurements as well as to show the influence of minimal changes in microscale climate.

Forests are important natural resources of Canada. Their renewal has been recognized to be important for continued wood supply and for other benefits. Consequently, the major emphasis of forest management activities focuses upon restocking clearcut forest lands. Effective planning and successful implementation of reforestation programs require efficient techniques for obtaining timely and accurate information regarding restocking status over cutover forest lands. In this thesis the potential of Landsat thematic mapper (TM) data for monitoring reforesting clearcuts was investigated. Landsat-5 TM data covering clearcut forest lands reforesting with lodgepole pine (Pinus contorta Dougl.) were analyzed. To assess spectral separability of various restocking classes and classifying reforestation areas according to their stocking status multivariate distance measures were employed to select the optimum three band subset from six reflective TM bands. Three commonly used vegetation indices, namely the ratio vegetation index, the normalized difference vegetation index, and the infrared index, were also studied for quantitative assessment of vegetation. The main conclusion of the study is that TM bands 3, 4, and 5 are the best for discriminating various restocking classes. The classification accuracy was estimated to be approximately 90 percent. The infrared index appears to be the most suitable vegetation index for quantitative assessment of reforestation.
Forestry, Faculty of
Graduate

Phosphorus flowing via rivers into the Baltic Sea is a major source of nutrients, and in some cases the limiting factor for the growth of algae which causes the phenomenon known as eutrophication. Remote sensing of phosphorus, here using Landsat TM-data, can help to give a better understanding of the process of eutrophication. Since Landsat TM-data is used, this could form a basis for further spatio-temporal analysis in the Baltic Sea region. A method originally described and previously applied for a Chinese river is here transferred and applied to three different rivers flowing into the Baltic Sea. The results show that by measuring the proxy variables of Secchi Depth and Chloryphyll-a the remote sensing model is able to explain 41% of the variance in total- phosphorus for the rivers Dalälven, Norrström and Gavleån without any consideration taken to CDOM, turbidity or other local features.

Detailed land cover information is crucial for mapping and managing complex urban environments across local and regional scales (Zhou and Qiu, 2015). This thesis is based on the proposition that spatial resolution is the most influential factor when mapping complex urban environments, compared to imagery’s spectral properties and type of classifier. As such, the modern “very high resolution” sensors (i.e., WorldView-2) offer a significant advantage for mapping, however using such imagery is a costly and resource-hungry approach. The coarser resolution of Landsat data (30m) is the key limitation for using these data, yet they are free and now have a temporal legacy. This doctoral research assesses the potential of using an approach that enhances the spatial resolution of Landsat data for urban land cover mapping, namely sparse representation. Focusing on the land cover mapping of the urban area of Nottingham, UK, and after establishing the superior role of spatial resolution on the accuracy of that mapping, this research demonstrates the potential of this approach. Moreover, some parameters around its use are established, in particular, the transferability of this method over space and time. It should be noted the potential of sparse representation can be even more significant by using finer spatial resolution products (i.e., Sentinel-2 and SPOT with 10m). This reaffirmed the importance of the spatial resolution for urban land cover mapping. Then it presents the sparse representation as a successful method to enhance the spatial resolution of Landsat data for urban land cover mapping.

The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite. To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat. Fusion of Landsat and MODIS is challenging because of differences in their spatial resolution, band designations, swath width, viewing angle and the noise level. Fusion is even more challenging for heterogeneous landscapes. In the first part of our work, the multiresolution analysis offered by the wavelet transform was explored as a suitable environment for Landsat and MODIS fusion. Our proposed Wavelet-based Spatiotemporal Adaptive Reflectance Fusion Model (WSTARFM) is the first model to merge Landsat and MODIS successfully. It handles the heterogeneity of the landscapes more effectively than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) does. The system has been tested on simulated data and on actual data of two study areas in North Carolina. For a challenging heterogeneous study area near Greensboro, North Carolina, WSTARFM produced results with median R-squared values of 0.98 and 0.95 for the near-infrared band over deciduous forests and developed areas, respectively. Those results were obtained by withholding an actual Landsat image, and comparing it with a predicted version of the same image. These values represent an improvement over results obtained using the well-known STARFM technique. Similar improvements were obtained for the red band. For the second (homogeneous) study area, WSTARFM produced comparable prediction results to STARFM. In the second part of our work, Landsat-MODIS fusion has been explored from the temporal perspective. The fusion is performed on the Landsat and MODIS per-pixel time series. A new Multisensor Adaptive Time Series Fitting Model (MATSFM) is proposed. MATSFM is the first model to use mapped MODIS values to guide the fitting applied to the sparse Landsat time series. MATSFM produced results with median R-squared of 0.98 over the NDVI images of the first heterogeneous study area compared to 0.97 produced by STARFM. For the second study area, MATSFM also produced better prediction accuracy than STARFM.
Ph. D.

Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth's land surface and vegetation.  There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate.   Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements.  In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001.  Harmonic regression had an R^2"0.9 over three quarters of all pixels. It gave the highest R_Predicted^2 values on two thirds of the pixels.  Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R^2"0.99 for most pixels.   We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data.  The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm.  EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery.   We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values.  These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential.  These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters.  Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase. These applications demonstrate the utility of harmonic regression for Landsat data.  They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects.
Ph. D.

One of the principal advantages of satellite data is the ability to provide terrain information over large areas, but past analyses of Landsat MSS data have tended to concentrate on developing techniques for small study areas. A method is developed for producing such large area land cover mapping from Landsat MSS data of Scotland. A stratified, interactive approach to image analysis produced the best results, incorporating a hybrid classification method involving a thorough selection process for training data pixels. Classification is implemented by either a minimum distance or a maximum likelihood technique which is further improved by post-classification editing and smoothing procedures. Results from a training and testing area produced a final classification statistically assessed as 87.3% correct. The method has subsequently been used to produce 3 maps of primary land cover types for Highland, Grampian and Tayside regions (total area 41,330 km2).

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California
Current Landsat 7 processing is based on a single-string, multifunction approach. A follow-on system has been designed that repartitions functions across multiple hardware platforms to provide increased flexibility and support for additional missions. Downlink bit stream acquisition has been moved to lower cost systems functioning as “capture appliances” with high-speed network interconnections to Level 0 processing on generic compute servers. This decouples serial data stream acquisition from the processing system to allow the addition or replacement of compute servers, without the reintegration of specialized high-speed capture hardware. Moreover, it also allows the easy integration of new systems and missions without extensive system redesign or additional software.

Orientador: Rubens Augusto Camargo Lamparelli
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola
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Resumo: A importância econômica da produção brasileira de café no mercado mundial é notória e contribui com uma grande parcela na balança comercial de exportação do país. Minas Gerais se destaca como o centro da atual produção cafeeira no Brasil e tem na região sul do estado a grande concentração da lavoura de café (Coffea arabica), onde o seu cultivo é realizado em pequenas propriedades bastante dispersas pela região montanhosa. A necessidade de adequação da agricultura cafeeira por meio do planejamento, controle de custos e melhoria da produtividade, tem acelerado a procura por técnicas e ferramentas para a previsão da produção agrícola passando, necessariamente, pela localização e quantificação das áreas cultivadas. Neste contexto, o objetivo desta pesquisa foi avaliar o fornecimento de informações dos dados do sensor TM/Landsat 5 utilizando as técnicas de Análise por Principais Componentes (APC) e separação de classes de iluminação sobre as áreas de lavoura de café em região montanhosa. A área de estudo escolhida foi o município de Guaxupé/MG por conter uma forte lavoura cafeeira mantida sob um organizado sistema cooperativo. Foram utilizadas imagens dos satélites Landsat 7, Landsat 5 e do sensor MODIS para a aplicação das técnicas de processamento digital, para correção atmosférica e normalização radiométrica, visando a análise do cafeeiro nas datas de 15/08/2001, 05/12/2001 e 12/04/2002, que caracterizam os estágios fenológicos do cafeeiro como períodos de colheita (repouso e senescência dos ramos), florada e início do crescimento da gemas florais, respectivamente. Também foi utilizada uma máscara da área cafeeira obtida por método de interpretação visual extraído de imagens Ikonos. A utilização de um modelo digital de elevação gerado por imagens do sensor ASTER/TERRA possibilitou a aplicação da técnica de determinação do fator de iluminação, que consistiu na criação de classes de iluminação que contribuíram na identificação de áreas de lavoura e áreas de mata sombreadas pelo relevo. Dados de campo foram levantados para auxiliar na identificação da lavoura cafeeira, separadas pelas classes amostrais de café adensado e café adulto em função dos espaçamentos de ruas e linhas adotados no plantio. A Análise por Principais Componentes (APC) foi aplicada com o objetivo de reduzir a redundância dos dados obtidos das imagens orbitais de maneira a permitir a seleção de amostras de treinamento para a utilização em classificação supervisionada. Utilizando o método da Distância de Mahalanobis como classificador, as imagens nas datas selecionadas para a pesquisa, mostraram dados importantes quando comparados os resultados das classificações com a máscara da área de café extraída das imagens Ikonos do município. Os resultados dessas classificações foram validados por meio da determinação da Exatidão Global e coeficiente Kappa que mostraram os valores de EG= 0,78 e K= 0,40 para a imagem de 15/08/2001, EG= 0,81 e K= 0.29 para a imagem de 05/12/2001 e, EG= 0,76 e K= 0,24 para a data de 12/04/2002, confirmando que o período seco (maio até outubro) é favorável para a classificação do cafeeiro que, neste período está sob processo de colheita onde, a queda de folhas e remoção de frutos ocorre, diferenciando das outras coberturas do solo como as matas. Os maiores valores atingidos na validação dos dados ocorreram na classificação da imagem gerada pela composição dos resultados das três datas, atingindo valores de EG = 0,81 e Kappa = 0,56. O valor da área, quantificada como sendo da cultura do café, encontrado pelo método de soma dos resultados das classificações em cada data, produziu um valor 73,06 % do total de área quantificada na máscara de café, utilizada como referência. Neste sentido a metodologia se mostrou bem adequada na quantificação de áreas de café em relevo montanhoso.
Abstract: The economic importance of Brazilian coffee growing in the world market is notorious and makes up significant portion of the country's foreign trade exports. Minas Gerais stands out as the core of Brazilian coffee growing, with most of the planting areas (Coffea arabica) concentrated in the south, where it is grown in small plots widely spread throughout the hills. The need to adequate coffee agriculture by planning, cost control and productivity improvement has increased the search for techniques and tools for the prediction of agricultural production, necessarily involving the location and quantification of cultivated areas. In this context, the goal of this research has been to evaluate data from the TM/Landsat-5 remote sensor, providing information about coffee growing areas in hilly regions. The city of Guaxupé/MG/Brazil was chosen for this study due to its strong coffee growing, kept under an organized cooperate system. Images from the Landsat-7 and Landsat-5 satellites and from the MODIS sensor have been employed for the purpose of using digital processing tools for atmospheric correction and radiometric normalization, in order to analyze coffee crops in three dates: 08/15/2001, 12/05/2001 and 04/12/2002, characterizing phenological stages such as harvesting periods (rest and senescence of boughs), flowering and beginning of flower bud growing, respectively. The use of a digital elevation model generated from ASTER/TERRA sensor enabled the use of a lighting factor determination technique, consisting in the creation of lighting classes that contributed in the identification of crop areas and terrain-shadowed vegetated areas. Field data were gathered to help identifying coffee plantation separated by sample classes of dense coffee and adult coffee as a function of the spacing of the field foods and lines used in planting. PCA (Principal Component Analysis) was applied in order to reduce the redundancy of the data obtained from orbital imaging in a way that allows the selection of training samples for use in supervised classification. Using the Mahalanobis Distance as a classifier, the images in the selected dates showed highly positive result when the classification was compared to the coffee area mask extracted from Ikonos images. The results of these classifications were validated through the determination of Global Accuracy and Kappa Index, which showed values of GA= 0.78 and K= 0.40 for the 08/15/2001 image, GA= 0.81 and K= 0.29 for the 12/05/2001 image, and GA= 0.76 and K= 0.24 for 04/12/2002, confirming that the dry season (May through October) is favorable for the classification of coffee, which is under the harvesting process in this period, during which the falling of leaves and remotion of fruit separates it from other ground cover such as vegetation. The spectral data obtained from satellite imaging through digital processing have proven themselves adequate for the location of coffee-growing areas in hilly regions when aided by a digital elevation model. The value of the area as being coffee crop, calculated by sum of the areas found from each date classification, produced 73,06% of the agreement with coffee mask considered as a reference data. Due this the methodology showed very suitable to quantify coffee areas in hilly region.
Mestrado
Planejamento e Desenvolvimento Rural Sustentável
Mestre em Engenharia Agrícola

Being able to accurately map forest carbon is a critical step in the global carbon cycle modeling and management process. This project is aimed at enhancing the current methodologies used for forest carbon mapping, and applying a method to account for any errors produced. By doing so, more accurate decisions can be made based on the knowledge gained from forest carbon maps; such as policy decisions on how to manage forests, or how to mitigate climate change. The use of remotely sensed images, in combination with Forest Inventory and Analysis (FIA) data, is one such way of doing this. This study compared three different methods; including linear regression, cosimulation, and up-scaled cosimulation to interpolate forest carbon based on a defined relationship between sample plots of national FIA data and satellite images. An uncertainty analysis was completed in an effort to quantify, and separate the different sources of error produced within a cosimulation mapping effort. The results indicated that the band ratio of TM4 / TM5 + TM4 / TM7 had the highest correlation coefficient, around 0.56, with the FIA forest carbon values. At a resolution of 90 m ×by 90 m, co-simulation predicted carbon values from about 14 Mg/ha, to 135 Mg/ha. The regression model, at the same resolution, estimated carbon values from about -17 Mg/ha, to 2,400 Mg/ha. Up-scaled cosimulation at a resolution of 990 m x× 990 m, predicted carbon values of ranging from 16 Mg/ha, to 133 Mg/ha. The uncertainty analysis was unable to produce any statistically significant results, with all R2 values below 0.1. These results showed that using a linear regression produced some impossible estimates, while cosimulation led to more realistic values. However, no conclusion can be made when comparing the methods based on the map validation techniques used. Although limited validation of the results was conducted, using both the FIA data and some independent sampling data; further work that focuses on validation is recommended.

Regional urban planning and natural resource management problems require efficient and accurate data concerning land use/land cover and temperature gradients for informed decision making. Remotely-sensed data provide a method for acquiring such information in a dependable and efficient manner. Regular data acquisition and a synoptic view make the Landsat Thematic Mapper (TM) an excellent resource for entities needing land cover and surface temperature information. Landsat 5 TM digital data (1985) are used to classify land cover in the vicinity of New Orleans, the study area encompassing approximately 185 square kilometers. The maximum likelihood, minimum distance to means, and the parallelepiped classifiers produce land cover classified images with highly significant differences and the maximum likelihood rule outperforms the other methods. The maximum likelihood land cover classification is used as ancillary data for the surface temperature conversions and meets the standard of 85% thematic accuracy set by the United States Geological Survey (USGS). The Landsat 5 TM thermal channel (band 6) provides exceptional spatial resolution and is an excellent tool for mapping surface temperatures. Variable emissivities of land cover types and atmospheric conditions often need to be incorporated into surface temperature calculations from TM data. The thermal channel digital counts are weighted according to land cover emissivity and converted into kinetic temperatures (atmospheric conditions are deemed negligible for the TM data) . Statistics generated and qualitative analyses demonstrate a strong relationship between surface temperatures and land cover types, allowing for the prediction of the surface temperature change that a change in land use/land cover will incur. Applications of the research include modeling and monitoring of land use/land cover in a region, urban planning, urban heat island mapping, and natural resource management/conservation.

Texte remanié de: Doctoral diss.--Austin--University of Texas, 1992. Titre de soutenance : Privatizing space-derived data : a case study of the effects of the Land remote-sensing commercialization act of 1984 on the academic geography community.
Notes bibliogr. Bibliogr. p.287-314. Index.

Thesis (Ph.D.)--Boston University
Land cover mapping and monitoring has been widely recognized as important for understanding global change and in particular, human contributions. This research emphasizes the use ofthe time domain for mapping land cover and changes in land cover using satellite images. Unlike most prior methods that compare pairs or sets of images for identifying change, this research compares observations with model predictions. Moreover, instead of classifying satellite images directly, it uses coefficients from time series models as inputs for land cover mapping. The methods developed are capable of detecting many kinds of land cover change as they occur and providing land cover maps for any given time at high temporal frequency. One key processing step of the satellite images is the elimination of "noisy" observations due to clouds, cloud shadows, and snow. I developed a new algorithm called Fmask that processes each Landsat scene individually using an object-based method. For a globally distributed set ofreference data, the overall cloud detection accuracy is 96%. A second step further improves cloud detection by using temporal information. The first application ofthe new methods based on time series analysis found change in forests in an area in Georgia and South Carolina. After the difference between observed and predicted reflectance exceeds a threshold three consecutive times a site is identified as forest disturbance. Accuracy assessment reveals that both the producers and users accuracies are higher than 95% in the spatial domain and approximately 94% in the temporal domain. The second application ofthis new approach extends the algorithm to include identification of a wide variety of land cover changes as well as land cover mapping. In this approach, the entire archive of Landsat imagery is analyzed to produce a comprehensive land cover history ofthe Boston region. The results are accurate for detecting change, with producers accuracy of 98% and users accuracies of 86% in the spatial domain and temporal accuracy of 80%. Overall, this research demonstrates the great potential for use of time series analysis of satellite images to monitor land cover change.

To successfully protect and manage wetlands, efficient and accurate tools are needed to identify where wetlands are located, the wetland type, what condition they are in, what are the stressors present, and the trend in their condition. Wetland mapping and monitoring are useful to accomplish these tasks. Wetland mapping and monitoring with optical remote sensing data has mainly focused on using a single image or using image acquired over two seasons within the same year. Now that Landsat data are available freely, a multi-temporal approach utilizing images that span multiple seasons and multiple years can potentially be used to characterize wetland dynamics in more detail. In addition, newer remote sensing techniques such as Light Detection and Ranging (lidar) can provide highly detailed and accurate topographic information, which can improve our ability to discriminate wetlands. Thus, the overall objective of this study was to investigate the utility of lidar and multi-temporal Landsat data for mapping and monitoring of wetlands. My research is presented as three independent studies related to wetland mapping and monitoring. In the first study, inter-annual time series of Landsat data from 1985 to 2009 was used to map changes in wetland ecosystems in northern Virginia. Z-scores calculated on tasseled cap images were used to develop temporal profile for wetlands delineated by the National Wetland Inventory. A change threshold was derived based on the Chi-square distribution of the Z-scores. The accuracy of a change/no change map produced was 89% with a kappa value of 0.79. Assessment of the change map showed that the method used was able to detect complete wetland loss together with other subtle changes resulting from development, harvesting, thinning and farming practices. The objective of the second study was to characterize differences in spectro-temporal profile of forested upland and wetland using intra and inter annual time series of Landsat data (1999-2012). The results show that the spector-temporal metrics derived from Landsat can accurately discriminate between forested upland and wetland (accuracy of 88.5%). The objective of the third study was to investigate the ability of topographic variables derived from lidar to map wetlands. Different topographic variables were derived from a high resolution lidar digital elevation model. Random forest model was used to assess the ability of these variables in mapping wetlands and uplands area. The result shows that lidar data can discriminate between wetlands and uplands with an accuracy of 72%. In summary, because of its spatial, spectral, temporal resolution, availability and cost Landsat data will be a primary data source for mapping and monitoring wetlands. The multi-temporal approach presented in this study has great potential for significantly improving our ability to detect and monitor wetlands. In addition, synergistic use of multi-temporal analysis of Landsat data combined with lidar data may be superior to using either data alone for future wetland mapping and monitoring approaches.
Ph. D.

The Antarctic Peninsula is larger than the UK and with limited geological mapping campaigns since the 1940s significant gaps in coverage remain, particularly in areas where access is difficult. Remote sensing offers potential for improving geological mapping on the peninsula but has not been used for these purposes. This thesis describes the use of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for lithological mapping of predominantly calc-alkaline subduction-related igneous rocks on the Antarctic Peninsula. The research encompassed lithological mapping of four study areas selected to provide an appropriate test of the potential of ASTER. Processing of ASTER reflectance and thermal emission data used spectral enhancement procedures and the matched filter (MF) spectral mapping method. This was supported by reflectance spectroscopy of rock samples, Hyperion data, thin section petrography, geochemical data for acid volcanic rocks, and fieldwork on Adelaide Island. The research shows although outcrop is limited in the polar context, weathering effects and vegetation cover do not cause significant problems. ASTER provides a range of lithologic information enabling validation of inferred field mapping and new observations of unmapped geology in the study areas. Granitoids and silicic volcanic rocks display distinctive spectral properties and are newly identified from unmapped parts of the Oscar II, Foyn, and Lassiter coasts. Areas of localised alteration in these rocks are readily discriminated based on the distinctive absorption features of the alteration mineral assemblages. ASTER is less successful at discriminating intermediate-mafic igneous, sedimentary and metamorphic lithologies that display more ambiguous spectral features. For these rocks lithological mapping is strongly reliant on existing field observations to resolve ambigious results. The research shows that although ASTER is limited in its ability to uniquely discriminate different rock types it can provide important lithological information in support of geological mapping on the Antarctic Peninsula.

The main objective of this study was to determine the spectral change technique best suited to detect complete forest harvests (clearcuts) in the Southern United States. In the pursuit of this objective eight existing change detection techniques were quantitatively evaluated and a hybrid method was also developed. Secondary objectives were to determine the impact of atmospheric corrections applied before the change detection, and the affect post-processing methods to eliminate small groups of misclassified pixels ("salt and pepper" effect) had on accuracy.

Landsat TM imagery of Louisa County, Virginia was acquired on anniversary dates in both 1996 and 1998 (Path 16, Row 34), clipped to the study area boundary, and registered to one another. Previous to the change detection exercise, two levels of atmospheric corrections were applied to the imagery separately to produce three data sets. The three data sets were evaluated to determine what level of pre-processing is necessary for harvest change detection. In addition, eight change detection techniques were evaluated: 1) the 345 TM band differencing, 2) 35 TM band differencing, 3) NDVI differencing, 4) principal component 1 differencing, 5) selection of a change band in a multitemporal PCA, 6) tasseled cap brightness differencing, 7) tasseled cap greenness differencing, and 8) univariate differencing using TM band 7. A hybrid method that used the results from the eight previous techniques was developed. After performing the change detection, majority filters using window sizes of 3x3 pixels, 5x5 pixels, and 7x7 pixels were applied to the change maps to determine how eliminating small groups of misclassified pixels would affect accuracies. Accuracy assessments of the binary (harvested or not harvested) change maps were used to evaluate the accuracies of the various methods described using 256 validation points collected by the Virginia Department of Forestry.

The atmospheric corrections did not seem to significantly benefit the change detection techniques, and in some cases actually degraded accuracies. Of the eight techniques applied to the original dataset, univariate differencing using TM band 7 performed the best with a 90.63% overall accuracy, while Tasseled Cap Greenness returned the worst result with an overall accuracy of 78.91%. Principal component 1 differencing and 35 differencing also performed well. The hybrid approach returned good results, but at its best returned an overall accuracy of 90.63%, matching the TM band 7 method. The majority filters using the 3x3 and 5x5 window sizes increased the accuracy in many cases, while the majority filter using the 7x7 window size degraded overall accuracy.
Master of Science

This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased.
Ph. D.

Obtaining up-to-date information concernmg the environment at reasonable costs is a challenge faced by many institutions today. Satellite images meet both demands and thus present a very attractive source of information. The following thesis deals with the comparison of satellite images and a vector based land use data base of the City of Vienna. The satellite data is transformed using the spectral mixture analysis, which allows an investigation at a sub-pixel level. The results of the transformation are used to determine how suitable this spectral mixture analysis is to distinguish different land use classes in an urban area. In a next step the results of the spectral mixture analysis of two different images (recorded in 1986 and 1991) are used to undertake a change detection. The aim is to show those areas, where building activities have taken place. This information may aid the update of data bases, by limiting a detailed examination of an area to those areas, which show up as changes in the change detection. The proposed method is a fast and inexpensive way of analysing large areas and highlighting those areas where changes have taken place. lt is not limited to urban areas but may easily be adapted for different environments. (author's abstract)
Series: Research Reports of the Institute for Economic Geography and GIScience

Since reaching their LIAMs, Himalayan glaciers have generally undergone a period of retreat, evident from large moraines left at former ice limits. Currently, however, detailed assessments of Himalayan glacier fluctuations over the past century are limited and fail to compare spatially or temporally to records available in Central Europe, North America and Scandinavia. Consequently, the variability and magnitude of glacial change across the Himalayas, which is a key indicator of climatic change in this region, is yet to be fully understood. Against a background of poor data availability, Corona imagery and historic GLIMS glacier outlines now offer an opportunity to assess glacier extent for regions of the Himalayas pre-1980. Corona imagery, acquired by a US space-borne reconnaissance mission operational from 1960 to 1970, represents a particularly unique dataset offering high resolution imagery (~1.8 m) with stereo-scopic capabilities. Utilising Corona imagery, there is an opportunity to produce detailed maps of Himalayan glacier extent and extract ice surface elevation estimations, in some instances, for the first time. Despite having been de-classified in 1995, the use of Corona data in the Himalayas has been neglected, mainly because of orthorectification challenges related to its unique geometric distortions. Hence, there remains a need to develop a low cost and easily replicable method of accurately orthorectifying Corona imagery enabling its use as a large-scale glacier mapping tool in the Himalayas. In response to this need, Corona images are orthorectified in this study through the use of: (1) a non-metric photogrammetry approach; and (2) horizontal and vertical reference data acquired from ortho-ASTER imagery and the freely available ASTER GDEM. By comparing glacier measurements derived from Corona imagery, GLIMS data and more contemporary ASTER data, changes in glacier area, length and in some instances volume, between the 1960/70s and early 2000s, were quantified for glaciers selected within four study areas located in Uttarakhand, India and Central Nepal. Importantly, this cross-regional glacier change dataset both complements and enhances current Himalayan records. Most notably, results indicate that glaciers selected in the Bhagirathi and Pindar/Kali basins, Uttarakhand, reduced in area by a relatively small 7.97±0.29% and 7.54±0.26%, respectively. Contrastingly, glaciers selected in the more easterly located Seti and Trisula basins reduced in area by 29.78±0.2% and 50.55±0.08%, respectively. Comparisons of Corona DEM (derived from Corona stereo-pairs) and ASTER Global DEM elevations at the terminus regions of four glaciers revealed extensive surface lowering, ranging from 87±27 m to 142±27 m. For Corona processing, the methods applied were shown to orthorectify Corona images to an accuracy that allows comparable glacier outlines to be delineated, further demonstrating the mapping potential of this dataset. However, for Corona DEM extraction, the use of ASTER spatial control data was shown to be inadequate and the presence of large vertical errors in the DEMs generated hindered the measurement of glacier volume change. For this purpose, it is therefore recommended that the methods developed are tested with the use of very high resolution spatial control data.

Glaciers and ice caps (GIC) are central parts of the hydrological cycle, are key to understanding regional and global climate change, and are important contributors to global sea level rise, regional water resources and local biodiversity. Multispectral (visible and near-infrared) remote sensing has been used for studying GIC and their changing characteristics for several decades. Glacier surfaces can be classified into a range of facies, or zones, which can be used as proxies for annual mass balance and also play a significant role in understanding glacier energy balance. However, multispectral sensors were not designed explicitly for snow and ice observation, so it is not self-evident that they should be optimal for remote sensing of glaciers. There are no universal techniques for glacier surface classification which have been optimized with in situ reflectance spectra. Therefore, the roles that the various spectral, spatial, and radiometric properties of each sensor play in the success and output of resulting classifications remain largely unknown. Therefore, this study approaches the problem from an inverse perspective. Starting with in situ reflectance spectra from the full range of surfaces measured on two glaciers at the end of the melt season in order to capture the largest range of facies (Midtre Lovénbreen, Svalbard & Langjökull, Iceland), optimal wavelengths for glacier facies identification are investigated with principal component analysis. Two linear combinations are produced which capture the vast majority of variance in the data; the first highlights broadband albedo while the second emphasizes the difference in reflectance between blue and near-infrared wavelengths for glacier surface classification. The results confirm previous work which limited distinction to snow, slush, and ice facies. Based on these in situ data, a simple, and more importantly completely transferrable, classification scheme for glacier surfaces is presented for a range of satellite multispectral sensors. Again starting with in situ data, application of relative response functions, scaling factors, and calibration coefficients shows that almost all simulated multispectral sensors (at certain gain settings) are qualified to classify glacier accumulation and ablation areas but confuse classification of partly ash-covered glacier surfaces. In order to consider the spatial as well as the spectral properties of multispectral sensors, airborne data are spatially degraded to emulate satellite imagery; while medium-resolution sensors (~20-60 m) successfully reproduce high-resolution (2 m) observations, low-resolution sensors (i.e. 250 m+) are unable to do so. These results give confidence in results from current sensors such as ASTER and Landsat ETM+ as well as ESA’s upcoming Sentinel-2 and NASA’s recently launched LDCM. In addition, images from the Landsat data archive are used to classify glacier facies and calculate the albedo of glaciers on the Brøgger Peninsula, Svalbard. The time series is used to observe seasonal and interannual trends and investigate the role of melt-albedo feedback in thinning of Svalbard glaciers. The dissertation concludes with recommendations for glacier surface classification over a range of current and future multispectral sensors. Application of the classification schemes suggested should help to improve the understanding of recent and continuing change to GIC around the world.

Thesis (Ph. D.)--Ohio State University, 1992.
Advisors: A.D. Ward and John G. Lyon, Dept. of Agricultural Engineering. Includes bibliographical references. Available online via OhioLINK's ETD Center

There is a common theme at play in our talk of data generally, of digital earth data more specifically, and of environmental monitoring most specifically: more data leads to more action and, ultimately, to societal good. This data-to-action framework is troubled. Its taken-for-grantedness prevents us from attending to the processes between data and action. It also dampens our drive to investigate the contexts of that data, that action, and that envisioned societal good. In this dissertation, I deconstruct this data-to-action model in the context of Landsat, the United States' first natural resource management satellite. First, I talk about the ways in which Landsat's data and instrumentation hold conflicting narratives and values within them. Therefore, Landsat data does not automatically or easily yield action toward environmental preservation, or toward any unified societal good. Furthermore, I point out a parallel dynamic in STS, where critique is somewhat analogous to data. We want our critiques to yield action, and to guide us toward a more just technoscience. However, critiques—like data—require intentional, reconstructive interventions toward change. Here is an opportunity for a diffractive intervention: one in which we read STS and remote sensing through each other, to create space for interdisciplinary dialogue around environmental preservation. A focus on this shared goal, I argue, is imperative. At stake are issues of environmental degradation, dwindling resources, and climate change. I conclude with beginnings rather than endings: with suggestions for how we might begin to create infrastructure that attends to that forgotten space between data, critique, action, and change.
Doctor of Philosophy
I have identified a problem I call the data-to-action paradigm. When we scroll around on Facebook and find articles –– citing pages and pages of statistics –– on our rapidly melting glaciers and increasingly unpredictable weather patterns, we are existing within this paradigm. We have been offered evidence of looming, catastrophic change, but no suggestions on what to do about it. This is not only happening with climatological data and large-scale environmental systems modelling. Rather, this is a general problem across the field of Earth Remote Sensing. The origins of this data-to-action paradigm, I argue, can be found in old and new rhetoric about Landsat, the United States’ first natural resource management satellite. This rhetoric often says that Landsat — and other natural resource management satellites’ — data is a way toward societal good. The more data we have, the more good will proliferate in the world. However, we haven’t been specific about what that good might look like, and what kinds of actions we might take toward that good using this data. This is because, I argue, Earth systems science is politically complicated, with many different conceptions of societal good. In order to be more specific about how we might use this data toward some kind of good we must (1) explore the history of environmental data, and figure out where this rhetoric comes from (which I I do in this dissertation), and (2) encourage interdisciplinary collaborations between Earth Remote Sensing scientists, social scientists, and humanists, to more specifically flesh out connections between digital Earth data, its analyses, and subsequent civic action on such data.

Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2005.
81 Leaves printed on single pages i-xi, preliminary pages and numbered pages 1- 70. Includes bibliography, list of tables and list of figures.
Digitized at 300 dpi color PDF format (OCR), using KODAK i 1220 PLUS scanner.
ENGLISH ABSTRACT: Geological field mapping is often limited by logistical and cost constraints as well as the scope and extent of observations possible using ground-based mapping. Remote sensing offers, among others, the advantages of an increased spectral range for observations and a regional perspective of areas under observation. This study aimed to determine the accuracy of a collection of image classification techniques when applied to ASTER reflectance data. Band rationing, the Crosta Technique, Constrained Energy Minimization, Spectral Correlation Mapping and the Maximum Likelihood Classifier were evaluated for their efficiency in detecting and discriminating between greenstone and granitoid material. The study area was the Archaean Barberton Greenstone Belt in the eastern Mpumalanga Province, South Africa. ASTER reflectance imagery was acquired and pre-processed. Training and reference data was extracted from the image through visual inspection and expert knowledge. The training data was used in conjunction with USGS mineral spectra to train the five classification algorithms using the ERDAS's software package. This resulted in abundance images for the target materials specified by the training data. The Maximum Likelihood Classifier produced a classified thematic map. The reference data was used to perform a rigorous classification accuracy assessment procedure. All abundance images were thresholded to varying levels, obtaining accuracy statistics at every level. In so doing, threshold levels could be defined for every abundance image in such a way that the reliability of the classification was optimized. For each abundance image, as well as for the output map of the Maximum Likelihood Classifier, user's- and producer's accuracies as well as kappa statistics were derived and used as comparative measures of efficiency between the five techniques. This information was also used to assess the spectral separability of the target materials. The Maximum Likelihood Classifier outperformed the other techniques significantly, achieving an overall classification accuracy of 81.1% and an overall kappa value of 0.748. Greenstone rocks were accurately discriminated from granitoid rocks with accuracies between 72.9% and 98.5%, while granitoid rocks showed very poor ability to be accurately distinguished from each other. The main recommendations from this study are that thermal infrared and gamma-ray data be considered, together with better vegetation masking and an investigation into object orientated techniques.
AFRIKAANSE OPSOMMING: Geologiese veldkartering word algemeen beperk deur logistiese en koste-verwante faktore, sowel as die beperkte bestek waartoe waarnemings met veld-gebasseerde tegnieke gemaak kan word. Afstandswaarneming bied, onder andere, 'n vergrote spekrale omvang vir waarnemings en 'n regionale perspektief van die area wat bestudeer word. Hierdie studie was gemik daarop om die akkuraatheid van 'n versameling beeld-klassifikasie tegnieke, toegepas op ASTER data, te bepaal. Bandverhoudings, die Crosta Tegniek, "Constrained Energy Minimization", Spektrale Korrellasie Kartering, en Maksimum Waarskynlikheid Klassifikasie is evalueer op grond van hul vermoë om groensteen en granitoied-rotse op te spoor en tussen hulle te onderskei. Die studiegebied was die Argalese Barberton Groensteengordel in die oostelike Mpumalanga Provinsie in Suid Afrika. 'n ASTER refleksie beeld is verkry, waarop voorverwerking uitgevoer is. Opleidings- en verwysingsdata is van die beeld verkry deur visuele inspeksie en vakkundige kennis. Die opleidingsdata is saam met VSGO mineraalspektra gebruik om die vyf klassifikasie algoritmes met behulp van die ERDAS sagteware pakket op te lei. Die resultaat was volopheidsbeelde vir die teikenmateriale gespesifiseer in die opleidingsdata. Die Maksimum Waarskynlikheid algoritme het 'n geklassifiseerde tematiese beeld gelewer. Met behulp van die verwysingsdata is 'n streng akkuraatheidstoetsing prosedure uitgevoer. Vir alle volopheidsbeelde is 'n reeks drempelwaardes gestel, en by elke drempelwaarde is akkuraatheidsstatistieke afgelei. Op hierdie manier kon 'n drempelwaarde vir elke volopheidsbeeld vasgestel word sodat die drempelwaarde die betroubaarheid van die klassifikasie optimeer. Vir elke volopheidsbeeld, asook vir die tematiese kaart verkry van die Maksimum Waarskynlikheid klassifikasie, is gebruikers- en produsent-akkuraathede en kappa statistieke bereken. Hierdie waardes is gebruik as vergelykende maatstawwe van akkuraatheid tussen die vyf tegnieke, asook van die spektrale skeibaarheid van die onderskeie teikenmateriale. Die Maksimum Waarskynlikheid klassifikasie het die beste resultate gelewer, met 'n algehele klassifikasie akkuraatheid van 81.1%, en 'n gemiddelde kappa waarde van 0.748. Groensteenrotse kon met hoë akkuraathede van tussen 72.9% en 98.5% van granitoiedrotse onderskei word, terwyl granitoiedrotse 'n swak vermoë getoon het om van mekaar onderskei te word. Die belangrikste aanbevelings vanuit hierdie studie is dat termiese uitstralingdata asook gamma-straal data geimplimenteer word. Beter verwydering van plantegroei en 'n studie na die lewensvatbaarheid van objekgeorienteerde metodes word ook aanbeveel.

Monitoring large forest areas is presently feasible with satellite remote sensing as opposed to time-consuming and expensive ground surveys as alternative. This study evaluated, for the first time, the potential of using freely available medium resolution (30 m) Landsat time series data for deforestation monitoring in tropical rainforests of Kalimantan, Indonesia, at sub-annual time scales. A simple, generic, data-driven algorithm for deforestation detection based on a consecutive anomalies criterion was proposed. An accuracy assessment in the spatial and the temporal domain was carried out using high-confidence reference sample pixels interpreted with the aid of multi-temporal very high spatial resolution image series. Results showed a promising spatial accuracy, when three consecutive anomalies were required to confirm a deforestation event. Recommendations in tuning the algorithm for different operational use cases were provided within the context of satisfying REDD+ requirements, depending on whether spatial accuracy or temporal accuracy need to be optimized.