15 research outputs found
Summaries of the Sixth Annual JPL Airborne Earth Science Workshop
The Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996, was divided into two smaller workshops:(1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, and The Airborne Synthetic Aperture Radar (AIRSAR) workshop. This current paper, Volume 2 of the Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, presents the summaries for The Airborne Synthetic Aperture Radar (AIRSAR) workshop
Comparing synthetic aperture radar and LiDAR for above-ground biomass estimation in Glen Affric, Scotland
Quantifying above-ground biomass (AGB) and carbon sequestration has been a
significant focus of attention within the UNFCCC and Kyoto Protocol for improvement
of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of
research has been carried out in relatively flat and homogeneous forests (Ranson & Sun,
1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al.,
2005), yet forests in the highlands, which generally form heterogeneous forest cover and
sparse woodlands with mountainous terrain have been largely neglected in AGB studies
(Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al.,
2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately
28% of the total global forest area (Price and Butt, 2000), a better understanding of the
slope effects is of primary importance in AGB estimation. The main objective of this
research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using
both SAR and LiDAR data.
Two types of Synthetic Aperture Radar (SAR) data were used in this research:
TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are
fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two
scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were
acquired on 09 June 2007. Two field measurement campaigns were carried out, one of
which was done from winter 2006 to spring 2007 where physical parameters of trees in
170 circular plots were measured by the Forestry Commission team. Another intensive
fieldwork was organised by myself with the help of my fellow colleagues and it
comprised of tree measurement in two transects of 200m x 50m at a relatively flat and
dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is
estimated for both the transects to investigate the effectiveness of the proposed method
at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data
for AGB estimation by investigating the relationship between the SAR backscattering
coefficient and AGB and also the relationship between the decomposed scattering
mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the
result from the backscatter-AGB analysis show that these forests present a challenge for
simple AGB estimation. As an alternative, polarimetric techniques were applied to the
problem by decomposing the backscattering information into scattering mechanisms
based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field
measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation
capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR
data are compared with AGB derived from LiDAR data. Since tree height is often
correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree
height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was
performed before AGB of individual trees were calculated allometrically. Results were
validated by comparison to the fieldwork data. The amount of overestimation varies
across the different canopy conditions. These results give some indication of when to
use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good
accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and
RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS
PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with
very accurate height measurement and consequent three-dimensional (3D) stand profiles
which allows investigation into the relationship between height, number density and
AGB, it's limited to small coverage area, or large areas but at large cost. ALOS
PALSAR, on the other hand, can cover big coverage area but it provide a lower
resolution, hence, lower estimation accuracy
Space-Based Remote Sensing of the Earth: A Report to the Congress
The commercialization of the LANDSAT Satellites, remote sensing research and development as applied to the Earth and its atmosphere as studied by NASA and NOAA is presented. Major gaps in the knowledge of the Earth and its atmosphere are identified and a series of space based measurement objectives are derived. The near-term space observations programs of the United States and other countries are detailed. The start is presented of the planning process to develop an integrated national program for research and development in Earth remote sensing for the remainder of this century and the many existing and proposed satellite and sensor systems that the program may include are described
Remote Sensing of Plant Biodiversity
This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale
Remote Sensing of Plant Biodiversity
At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally.
This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing
instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution.
The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity.
Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely.
Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON).
This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing
Estimativa de biomassa na região amazônica utilizando técnicas de aprendizado de máquina
Tese (doutorado) — Universidade de BrasÃlia, Instituto de Geociências, Pós-Graduação em Geociências Aplicadas, 2021.No ano de 2016 mais de 190 paÃses participaram da 21ª Conferência das Partes das Nações Unidas
sobre Mudança Climática, realizada em Paris. Apesar de intensos trabalhos visando elaborar um
tratado, os resultados não atenderam às expectativas devido à falta de metodologias que medem
com precisão a quantidade de biomassa florestal. Imagens de sensoriamento remoto podem ser
usadas para que seja realizada uma quantificação mais precisa e viável da biomassa existente em
regiões de difÃcil acesso, como a região amazônica, com destaque para as imagens na faixa do
micro-ondas, mais especificamente as de radares. Em função da grande quantidade de dados de
sensoriamento remoto disponÃveis, faz-se necessário o desenvolvimento de técnicas e ferramentas
que visem organizá-los e analisá-los de forma inteligente e automática, como as técnicas de
Aprendizado de Máquina (Machine Learning). A presente tese tem por objetivo geral desenvolver e
aplicar uma metodologia para estimar a quantidade de biomassa arbórea em uma área da região
amazônica, a partir de dados de SAR, utilizando técnicas de Aprendizado de Máquina. As etapas
metodológicas de tese encontra-se divididas em três artigos técnicos sequenciais que cobrem os
objetivos propostos. O primeiro artigo possui como hipótese a possibilidade de ajuste da altura
interferométrica, atributos de InSAR, a partir da identificação de áreas de solo exposto, isto é, onde
o valor é teoricamente igual a 0 (zero). Além de inovadora, a hipótese previa o ajuste do modelo
digital da região visando aprimorar a modelagem referente à estimativa de biomassa. Entretanto,
como resultado, o método proposto no primeiro artigo não possibilitou a melhora significativa da
estimativa de biomassa florestal, não sendo adotado nas próximas etapas do trabalho. O segundo
artigo dá continuidade ao primeiro e apresenta a aplicação de técnicas de Aprendizado de Máquina
sobre os atributos de SAR extraÃdos dos dados disponÃveis. De forma inédita avalia e compara
modelos de estimativa de biomassa baseados em atributos qualitativos e quantitativos. O segundo
artigo conclui que as diferentes regiões da Floresta Amazônica e suas respectivas caracterÃsticas
demandam modelos e técnicas especÃficas, não se enquadrando em um único padrão. Neste caso
não foi possÃvel identificar uma única técnica de Aprendizado de Máquina que se mostrasse como a
mais adequada ao objetivo, apesar dos melhores resultados apontarem para o uso das redes neurais
artificiais. O terceiro e último artigo conclui o trabalho da presente tese por meio da análise e
construção de produtos temáticos de biomassa. Neste último artigo é apresentado um sistema
computacional desenvolvido que visa otimizar o processo de categorização, necessário Ã
representação visual da geoinformação. Os resultados obtidos no terceiro artigo mostram que o
algoritmo de Otimização de Categorização proposto demonstrou capacidade de encontrar novos
subintervalos de categorias que aumentaram o Ãndice de concordância Kappa. Como resultado,
foram construÃdos produtos temáticos que apresentaram acurácia temática superior aos obtidos
pelos métodos clássicos de categorização. Juntamente, do ponto de vista computacional, a heurÃstica
proposta no algoritmo possibilitou a identificação de resultados de forma eficiente, evitando os altos
custos de processamento. A hipótese proposta na tese, isto é, de que a aplicação de técnicas de
aprendizado de máquina sobre dados de SAR permitem obter a estimativa de biomassa da região
amazônica com erros abaixo de 20%, atendendo os padrões preceituados por organismos
internacionais, não foi confirmada. Os resultados obtidos nos modelos elaborados são classificados
somente como moderados. Dentre os fatores que podem ter contribuÃdo para este resultado, está a
quantidade reduzida de amostras de biomassa, com pequena variação de valores, o que prejudicou o
ajuste dos modelos gerados e o acesso restrito aos dados de SAR das bandas X e P, não sendo
possÃvel gerar novos atributos coerentes.In 2016, more than 190 countries participated in the 21st United Nations Conference of Parties on
Climate Change, held in Paris. Despite the intense work aiming at preparing a treaty, the results did
not meet expectations due to the lack of methodologies that accurately measures the amount of
forest biomass. Remote sensing images can be used to make a more accurate and viable
quantification of the existing biomass in regions with difficult access, such as the Amazon region,
with emphasis on images in the microwave range, more specifically those from radar. Due to the
large amount of remote sensing data available, it is necessary to develop techniques and tools that
aims to organize and analyze them in an intelligent and automatic way, such as Machine Learning
techniques. The present thesis has as general objective to develop and apply a methodology to
estimate the amount of arboreal biomass in an area of the Amazon region, using SAR data and
Machine Learning techniques. The thesis methodological steps are divided into three sequential
technical articles that covers the proposed objectives. The first article hypothesizes the possibility of
adjusting the interferometric height, InSAR feature, using the exposed soil areas identified in the
image, that is, where the value is theoretically equal to 0 (zero). In addition to being innovative, the
hypothesis predicted the adjustment of the region digital model in order to improve the biomass
estimation modeling. However, as a result, the method proposed in the first article did not present a
significant improvement in the estimation of forest biomass and was not adopted in the next stages
of the work. The second article gives sequence for the first and presents the application of Machine
Learning techniques over SAR features extracted from the available data. In an unprecedented way,
it presents a methodology that evaluates and compares biomass estimation models based on
qualitative and quantitative features. The second article concludes that the different Amazon Forest
regions and their respective characteristics demands specific models and techniques, not fitting into
a single pattern. In this case, it was not possible to identify a single Machine Learning technique
that proved to be the most adequate for the purpose, despite the best results pointing to the use of
artificial neural networks. The third and last article concludes the work of this thesis through the
analysis and construction of thematic biomass products. In this last article, a computational system
that aims to optimize the categorization process was developed, necessary for the visual
representation of geoinformation. The results obtained in the third article shows that the proposed
Categorization Optimization algorithm demonstrated the ability to find new subintervals of
categories that increased the Kappa agreement index. As a result, thematic products were
constructed and presented thematic accuracy superior to those obtained by the classical
categorization methods. Besides that, from a computational point of view, the heuristic proposed in
the algorithm enabled the identification of results in an efficient way, avoiding high processing
costs. The hypothesis proposed in the thesis, that is, that the application of machine learning
techniques over SAR data allows to obtain an estimate of biomass in the Amazon region with errors
below 20%, attending to the standards established by international organizations, was not
confirmed. The results obtained in the constructed models were classified only moderate. Among
the factors that may have contributed to this result, there is the reduced amount of biomass samples,
with little variation in values, which impaired the adjustment of the generated models and the
restricted access to the X and P bands SAR data, not being possible to generate new coherent
features
Earth Resources. A continuing bibliography with indexes, issue 34, July 1982
This bibliography lists 567 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between April 1, and June 30, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis
Topics in environmental and physical geodesy
A compilation of mathematical techniques and physical basic knowledge in order to prepare the post graduate students of the subjects of physical geodesy, environmental physics and the visiting students of Erasmus-Socrates projects of the Mediterranean Institute of Oceanography of Toulon and the Campus Universitari de la Mediterrania in Vilanova i la Geltru, Barcelona.Postprint (published version