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The mapping of sea ice is an important task for understanding global climate and for safe shipping. Currently, sea ice maps are created by human analysts with the help of remote sensing imagery, including synthetic aperture radar (SAR) imagery. While the maps are generally correct, they can be somewhat subjective and do not have pixel-level resolution due to the time consuming nature of manual segmentation. Therefore, automated sea ice mapping algorithms such as the multivariate iterative region growing with semantics (MIRGS) sea ice image segmentation algorithm are needed. MIRGS was designed to work with one-channel single-polarization SAR imagery from the RADARSAT-1 satellite. The launch of RADARSAT-2 has made available two-channel dual-polarization SAR imagery for the purposes of sea ice mapping. Dual-polarization imagery provides more information for distinguishing ice types, and one of the channels is less sensitive to changes in the backscatter caused by the SAR incidence angle parameter. In the past, this change in backscatter due to the incidence angle was a key limitation that prevented automatic segmentation of full SAR scenes. This thesis investigates techniques to make use of the dual-polarization data in MIRGS. An evaluation of MIRGS with RADARSAT-2 data was performed and showed that some detail was lost and that the incidence angle caused errors in segmentation. Several data fusion schemes were investigated to determine if they can improve performance. Gradient generation methods designed to take advantage of dual-polarization data, feature space fusion using linear and non-linear transforms as well as image fusion methods based on wavelet combination rules were implemented and tested. Tuning of the MIRGS parameters was performed to find the best set of parameters for segmentation of dual-polarization data. Results show that the standard MIRGS algorithm with default parameters provides the highest accuracy, so no changes are necessary for dual-polarization data. A hierarchical segmentation scheme that segments the dual-polarization channels separately was implemented to overcome the incidence angle errors. The technique is effective but requires more user input than the standard MIRGS algorithm.

The mapping of sea ice is an important task for understanding global climate and for safe shipping. Currently, sea ice maps are created by human analysts with the help of remote sensing imagery, including synthetic aperture radar (SAR) imagery. While the maps are generally correct, they can be somewhat subjective and do not have pixel-level resolution due to the time consuming nature of manual segmentation. Therefore, automated sea ice mapping algorithms such as the multivariate iterative region growing with semantics (MIRGS) sea ice image segmentation algorithm are needed. MIRGS was designed to work with one-channel single-polarization SAR imagery from the RADARSAT-1 satellite. The launch of RADARSAT-2 has made available two-channel dual-polarization SAR imagery for the purposes of sea ice mapping. Dual-polarization imagery provides more information for distinguishing ice types, and one of the channels is less sensitive to changes in the backscatter caused by the SAR incidence angle parameter. In the past, this change in backscatter due to the incidence angle was a key limitation that prevented automatic segmentation of full SAR scenes. This thesis investigates techniques to make use of the dual-polarization data in MIRGS. An evaluation of MIRGS with RADARSAT-2 data was performed and showed that some detail was lost and that the incidence angle caused errors in segmentation. Several data fusion schemes were investigated to determine if they can improve performance. Gradient generation methods designed to take advantage of dual-polarization data, feature space fusion using linear and non-linear transforms as well as image fusion methods based on wavelet combination rules were implemented and tested. Tuning of the MIRGS parameters was performed to find the best set of parameters for segmentation of dual-polarization data. Results show that the standard MIRGS algorithm with default parameters provides the highest accuracy, so no changes are necessary for dual-polarization data. A hierarchical segmentation scheme that segments the dual-polarization channels separately was implemented to overcome the incidence angle errors. The technique is effective but requires more user input than the standard MIRGS algorithm.

This thesis is focused on the utilization of RADARSAT-2 polarimetric SAR data for mapping two surficial aspects of the Umiujalik Lake area, Nunavut, Canada: i) materials, such as bedrock, boulders, organic material, sand and gravel, thick and thin till; and ii) lineaments. To achieve these tasks, RADARSAT-2 polarimetric SAR images with three west-looking, increasing incidence angles (FQ1, FQ12, and FQ20, respectively) were used alone and in combination with LANDSAT-7 ETM+ and Digital Elevation Model (DEM) image data. The surficial materials mapping study tested: i) the effects of incidence angles on mapping accuracy; and ii) non-polarimetric and polarimetric classifiers. For non-polarimetric analysis, a Maximum Likelihood Classification (MLC) algorithm was applied to different combinations of RADARSAT-2, LANDSAT-7 ETM+, and DEM images, achieving a maximum overall classification accuracy of 85%. Polarimetric analyses first included computation of polarimetric signatures to understand the scattering mechanisms of the considered surficial materials, i.e., surface, volume, and multiple scatterings. It also tested three polarimetric classifiers: supervised Wishart (overall accuracy of 48.7% from FQ12 image), and unsupervised Freeman-Wishart, and Wishart-H/ /A. Three main conclusions were reached: i) high incidence angle greatly decreases classification accuracy for the HH polarized image when used alone, but incidence angle has little effect when the HV polarization is added; ii) combining images with three incidence angles (FQ1, FQ12, and FQ20) gives higher accuracy with the maximum likelihood classifier; and iii) the medium incidence angle image (FQ12) produces the best classification accuracy using polarimetric classifiers. In the second part of the study, surface lineaments were mapped using RADARSAT-2 SAR single-polarized images, RGB HH, HV, VV composites, polarimetric total power images, and LANDSAT-7 ETM+ principal component images. Polarization effect analysis showed that regardless of beam mode, more lineaments were identified on the HH image than on the HV image, and the maximum number of lineaments was identified on the multi-polarized RGB composite. Incidence angle effects results showed that regardless of polarization modes, the FQ12 image yielded more lineaments than the FQ1 or FQ20 images. The majority of lineaments are oriented in NW and NNW directions, which correspond to the ice flow direction during the last glaciation.

The boreal forest covers 11% of the earth’s land surface and contains 37 percent of the planet’s terrestrial carbon, which is more than the combined total of both the tropical and the temperate forests [1]. This estimate translates to 703 Pg of carbon with the vast majority contained within the organic soils and peat layers [2-4]. The western-north American boreal forest is a fire ecosystem [2, 5-7] where fires typically occur every 50 to 200 years [8, 9], allowing vast quantities of carbon to re-enter the atmosphere. Understanding and estimating past fire history and the related changes in carbon budget [3, 4, 7, 10] in this biome is of significant importance for climate researchers as they attempt to model for future changes in the planet’s climate [2, 4, 11-14]. Many techniques are available to remotely sense wildfires - using optical, thermal and passive microwave remote sensors - during and immediately after an event - although resolution and availability of images due to cloud cover can make these techniques operationally challenging. Radar remote sensing can provide a complement to these optical and passive microwave techniques, since radar is not affected by cloud cover and solar illumination levels. The Advanced Land Observatory Satellite (ALOS) operates a phased array L band synthetic aperture radar (PALSAR) and Canada’s Radarsat-2 contains a C-Band (SAR) instrument. These radar satellites can be used to detect information about the boreal forest environment including the effects of wildfire. Polarimetric radar is an emerging technology whose full potential is still being actively explored and discovered. More specifically, this research is ground-breaking since very little work has been performed investigating the relationship between polarimetric radar data and historical boreal wildfire events. This area of investigation is a complex marriage of forestry, geospatial information and radar engineering that requires an extensive array of data sets to facilitate analysis. This research has demonstrated that both PALSAR L-Band and Canada’s Radarsat-2 C-Band full polarimetric radars can be used to detect and classify wildfire scars within individual images. The boreal forest is a dynamic ecosystem where both the level of burn severity and the subsequent regeneration of the forest is affected by many factors that can vary widely across small distances. This work contributes to the understanding of the relationships between remotely sensed quad-pol radar signals and both the boreal ecosystem and how wildfire interacts in this environment. / Graduate / 0478 / 0538 / 0984 / ghobart@nrcan.gc.ca

This project presents regional velocity measurements for the eastern portion of the St. Elias Mountains, including the entire glaciated area of Kluane National Park, derived from speckle tracking of Radarsat-2 imagery acquired in winter 2011 and 2012. This technique uses a cross-correlation approach to determine the displacement of the ‘speckle’ pattern of radar phase returns between two repeat-pass images. Further reconstruction of past velocities is performed on a selection of key glaciers using feature tracking of Landsat-5 imagery, allowing for the investigation of variability in glacier motion on interannual and decadal time scales. The results of the analysis showed that there is a strong velocity gradient across the region reflecting high accumulation rates on the Pacific-facing slope of the mountain range. These glaciers may have velocities an order of magnitude greater than glaciers of a similar size on the landward slope. Interannual variability was high, both in relation to surge events, of which a number were identified, and variation of other unknown controls on glacier motion. A long-term trend of velocity decrease was observed on the Kaskawulsh Glacier when comparing the results of this analysis to work carried out in the 1960s, the pattern of which is broadly congruent to measurements of surface elevation change over a similar period.

Thesis (M.A.) - Carleton University, 2002. / Includes bibliographical references (p. 130-144). Also available in electronic format on the Internet.

Thesis (MA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Image classification has long been used in earth observation and is driven by the need for accurate maps to develop conceptual and predictive models of Earth system processes. Synthetic aperture radar (SAR) imagery is used ever more frequently in land cover classification due to its complementary nature with optical data. There is therefore a growing need for reliable, accurate methods for using SAR and optical data together in land use and land cover classifications. However, combining data sets inevitably increases data dimensionality and these large, complex data sets are difficult to handle. It is therefore important to assess the benefits and limitations of using multi-temporal, dual-sensor data for applications such as land cover classification. This thesis undertakes this assessment through four main experiments based on combined RADARSAT-2 and SPOT-5 imagery of the southern part of Reunion Island. In Experiment 1, the use of feature selection for dimensionality reduction was considered. The rankings of important features for both single-sensor and dual-sensor data were assessed for four dates spanning a 6-month period, which coincided with both the wet and dry season. The mean textural features produced from the optical bands were consistently ranked highly across all dates. In the two later dates (29 May and 9 August 2014), the SAR features were more prevalent, showing that SAR and optical data have complementary natures. SAR data can be used to separate classes when optical imagery is insufficient. Experiment 2 compared the accuracy of six supervised and machine learning classification algorithms to determine which performed best with this complex data set. The Random Forest classification algorithm produced the highest accuracies and was therefore used in Experiments 3 and 4. Experiment 3 assessed the benefits of using combined SAR-optical imagery over single-sensor imagery for land cover classifications on four separate dates. The fused imagery produced consistently higher overall accuracies. The 29 May 2014 fused data produced the best accuracy of 69.8%. The fused classifications had more consistent results over the four dates than the single-sensor imagery, which suffered lower accuracies, especially for imagery acquired later in the season. In Experiment 4, the use of multi-temporal, dual-sensor data for classification was evaluated. Feature selection was used to reduce the data set from 638 potential training features to 50, which produced the best accuracy of 74.1% in comparison to 71.9% using all of the features. This result validated the use of multi-temporal data over single-date data for land cover classifications. It also validated the use of feature selection to successfully inform data reduction without compromising the accuracy of the final product. Multi-temporal and dual-sensor data shows potential for mapping land cover in a tropical, mountainous region that would otherwise be challenging to map using single-sensor data. However, accuracies Stellenbosch University https://scholar.sun.ac.za iv generally remained lower than would allow for transferability and replication of the current methodology. Classification algorithm optimisation, supervised segmentation and improved training data should be considered to improve these results. / AFRIKAANSE OPSOMMING: Beeld-klassifikasie word al ‘n geruime tyd in aardwaarneming gebruik en word gedryf deur die behoefte aan akkurate kaarte om konseptuele en voorspellende modelle van aard-stelsel prosesse te ontwikkel. Sintetiese apertuur radar (SAR) beelde word ook meer dikwels in landdekking klassifikasie gebruik as gevolg van die aanvullende waarde daarvan met optiese data. Daar is dus 'n groeiende behoefte aan betroubare, akkurate metodes vir die gesamentlike gebruik van SAR en optiese data in landdekking klassifikasies. Die kombinasie van datastelle bring egter ‘n onvermydelike verhoging in data dimensionaliteit mee, en hierdie groot, komplekse datastelle is moeilik om te hanteer. Dus is dit belangrik om die voordele en beperkings van die gebruik van multi-temporale, dubbel-sensor data vir toepassings soos landdekking-klassifikasie te evalueer. Die waarde van gekombineerde (versmelte) RADARSAT-2 en SPOT-5 beelde word in hierdie tesis deur middel van vier eksperimente geevalueer. In Eksperiment 1 is die gebruik van kenmerk seleksie vir dimensionaliteit-vermindering toegepas. Die ranglys van belangrike kenmerke vir beide enkel-sensor en 'n dubbel-sensor data is beoordeel vir vier datums wat oor 'n tydperk van 6 maande strek. Die gemiddelde tekstuur kenmerke uit die optiese lae is konsekwent hoog oor alle datums geplaas. In die twee later datums (29 Mei en 9 Augustus 2014) was die SAR kenmerke meer algemeen, wat dui op die aanvullende aard van SAR en optiese data. SAR data dus gebruik kan word om klasse te onderskei wanneer optiese beelde onvoldoende daarvoor is. Eksperiment 2 het die akkuraatheid van ses gerigte en masjien-leer klassifikasie algoritmes vergelyk om te bepaal watter die beste met hierdie komplekse datastel presteer. Die random gorest klassifikasie algoritme het die hoogste akkuraatheid bereik en is dus in Eksperimente 3 en 4 gebruik. Eksperiment 3 het die voordele van gekombineerde SAR-optiese beelde oor enkel-sensor beelde vir landdekking klassifikasies op vier afsonderlike datums beoordeel. Die versmelte beelde het konsekwent hoër algehele akkuraathede as enkel-sensor beelde gelewer. Die 29 Mei 2014 data het die hoogste akkuraatheid van 69,8% bereik. Die versmelte klassifikasies het ook meer konsekwente resultate oor die vier datums gelewer en die enkel-sensor beelde het tot laer akkuraathede gelei, veral vir die later datums. In Eksperiment 4 is die gebruik van multi-temporale, dubbel-sensor data vir klassifikasie ge-evalueer. Kenmerkseleksie is gebruik om die data stel van 638 potensiële kenmerke na 50 te verminder, wat die beste akkuraatheid van 74,1% gelewer het. Hierdie resultaat bevestig die belangrikheid van multi-temporale data vir grond dekking klassifikasies. Dit bekragtig ook die gebruik van kenmerkseleksie om data vermindering suksesvol te rig sonder om die akkuraatheid van die finale produk te belemmer. Stellenbosch University https://scholar.sun.ac.za vi Multi-temporale en dubbel-sensor data toon potensiaal vir die kartering van landdekking in 'n tropiese, bergagtige streek wat andersins uitdagend sou wees om te karteer met behulp van enkel-sensor data. Oor die algemeen het akkuraathede egter te laag gebly om vir oordraagbaarheid en herhaling van die huidige metode toe te laat. Klassifikasie algoritme optimalisering, gerigte segmentering en verbeterde opleiding data moet oorweeg word om hierdie resultate te verbeter.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-04-17T23:16:01Z No. of bitstreams: 1 BrunoCesarPereiraDaCosta_TESE.pdf: 9035673 bytes, checksum: b47a178b546c68bed34b29cd6050cae3 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-04-20T22:33:39Z (GMT) No. of bitstreams: 1 BrunoCesarPereiraDaCosta_TESE.pdf: 9035673 bytes, checksum: b47a178b546c68bed34b29cd6050cae3 (MD5) / Made available in DSpace on 2017-04-20T22:33:39Z (GMT). No. of bitstreams: 1 BrunoCesarPereiraDaCosta_TESE.pdf: 9035673 bytes, checksum: b47a178b546c68bed34b29cd6050cae3 (MD5) Previous issue date: 2016-11-18 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / As imagens de sat?lites t?m sido utilizadas para mapear, monitorar e quantificar a qualidade dos recursos naturais. O mapeamento detalhado da vegeta??o de mangue ? uma demanda crescente por se tratar de um valoroso instrumento de conhecimento, manuten??o e gest?o do ecossistema manguezal em rela??o ?s modifica??es provocadas pelas atua??es antr?picas e/ou naturais, frente ?s mudan?as globais. Este trabalho combinou dados multiespectrais da regi?o do vis?vel e infravermelho pr?ximo do sistema LANDSAT-8 com dados monoespectrais do RADARSAT-2, aliado a dados hiperespectrais de espectrorradiometria e ?ndice de Vegeta??o na segmenta??o e classifica??o de alguns manguezais no Nordeste do Brasil, levando em considera??o a diversidade de ambientes presentes na ?rea total do estudo. Como resultado final do processo de segmenta??o e classifica??o, calculamos que os manguezais da ?rea estudada ocupam ?rea total de aproximadamente 5.392 ha. A esp?cie R. mangle ? a esp?cie dominante, totalizando uma ?rea de 3.350 ha, representando 62,13% da ?rea total, deste total cerca de 2.861 ha s?o ocupados pela R. mangle I (porte e adensamento convencional), representando 53,06% da ?rea total; 489 ha pela condi??o fitoestrutural R. mangle II (porte baixo e bastante adensado), cerca de 9,07% da ?rea total; regi?es mistas de esp?cies ou de transi??o entre elas ocupam ?rea de 1.092 ha, cerca de 20,25% da ?rea total, seguida da esp?cie A. schaueriana ocupando uma ?rea de 950 ha, cerca de 17,62% da ?rea total. Este estudo atendeu ?s expectativas em obter uma maior efici?ncia no levantamento espacial com alta acur?cia para o monitoramento da qualidade desse ecossistema altamente sens?vel ?s altera??es ambientais: como subs?dio ? sua preserva??o e transforma??o em um Projeto de Mecanismo de Desenvolvimento Limpo. Para tal, foi realizada a estimativa de CO2 aprisionado nas florestas de mangue pertencentes a Reserva de Desenvolvimento Sustent?vel Estadual Ponta do Tubar?o (RDSEPT) por meio de M?todo Indireto n?o destrutivo. As estimativas totais das m?dias de CO2 aprisionado em cada hectare ocupado pela esp?cie R. mangle ? de 39,93 t, L. schaueriana ? de 28,47 t e as regi?es de esp?cies mistas ? de 34,20 t. Estima-se que a quantifica??o total de CO2 aprisionado na RDSEPT seja 17.156,51 t. Mediante os valores obtidos, percebemos que o manguezal da RDSEPT de maneira geral possui um grande potencial de gerar biomassa e consequentemente aprisionar CO2. Podendo gerar uma valiosa oportunidade financeira, justificando a preserva??o deste ecossistema. / The satellite images have been used to map, monitor and quantify the quality of natural resource. The detailed mapping of mangrove vegetation is an increasing demand because it is a valorous Valorous instrument of knowledge, maintenance and management of the mangrove ecosystem in relation to changes caused by anthropic actions and/or natural activities to global changes. This work combined multispectral data from the visible and near infrared of the LANDSAT-8 system with Monospectral data from RADARSAT-2, combined with hyperspectral data of the spectroradiometry and Vegetation Index in the segmentation and classification of some mangroves in the Northeast of Brazil, taking into account the diversity of environments present in the total area of the study. As the final result of the segmentation and classification process, we calculate that the mangroves in the study area occupy a total area of approximately 5.392ha. The species R. mangle is the dominant species, totaling an area of 3,350 ha, representing 62.13% of the total area, of this total about 2,861 ha are occupied by R. mangle I (size and conventional densification), representing 53.06% of the total area; 489 ha for structural phyto condition R. mangle II (short stature and very dense), about 9.07% of the total area; mixed regions of species or transition between them occupy area of 1092 ha, about 20.25% of total area, then the specie A. schaueriana occupying an area of 950 ha, about 17.62% of the total area. This study met the expectations to get the greater efficiency in the spatial lifting for monitoring the quality of this highly sensitive ecosystem on the environmental changes: as support their preservation and transformation into a Clean Development Mechanism Project. For such, was made the estimation of trapped CO2 in mangrove forests belonging to Reserva de Desenvolvimento Sustent?vel Estadual Ponta do Tubar?o (RDSEPT) by Indirect Method nondestructive. The total estimates on the mean of trapped CO2 in each hectare occupied by the species R. mangle is 39.93t, L. schaueriana is 28.47t and the regions of mixed species is 34.20t. It is estimated that the total quantification of trapped CO2 in the RDSEPT is 17156.51t. By the obtained values, we realized that the values of mangrove in the RDSEPT in general have a great potential to generate biomass and consequently imprison CO2. It is able to generate a valuable financial opportunity, justifying the preservation of this ecosystem.

This project presents regional velocity measurements for the eastern portion of the St. Elias Mountains, including the entire glaciated area of Kluane National Park, derived from speckle tracking of Radarsat-2 imagery acquired in winter 2011 and 2012. This technique uses a cross-correlation approach to determine the displacement of the ‘speckle’ pattern of radar phase returns between two repeat-pass images. Further reconstruction of past velocities is performed on a selection of key glaciers using feature tracking of Landsat-5 imagery, allowing for the investigation of variability in glacier motion on interannual and decadal time scales. The results of the analysis showed that there is a strong velocity gradient across the region reflecting high accumulation rates on the Pacific-facing slope of the mountain range. These glaciers may have velocities an order of magnitude greater than glaciers of a similar size on the landward slope. Interannual variability was high, both in relation to surge events, of which a number were identified, and variation of other unknown controls on glacier motion. A long-term trend of velocity decrease was observed on the Kaskawulsh Glacier when comparing the results of this analysis to work carried out in the 1960s, the pattern of which is broadly congruent to measurements of surface elevation change over a similar period.