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% Generated by Paperpile. Check out https://paperpile.com for more information.
% BibTeX export options can be customized via Settings -> BibTeX.
@INCOLLECTION{Field1991-pk,
title = "Ecological scaling of carbon gain to stress and resource",
booktitle = "Response of plants to multiple stresses",
author = "Field, Christopher B",
abstract = "The challenge of understanding global change must be met with a
program for integrating research at many scales, from the
biochemical and molecular to the global (Committee on Global
Change, 1988). For many of the critical questions concerning the
responses of ecosystems to climate change and, especially,
feedbacks of vegetation to climate, understanding the responses
of plant carbon balance and growth to stress is a critical
prerequisite. A bottom-up physiological approach, synthesizing
results of detailed",
publisher = "Academic Press San Diego",
pages = "35--65",
year = 1991
}
@TECHREPORT{Geo_bon2017-ak,
title = "{GEO} {BON} Strategy for development of Essential Biodiversity
Variables",
author = "{GEO BON}",
institution = "GEO BON",
month = jan,
year = 2017
}
@ARTICLE{Pohl1998-ze,
title = "Review article Multisensor image fusion in remote sensing:
Concepts, methods and applications",
author = "Pohl, C and Van Genderen, J L",
abstract = "With the availability of multisensor, multitemporal,
multiresolution and multifrequency image data from operational
Earth observation satellites the fusion of digital image data
has become a valuable tool in remote sensing image evaluation.
Digital image fusion is a relatively new research field at the
leading edge of available technology. It forms a rapidly
developing area of research in remote sensing. This review paper
describes and explains mainly pixel based image fusion of Earth
observation satellite data as a contribution to multisensor
integration oriented data processing.",
journal = "Int. J. Remote Sens.",
publisher = "Taylor \& Francis",
volume = 19,
number = 5,
pages = "823--854",
month = jan,
year = 1998
}
@ARTICLE{Clark2005-kr,
title = "Hyperspectral discrimination of tropical rain forest tree
species at leaf to crown scales",
author = "Clark, Matthew L and Roberts, Dar A and Clark, David B",
abstract = "We investigated the utility of high spectral and spatial
resolution imagery for the automated species-level
classification of individual tree crowns (ITCs) in a tropical
rain forest (TRF). Laboratory spectrometer and airborne
reflectance spectra (161 bands, 437--2434 nm) were acquired from
seven species of emergent trees. Analyses focused on leaf-,
pixel- and crown-scale spectra. We first described the spectral
regions and factors that most influence spectral separability
among species. Next, spectral-based species classification was
performed using linear discriminant analysis (LDA), maximum
likelihood (ML) and spectral angle mapper (SAM) classifiers
applied to combinations of bands from a stepwise-selection
procedure. Optimal regions of the spectrum for species
discrimination varied with scale. However, near-infrared
(700--1327 nm) bands were consistently important regions across
all scales. Bands in the visible region (437--700 nm) and
shortwave infrared (1994--2435 nm) were more important at pixel
and crown scales. Overall classification accuracy decreased from
leaf scales measured in the laboratory to pixel and crown scales
measured from the airborne sensor. Leaf-scale classification
using LDA and 40 bands had 100\% overall accuracy. Pixel-scale
spectra from sunlit regions of crowns were classified with 88\%
overall accuracy using a ML classifier and 60 bands. The highest
crown-scale (ITC) accuracy was 92\% with LDA and 30 bands.
Producer's accuracies ranged from 70\% to 100\% and User's
accuracies ranged from 81\% to 100\%. The SAM classifier
performed poorly at all scales and spectral regions of analysis.
ITCs were also classified using an object-based approach in
which crown species labels were assigned according to the
majority class of classified pixels within a crown. An overall
accuracy of 86\% was achieved with an object-based LDA
classifier applied to 30 bands of data. Object-based and
crown-scale ITC classifications were significantly more accurate
with 10 narrow-bands relative to accuracies achieved with
simulated multispectral, broadband data. We concluded that high
spectral and spatial resolution imagery acquired over TRF canopy
has substantial potential for automated ITC species
discrimination.",
journal = "Remote Sens. Environ.",
publisher = "Elsevier",
volume = 96,
number = 3,
pages = "375--398",
month = jun,
year = 2005,
keywords = "Tropical rain forest; Individual tree crown classification; Tree
floristic classification; Hyperspectral sensors; Multispectral
data; High spatial and spectral resolution; Linear discriminant
analysis; Spectral angle mapper; Maximum likelihood"
}
@ARTICLE{Araujo2006-cc,
title = "Five (or so) challenges for species distribution modelling",
author = "Ara{\'u}jo, Miguel B and Guisan, Antoine",
abstract = "Abstract Species distribution modelling is central to both
fundamental and applied research in biogeography. Despite
widespread use of models, there are still important conceptual
ambiguities as well as biotic and algorithmic uncertainties that
need to be investigated in order to increase confidence in model
results. We identify and discuss five areas of enquiry that are
of high importance for species distribution modelling: (1)
clarification of the niche concept; (2) improved designs for
sampling data for building models; (3) improved
parameterization; (4) improved model selection and predictor
contribution; and (5) improved model evaluation. The challenges
discussed in this essay do not preclude the need for
developments of other areas of research in this field. However,
they are critical for allowing the science of species
distribution modelling to move forward.",
journal = "J. Biogeogr.",
publisher = "Blackwell Publishing Ltd",
volume = 33,
number = 10,
pages = "1677--1688",
month = oct,
year = 2006,
keywords = "Fundamental niche; model evaluation; niche models;
parameterization; realized niche; sampling; variable
selection;SDM",
language = "en"
}
@ARTICLE{Serbin2014-ta,
title = "Spectroscopic determination of leaf morphological and
biochemical traits for northern temperate and boreal tree
species",
author = "Serbin, Shawn P and Singh, Aditya and McNeil, Brenden E and
Kingdon, Clayton C and Townsend, Philip A",
abstract = "The morphological and biochemical properties of plant canopies
are strong predictors of photosynthetic capacity and nutrient
cycling. Remote sensing research at the leaf and canopy scales
has demonstrated the ability to characterize the biochemical
status of vegetation canopies using reflectance spectroscopy,
including at the leaf level and canopy level from air? and
spaceborne imaging spectrometers. We developed a set of accurate
and precise spectroscopic calibrations for the determination of
leaf chemistry (contents of nitrogen, carbon, and fiber
constituents), morphology (leaf mass per area, Marea), and
isotopic composition ($\delta$15N) of temperate and boreal tree
species using spectra of dried and ground leaf material. The
data set consisted of leaves from both broadleaf and needle?leaf
conifer species and displayed a wide range in values, determined
with standard analytical approaches: 0.7?4.4\% for nitrogen
(Nmass), 42?54\% for carbon (Cmass), 17?58\% for fiber
(acid?digestible fiber, ADF), 7?44\% for lignin (acid?digestible
lignin, ADL), 3?31\% for cellulose, 17?265 g/m2 for Marea, and
?9.4? to 0.8? for $\delta$15N. The calibrations were developed
using a partial least?squares regression (PLSR) modeling
approach combined with a novel uncertainty analysis. Our PLSR
models yielded model calibration (independent validation shown
in parentheses) R2 and the root mean square error (RMSE) values,
respectively, of 0.98 (0.97) and 0.10\% (0.13\%) for Nmass, R2 =
0.77 (0.73) and RMSE = 0.88\% (0.95\%) for Cmass, R2 = 0.89
(0.84) and RMSE = 2.8\% (3.4\%) for ADF, R2 = 0.77 (0.69) and
RMSE = 2.4\% (3.9\%) for ADL, R2 = 0.77 (0.72) and RMSE = 1.4\%
(1.9\%) for leaf cellulose, R2 = 0.62 (0.60) and RMSE = 0.91?
(1.5?) for $\delta$15N, and R2 = 0.88 (0.87) with RMSE = 17.2
g/m2 (22.8 g/m2) for Marea. This study demonstrates the
potential for rapid and accurate estimation of key foliar traits
of forest canopies that are important for ecological research
and modeling activities, with a single calibration equation
valid over a wide range of northern temperate and boreal species
and leaf physiognomies. The results provide the basis to
characterize important variability between and within species,
and across ecological gradients using a rapid, cost?effective,
easily replicated method.",
journal = "Ecol. Appl.",
publisher = "Ecological Society of America",
volume = 24,
number = 7,
pages = "1651--1669",
month = oct,
year = 2014,
keywords = "foliar chemistry; forests; partial least-squares regression,
PLSR; plant functional traits; reflectance spectroscopy; remote
sensing",
language = "en"
}
@ARTICLE{Wang2010-ev,
title = "Remote sensing of ecology, biodiversity and conservation: a
review from the perspective of remote sensing specialists",
author = "Wang, Kai and Franklin, Steven E and Guo, Xulin and Cattet, Marc",
abstract = "Remote sensing, the science of obtaining information via
noncontact recording, has swept the fields of ecology,
biodiversity and conservation (EBC). Several quality review
papers have contributed to this field. However, these papers
often discuss the issues from the standpoint of an ecologist or a
biodiversity specialist. This review focuses on the spaceborne
remote sensing of EBC from the perspective of remote sensing
specialists, i.e., it is organized in the context of
state-of-the-art remote sensing technology, including instruments
and techniques. Herein, the instruments to be discussed consist
of high spatial resolution, hyperspectral, thermal infrared,
small-satellite constellation, and LIDAR sensors; and the
techniques refer to image classification, vegetation index (VI),
inversion algorithm, data fusion, and the integration of remote
sensing (RS) and geographic information system (GIS).",
journal = "Sensors",
volume = 10,
number = 11,
pages = "9647--9667",
month = nov,
year = 2010,
keywords = "EBC (ecology, biodiversity and conservation); LIDAR; data fusion;
image classification; integration of remote sensing (RS) and
geographic information system (GIS); remote sensing;
small-satellite constellation; thermal infrared",
language = "en"
}
@ARTICLE{Pereira2013-pk,
title = "Ecology. Essential biodiversity variables",
author = "Pereira, H M and Ferrier, S and Walters, M and Geller, G N and
Jongman, R H G and Scholes, R J and Bruford, M W and Brummitt, N
and Butchart, S H M and Cardoso, A C and Coops, N C and Dulloo, E
and Faith, D P and Freyhof, J and Gregory, R D and Heip, C and
H{\"o}ft, R and Hurtt, G and Jetz, W and Karp, D S and McGeoch, M
A and Obura, D and Onoda, Y and Pettorelli, N and Reyers, B and
Sayre, R and Scharlemann, J P W and Stuart, S N and Turak, E and
Walpole, M and Wegmann, M",
abstract = "Reducing the rate of biodiversity loss and averting dangerous
biodiversity change are international goals, reasserted by the
Aichi Targets for 2020 by Parties to the United Nations (UN)
Convention on Biological Diversity (CBD) after failure to meet
the 2010 target (1, 2). However, there is no global, harmonized
observation system for delivering regular, timely data on
biodiversity change (3). With the first plenary meeting of the
Intergovernmental Science-Policy Platform on Biodiversity and
Ecosystem Services (IPBES) soon under way, partners from the
Group on Earth Observations Biodiversity Observation Network (GEO
BON) (4) are developing---and seeking consensus
around---Essential Biodiversity Variables (EBVs) that could form
the basis of monitoring programs worldwide. A global system of
harmonized observations is needed to inform scientists and
policy-makers. A global system of harmonized observations is
needed to inform scientists and policy-makers.",
journal = "Science",
volume = 339,
number = 6117,
pages = "277--278",
month = jan,
year = 2013,
language = "en"
}
@ARTICLE{De_Bello2010-cl,
title = "Towards an assessment of multiple ecosystem processes and
services via functional traits",
author = "de Bello, Francesco and Lavorel, Sandra and D{\'\i}az, Sandra
and Harrington, Richard and Cornelissen, Johannes H C and
Bardgett, Richard D and Berg, Matty P and Cipriotti, Pablo and
Feld, Christian K and Hering, Daniel and Martins da Silva, Pedro
and Potts, Simon G and Sandin, Leonard and Sousa, Jose Paulo and
Storkey, Jonathan and Wardle, David A and Harrison, Paula A",
abstract = "Managing ecosystems to ensure the provision of multiple
ecosystem services is a key challenge for applied ecology.
Functional traits are receiving increasing attention as the main
ecological attributes by which different organisms and
biological communities influence ecosystem services through
their effects on underlying ecosystem processes. Here we
synthesize concepts and empirical evidence on linkages between
functional traits and ecosystem services across different
trophic levels. Most of the 247 studies reviewed considered
plants and soil invertebrates, but quantitative trait--service
associations have been documented for a range of organisms and
ecosystems, illustrating the wide applicability of the trait
approach. Within each trophic level, specific processes are
affected by a combination of traits while particular key traits
are simultaneously involved in the control of multiple
processes. These multiple associations between traits and
ecosystem processes can help to identify predictable
trait--service clusters that depend on several trophic levels,
such as clusters of traits of plants and soil organisms that
underlie nutrient cycling, herbivory, and fodder and fibre
production. We propose that the assessment of trait--service
clusters will represent a crucial step in ecosystem service
monitoring and in balancing the delivery of multiple, and
sometimes conflicting, services in ecosystem management.",
journal = "Biodivers. Conserv.",
publisher = "Springer Netherlands",
volume = 19,
number = 10,
pages = "2873--2893",
month = sep,
year = 2010,
language = "en"
}
@ARTICLE{Legendre2005-zb,
title = "Analyzing Beta Diversity: Partitioning the Spatial Variation of
Community Composition Data",
author = "Legendre, Pierre and Borcard, Daniel and Peres-Neto, Pedro R",
abstract = "Robert H. Whittaker defined beta diversity as the variation in
species composition among sites in a geographic area. Beta
diversity is a key concept for understanding the functioning of
ecosystems, for the conservation of biodiversity, and for
ecosystem management. This paper explains how hypotheses about
the origin of beta diversity can be tested by partitioning the
spatial variation of community composition data (presence?
absence or abundance data) with respect to environmental
variables and spatial base functions. We compare two statistical
methods to accomplish that. The sum?of?squares of a community
composition data table, which is one possible measure of beta
diversity, is correctly partitioned by canonical ordination;
hence, canonical partitioning produces correct estimates of the
different portions of community composition variation. In recent
years, several authors interested in the variation in community
composition among sites (beta diversity) have used another
method, variation partitioning on distance matrices (Mantel
approach). Their results led us to compare the two partitioning
approaches, using simulated data generated under hypotheses
about the variation of community composition among sites. The
theoretical developments and simulation results led to the
following observations: (1) the variance of a community
composition table is a measure of beta diversity. (2) The
variance of a dissimilarity matrix among sites is not the
variance of the community composition table nor a measure of
beta diversity; hence, partitioning on distance matrices should
not be used to study the variation in community composition
among sites. (3) In all of our simulations, partitioning on
distance matrices underestimated the amount of variation in
community composition explained by the raw?data approach, and
(4) the tests of significance had less power than the tests of
canonical ordination. Hence, the proper statistical procedure
for partitioning the spatial variation of community composition
data among environmental and spatial components, and for testing
hypotheses about the origin and maintenance of variation in
community composition among sites, is canonical partitioning.
The Mantel approach is appropriate for testing other hypotheses,
such as the variation in beta diversity among groups of sites.
Regression on distance matrices is also appropriate for fitting
models to similarity decay plots.",
journal = "Ecol. Monogr.",
publisher = "Ecological Society of America",
volume = 75,
number = 4,
pages = "435--450",
month = nov,
year = 2005,
keywords = "beta diversity; canonical ordination; community composition;
Mantel test; PCNM analysis; regression on distance matrices;
simulation study; spatial variation; variation partitioning"
}
@ARTICLE{Chambers2009-bl,
title = "Lack of intermediate-scale disturbance data prevents robust
extrapolation of plot-level tree mortality rates for old-growth
tropical forests",
author = "Chambers, Jeffrey Q and Negr{\'o}n-Ju{\'a}rez, Robinson I and
Hurtt, George C and Marra, Daniel M and Higuchi, Niro",
abstract = "Abstract Lloyd et al.(2009) question the methods, concepts and
conclusions of Fisher et al.(2008). We address these assertions,
and place our work into a broader context. We demonstrate the
veracity of Fisher et al., and further show that lack of data
for intermediate- scale tree mortality disturbance events for
old-growth tropical forests might prevent robust extrapolation
of forest plot biomass accumulation data, and accurate estimates
of distribution parameters such as power-law exponents
($\alpha$).",
journal = "Ecol. Lett.",
publisher = "Wiley Online Library",
volume = 12,
number = 12,
year = 2009
}
@ARTICLE{Rocchini2007-ew,
title = "Effects of spatial and spectral resolution in estimating
ecosystem $\alpha$-diversity by satellite imagery",
author = "Rocchini, Duccio",
abstract = "Remote sensing represents a powerful tool to derive quantitative
and qualitative information about ecosystem biodiversity. In
particular, since plant species richness is a fundamental
indicator of biodiversity at the community and regional scales,
attempts were made to predict species richness (spatial
heterogeneity) by means of spectral heterogeneity. The
possibility of using spectral variance of satellite images for
predicting species richness is known as Spectral Variation
Hypothesis. However, when using remotely sensed data,
researchers are limited to specific scales of investigation.
This paper aims to investigate the effects of scale (both as
spatial and spectral resolution) when searching for a relation
between spectral and spatial (related to plant species richness)
heterogeneity, by using satellite data with different spatial
and spectral resolution. Species composition was sampled within
square plots of 100 m2 nested in macroplots of 10,000 m2.
Spectral heterogeneity of each macroplot was calculated using
satellite images with different spatial and spectral resolution:
a Quickbird multispectral image (4 bands, spatial resolution of
3 m), an Aster multispectral image (first 9 bands used, spatial
resolution of 15 m for bands 1 to 3 and 30 m for bands 4 to 9),
an ortho-Landsat ETM+ multispectral image (bands 1 to 5 and band
7 used; spatial resolution, 30 m), a resampled 60 m Landsat ETM+
image. Quickbird image heterogeneity showed a statistically
highly significant correlation with species richness (r=0.69)
while coarse resolution images showed contrasting results
(r=0.43, r=0.67, and r=0.69 considering the Aster, Landsat ETM+,
and the resampled 60 m Landsat ETM+ images respectively). It
should be stressed that spectral variability is scene and sensor
dependent. Considering coarser spatial resolution images, in
such a case even using SWIR Aster bands (i.e. the additional
spectral information with respect to Quickbird image) such an
image showed a very low power in catching spectral and thus
spatial variability with respect to Landsat ETM+ imagery.
Obviously coarser resolution data tend to have mixed pixel
problems and hence less sensitive to spatial complexity. Thus,
one might argue that using a finer pixel dimension should
inevitably result in a higher level of detail. On the other
hand, the spectral response from different land-cover features
(and thus different species) in images with higher spectral
resolution should exhibit higher complexity. Spectral Variation
Hypothesis could be a basis for improving sampling designs and
strategies for species inventory fieldwork. However, researchers
must be aware on scale effects when measuring spectral (and thus
spatial) heterogeneity and relating it to field data, hence
considering the concept of scale not only related to a spatial
framework but even to a spectral one.",
journal = "Remote Sens. Environ.",
publisher = "Elsevier",
volume = 111,
number = 4,
pages = "423--434",
month = dec,
year = 2007,
keywords = "$\alpha$-Diversity; Grain; Plant species richness; Spatial
resolution; Spectral resolution; Spectral Variation Hypothesis"
}
@article{fisher1997pixel,
title={The pixel: a snare and a delusion},
author={Fisher, Peter},
journal={International Journal of Remote Sensing},
volume={18},
number={3},
pages={679--685},
year={1997},
publisher={Taylor \& Francis}
}
@book{blaschke2008object,
title={Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications},
author={Blaschke, Thomas and Lang, Stefan and Hay, Geoffrey},
year={2008},
publisher={Springer Science \& Business Media}
}
@ARTICLE{Fourcade2018-ws,
title = "Paintings predict the distribution of species, or the challenge
of selecting environmental predictors and evaluation statistics",
author = "Fourcade, Yoan and Besnard, Aur{\'e}lien G and Secondi, Jean",
abstract = "Abstract Aim Species distribution modelling, a family of
statistical methods that predicts species distributions from a
set of occurrences and environmental predictors, is now
routinely applied in many macroecological studies. However, the
reliability of evaluation metrics usually employed to validate
these models remains questioned. Moreover, the emergence of
online databases of environmental variables with global
coverage, especially climatic, has favoured the use of the same
set of standard predictors. Unfortunately, the selection of
variables is too rarely based on a careful examination of the
species' ecology. In this context, our aim was to highlight the
importance of selecting ad hoc variables in species distribution
models, and to assess the ability of classical evaluation
statistics to identify models with no biological realism.
Innovation First, we reviewed the current practices in the field
of species distribution modelling in terms of variable selection
and model evaluation. Then, we computed distribution models of
509 European species using pseudo?predictors derived from
paintings or using a real set of climatic and topographic
predictors. We calculated model performance based on the area
under the receiver operating curve (AUC) and true skill
statistics (TSS), partitioning occurrences into training and
test data with different levels of spatial independence. Most
models computed from pseudo?predictors were classified as good
and sometimes were even better evaluated than models computed
using real environmental variables. However, on average they
were better discriminated when the partitioning of occurrences
allowed testing for model transferability. Main conclusions
These findings confirm the crucial importance of variable
selection and the inability of current evaluation metrics to
assess the biological significance of distribution models. We
recommend that researchers carefully select variables according
to the species' ecology and evaluate models only according to
their capacity to be transfered in distant areas. Nevertheless,
statistics of model evaluations must still be interpreted with
great caution.",
journal = "Glob. Ecol. Biogeogr.",
publisher = "Wiley Online Library",
volume = 27,
number = 2,
pages = "245--256",
month = feb,
year = 2018,
keywords = "AUC; environmental predictors; environmental variables; MaxEnt;
model evaluation; ROC curve; species distribution modelling; TSS"
}
@ARTICLE{Dray2012-ia,
title = "Community ecology in the age of multivariate multiscale spatial
analysis",
author = "Dray, S and P{\'e}lissier, R and Couteron, P and Fortin, M-J and
Legendre, P and Peres-Neto, P R and Bellier, E and Bivand, R and
Blanchet, F G and De C{\'a}ceres, M and Dufour, A-B and
Heegaard, E and Jombart, T and Munoz, F and Oksanen, J and
Thioulouse, J and Wagner, H H",
abstract = "Species spatial distributions are the result of population
demography, behavioral traits, and species interactions in
spatially heterogeneous environmental conditions. Hence the
composition of species assemblages is an integrative response
variable, and its variability can be explained by the complex
interplay among several structuring factors. The thorough
analysis of spatial variation in species assemblages may help
infer processes shaping ecological communities. We suggest that
ecological studies would benefit from the combined use of the
classical statistical models of community composition data, such
as constrained or unconstrained multivariate analyses of
site?by?species abundance tables, with rapidly emerging and
diversifying methods of spatial pattern analysis. Doing so
allows one to deal with spatially explicit ecological models of
beta diversity in a biogeographic context through the multiscale
analysis of spatial patterns in original species data tables,
including spatial characterization of fitted or residual
variation from environmental models. We summarize here the
recent progress for specifying spatial features through spatial
weighting matrices and spatial eigenfunctions in order to define
spatially constrained or scale?explicit multivariate analyses.
Through a worked example on tropical tree communities, we also
show the potential of the overall approach to identify
significant residual spatial patterns that could arise from the
omission of important unmeasured explanatory variables or
processes.",
journal = "Ecol. Monogr.",
publisher = "Ecological Society of America",
volume = 82,
number = 3,
pages = "257--275",
month = aug,
year = 2012,
keywords = "ecological community; multivariate spatial data; ordination;
spatial autocorrelation; spatial connectivity; spatial
eigenfunction; spatial structure; spatial weight"
}
@ARTICLE{Pettorelli2014-yz,
title = "Satellite remote sensing for applied ecologists: opportunities
and challenges",
author = "Pettorelli, Nathalie and Laurance, William F and O'Brien, Timothy
G and Wegmann, Martin and Nagendra, Harini and Turner, Woody",
editor = "Milner-Gulland, E J",
abstract = "Summary Habitat loss and degradation, overexploitation, climate
change and the spread of invasive species are drastically
depleting the Earth's biological diversity, leading to
detrimental impacts on ecosystem services and human well?being.
Our ability to monitor the state of biodiversity and the impacts
of global environmental change on this natural capital is
fundamental to designing effective adaptation and mitigation
strategies for preventing further loss of biological diversity.
This requires the scientific community to assess spatio?temporal
changes in the distribution of abiotic conditions (e.g.
temperature, rainfall) and in the distribution, structure,
composition and functioning of ecosystems. The potential for
satellite remote sensing (SRS) to provide key data has been
highlighted by many researchers, with SRS offering repeatable,
standardized and verifiable information on long?term trends in
biodiversity indicators. SRS permits one to address questions on
scales inaccessible to ground?based methods alone, facilitating
the development of an integrated approach to natural resource
management, where biodiversity, pressures to biodiversity and
consequences of management decisions can all be monitored.
Synthesis and applications. Here, we provide an interdisciplinary
perspective on the prospects of satellite remote sensing (SRS)
for ecological applications, reviewing established avenues and
highlighting new research and technological developments that
have a high potential to make a difference in environmental
management. We also discuss current barriers to the ecological
application of SRS?based approaches and identify possible ways to
overcome some of these limitations.",
journal = "J. Appl. Ecol.",
volume = 51,
number = 4,
pages = "839--848",
month = aug,
year = 2014,
keywords = "biodiversity; Earth observations; environmental management;
natural capital; sensor; technology; wildlife management",
language = "en"
}
@ARTICLE{Levin1992-ga,
title = "The Problem of Pattern and Scale in Ecology: The Robert H.
{MacArthur} Award Lecture",
author = "Levin, Simon A",
abstract = "It is argued that the problem of pattern and scale is the
central problem in ecology, unifying population biology and
ecosystems science, and marrying basic and applied ecology.
Applied challenges, such as the prediction of the ecological
causes and consequences of global climate change, require the
interfacing of phenomena that occur on very different scales of
space, time, and ecological organization. Furthermore, there is
no single natural scale at which ecological phenomena should be
studied; systems generally show characteristic variability on a
range of spatial, temporal, and organizational scales. The
observer imposes a perceptual bias, a filter through which the
system is viewed. This has fundamental evolutionary
significance, since every organism is an 'observer' of the
environment, and life history adaptations such as dispersal and
dormancy alter the perceptual scales of the species, and the
observed variability. It likewise has fundamental significance
for our own study of ecological systems, since the patterns that
are unique to any range of scales will have unique causes and
biological consequences. The key to prediction and understanding
lies in the elucidation of mechanisms underlying observed
patterns. Typically, these mechanisms operate at different
scales than those on which the patterns are observed; in some
cases, the patterns must be understood as emerging form the
collective behaviors of large ensembles of smaller scale units.
In other cases, the pattern is imposed by larger scale
constraints. Examination of such phenomena requires the study of
how pattern and variability change with the scale of
description, and the development of laws for simplification,
aggregation, and scaling. Examples are given from the marine and
terrestrial literatures.",
journal = "Ecology",
publisher = "Ecological Society of America",
volume = 73,
number = 6,
pages = "1943--1967",
month = dec,
year = 1992,
language = "en"
}
@ARTICLE{Asner2016-qd,
title = "Large-scale climatic and geophysical controls on the leaf
economics spectrum",
author = "Asner, Gregory P and Knapp, David E and Anderson, Christopher B
and Martin, Roberta E and Vaughn, Nicholas",
abstract = "Leaf economics spectrum (LES) theory suggests a universal
trade-off between resource acquisition and storage strategies in
plants, expressed in relationships between foliar nitrogen (N)
and phosphorus (P), leaf mass per area (LMA), and photosynthesis.
However, how environmental conditions mediate LES trait
interrelationships, particularly at large biospheric scales,
remains unknown because of a lack of spatially explicit data,
which ultimately limits our understanding of ecosystem processes,
such as primary productivity and biogeochemical cycles. We used
airborne imaging spectroscopy and geospatial modeling to
generate, to our knowledge, the first biospheric maps of LES
traits, here centered on 76 million ha of Andean and Amazonian
forest, to assess climatic and geophysical determinants of LES
traits and their interrelationships. Elevation and substrate were
codominant drivers of leaf trait distributions. Multiple
additional climatic and geophysical factors were secondary
determinants of plant traits. Anticorrelations between N and LMA
followed general LES theory, but topo-edaphic conditions strongly
mediated and, at times, eliminated this classic relationship. We
found no evidence for simple P-LMA or N-P trade-offs in forest
canopies; rather, we mapped a continuum of N-P-LMA interactions
that are sensitive to elevation and temperature. Our results
reveal nested climatic and geophysical filtering of LES traits
and their interrelationships, with important implications for
predictions of forest productivity and acclimation to rapid
climate change.",
journal = "Proc. Natl. Acad. Sci. U. S. A.",
volume = 113,
number = 28,
pages = "E4043--51",
month = jul,
year = 2016,
keywords = "Amazon basin; functional biogeography; leaf traits; plant traits;
tropical forests;Anderson",
language = "en"
}
@ARTICLE{Irons2012-fm,
title = "The next Landsat satellite: The Landsat Data Continuity Mission",
author = "Irons, James R and Dwyer, John L and Barsi, Julia A",
abstract = "The National Aeronautics and Space Administration (NASA) and the
Department of Interior United States Geological Survey (USGS)
are developing the successor mission to Landsat 7 that is
currently known as the Landsat Data Continuity Mission (LDCM).
NASA is responsible for building and launching the LDCM
satellite observatory. USGS is building the ground system and
will assume responsibility for satellite operations and for
collecting, archiving, and distributing data following launch.
The observatory will consist of a spacecraft in low-Earth orbit
with a two-sensor payload. One sensor, the Operational Land
Imager (OLI), will collect image data for nine shortwave
spectral bands over a 185km swath with a 30m spatial resolution
for all bands except a 15m panchromatic band. The other
instrument, the Thermal Infrared Sensor (TIRS), will collect
image data for two thermal bands with a 100m resolution over a
185km swath. Both sensors offer technical advancements over
earlier Landsat instruments. OLI and TIRS will coincidently
collect data and the observatory will transmit the data to the
ground system where it will be archived, processed to Level 1
data products containing well calibrated and co-registered OLI
and TIRS data, and made available for free distribution to the
general public. The LDCM development is on schedule for a
December 2012 launch. The USGS intends to rename the satellite
``Landsat 8'' following launch. By either name a successful
mission will fulfill a mandate for Landsat data continuity. The
mission will extend the almost 40-year Landsat data archive with
images sufficiently consistent with data from the earlier
missions to allow long-term studies of regional and global land
cover change.",
journal = "Remote Sens. Environ.",
publisher = "Elsevier",
volume = 122,
number = "Supplement C",
pages = "11--21",
month = jul,
year = 2012,
keywords = "Landsat Data Continuity Mission; Operational Land Imager;
Thermal Infrared Sensor; National Aeronautics and Space
Administration; Goddard Space Flight Center; United States
Geological Survey; Earth Resources Science and Observation
Center"
}
@ARTICLE{Roy2008-zx,
title = "Multi-temporal {MODIS--Landsat} data fusion for relative
radiometric normalization, gap filling, and prediction of
Landsat data",
author = "Roy, David P and Ju, Junchang and Lewis, Philip and Schaaf,
Crystal and Gao, Feng and Hansen, Matt and Lindquist, Erik",
abstract = "A semi-physical fusion approach that uses the MODIS BRDF/Albedo
land surface characterization product and Landsat ETM+ data to
predict ETM+ reflectance on the same, an antecedent, or
subsequent date is presented. The method may be used for ETM+
cloud/cloud shadow and SLC-off gap filling and for relative
radiometric normalization. It is demonstrated over three study
sites, one in Africa and two in the U.S. (Oregon and Idaho) that
were selected to encompass a range of land cover land use types
and temporal variations in solar illumination, land cover, land
use, and phenology. Specifically, the 30 m ETM+ spectral
reflectance is predicted for a desired date as the product of
observed ETM+ reflectance and the ratio of the 500 m surface
reflectance modeled using the MODIS BRDF spectral model
parameters and the sun-sensor geometry on the predicted and
observed Landsat dates. The difference between the predicted and
observed ETM+ reflectance (prediction residual) is compared with
the difference between the ETM+ reflectance observed on the two
dates (temporal residual) and with respect to the MODIS BRDF
model parameter quality. For all three scenes, and all but the
shortest wavelength band, the mean prediction residual is
smaller than the mean temporal residual, by up to a factor of
three. The accuracy is typically higher at ETM+ pixel locations
where the MODIS BRDF model parameters are derived using the best
quality inversions. The method is most accurate for the ETM+
near-infrared (NIR) band; mean NIR prediction residuals are 9\%,
12\% and 14\% of the mean NIR scene reflectance of the African,
Oregon and Idaho sites respectively. The developed fusion
approach may be applied to any high spatial resolution satellite
data, does not require any tuning parameters and so may be
automated, is applied on a per-pixel basis and is unaffected by
the presence of missing or contaminated neighboring Landsat
pixels, accommodates for temporal variations due to surface
changes (e.g., phenological, land cover/land use variations)
observable at the 500 m MODIS BRDF/Albedo product resolution,
and allows for future improvements through BRDF model refinement
and error assessment.",
journal = "Remote Sens. Environ.",
publisher = "Elsevier",
volume = 112,
number = 6,
pages = "3112--3130",
month = jun,
year = 2008,
keywords = "ETM+; MODIS; Data fusion; Radiometric normalization; BRDF;
SLC-off gap filling; Image mosaicking"
}
@ARTICLE{Badgley2017-ua,
title = "Canopy near-infrared reflectance and terrestrial photosynthesis",
author = "Badgley, Grayson and Field, Christopher B and Berry, Joseph A",
abstract = "Global estimates of terrestrial gross primary production (GPP)
remain highly uncertain, despite decades of satellite
measurements and intensive in situ monitoring. We report a new
approach for quantifying the near-infrared reflectance of
terrestrial vegetation (NIRV). NIRV provides a foundation for a
new approach to estimate GPP that consistently untangles the
confounding effects of background brightness, leaf area, and the
distribution of photosynthetic capacity with depth in canopies
using existing moderate spatial and spectral resolution satellite
sensors. NIRV is strongly correlated with solar-induced
chlorophyll fluorescence, a direct index of photons intercepted
by chlorophyll, and with site-level and globally gridded
estimates of GPP. NIRV makes it possible to use existing and
future reflectance data as a starting point for accurately
estimating GPP.",
journal = "Sci Adv",
volume = 3,
number = 3,
pages = "e1602244",
month = mar,
year = 2017,
keywords = "gross primary production; near-infrared reflectance;
photosynthesis",
language = "en"
}
% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Wisz2008-hx,
title = "Effects of sample size on the performance of species
distribution models",
author = "Wisz, M S and Hijmans, R J and Li, J and Peterson, A T and
Graham, C H and Guisan, A and {NCEAS Predicting Species
Distributions Working Group†}",
abstract = "ABSTRACT A wide range of modelling algorithms is used by
ecologists, conservation practitioners, and others to predict
species ranges from point locality data. Unfortunately, the
amount of data available is limited for many taxa and regions,
making it essential to quantify the sensitivity of these
algorithms to sample size. This is the first study to address
this need by rigorously evaluating a broad suite of algorithms
with independent presence?absence data from multiple species and
regions. We evaluated predictions from 12 algorithms for 46
species (from six different regions of the world) at three
sample sizes (100, 30, and 10 records). We used data from
natural history collections to run the models, and evaluated the
quality of model predictions with area under the receiver
operating characteristic curve (AUC). With decreasing sample
size, model accuracy decreased and variability increased across
species and between models. Novel modelling methods that
incorporate both interactions between predictor variables and
complex response shapes (i.e. GBM, MARS?INT, BRUTO) performed
better than most methods at large sample sizes but not at the
smallest sample sizes. Other algorithms were much less sensitive
to sample size, including an algorithm based on maximum entropy
(MAXENT) that had among the best predictive power across all
sample sizes. Relative to other algorithms, a distance metric
algorithm (DOMAIN) and a genetic algorithm (OM?GARP) had
intermediate performance at the largest sample size and among
the best performance at the lowest sample size. No algorithm
predicted consistently well with small sample size (n < 30) and
this should encourage highly conservative use of predictions
based on small sample size and restrict their use to exploratory
modelling.",
journal = "Diversity and Distributions",
publisher = "Blackwell Publishing Ltd",
volume = 14,
number = 5,
pages = "763--773",
month = sep,
year = 2008,
keywords = "Ecological niche model; MAXENT; model comparison; OM-GARP;
sample size; species distribution model"
}
@ARTICLE{Nagendra2010-cc,
title = "Assessing Plant Diversity in a Dry Tropical Forest: Comparing
the Utility of Landsat and Ikonos Satellite Images",
author = "Nagendra, Harini and Rocchini, Duccio and Ghate, Rucha and
Sharma, Bhawna and Pareeth, Sajid",
abstract = "While high expectations have been raised about the utility of
high resolution satellite imagery for biodiversity assessment,
there has been almost no empirical assessment of its use,
particularly in the biodiverse tropics which represent a very
challenging environment for such assessment challenge. This
research evaluates the use of high spatial resolution (IKONOS)
and medium spatial resolution (Landsat ETM+) satellite imagery
for assessing vegetation diversity in a dry tropical forest in
central India. Contrary to expectations, across multiple
measures of plant distribution and diversity, the resolution of
IKONOS data is too fine for the purpose of plant diversity
assessment and Landsat imagery performs better.",
journal = "Remote Sensing",
publisher = "Molecular Diversity Preservation International",
volume = 2,
number = 2,
pages = "478--496",
month = feb,
year = 2010,
language = "en"
}
@INPROCEEDINGS{Baumgartner2012-sg,
title = "Characterisation methods for the hyperspectral sensor
{HySpex} at {DLR's} calibration home base",
booktitle = "SPIE Remote Sensing 2012",
author = "Baumgartner, Andreas and Gege, Peter and K{\"o}hler, Claas
and Lenhard, Karim and Schwarzmaier, Thomas",
abstract = "The German Aerospace Center's (DLR) Remote Sensing
Technology Institute (IMF) operates a laboratory for the
characterisation of imaging spectrometers. Originally
designed as Calibration Home Base (CHB) for the imaging
spectrometer APEX, the laboratory can be used to
characterise nearly every airborne hyperspectral system.
Characterisation methods will be demonstrated exemplarily
with HySpex, an airborne imaging spectrometer system from
Norsk Elektro Optikks A/S (NEO). Consisting of two
separate devices (VNIR-1600 and SWIR-320me) the setup
covers the spectral range from 400 nm to 2500 nm. Both
airborne sensors have been characterised at NEO. This
includes measurement of spectral and spatial resolution
and misregistration, polarisation sensitivity, signal to
noise ratios and the radiometric response. The same
parameters have been examined at the CHB and were used to
validate the NEO measurements. Additionally, the line
spread functions (LSF) in across and along track direction
and the spectral response functions (SRF) for certain
detector pixels were measured. The high degree of lab
automation allows the determination of the SRFs and LSFs
for a large amount of sampling points. Despite this, the
measurement of these functions for every detector element
would be too time-consuming as typical detectors have 105
elements. But with enough sampling points it is possible
to interpolate the attributes of the remaining pixels. The
knowledge of these properties for every detector element
allows the quantification of spectral and spatial
misregistration (smile and keystone) and a better
calibration of airborne data. Further laboratory
measurements are used to validate the models for the
spectral and spatial properties of the imaging
spectrometers. Compared to the future German spaceborne
hyperspectral Imager EnMAP, the HySpex sensors have the
same or higher spectral and spatial resolution. Therefore,
airborne data will be used to prepare for and validate the
spaceborne system's data.",
publisher = "elib.dlr.de",
pages = "1--8",
institution = "SPIE",
month = sep,
year = 2012,
keywords = "Hyperspectral, Imaging Spectrometer, Smile, Keystone,
Calibration, Radiometry, LSF, SRF, HySpex, CHB",
language = "en",
conference = "SPIE Remote Sensing 2012",
location = "Edinburgh, Gro{\ss}britannien"
}
@ARTICLE{Nekola1999-ks,
title = "The distance decay of similarity in biogeography and ecology",
author = "Nekola, Jeffrey C and White, Peter S",
abstract = "Summary Aim Our aim was to understand how similarity changes
with distance in biological communities, to use the distance
decay perspective as quantitative technique to describe
biogeographic pattern, and to explore whether growth form,
dispersal type, rarity, or support affected the rate of distance
decay in similarity. Location North American spruce?fir forests,
Appalachian montane spruce?fir forests. Methods We estimated
rates of distance decay through regression of log?transformed
compositional similarity against distance for pairwise
comparisons of thirty?four white spruce plots and twenty?six
black spruce plots distributed from eastern Canada to Alaska,
six regional floras along the crest of the Appalachians, and six
regional floras along the east?west extent of the boreal forest.
Results Similarity decreased significantly with distance, with
the most linear models relating the log of similarity to
untransformed distance. The rate of similarity decay was 1.5?1.9
times higher for vascular plants than for bryophytes. The rate
of distance decay was highest for berry?fruited and nut?bearing
species (1.7 times higher than plumose?seeded species and 1.9
times higher than microseeded/spore species) and 2.1 times
higher for herbs than woody plants. There was no distance decay
for rare species, while species of intermediate frequency had
2.0 times higher distance decay rates than common species. The
rate of distance decay was 2.7 times higher for floras from the
fragmented Appalachians than for floras from the contiguous
boreal forest. Main conclusions The distance decay of similarity
can be caused by either a decrease in environmental similarity
with distance (e.g. climatic gradients) or by limits to
dispersal and niche width differences among taxa. Regardless of
cause, the distance decay of similarity provides a simple
descriptor of how biological diversity is distributed and
therefore has consequences for conservation strategy.",
journal = "J. Biogeogr.",
publisher = "Blackwell Science Ltd",
volume = 26,
number = 4,
pages = "867--878",
month = jul,
year = 1999,
keywords = "Similarity; spatial dependence; distance decay; biological
diversity; boreal forest",
language = "en"
}
@ARTICLE{Bartsch2009-ze,
title = "Global monitoring of wetlands--the value of {ENVISAT} {ASAR}