Bibliography: leaves 176-179.

Lidar is able to provide height and cover information which can be used to estimate selected forest attributes precisely. However, for users to evaluate whether the additional cost and complication associated with using Lidar merits adoption requires that the protocol to use lidar be thoroughly described and that a basis for selection of design parameters such as number of field plots and lidar pulse density be described. In our first analysis, we examine these issues by looking at the effects of pulse density and sample size on estimation when wall-to-wall lidar is used with a regression estimator. The effects were explored using resampling simulations. We examine both the effects on precision, and on the validity of inference. Pulse density had almost no effect on precision for the range examined, from 3 to .0625 pulses / m��. The effect of sample size on estimator precision was roughly in accordance with the behavior indicated by the variance estimator, except that for small samples the variance estimator had positive bias (the variance estimates were too small), compromising the validity of inference. In future analyses we plan to provide further context for wall-to-wall lidar-assisted estimation. While there is a lot of literature on modeling, there is limited information on how lidar-assisted approaches compare to existing methods, and what variables can or cannot be acquired, or may be acquired with reduced confidence. We expand our investigation of estimation in our second analysis by examining lidar obtained in a sampling mode in combination with Landsat. In this case we make inference about the feasibility of a lidar-assisted estimation strategy by contrasting its variance estimate with variance estimates from a variety of other sampling designs and estimators. Of key interest was how the precision of a two-stage estimator with lidar strips compared with a plot-only estimator from a simple random sampling design. We found that because the long and narrow lidar strips incorporate much of the landscape variability, if the number of lidar strips was increased from 7 to 15 strips, the precision of estimators with lidar can exceed that of estimators applied to plot-only SRS data for a much larger number of plots. Increasing the number of lidar strips is considered to be highly viable since the costs of field plots can be quite expensive in Alaska, often exceeding the cost of a lidar strip. A Landsat-assisted approach used for either an SRS or a two-stage sample was also found to perform well relative to estimators for plot-only SRS data. This proved beneficial when we combined lidar and Landsat-assisted regression estimators for two-stage designs using a composite estimator. The composite estimator yielded much better results than either estimator used alone. We did not assess the effects of changing the number of lidar strips in combination with using a composite estimator, but this is an important analysis we plan to perform in a future study. In our final analysis we leverage the synergy between lidar and Landsat to improve the explanatory power of auxiliary Landsat using a multilevel modeling strategy. We also incorporate a more sophisticated approach to processing Landsat which reflects temporal trends in individual pixels values. Our approach used lidar as an intermediary step to better match the spatial resolution of Landsat and increase the proportion of area overlapped between measurement units for the different sources of data. We developed two separate approaches for two different resolutions of data (30 m and 90 m) using multiple modeling alternatives including OLS and k nearest neighbors (KNN), and found that both resolution and the modeling approach affected estimates of residual variability, although there was no combination of model types which was a clear winner for all responses. The modeling strategies generally fared better for the 90 m approaches, and future analyses will examine a broader range of resolutions. Fortunately the approaches used are fairly flexible and there is nothing prohibiting a 1000 m implementation. In the future we also plan to look at using a more sophisticated Landsat time-series approach. The current approach essentially dampened the noise in the temporal trend for a pixel, but did not make use of information in the trend such as slope or indications of disturbance ��� which may provide additional explanatory power. In a future study we will also incorporate a multilevel modeling into estimation or mapping strategies and evaluate the contribution of the multilevel modeling strategy relative to alternate approaches. / Graduation date: 2013 / Access restricted to the OSU Community at author's request from June 21, 2012 - Dec. 21, 2012

No description available.

Includes bibliographical references (leaves 176-179) / xiii, 202 leaves : ill. (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, 1989

Thesis (M. En.)--Miami University, Institute of Environmental Sciences, 2008. / Title from first page of PDF document. Includes bibliographical references (p. 16-21-Xx).

This study compared two remote sensing water indices: the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI). Both indices were calculated using publically-available data from the Landsat 8 Operational Land Imager (OLI). The research goal was to determine whether the indices are effective in locating open water and measuring surface soil moisture. To demonstrate the application of water indices, analysis was conducted for freshwater wetlands in the Tualatin River Basin in northwestern Oregon to estimate hydrologic connectivity and hydrological permanence between these wetlands and nearby water bodies. Remote sensing techniques have been used to study wetlands in recent decades; however, scientific studies have rarely addressed hydrologic connectivity and hydrologic permanence, in spite of the documented importance of these properties. Research steps were designed to be straightforward for easy repeatability: 1) locate sample sites, 2) predict wetness with water indices, 3) estimate wetness with soil samples from the field, 4) validate the index predictions against the soil samples from the field, and 5) in the demonstration step, estimate hydrologic connectivity and hydrological permanence. Results indicate that both indices predicted the presence of large, open water features with clarity; that dry conditions were predicted by MNDWI with more subtle differentiation; and that NDWI results seem more sensitive to sites with vegetation. Use of this low-cost method to discover patterns of surface moisture in the landscape could directly improve the ability to manage wetland environments.

Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Geological remote sensing is a powerful tool for lithological discrimination, especially in arid regions with minimal vegetative cover to obscure rock exposures. Commercial multispectral imaging satellites provide a broad spectral range with which to target specific rock types. Landsat ETM+ (7), ASTER, and SPOT 5 multispectral images were acquired and digitally processed: band ratioing, principle components analysis, and maximum likelihood supervised classification. The sensors were evaluated on the ability to discriminate between sedimentary rocks in a structurally complex setting. The study focusses on the formations of the Naukluft Nappe Complex, Namibia. Previous work of the area had to be consulted in order to identify the main target rock types. Dolomite, limestone, quartzite, and shale were determined to make up the majority of rock types in the area. Landsat, ASTER, and SPOT 5 imagery were acquired and pre-processed. Each was subjected to transform techniques: band ratios and PCA. Band ratios were tailored to highlighted target rock types as well as a number of control ratios to ensure the integrity of important ratios. PCA components were inspected to find the most useful ones which were combined into FCCs. Transform results, expert knowledge, and a geological map were consulted to identify training and accuracy samples for the supervised classifications. All three classifications made use of the same set of training and accuracy samples to facilitate useful comparisons. Transform results were promising for Landsat and ASTER images, while SPOT 5 struggled. The limited spectral resolution of SPOT 5 limited its use for identifying target rock types, with the superior spatial resolution contributing very little. Landsat benefitted from good spectral resolution. This allowed for good performance with highlighting limestone and dolomite, while being less successful with shale. Quartzite was a real problem as the spectral resolution of Landsat could not cover this range as well. ASTER, having the highest spectral resolution, could distinguish between all four target rock types. Landsat and ASTER results suffered in areas where formations were relatively thin (smaller than sensor spatial resolution). The supervised classification results were similar to the transforms in that both Landsat and ASTER provided useful results, while SPOT 5 failed to yield definitive results. Accuracy assessment determined that ASTER performed the best at 98.72%. Landsat produced an accuracy of 93.29% while SPOT 5 was 80.17% accuracy. Landsat completely overestimated the amount of quartzite present, while all results classified significant proportions Quaternary sediments as shale. Limestone was well represented in even the poorest results, while dolomite usually struggled in areas where it was in close association with quartzite. Silica yields relatively strong responses in the TIR spectrum which could lead to misclassification of dolomite, which also has strong TIR signatures. / AFRIKAANSE OPSOMMING: Geologiese afstandswaarneming is 'n kragtige tegniek vir litologiese diskriminasie, veral in droë streke met minimale plantbedekking om dagsome te verduister. Kommersiële multispektrale satelliete beelde bied 'n breë spektrale reeks waarmee spesifieke gesteentetipes geteiken kan word. Landsat ETM + (7), ASTER, en SPOT 5 multispektrale beelde was bekom en digitaal verwerk: bandverhoudings, hoofkomponente-ontleding, en maksimum waarskynlikheid klassifikasie. Die sensors is geëvalueer op hul vermoë om te onderskei tussen sedimentêre gesteentes in 'n struktureel komplekse omgewing. Die studie fokus op die formasies van die Naukluft Dekblad Kompleks, Namibië. Vorige werk van die area was geraadpleeg om die hoofgesteentetipes te identifiseer. Dit was bepaal dat dolomiet, kalksteen, kwartsiet, en skalie die oorgrote meerderheid van kliptipes in area opgemaak het. Landsat, ASTER, en SPOT 5 beelde is verkry en voorverwerk. Elke beeld was onderwerp aan transformasietegnieke: bandverhoudings en hoofkomponente-ontleding. Bandverhoudings is aangepas om teiken rotstipes uit te lig asook 'n aantal kontrole bandverhoudings om die integriteit van belangrike verhoudings te verseker. Hoofkomponente-ontleding komponente is ondersoek om die mees bruikbares te vind en dié was gekombineer in valse kleur samestellings. Transformasie resultate, deskundige kennis, en 'n geologiese kaart was geraadpleeg om opleidings- en verwysingsmonsters was verkry vanaf die beelde vir die klassifikasies. Al drie klassifikasies gebruik gemaak van dieselfde stel van die opleiding- en akkuraatheidsmonsters om sodoende betekenisvolle vergelykings te verseker. Transformasie resultate is belowend vir Landsat en ASTER beelde, terwyl SPOT 5 minder bruikbaar was. Die noue spektrale resolusie van SPOT 5 beperk die gebruik daarvan vir die identifisering van teiken gesteentetipes terwyl die hoë ruimtelike resolusie baie min bydra. Landsat het voordeel getrek uit goeie spektrale resolusie. Dit goeie resultate opgelwer met die klem op kalksteen en dolomiet, terwyl skalie aansienlik swakker resultate opgelewer het. Kwartsiet was 'n werklike probleem omdat die spektrale resolusie van Landsat nie breed genoeg was om hierdie kliptipe te onderskei nie. ASTER, met die hoogste spektrale resolusie, kon onderskei tussen al vier teiken rotstipes. Landsat en ASTER resultate was baie negatief beïnvloed in gebiede waar formasies relatief dun was (kleiner as sensor ruimtelike resolusie). Die klassifikasie resultate was soortgelyk aan die transformasies in dat beide Landsat en ASTER nuttige resultate opgelewer het, terwyl SPOT 5 misluk het. Akkuraatheids assessering het bepaal dat ASTER die beste gevaar het met 98,72%. Landsat het 'n akkuraatheid van 93,29% opgelewer, terwyl SPOT 5 80,17% akkuraat was. Landsat het die hoeveelheid kwartsiet heeltemal oorskat, terwyl al die resultate groot hoeveelhede Kwaternêre sedimente as skalie geklassifiseer het. Kalksteen is goed verteenwoordig in tot die armste resultate, terwyl resultate gewoonlik afgeneem het waar dolomiet in noue verband met kwartsiet was. Dit is moontlik asgevolg van silika se relatiewe sterk reaksies in die termiese infra-rooi spektrum wat kan lei tot die foutiewe klassifisering met dolomiet (wat ook sterk reageer in die TIR spektrum).

Improved monitoring of forest biomass is needed to quantify natural and anthropogenic effects on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, fine spatial grain, and long history of earth observations provide a unique opportunity for measuring biophysical properties of vegetation across large areas and long time scales. However, like other multi-spectral data, the relationship between single-date reflectance and forest biomass weakens under certain canopy conditions. Because the structure and composition of a forest stand at any point in time is linked to the stand's disturbance history, one potential means of enhancing Landsat's spectral relationships with biomass is by including information on vegetation trends prior to the date for which estimates are desired. The purpose of this research was to develop and assess a method that links field data, airborne lidar, and Landsat-derived disturbance and recovery history for mapping of forest biomass and biomass change. Our study area is located in eastern Oregon (US), an area dominated by mixed conifer and single species forests. In Chapter 2, we test and demonstrate the utility of Landsat-derived disturbance and recovery metrics to predict current forest structure (live and dead biomass, basal area, and stand height) for 51 field plots, and compare the results with estimates from airborne lidar and single-date Landsat imagery. To characterize the complex nature of long-term (insect, growth) and short-term (fire, harvest) vegetation changes found in this area, we use annual Landsat time series between 1972 and 2010. This required integrating Landsat data from MSS (1972-1992) and TM/ETM+ (1982-present) sensors. In Chapter 2, we describe a method to bridge spectral differences between Landsat sensors, and therefore extent Landsat time-series analyses back to 1972. In Chapter 3, we extend and automate our approach and develop maps of current (2009) and historic (1993-2009) live forest biomass. We use lidar data for model training and evaluate the results with forest inventory data. We further conduct a sensitivity analysis to determine the effects of forest structure, time-series length, terrain and sampling design on model predictions. Our research showed that including disturbance and recovery trends in empirical models significantly improved predictions of forest biomass, and that the approach can be applied across a larger landscape and across time for estimating biomass change. / Graduation date: 2012 / Access restricted to the OSU Community at author's request from Nov. 29, 2011 - Nov. 29, 2012

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-27T15:55:07Z No. of bitstreams: 1 JOHN ELTON DE BRITO LEITE CUNHA - TESE PPGRN 2018.pdf: 4725678 bytes, checksum: 60bf2159f5477dc3356a1c23f7c2247e (MD5) / Made available in DSpace on 2018-08-27T15:55:07Z (GMT). No. of bitstreams: 1 JOHN ELTON DE BRITO LEITE CUNHA - TESE PPGRN 2018.pdf: 4725678 bytes, checksum: 60bf2159f5477dc3356a1c23f7c2247e (MD5) Previous issue date: 2018 / Capes / O baixo monitoramento e altas pressões climáticas e antrópicas fazem do bioma Caatinga, semiárido brasileiro, um dos mais vulneráveis do mundo. Séries temporais de sensoriamento remoto são valiosas para analisar as LCC em áreas com alta sazonalidade, mas demandam muitos recursos computacionais. Estudos anteriores utilizam séries temporais superiores a 30 anos de índices de vegetação com baixa resolução espacial (1 a 8 km). No entanto, esta resolução espacial geralmente não permite identificar ações humanas (impactos) no meio ambiente. Nos últimos anos, houve melhorias na qualidade da imagem do Landsat (radiométrica e geométrica) e agora estão prontas para suportar o monitoramento e análise dos processos na superfície terrestre. O objetivo deste estudo é analisar, a partir de sensores de média resolução espadai, as alterações na cobertura do solo de origem antrópica numa área do bioma Caatinga. Para este fim, utilizou-se algoritmos para gerar índices de vegetação, albedo de superfície e evapotranspiração a partir de dados dos sensores a bordo dos satélites da família Landsat. Para aumentar a eficiência na geração dessas informações, os algoritmos foram conduzidos para operar com baixa demanda por dados de estações meteorológicas e sem intervenção humana durante o processamento. Além disso, um serviço de alto desempenho para processamento de dados orbitais é proposto. Os dados gerados por estes algoritmos foram testados com a informações de campo, demonstrando a possibilidade de utilizar os algoritmos em processos automáticos. As técnicas de computação em nuvem e paralelização utilizadas neste estudo foram eficientes na produção de séries temporais superiores a 30 anos de variáveis em média resolução espacial. A principal aplicação desenvolvida neste trabalho utilizou séries temporais do Landsat por um período de 31 anos em resolução temporal mensal, a fim de investigar os padrões espaciais e temporais da mudança de cobertura do solo em uma área de Caatinga, semiárido do estado da Paraíba, no Brasil. Um novo índice espectral - índice Surface Albedo (SAI) - é proposto para melhorar a observação da condição biofísica da vegetação. Os índices NDVI, EVI e SAI foram utilizados para avaliar o monitoramento das LCC impulsionadas por ações humanas em contraste a alteração induzida pelo clima. Séries temporais dos índices foram aplicados ao método TSS RESTREND para monitoramento das LCC. O método é empregado para remover as influências a curto prazo da precipitação na fisionomia da cobertura do solo, permitindo assim avaliar a capacidade dos índices utilizados para discriminar alterações nas regiões semiáridas. Google Earth, imagens RapidEye e observações in situ (a partir de outubro de 2017) foram usadas para observar condições de preservação/degradação ao longo do tempo. Os resultados mostram que o índice SAI é capaz de distinguir entre cobertura do solo "alterada" e "inalterada" com uma alta acurácia, 87%, para detectar corretamente o ano da LCC. Quando utilizado o índice SAI, o TSS RESTREND demonstrou-se adequado para detectar LCC na Caatinga, e seu melhor desempenho foi alcançado quando o evento de mudança ocorre na região central da série temporal (1990-2010), com algumas imprecisões em anos secos. O menor desempenho dos índices EVI e NDVI na detecção das LCC no bioma da Caatinga é explicado pela sua alta sensibilidade às variações da cobertura de folhas, como resultado de condições sazonais ou extremas de seca. O LCC afeta todo o sistema soloplanta-atmosfera, como remoção de biomassa e mudanças nas propriedades do solo, bem como no microclima, devido à exposição direta à radiação, precipitação e vento. A este respeito, a SAI é suposto ser mais sensível às alterações artificiais na superfície terrestre, devido à sua capacidade de capturar uma maior quantidade de feedback ambiental. / Low monitoring plus high human and climate pressures make the Caatinga biome one of the most vulnerabte biomes in the world. Time series of remote sensing are vafuable for analyzing LCC in áreas with high seasonalrty, but they require a lot of computationai resources. Earlier studíes mostly use > 30- years time series of vegetatíon indexes at low spatial resolution (1 to 8 km). However, this spatial resolution usually does not allow to identify human actions (impacts) on the environment. Landsat imagery quality (radiometricalfy as weli as geometrically) and availability has improved in recent years and is now ready to support high temporal resolution monitoring and analysis of land surface processes. The objective of this study is to analyze, from sensors of médium spatial resolution, the changes in land cover of anthropic origin in an area of the Caatinga biome. For this purpose, algorithms were used to generate vegetation índices, surface albedo and evapotranspiration from sensor data on the satellites of the Landsat family. To increase the efficiency in generating this information, the algorithms were conducted to operate with low demand for meteorological station data and without human intervention during processing. In addition, a high performance service for orbitai data processing is proposed. The data generated by these algorithms were tested to field observations, demonstrating the possibility of using these algorithms in automatic processes. The techniques of cloud computing and parallelization used in this study were efficient in producing long time series (over 30 years) of these variables in average spatial resolution. The main application developed in this work, used Landsat time series for a period of 31 years at monthly resolution in order to investigate spatial and temporal pattems of hotspots of land cover change in a Caatinga area of the semi-arid region of the Paraiba state, Brazil. A new spectral index - Surface Albedo Index (SAI) - is proposed to improve the observation of vegetation biophysica!condition and change. SAI, NDV1 and EVI are compared in order to evaluate the suitability of monitoring LCC driven by human actions in contrast to climate induced (drought) alteration. The TSS RESTREND method was successfully applied to Landsat time series for LCC monitoring. It is employed in order to remove the short-term influences of precípitation on land cover physiognomy, thus allowing to assess the ability of the index time series to discriminate LCC in drylands. Google Earth, Rapid Eye images and in situ observations (from October 2017) were used to observe preservation / degradation conditions along the time. Results showthat SAI is able to distinguish between "changed" and "unchanged" land cover with a high accuracy (87%) to detect the year of change. When using the SAI index, TSS RESTREND is suitable to detect LCC in the Caatinga, and its best performance was achieved when the change event occurred in the middle of the time series (1990-2010), with some inaccuracies in dry years. The lower ability of EVI and NDVI in the detection of LCC in the Caatinga biome is explained by their high sensitivity to leaf cover variations (as a result of seasonal or extreme drought conditions). LCC impacts the whole soilplant-atmosphere system, such as biomass remova!and changes in soil properties as weil as mícroclimate, due to the direct exposure to radiation, precípitation, and wind. In this regard, SAI is supposed to be more sensitive to man-made alterations of the land surface, due to its ability to capture a higher number of environmental feedbacks.

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