Modeling Land-Cover Types Using Multiple Endmember Spectral Mixture Analysis in a Desert City

Spectral mixture analysis is probably the most commonly used approach among sub-pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (=3x17x4) total four-endmember models for the urban subset and 96 (=6x6x2x4) total five-endmember models for the non-urban subset to identify fractions of soil, impervious surface, vegetation, and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub- pixel level.

Interpreting Vegetation and Soil Anomalies in the Guarumen Area of Northwestern Venezuela Using Remote Sensing Applications

The Guarumen area of Venezuela is a tectonically active region that is approximately 1,640 mi2 across the northern portions of the Barinas Basin and the foothills of the Mérida Andes. It is structurally influenced by the Caribbean plate to the north, the Nazca plate to the west, and the Maracaibo block against the Guyana Shield of the South American Plate. These result in an oblique boundary that gives rise to the fold-and-thrust belt of the Mérida Andes to the west, and the Caribbean Mountain system to the north, in concordance to the right-lateral shearing that is evidenced by the Boconó fault system. The goal of this research was to investigate the geological setting of northwestern Venezuela and further understand the geologic controls of the region, as it has become a region of interest for mineral, oil, and gas exploration. To achieve the goal, hyperspectral and multispectral data analysis were used to address land cover types by reducing hyperspectral and multispectral spectra to unique endmembers for use in classification. Then, provide an accurate land cover analysis using derived endmembers to characterize the outcomes concerning the influence of geological phenomena, and determine if microclimate analysis using satellite-based land surface temperature data can be effectively used to infer geologic structure or geomorphology, particularly soils and vegetation. Based on the hyperspectral data, an in-depth endmember analysis was conducted with image-derived spectra. These spectra were plotted in comparison with spectral libraries to identify the anomaly classification. It was determined that the natural vegetation make up of a specific region helped identify soil type. The Guarumen area was influenced by the sediment transport of the alluvial stream geomorphology of both the Merida Andes and the Caribbean Mountain System and both its respective geologies. The microclimate analysis shoa land surface temperature comparison of two separate Landscenes. Both shoa similar mean temperature range due to Venezuela’s tropical climate, but differed in other classifications. Results from this research show that remote sensing applications with limited field data can provide accurate land cover analysis concerning geological phenomena, but further field analysis is needed for more detailed classification

Master of Science

thesisPersistent drought conditions and associated vegetation mortality in the central Sierra Nevada of California were analyzed from 2013-2015 using a combination of field-derived polygons and AVIRIS hyperspectral data. Linear Discriminant Analysis (LDA) was used to classify hyperspectral data into five land cover classes based on dominant flora. LDA accuracies were compared across years in order to determine whether classification accuracy was correlated with increasing drought severity. It was determined that 2013 had the greatest accuracy and 2015 had the lowest. However, this trend was influenced by Bidirectional Reflectance Distribution Function (BRDF) effects in the densely forested landscape. Fractional cover data of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil were obtained from the US Forest Service to analyze which land cover classes and which elevation intervals experienced the greatest fractional cover change, which are both indicators of vegetation senescence and mortality. GV loss deemed the most appropriate indicator of vegetation senescence and mortality as NPV and soil appeared to be confused by the Multiple Endmember Spectral Mixture Analysis (MESMA) method used to obtain the fractional cover images. Mixed oak woodland (MO) and mixed low conifer (LC) forests experienced the greatest and second-greatest decreases in GV, respectively. Lower elevation areas (695-1369 m) generally experienced greater GV loss than higher elevation areas (2167-2779), which coincided with both MO and LC forest classes. The MO forest class, which occurs most in lower elevation areas, was comprised of dominantly drought resistant flora and experienced the greatest GV loss during the study period (16%). Conversely, the HC forest, which occurs dominantly in higher elevation areas, was comprised of dominantly non-drought-tolerant flora but experienced less GV loss (5%). This suggests that the differences in elevation and location of vegetation within the landscape played a larger role than the dominant floras' degrees of drought tolerance. Variations in seasonal senescence may have influenced the measured loss of GV for the MO and LC classes, which contained deciduous vegetation. However, overall GV loss in all classes, even those without trees, indicates that the landscape likely experienced vegetation mortality, especially at low elevations in the MO and LC classes

Remote Sensing Methods and Applications for Detecting Change in Forest Ecosystems

Forest ecosystems are being altered by climate change, invasive species, and additional stressors. Our ability to detect these changes and quantify their impacts relies on detailed data across spatial and temporal scales. This dissertation expands the ecological utility of long-term satellite imagery by developing high quality forest mapping products and examining spatiotemporal changes in tree species abundance and phenology across the northeastern United States (US; the ‘Northeast’). Species/genus-level forest composition maps were developed by integrating field data and Landsat images to model abundance at a sub-pixel scale. These abundance maps were then used to 1) produce a more detailed, accurate forest classification compared to similar products and 2) construct a 30-year time-series of abundance for eight common species/genera. Analyzing the time-series data revealed significant abundance trends in notable species, including increases in American beech (Fagus grandifolia) at the expense of sugar maple (Acer saccharum). Climate was the dominant predictor of abundance trends, indicating climate change may be altering competitive relationships. Spatiotemporal trends in deciduous forest phenology – start and end of the growing season (SOS/EOS) – were examined based on MODIS imagery from 2001-2015. SOS exhibited a slight advancing trend across the Northeast, but with a distinct spatial pattern: eastern ecoregions showed advance and western ecoregions delay. EOS trended substantially later almost everywhere. SOS trends were linked to winter-spring temperature and precipitation trends; areas with higher elevation and fall precipitation anomalies had negative associations with EOS trends. Together, this work demonstrates the value of remote sensing in furthering our understanding of long-term forest responses to changing environmental conditions. By highlighting potential changes in forest composition and function, the research presented here can be used to develop forest conservation and management strategies in the Northeast

Use of Earth observation for monitoring soil sealing trends in Flanders and Brussels between 1976 and 2013

The on-going growth of urban area in Flanders and in the Brussels Capital Region over the past decades has resulted in a highly sprawled urban tissue, consisting of large and smaller urban agglomerations, connected by a well-developed transportation network. The conversion of open land to urban area is accompanied by an increase in soil sealing, affecting the hydrological cycle and the urban climate. Despite a growing interest in monitoring the process of soil sealing in urban areas, to date no detailed information on the presence and evolution of sealed surfaces is available for Flanders. In this paper a linear regression unmixing approach is proposed to map and monitor changes of sealed surface cover at the regional scale, using medium as well as high resolution remote sensing data. Applied to Flanders and the Brussels Capital Region, a total sealed area of 2687 km² for 2013 is found, corresponding to an increase of 82% since 1976. Residential areas account for nearly half of the sealed area and show the largest increase in sealed surface cover over the past 37 years.De toenemende verstedelijking van Vlaanderen en het Brussels Hoofdstedelijk Gewest in de laatste decennia heeft geleid tot een sterk gefragmenteerde stedelijke ruimte die zich heeft ontwikkeld rond grote en kleinere bebouwingskernen en langs het dicht vertakte transportnetwerk dat deze kernen verbindt. De conversie van open ruimte naar stedelijk gebied gaat gepaard met een toenemende afdichting van de bodem met verharde oppervlakken. Deze afdichting heeft een impact heeft op de hydrologische cyclus en het klimaat van verstedelijkte zones. Ondanks de groeiende aandacht voor het opvolgen van bodemafdichting in verstedelijkte gebieden is op dit moment geen gedetailleerde informatie omtrent de evolutie van bodemverharding in Vlaanderen beschikbaar. In dit artikel wordt een methode voorgesteld om bodemverharding en veranderingen in verharding doorheen de tijd op regionale schaal in kaart te brengen, gebruik makend van satellietdata. De methode is gebaseerd op spectrale ontmenging van medium resolutie satellietdata, en gebruikt gedetailleerd, hoge resolutie beeldmateriaal om een op lineaire regressie gebaseerd ontmengingsmodel te calibreren en valideren. Toepassing van de methode op Vlaanderen en het Brussels Hoofdstedelijk Gewest resulteert voor 2013 in een totale verharde oppervlakte van 2687 km2, wat overeenstemt met een toename van 82% sinds 1976. Bijna de helft van de verharde oppervlakte situeert zich in residentiële gebieden, die vergeleken met andere landgebruiken ook de grootste toename in verharding kennen

Examining Urban Heat Island Effect and Its Public Health Implications with Remotely Sensed Data

The Urban heat island (UHI) as a byproduct of urbanization has long been studied utilizing remote sensing technologies. However, issues remain to be addressed. Land surface temperature (LST) as the indicator of surface UHI can be retrieved from remotely sensed data, but its accuracy is limited as existing studies neglect the neighboring effect. Further, while LST serves well as an indicator of surface thermal condition, it lacks the ability to reveal human heat stress, which is an environmental hazard that can seriously affect productivity, health or even survival of individuals. Although human heat stress has long been studied and can be quantified by many heat stress indices, it has never been explored across continuous spaces. Aiming to address these issues, the objectives of this research include: (1) taking into account the neighboring effect during LST retrieval using a moving window method; (2) revealing human heat stress with remotely sensed data; and (3) exploring the relationship between heat stress and land cover composition and configuration. My results indicate that the accuracy of LST estimation is improved when neighboring effect is considered. Discomfort index (DI) as an indicator of human heat stress can be retrieved from remotely sensed data, and its spatial distribution and relationship with land cover composition is largely affected by relative humidity. Spatial configuration of different land covers has an impact on DI, which may provide insights for policy makers and urban designers on mitigating hazardous environmental effect brought by urbanization

Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping

The emergence of hyperspectral imaging provides a new perspective for Earth observation, in addition to previously available orthophoto and multispectral imagery. This thesis focused on both the new data and new methodology in the field of hyperspectral imaging. First, the application of the future hyperspectral satellite EnMAP in impervious surface area (ISA) mapping was studied. During the search for the appropriate ISA mapping procedure for the new data, the subpixel classification based on nonnegative matrix factorization (NMF) achieved the best success. The simulated EnMAP image shows great potential in urban ISA mapping with over 85% accuracy. Unfortunately, the NMF based on the linear algebra only considers the spectral information and neglects the spatial information in the original image. The recent wide interest of applying the multilinear algebra in computer vision sheds light on this problem and raised the idea of nonnegative tensor factorization (NTF). This thesis found that the NTF has more advantages over the NMF when work with medium- rather than the high-spatial-resolution hyperspectral image. Furthermore, this thesis proposed to equip the NTF-based subpixel classification methods with the variations adopted from the NMF. By adopting the variations from the NMF, the urban ISA mapping results from the NTF were improved by ~2%. Lastly, the problem known as the curse of dimensionality is an obstacle in hyperspectral image applications. The majority of current dimension reduction (DR) methods are restricted to using only the spectral information, when the spatial information is neglected. To overcome this defect, two spectral-spatial methods: patch-based and tensor-patch-based, were thoroughly studied and compared in this thesis. To date, the popularity of the two solutions remains in computer vision studies and their applications in hyperspectral DR are limited. The patch-based and tensor-patch-based variations greatly improved the quality of dimension-reduced hyperspectral images, which then improved the land cover mapping results from them. In addition, this thesis proposed to use an improved method to produce an important intermediate result in the patch-based and tensor-patch-based DR process, which further improved the land cover mapping results

Intraurban Analysis of Surface Urban Heat Island From Disagregated Thermal Radiance Images

Surface Urban Heat Islands (SUHI) are areas with higher surface temperatures than their surroundings. Several studies have used thermal images from satellites to research the influence of urbanization on surface temperature patterns, however the low spatial resolution of thermal sensors limits the analysis of LST intraurban variations. Attempting to overcome this limitation, we used the Enhanced Physical Model (EPM) for disaggregation of land surface temperature (DLST) to generate fine scale LST for Sao Paulo city in Brazil. This method uses a linear regression and Planck’s law to combine NDVI, NDWI and UI to estimate LST at finer spatial detail. First, we calibrate the method by upscaling an ASTER thermal band to 1000 m and using EPM to estimate the original 100 m thermal band. The original and estimated ASTER thermal bands achieved and R² of 0.66. Following, we apply the EPM model to estimate the LST at 15 m and compare it with data from meteorological stations. The 15 m LST image facilitated the identification of potential SUHIs. The EPM model provides an enhanced product with higher level of spatial detail, which allows researchers to identify changes of surface temperature that would not be evident from an ASTER LST (90 m spatial resolution) product. In summary, the model allowed us to quantify and map the influence of different urbanization patterns on the LST distribution.Ilhas de calor de superfície (ICS)são áreas com temperature de superfície maior do que as áreas ao redor. Vários estudos tem usado imagens termais de satélite para investigar a influência da urbanização nos padrões de temperatura de superfície; entretanto a baixa resolução espacial dos atuais sensores termais limita a análise dos padrões de variação intraurbana de temperatura de superfície. Com o objetivo de surpassar essa limitação, nós utilizamos o the Enhanced Physical Model (EPM) para gerar dados de temperatura de superfície com maior nível de detalhamento para a cidade de São Paulo- Brasil. Esse método utiliza um modelo de regressão linear e a lei de Planck para combinar NDVI, NDWI e UI para estimar a temperatura de superfície com maior nível de detalhes espaciais.  Primeiro, para calibrar o modelo, nós reamostramos uma banda termal ASTER para 1000 m e utilizamos o método EPM para estimar a banda original de 100 m. A banda termal estimatada de 100 m atingiu um R2= 0.66 em relação a banda termal original. A seguir,  nós aplicamos o método EPM para estimar a temperatura de superfície à 15 m. A imagem de temperatura de superfície de 15 m facilitou a identificação de potenciais ilhas de calor de superfície. O modelo EPM fornece um produto com alto grau de detalhamento espacial, o que permite que pesquisadores identifiquem as mudanças de temperatura de superfície que não seriam evidentes na imagem  termal ASTER original (90 m de resolução espacial). Em suma, o modelo nos permitiu quantificar e mapear a influência de diferentes padrões de urbanização na distribuição dos padrões de temperatura de superfície

Tree Cover Variability in the District of Columbia

Urban forests are increasingly a focus of interest as urbanized populations grow and urban areas expand. Urban forests change as trees are planted, grow, die, and are removed. These processes alter a city's tree cover over time, but this inherent dynamism is poorly understood. Better understanding of how tree cover is a variable land cover component will enhance knowledge of the urban environment and provide new perspectives for management of urban resources. In this study, tree cover variability within a major urban center was observed over a 20 year period. Changes in tree cover proportion were measured in the District of Columbia between 1984-2004 utilizing highly calibrated satellite remote sensing data. Testing of alternate methodologies demonstrated that an approach utilizing support vector regression provided most consistent accuracy across land use types. Tree cover maps were validated using aerial photography imagery and data from field surveys. Between 1984-2004, the city-wide tree cover remained between 22.1(+/-2.9)% and 28.8(+/-2.9)% of total land surface area. The District of Columbia did not experience an overall increase or decrease in total tree canopy area. Spatial patterns of tree cover variability were investigated to identify local scale changes in tree cover and connections with urban land use. Within the city, greatest variability was observed in low density residential zones. Tree cover proportion in these zones declined 7.4(+/-5.4)% in the years between 1990-1996 and recovered after 1996. Changes in tree cover were observed with high resolution aerial photography to determine relative contribution from fluctuation in the number of standing trees and changes in crown sizes. Land cover conversion removed dense tree cover from 50.2 hectares of the city's land surface between 1984-2004. The results demonstrate that tree cover variability in the District of Columbia occurred primarily within low population density residential areas. Neighborhoods within these zones were analyzed to identify factors correlated with tree cover. Implications of the results include enhanced understanding of the possible impact of urban forest management, and how a focus on low density residential zones is appropriate in setting goals for expansion of urban tree cover