Earth Observation Satellite Sensors for Biodiversity Monitoring: Potentials and Bottlenecks

Abstract

Many biologists, ecologists, and conservationists are interested in the possibilities that remote sensing offers for their daily work and study site analyses as well as for the assessment of biodiversity. However, due to differing technical backgrounds and languages, cross-sectorial communication between this group and remote-sensing scientists is often hampered. Hardly any really comprehensive studies exist that are directed towards the conservation community and provide a solid overview of available Earth observation sensors and their different characteristics. This article presents, categorizes, and discusses what spaceborne remote sensing has contributed to the study of animal and vegetation biodiversity, which different types of variables of value for
the biodiversity community can be derived from remote-sensing data, and which types of spaceborne sensor data are available for which time spans, and at which spatial and temporal resolution. We categorize all current and important past sensors with respect to application fields relevant for biologists, ecologists, and conservationists. Furthermore, sensor gaps and current challenges for Earth observation with respect to data access and provision are presented.

Full text

REVIEW ARTICLE
Earth observation satellite sensors for biodiversity monitoring:
potentials and bottlenecks
Claudia Kuenzer
a
*, Marco Ottinger
b
, Martin Wegmann
b
, Huadong Guo
c
,
Changlin Wang
c
, Jianzhong Zhang
d
, Stefan Dech
a,b
, and Martin Wikelski
e†
a
Earth Observation Center, EOC of the German Aerospace Center, DLR, Oberpfaffenhofen, 82234
Wessling, Germany;
b
Department of Remote Sensing, University of Wuerzburg, Institute of
Geography and Geology, 97070 Wuerzburg, Germany;
c
Institute for Remote Sensing and Digital
Earth, RADI, Chinese Academy of Sciences, CAS, 100094 Beijing, China;
d
Freelance Remote
Sensing Consultant, 86916 Kaufering, Germany;
e
Max Planck Institute for Ornithology, 78315
Radolfzell, Germany
(Received 13 August 2014; accepted 26 August 2014)
Many biologists, ecologists, and conservationists are interested in the possibilities that
remote sensing offers for their daily work and study site analyses as well as for the
assessment of biodiversity. However, due to differing technical backgrounds and
languages, cross-sectorial communication between this group and remote-sensing
scientists is often hampered. Hardly any really comprehensive studies exist that are
directed towards the conservation community and provide a solid overview of avail-
able Earth observation sensors and their different characteristics. This article presents,
categorizes, and discusses what spaceborne remote sensing has contributed to the study
of animal and vegetation biodiversity, which different types of variables of value for
the biodiversity community can be derived from remote-sensing data, and which types
of spaceborne sensor data are available for which time spans, and at which spatial and
temporal resolution. We categorize all current and important past sensors with respect
to application fields relevant for biologists, ecologists, and conservationists.
Furthermore, sensor gaps and current challenges for Earth observation with respect
to data access and provision are presented.
1. Introduction: remote sensing for biodiversity assessments and conservation.
Common cross-disciplinary challenges
Biodiversity is a measure for the number and variety of biotic species found within a
defined geographic region. Such a region can be an ecosystem, a biome, or a global
climate zone. According to the Convention on Biodiversity, it is defined as diversity
within species, between species, and of ecosystems. On our planet, terrestrial biodiversity
is highest near the equator (Gaston 2000), where a warm climate leads to high primary
productivity. Also in our oceans, biodiversity shows a general trend with latitudinal
gradients (Gaston 2000; O’dor, Fennel, and Berghe 2009). All over the world, we can
find ‘hot spots’of biodiversity, clusters with an exceptional diversity of plant or animal
*Corresponding author. Email: claudia.kuenzer@dlr.de
†
Present address: University of Konstanz, 78457 Konstanz, Germany.
International Journal of Remote Sensing, 2014
Vol. 35, Nos. 17–18, 6599–6647, http://dx.doi.org/10.1080/01431161.2014.964349
© 2014 The Author(s). Published by Taylor & Francis.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited. The moral rights of the named author(s) have been asserted.

species (Gaston 2000; Leyequien et al. 2007) which are relevant for priority decisions on
conservation (Myers et al. 2000).
Long before the dawn of mankind on our planet, biodiversity distribution was impacted
by a variety of factors, such as endogenic forcing (e.g. the birth of new volcanic islands in the
oceans, or eruptive activity on land); local, regional, and even global natural hazards; and
mass extinction events (e.g. meteorite impacts, forest fires, tsunamis, floods, etc.) (Kataoka
et al. 2014; Twitchett 2013; Brand et al. 2012), not to mention the impact on biodiversity by
the biosphere itself (e.g. food chain impacts, etc.) (Legović, Klanjšček, and Geček 2010;
Karlsson, Jonsson, and Jonsson 2007; Jonsson, Karlsson, and Jonsson 2006). However,
especially since the onset of industrialization, it is humans and their undertakings that are
the main drivers of biodiversity dynamics and that also pose a severe threat to global
biodiversity (Barnosky et al. 2011; Steffen, Crutzen, and that McNeill 2007; Kachelriess
et al. 2014). Urbanization with its ever continuing expansion of infrastructure; agro-indus-
trialization; and the constant hunger for food, construction, geological, and energy resources
has led to a drastic increase in species extinction (Zalasiewicz et al. 2011). Examples are the
alarming loss of rainforest habitats in Africa, South America, and Asia (Sassen et al. 2013;
Biggs et al. 2008; Hansen et al. 2013; Potapov et al. 2012; Stehman et al. 2011); the decline of
marine resources and the destruction of coral reefs in our oceans (Kachelriess et al. 2014;
Baker, Glynn, and Riegl 2008; White, Vogt, and Arin 2000); or the observed decrease in
migratory species in many regions globally (Knudsen et al. 2011). Additionally, the collection
of rare animals or parts of their bodies for spiritual, medicinal, or status reasons poses a threat
to many populations globally (Abraham 2014; Wielgus et al. 2013; Burton 1999). Over the
past few decades, numerous bodies, such as the United Nations Environmental Programme
(UNEP), Conservation International (CI), the International Union for the Conservation of
Nature (IUCN), and the World Wide Fund for Nature (WWF), have called for an increased
protection of biodiversity. For example, a recent outcome document of the Rio+20 United
Nations Conference on Sustainable Development calls for a framework plan to set up trans-
border protected marine areas (Kachelriess et al. 2014), and many bodies call for the fast
prioritization of areas for conservation (Hazen 2009) on the basis of already existing large
global categorizations, such as the ‘Terrestrial ecoregions of the world’map by Olson et al.
(2001) or the global biodiversity hot spot map of Myers et al. (2000). In Europe, the EU
Habitats Directive requests member states to report every six years on the status of habitat
conservation by submitting information on habitat area, range, indicators of habitat quality,
and future prospects for habitat protection (Nagendra et al. 2013).
It is undoubtedly of the utmost importance to map and monitor biodiversity and changes
thereto (Hazen 2009). This ongoing challenge can –besides other means –also be tackled
using remote-sensing-derived information; the debate on a standardized set of indicators is
ongoing (Pereira et al. 2013; Costello et al. 2013; Costello and Wieczorek 2014; Gardner
et al. 2012; Cord et al. 2014). A further challenge for the conservation and remote-sensing
communities alike is the fact that biodiversity is hierarchical –from molecule, to ecosystem,
to even the global level –and whether biodiversity should be assessed at the cellular,
species, habitat, or landscape level is a topic of discussion for many biologists (Hazen
2009). Landscape level environmental characteristics have been found to be good proxies or
surrogates for biodiversity, as long as one is aware of the pitfalls that this approximation can
have. Remote sensing can contribute to species, habitat, and landscape characterization, and
groups such as GEO BON (the Group on Earth Observations Biodiversity Observation
Network) advertise the advantages of remote sensing to the biodiversity community.
However, many cross-sectorial gaps exist between conservationists and remote-sensing
scientists, and a common understanding has to be established.
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When biologists, ecologists, and conservationists talk with a remote-sensing expert, an
often observed dialogue (a bit over-accentuated here to make the point) goes somewhat
like this:
Biologist 1: ‘I am very interested in the population size of flying foxes,
which usually roost in large trees. Which type of earth
observation data should I get to monitor them?’
Remote-sensing expert: ‘Hmm …these animals are pretty small, right?’
Biologist 1: ‘Yes, only about 40 centimetres long, brown in colour, just
like the branches of the trees; and I would like to count them
every week, at least.’
Remote-sensing expert: ‘Sorry, I think satellite data, or even airborne data, cannot
help you.’
Biologist 1 (disappointed): ‘Well, what can I use your earth observation data for?’
Biologist 2: (excited) ‘Maybe for my endeavour. I am especially inter-
ested in butterfly swarms that migrate in the Americas. They
are very colourful. I think it should be possible to see them.
And also they rest especially in certain types of fruit trees. I
am sure you can differentiate these trees from other types of
trees, right?’
Remote-sensing expert: ‘Eh …, this might not be possible either, I am afraid.
Butterflies are really very small, change their location
often, and this certain tree …well, I am not so sure it is
differentiable from other tree types, especially as we have
very few real hyperspectral sensors in orbit.’
Biologist 2: ‘Hmm, you turn me down as well? I wonder if there is any
way at all to monitor biodiversity with remote sensing
data …’
Biologist 3: ‘Ah, but I have a more realistic request. I am focusing on
elephant migration, and I do know that it is possible to see
elephants in high-resolution remote-sensing data. We would
like to monitor where the elephants are on a weekly basis. The
region that needs to be covered is about 1000 by 1000 km in
size. Unfortunately, our conservation project only has very
limited funds, but I think it should be no problem to cover
such an area and obtain very high resolution data once a week,
right?’Furthermore, we want to map different species of
Savannah grasses that are commonly eaten by elephants’.
Remote-sensing expert: ‘I am so sorry, but animal monitoring is also a very tricky
request. It is true that in the highest resolution data from
satellite sensors such as QuickBird, IKONOS, WorldView, or
the like one can see elephant herds. But the scenes of these
highest-resolution sensors only cover very small areas, actu-
ally, only a few square kilometres, and furthermore, the data
are relatively costly. With your limited budget it will most
likely not be possible to cover such a large area at such dense
time intervals. However, to distinguish different plant species
might maybe be doable –especially, as plants are stationary.’
International Journal of Remote Sensing 6601

All biologists: ‘So what can your earth observation data really do for us
biodiversity experts? What has remote sensing contributed
so far for us biologists and ecologists? What can we see,
and not see, with current sensors? Is satellite data useful at
all for biodiversity monitoring, and if yes, how? Which
sensor’s data should I use? Is it for free?’
In this article, we address exactly these questions, and –beyond –shed light on currently
available spaceborne sensors, their characteristics, fields of applications, as well as
problems of data provision. The questions we aim to answer are the following.
●What has spaceborne remote sensing so far contributed to biodiversity assessment
and monitoring?
●Which geophysical and other variables can we derive based on imagery from
current spaceborne sensors? Which phenomena relevant for biodiversity assessment
can be observed? When is unitemporal data sufficient, and what advantages does
multitemporal data have?
●Which type of spaceborne sensor data is available, and which sensor continuity is
given, enabling the long-term monitoring of areas? Which countries operate which
sensors?
●What are typical spatial, spectral, and temporal resolutions of sensors and in which
fields of application (with respect to biodiversity and conservation) is their data
usually exploited?
●Where are current sensor gaps and which sensors are urgently needed? Which
changes in data provision need to occur?
2. Remote sensing of biodiversity: a brief overview
2.1. Spaceborne remote sensing of animals
Spaceborne remote sensing of animals has been undertaken since the early 1980s. Löffler
and Margules (1980) mapped the expansion of hairy-nosed wombats in Nullarbor Plain,
southern Australia, using 60 m-resolution Landsat multispectral scanner system (MSS)
imagery to identify wombat warrens. Due to the animals’grazing behaviour around the
warrens, these plots are characterized by degraded vegetation and bare ground, clearly
recognizable as light spots in the imagery, and highly reflecting tracks around the warrens
could also be clearly identified. Saxon (1983) used remote-sensing data to map the
habitats of re-introduced rufous hare-wallabies in Australia. Nellis and Bussing (1990)
investigated the destruction of vegetation by elephants in the Zambezi teak forests of
Botswana. Multispectral spaceborne SPOT-HRV (Système Probatoire d’Observation de la
Terre High-Resolution Visible) data at 20 m spatial resolution was categorized into
different landscape units to illustrate elephant-related impacts. Especially for monitoring
elephants’behaviour, numerous remote-sensing studies have been undertaken; amongst
many a study by Murwira et al. (2010), which investigated the link between arable field
distribution and changes in the distribution of elephants, and a study by Sibanda and
Murwira (2012), which elucidated how cotton field development drives elephant habitat
fragmentation in the Zambezi valley of Zimbabwe. Herd distribution, migration pathways
and habitat conditions of other larger mammals such as bison (Ware, Terletzky, and Adler
2014; Kuemmerle et al. 2010), reindeer (Kivinen and Kumpula 2014), cattle (Raizman
6602 C. Kuenzer et al.

et al. 2013), and smaller livestock such as sheep and goats (Röder et al. 2008) have also
been assessed. Many authors have found direct relationships between the seasonal
migration of herds and vegetation vigour, especially for herds driven by green vegetation
availability (Leyequien et al. 2007).
In general, there are only a few studies about monitoring mammals directly, in
comparison to the number of studies interpreting remote-sensing imagery for other related
purposes. The latter studies focus on characterizing and assessing changes in mammal
habitats rather than on direct observation of individual animals or herds. Although this is a
valuable approach for mammals and other animals with clear habitat preferences, it has to
be kept in mind that some species use more than one distinct vegetation type and might
not be directly associated with a single habitat (Leyequien et al. 2007).
A good in-depth review of the application of remote sensing for assessing terrestrial
animal distribution and diversity was compiled by Leyequien et al. (2007).
Numerous studies have been published assessing large bird populations. However,
most authors utilize airborne data (Groom et al. 2013), and only relatively little work is
being undertaken based on data acquired from space. Sasamal et al. (2008) observed the
wintering habitat of flamingos in Chilika Lake, India, using high-resolution QuickBird
and IKONOS data. Flamingo congregations, mapped as bright spots, contrasted strongly
with the dark surrounding water, which enabled a precise count of the flamingos.
Schwaller et al. (1989) acquired Landsat imagery to estimate changes in the size of
Adélie penguin nesting sites, which was considered to be an indicator of the availability
of krill. A comprehensive study by Fretwell and Trathan (2009) demonstrated the use of
Landsat data for the detection of faecal stains, revealing the location of emperor penguin
colonies at 38 sites scattered along the shore of Antarctica. However, as for mammals,
studies dealing with remote-sensing-based assessments of bird habitats and the identifica-
tion of preferred resting, nesting, and feeding areas in remote-sensing data (Sheeren,
Bonthoux, and Balent 2014; Flaspohler et al. 2010; Fuller et al. 1998; Leyequien et al.
2007) by far outweigh the studies that observe bird populations directly.
Indirect biodiversity-related information from remote-sensing data is even retrieved for
the smallest animals, such as insects. The field of ‘geo-health’which evolved over the past
few decades uses remote-sensing data to map the potential breeding grounds of mosquitos
that can carry malaria or dengue viruses, or habitats of certain snails that may spread
schistosoma. However, also here, habitat mapping of the virus hosts is the main focus
(Benali et al. 2014; Chuang et al. 2012; Hay, Snow, and Rogers 1998). For this field,
Herbreteau et al. (2007) presented a comprehensive overview of thirty years of remote
sensing applied to epidemiology. The subtitle ‘From Early Promises to Lasting Frustration’
and the quote, ‘Despite the potential of remote sensed images and processing techniques for
a better knowledge of disease dynamics, an exhaustive analysis of the bibliography shows a
generalized use of pre-processed spatial data and low-cost images, resulting in a limited
adaptability when addressing biological questions’(1) clearly indicate that more was
expected from spaceborne data and the remote-sensing community than could actually be
provided to epidemiologists. A more successful field has been the indirect monitoring of
swarming insects that have the potential to destroy their own habitats. Defoliating insects
(e.g. locusts in maize fields, etc.) consume their primary food source, and defoliated fields
can be spotted in high-resolution remote-sensing data (Leyequien et al. 2007).
Last but not least, limnologists, marine biologists, and oceanographers also employ
remote-sensing imagery to monitor their objects of interest. It goes without saying that
this endeavour is very complex, as marine mammals and fish usually live below the water
surface. A comprehensive review of the use of remote sensing in fisheries oceanography
International Journal of Remote Sensing 6603

has been presented by Santos (2000), as well as Klemas (2013). Spaceborne imagery that
depicts whales (Fretwell, Staniland, and Forcada 2014), dolphins, or even fish swarms at
the water surface is usually a product of chance and cannot be acquired according to a
planned schedule. Therefore, also for lake and marine ecologists, remote sensing is
usually a tool for analysing proxies and surrogates and focusing on habitats and habitat
dynamics. Sea surface temperature (SST) is one such proxy (Santos 2000). Albacore tuna,
anchovy, sardine, and jack mackerel all prefer the sharp contrast areas where cold and
warm currents meet (Klemas 2013). Further proxies for the aquatic sphere –next to water
temperature –include ocean thermal boundaries, water colour, related chlorophyll
(coloured dissolved organic matter, CDOM) or sediment content (suspended particular
matter, SPM), ocean colour boundaries, water salinity, wind speed and direction, wave
height and direction, shoreline vegetation, and anthropogenic impacts such as ship traffic,
oil spills, and other pollution (Blondeau-Patissier et al. 2014; Fingas and Brown 2014;
Kachelriess et al. 2014; Leifer et al. 2012; Hoepffner and Zibordi 2009; Santos 2000). It
is, for example, known that many fish swarm species have preferred temperature ranges,
and many are found at thermal fronts. The climate change-related impacts of sea level rise
and ocean acidification are especially perceived by coral reefs. For these special marine
environments of outstanding biodiversity, Xu and Zhao (2014) presented a comprehensive
review of remote-sensing-based studies and findings.
A common approach in many of the studies using remote-sensing data to assess
animal biodiversity, be it mammals, avifauna, reptiles, and amphibians, or even inverte-
brates, is to correlate information collected in situ on species numbers and diversity with
certain variables detectable in remote-sensing data. Good correlations between species
representations and certain variables have been found by many authors (Leyequien et al.
2007). They use this as an argument that –despite the fact that remote sensing of animals
is ‘trying to capture the fugitive’–it does have a high value for biodiversity assessments
as habitat suitability derived from remote-sensing data can in many cases act as a proxy
for species occurrence or richness.
Although not strictly ‘Earth observation remote sensing’–as no spatial images are
acquired –the GPS tracking of animals from space is closely linked with remote-sensing-
based biodiversity applications. Animals equipped with small GPS sensors can be located
via satellites (e.g. Argos). In this way, animal movements and migration routes can be
tracked (Kranstauber et al. 2011; Witt et al. 2010; Buerkert and Schlecht 2009; Hughes et al.
1998; Papi et al. 1997). Already in the early 1990s, Priede and French (1991) described in
detail how transmitters with antennas can be attached to the animal with harnesses (e.g. on
birds), collars, or anchors, also addressing the general problem of attaching sensors to fish.
Fish or marine mammals can only be detected when the transmitter is located on the sea
surface during the satellite overpass. Due to their gill breathing, most fish never need to
break the surface. Even lung-breathing dolphins often come to the surface for only a few
seconds to breathe. Therefore, marine species tracking is a large challenge. The topic of
global animal tracking and the synergetic analysis of such tracking data (vector files) with
remote-sensing-derived information products on land-cover dynamics is experiencing grow-
ing popularity based on the work presented by the Max Planck Institute of Ornithology,
Germany. The group has set up an internet platform named ‘Movebank’, which depicts
migratory routes of animals (mainly birds) globally and in near real time. The migration
routes of thousands of sensor-carrying storks, cranes, gulls, geese, and also of several
mammals can be traced in this web portal in a dynamic manner –some of them even in
near real time (Safi et al. 2013; Brown et al. 2012; Kranstauber et al. 2011). Synergetic data
analysis of the migratory (vector) tracks stored in Movebank with intelligent information
6604 C. Kuenzer et al.

products from remote-sensing imagery can help to answer pressing questions such as why
certain migratory bird numbers are decreasing, why certain species start to migrate earlier or
later in the year, or why resting or breeding habitats are given up (Dell et al. 2014; Cooke
et al. 2004). The answers usually lie in human-induced habitat changes to these spaces, such
as the drainage and destruction of wetlands; the decrease of prey species in lakes, rivers, or
coastal waters (e.g. fish); the impacts of air, water, and soil pollution (Duncan et al. 2014);
and –with respect to the shift of migration onset –in climate change, changes in land
surface temperature (LST) and SST, or precipitation patterns.
The future of biodiversity-related remote-sensing analysis, especially for animals that,
unlike plants, do not exhibit stationary behaviour, is definitely in the combination of
remote-sensing products and ancillary biodiversity or animal-related information, which
should be made publicly available (Costello and Wieczorek 2014; Costello et al. 2013;
Pino-Del-Carpio et al. 2014).
2.2. Spaceborne remote sensing of vegetation biodiversity
Spaceborne remote sensing allows for the accurate assessment of vegetation habitats in
inaccessible, vast areas. Depending on spectral, spatial, and temporal resolution, it enables
the mapping of general vegetation vigour and development down to the precise classifica-
tion of individual plant species. Vegetation observed in remote-sensing data is usually
described in terms of derived variables such as vegetation indices (normalized difference
vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation
index (EVI), etc.) (Tucker 1979; Tarpley 1991; Jackson and Huete 1991; Baret and
Guyot 1991; Gupta 1993; Huete et al. 1997), leaf area index (LAI) (Gupta, Prasad, and
Vijayan 2000; Fensholt, Sandholt, and Rasmussen 2004; Casa and Jones 2005), tree cover
density (Bai et al. 2005; Yang, Weisberg, and Bristow 2012; Leinenkugel et al. 2014,
forthcoming), net primary productivity (NPP) and biomass (gC m
−2
) (Wagner et al. 2003;
Hese et al. 2005; Lu 2006; Eisfelder, Kuenzer, and Dech 2011), canopy moisture (Brakke
et al. 1981), canopy height, expected crop yield (Birnie, Robertson, and Stove 1982;
Hatfield 1983; Horie, Yajima, and Nakagawa 1992), measures of fragmentation and
connectivity (Stenhouse 2004; Pueyo and Alados 2007; Briant, Gond, and Laurance
2010), or as detailed classification-derived map products breaking down vegetation
distribution to the species level (Foody and Cutler 2006; Kutser and Jupp 2006; Pu and
Landry 2012; Engler et al. 2013). Vegetation height can also be derived from digital
elevation model (DEM) data (Walker et al. 2007). With these variables, it is possible to
address vegetation vigour, height, age, density, productivity and biodiversity, as well as
stress and disturbances.
Literally thousands of published studies exist on the remote sensing of vegetation. It is
not within the scope of this article to present a comprehensive overview in one paragraph.
However, elaborate and overarching review articles presenting the state of the art in
remote sensing for a defined vegetation ecosystem are mentioned, and some studies
especially addressing vegetation biodiversity are introduced. A comprehensive review of
the potential of remote sensing for monitoring tropical forest ecosystems was provided by
Justice (1992). Reviews on the remote sensing of other forest ecosystems were provided
by Malingreau (1992). Reviews on the remote sensing of coastal mangrove ecosystems
have been published by Kuenzer et al. (2011) and Blasco et al. (1998). Rundquist,
Narumalani, and Narayanan (2001), Henderson and Lewis (2008), Lawler (2001), and
Kuenzer and Knauer (2013) published overarching articles on remote-sensing techniques
for assessing further natural and agricultural wetland ecosystems, and Zhang et al. (1997)
International Journal of Remote Sensing 6605

focused on remote sensing for saltmarsh ecosystems. Remote-sensing vegetation applica-
tions in African savannahs were reviewed by Knauer et al. (2014), and tundra vegetation
applications were summarized by Stow et al. (2004).
Selected authors have also presented reviews on the potential and contribution of
remote sensing for assessing plant species diversity (Gould 2000; Griffiths, Lee, and
Eversham 2000; Nagendra 2001). Examples of studies where vegetation biodiversity was
mapped down to the species level are to be found in Walsh et al. (2008), Saatchi et al.
(2008), and in many other publications. Walsh et al. (2008) employed spaceborne high-
resolution QuickBird and hyperspectral Hyperion data to map and analyse the dynamics
of different invasive plant species on the Galapagos Islands of Ecuador, deriving recom-
mendations for control and land-use management. Yang, Everitt, and Johnson (2009)
mapped invasive species in Texas, USA, also using high-resolution QuickBird data. Also
at the species level, Belluco et al. (2006) mapped five different halophytes in an intertidal
salt marsh over a five-year period employing spaceborne and airborne sensors (IKONOS,
QuickBird, ROSIS, CASI, MIVIS). Saatchi et al. (2008) monitored the potential distribu-
tion of five Amazonian tree species and their diversity in floodplains, swamps, and on
terra firma. The LAI of Moderate Resolution Imaging Spectroradiometer (MODIS)
imagery served as an indicator for vegetation seasonality; vegetation continuous field
(VCF) products supported the estimation of percentile canopy cover, and QuickScat
scatterometer and Shuttle Radar Topography Mission (QSCAT and SRTM) radar data
were employed to determine canopy roughness. QSCAT was additionally used to obtain
the relative moisture of the canopy and soil. Indicators such as soil moisture, elevation,
distance from the sea, seasonality, LAI, canopy density, and roughness, combined with
ancillary data on temperature and precipitation, enabled the potential distribution of the
tree species to be determined. It should be mentioned that accurate discrimination of top
canopy species becomes more difficult, the denser the vegetation stands, for example in
tropical areas, where there is a substantial amount of overlap between the leaves and
branches of individual plants of different species (Nagendra et al. 2013). Here the mixed
pixel problem can be circumvented by higher resolution data with sufficient bands in the
optical and near infrared range (e.g. WorldView).
Overall, studies focusing on the derivation of vegetation habitats down to the
mapping of individual vegetation species by far outweigh animal-related applications.
However, as animal habitats are usually closely related to the habitats of certain
vegetation types, these mapping activities contribute greatly to the indirect remote
sensing of animal habitats as well.
3. What can realistically be done? A categorization of biodiversity-relevant
variables derivable from remote-sensing data
As stated above, information relevant for biodiversity analysis derivable from remote-
sensing data is usually indirect information, also termed proxy or surrogate. Especially for
animal-related studies, even the highest resolution spaceborne sensors hardly ever allow
the object of interest to be directly observed. Thus, studies usually derive variables
describing the habitat of a certain species. Every animal and plant species has certain
habitat limits defined by temperature, precipitation, altitude or topography limits, resource
availability (soil type, food, prey), as well as proximity to other species (including
humans), to give only some examples. Remote sensing allows such defining habitat
boundary conditions to be extracted and thus a certain habitat to be mapped based on
the derivation of these variables. To enable a comprehensive overview of the variables
6606 C. Kuenzer et al.

that can be extracted from remote-sensing data, we collected and then categorized all
common possible variables into five groups, namely geophysical, index, thematic, topo-
graphic, or texture variables (see yellow boxes, Figure 1). We furthermore differentiate the
unitemporal (Figure 1) versus the multitemporal (Figure 2) case.
Geophysical variables are defined by physical units quantifying LST, SST, atmo-
spheric temperature (AT)), or available photosynthetically active radiation (PAR). Index
variables are dimensionless and provide a pseudo-quantitative measure of the state of an
ecosystem; e.g. the NDVI represents vegetation state and vigour. While geophysical
variables and index variables are derived via clearly defined mathematical formulae,
thematic variables are usually extracted with a higher degree of analyst bias. Thematic
variables are for example land-cover and land-use information, derived either in the
context of complex supervised multi-class classifications or as binary masks (water/non-
water, urban/non-urban). In this category also falls the direct observation of biodiversity
such as –especially in the case of vegetation –the supervised mapping of individual
species based on knowledge of their spectral characteristics. Topographic variables such
as height, slope, aspect, or surface roughness can be derived from DEMs (generated from
either radar or optical stereo data), and texture variables such as object size, homogeneity,
heterogeneity, or neighbourhood relationships depend on image segmentation or distinct
object detection and subsequent object-oriented analysis. These latter variables are often
used for direct vegetation biodiversity assessment (e.g. tree type differentiation based on
Figure 1. Information relevant for biodiversity analysis that can be gained from remotely sensed
data. We classify five types of variables, and examples of further products relevant for habitat
analysis are presented. This is shown for the unitemporal case, assuming that only one data set is
available and that multitemporal data or time series of data do not exist.
International Journal of Remote Sensing 6607

crown size and shape). Fragmentation and connectivity are also extremely important
measures, since ‘landscape fragmentation, through the disruption of habitat connectivity,
can impact species dispersion and habitat colonization, gene flows and population diver-
sity, and species mortality and reproduction’(Nagendra et al. 2013, 50). Furthermore, it
has been found that landscape and habitat heterogeneity is a driving factor for species
richness (Leyequien et al. 2007). The green boxes in Figure 1 depict which biodiversity-
relevant information can be retrieved based on these variables. Geophysical and topo-
graphic variables especially allow for the approximation of habitat boundary limits, for
example defined by temperature, altitude, precipitation, or slope limits. The same applies
for index variables, which furthermore depict the direct status of ecosystems. Thematic
variables –including land-cover information –characterize an ecosystem and also eluci-
date resource availability (e.g. distance to waterbodies) or proximity to threats (humans,
infrastructure). Texture variables can support the differentiation of species, estimation of
population size or status, as well as the derivation of habitat fragmentation or connectivity.
Figure 2, which is similar to Figure 1 except that it is for multitemporal rather than
unitemporal sources of data, depicts the five groups of variables as well as the relevant
information for biodiversity analysis. It is the opportunity to observe the dynamics of
variables that makes remote sensing a powerful tool for biodiversity-related studies.
Whereas unitemporal studies only allow the assessment of the current situation, the
Figure 2. Information relevant for biodiversity analysis that can be gained from multitemporal
remotely sensed data up to dense time series of data. Multitemporal data allow the monitoring of
objects and habitats over time, and when long-term time series are available one can even observe
trends (e.g. temperature shifts, snow cover duration shifts, etc.), and therefore slight geographic
shifts of habitats.
6608 C. Kuenzer et al.

multitemporal –and in the optimal case the time series –capability of some sensors
allows a much larger range of biodiversity-relevant applications considering the past as
well as the future (see Figure 2).
As depicted in the green boxes of Figure 2, multitemporal analysis of geophysical,
index, thematic, topographic, and texture variables allows statements on variable condi-
tions over the course of one month, one year, or even one or several decades, including
the calculation of daily, weekly, monthly, and annual means, deviations, anomalies,
general variability, and –given a long enough observation time span –even trends (e.g.
LST annual range). Changes in growth conditions, phenologic metrics (e.g. start of
season, mid of season, end of season), or growing season length can be observed over
multiple years, and shifts in vegetation habitats can be depicted. One-land-cover-class
monitoring, for example of water surfaces, including waterbodies and floods, allows the
derivation of monthly, annual and even decadal minimum and maximum waterbody
extent, flood frequency, and inundation variability. The same applies to land-cover and
land-use change patterns that persist for decades and can be projected to continue into the
future (e.g. urban sprawl or glacial retreat). Multitemporal elevation information allows
the monitoring of surface displacement induced by land subsidence, which is often driven
by groundwater, oil or gas extraction, other mining activities, or urban sprawl (heavy
structures compacting the ground). While subsidence phenomena might not directly affect
biodiversity, they are of relevance especially in the coastal zone. Many coastal ecosystems
on shallow coastal shelves or in river deltas are under threat of ‘drowning’, as subsidence
and sea level rise combine and lead to aggravated land submersion. Several sensors allow
the monitoring of habitat development up to four decades into the past.
Next to highest resolution sensors, which partially enable mapping at the species level,
especially sensors allowing long-term monitoring can be the main workhorses for extract-
ing biodiversity-related environmental dynamics and delineating past, present, and future
habitats. However, comprehensive overviews of all the different Earth observation satel-
lite sensors and their data hardly exist. But it is exactly this amalgamated information on
sensor types, specific sensors and their spatial and temporal resolution, time span of
operation, and data availability that is essential for biologists, conservationists, environ-
mental scientists, and even many remote-sensing experts who need to make decisions
about which data can be employed to address their most pressing questions. Although
some grey literature documents and websites with sensor overviews have been published
by large space agencies such as the National Aeronautics and Space Administration
(NASA), European Space Agency (ESA), Japan Aerospace Exploration Agency
(JAXA) etc., they mostly present the sensors they themselves operate. To our knowledge,
no overarching, well-structured overview of all available spaceborne remote-sensing
sensors tailored to the needs of the biodiversity community exists. We provide this in
the following sections and concentrate on up-to-date sensors (sensors, which are still in
orbit, or long sensors lines, which have been frequently exploited).
4. Satellite sensor categorization: optical/infrared, thermal infrared, and radar
sensors
A review and comparison of past, present, and future sensors used for Earth observation is
essential for providing a good overview of their technical capabilities to the remote-
sensing and user communities, exposing existing gaps, and listing requirements for future
missions. Several authors have provided sectorial reviews of different types of sensors.
About a decade ago, Melesse et al. (2007) reviewed the history of remote sensing and the
International Journal of Remote Sensing 6609

development of different sensors for environmental and natural resource mapping and data
acquisition. Gillespie et al. (2007) presented an overview of recent satellites with passive
sensors that provide panchromatic, visible, near-infrared, shortwave infrared, and thermal
infrared (TIR) information suitable for the remote sensing of hazards. Goetz (2009)
discussed the development of airborne and spaceborne hyperspectral sensors and data
analysis techniques during the last 30 years and pointed out current limitations and future
needs. CubeSats have been addressed by Sandau (2010) and Cracknell (2010), amongst
others. A comprehensive work of Kramer (2002) presents an overview of available
satellite sensors, but this does not contain developments of the past decade. However,
an updated edition of the book will be published around the end of 2014. Only a few
articles illustrate the use and potential of airborne and spaceborne sensors for observations
in the field of biodiversity and conservation. Ten years ago, Turner et al. (2003) presented
a review of sensors available for indirect biodiversity monitoring via the derivation of
biophysical parameters, and Wang et al. (2010) reviewed spaceborne remote sensing for
ecology, biodiversity, and conservation studies, mainly focusing on high-resolution,
hyperspectral, lidar, and small CubeSat sensors. However, no publication exists that is
half-way up to date and presents a holistic overview of all spaceborne available optical,
infrared, thermal, and radar imaging sensors as well as highlighting their most suitable
fields of application related to biodiversity-type assessments.
Based on information from published Science Citation Index studies and sectorial
reviews, grey literature, and websites provided by space agencies and sensor operators, we
compiled timeline charts of currently active sensors acquiring data in the optical and near-
infrared domain (Figure 3), the TIR domain (Figure 4), and radar domain (Figure 5). After
a first overview, we go into detail on spatial and temporal sensor characteristics and
preferred application domains (Section 5). This is followed by a section on data avail-
ability, access, and costs (Section 6).
Figure 3. Timelines of major Earth observation satellites with optical/multispectral sensors.
6610 C. Kuenzer et al.

Figure 3 presents an overview of optical and (near-/mid-) infrared spaceborne sensors,
their active data acquisition period, as well as their country of origin. As a general rule, we
only list sensors that are still active (indicated by an arrow at the right end of the yellow
bar). However, in exceptional cases, past sensors were included in the case of missions
designed to provide long-term data continuity and which are more or less seamlessly
complemented by later missions.
It can be seen that only a few optical and multispectral sensors really allow long-term
monitoring. Only the multispectral NOAA-AVHRR (National Oceanic and Atmospheric
Figure 4. Timelines of major Earth observation satellites with thermal sensors.
Figure 5. Timelines of major Earth observation satellites with radar and passive microwave
sensing instruments.
International Journal of Remote Sensing 6611

Administration Advanced Very High Resolution Radiometer) sensor offering 1 km spatial
resolution at a better than daily revisiting time enables an area to be monitored back in time
to the late 1970s. Due to its very high temporal resolution (at least daily revisits, and twice a
day for many places), even in the most cloud-covered regions of Earth, it is likely to acquire
cloud-free imagery at least once a month. At the same time, 1 km spatial resolution is too
coarse for many species or habitat mapping applications. Only the Landsat fleet, starting
with the Landsat MSS, followed by TM and ETM+, and now continued via the Landsat-
DCM (8) satellite launched in February 2013, allows long-term monitoring of an area for
over 30 years at a spatial resolution averaging 30 m up to 10 m (ETM+ and LDCM
panchromatic). The development of the Landsat mission has been reviewed by Wulder et al.
(2011), Irons, Dwyer, and Barsi (2012), and Loveland and Dwyer (2012). The latter
discussed the strategy of the Landsat programme with its numerous missions as well as
the progress of the Landsat archive of the US Geological Survey (USGS) and specified US
Federal efforts including the development of Landsat 9 and 10 in the future. It is for this
reason that in all remote-sensing journal articles, it is the Landsat sensor that has been used
most frequently for local to regional analyses. The exploitation of medium-resolution
AVHRR time series has also been undertaken by numerous authors, however, not as
frequently as published studies based on medium-resolution MODIS data. MODIS –
contrary to AVHRR –can only cover the past decade, but it offers spatial resolution up
to 250 m. Numerous value-added products are freely made available by the MODIS science
team, and the sensor has been continuously orbiting since 2000 so that data intercalibration
challenges, as occur with the fleet of consecutive AVHRR sensors, are not an issue.
Nagendra et al. (2013) underline that there is a broad assumption in the community of
conservationists that higher spatial resolution is always better, and thus there is a current
preference for ordering the highest available spatial resolution data, such as QuickBird,
IKONOS, GeoEye, or WorldView. However, many of these sensors lack an infrared or
thermal band, which can be very useful for vegetation type discrimination. Furthermore,
the value of high-temporal resolution data (time series) is still underestimated to date.
There is a clear nation-related tendency: although one third of the sensors or sensor-
lines presented are operated by the USA, European and here especially French sensors
have also played a role since the beginning of remote-sensing imaging. Since the year
2000, already 14 of the listed optical/multispectral spaceborne sensors have been launched
and operated by China. India also launched at least seven optical/multispectral sensors
into orbit within only the past 10 years.
The high value of thermal sensors and extractable information such as monotemporal
LST, SST, emissivity, thermal inertia, and thermal anomalies, as well as temperature time
series, is often underestimated (Kuenzer and Dech 2013; Kuenzer et al. 2008). However,
as upper and lower LST/SST values are defining factors for many vegetation and animal
habitats, temperature information should be part of any biodiversity-related analyses.
Figure 4 depicts all current TIR Earth observation sensors in orbit. Furthermore, thermal
data are a valuable tool for fire ecology applications (forest fires, slash and burn, etc.). The
same patterns as with optical/shortwave and mid-infrared sensors can also be observed in
the domain of thermal sensors. Except for AVHRR and Landsat, no other sensor or sensor
line offers the chance of long-term monitoring of thermal patterns covering three to four
decades. While AVHRR offers on average two thermal observations per day, Landsat still
has a 16 day repeat cycle, and especially in cloudy latitudes, completely cloud-free
observations might therefore only be available a few times per year.
Just as for optical and near-/mid-infrared data, the MODIS sensor is also used for
many LST, SST, and hot spot detection studies. Up to four observations per day (two from
6612 C. Kuenzer et al.

the Terra platform, two from the Aqua platform) as well as two suitable bands (31, 32) in
the TIR domain allow the derivation of complex thermal products.
Also for thermal sensors (most of them flown jointly with optical/multispectral
sensors), a clear dominance of USA-operated instruments catches the eye. Europe follows
second, but China’s launch activities in the past decade in the TIR domain are also
outstanding. While AVHRR, MODIS, and Landsat, as well as the Aster satellite (which
has five thermal bands and is thus often employed for emissivity spectra applications), are
mainly used for global, regional and, local thermal analysis, respectively, thermal data
from the geostationary sensor Meteosat enable the acquisition of thermal imagery of a
fixed view (e.g. the European and African part of the northern and southern hemispheres).
Meteosat data are usually analysed by the meteorological science community.
Figure 5 depicts all passive and active microwave sensors that either are still in orbit
or are of broader significance as they were employed for numerous studies. Radar data –
especially if combined with optical data –offer several advantages, such as the extraction
of the three-dimensional structure of habitats (Nagendra et al. 2013). The workhorse for
the radar remote-sensing community has been the European Remote Sensing Satellite
(ERS) sensor, with data available since the early 1990s. ERS data provided by ESA have
been used for numerous global product developments and applications. ESA has granted
easy and low-cost access to ERS-1 and ERS-2 data (data access will be addressed in
subsection 6), and the same applies to ESA’s Envisat ASAR (Advanced Synthetic
Aperture Radar) data, available for the time period 2002 to 2012. Thus, it is especially
these two sensors that are extensively used for environmental analysis. Since 2007,
TerraSAR-X sensor data have also been increasingly used for local studies. Other radar
sensor data, such as from Canada’s Radarsat, Italy’s Cosmo SkyMed, as well as many
other sources listed in Figure 6, are either very costly to acquire or not easily accessible.
Figure 6. Optical/infrared (yellow) and radar (blue) sensors used for land-related applications.
International Journal of Remote Sensing 6613

Contrary to optical/infrared data (Figure 4), which provide an archive going back 40 years
into the past, radar-based time-series analyses can only provide an archive going back to
the early 1990s (ERS). This relatively short time span, as well as additional years with
data gaps, rules out these radar time series as a data source for the observation of climate
change-relevant trends. The low spatial resolution of ERS data –25 km –further
decreases the attractiveness of derived products for stakeholders and decision-makers.
However, for long-term habitat analysis, typical data products such as soil moisture,
biomass, sea ice, or waterbody maps are of great importance for monitoring habitat
boundary shifts over time.
5. Satellite sensor categorization: application domains, spatial and temporal
resolution, and suitability for various biodiversity applications
We designed Figures 6 to 10 to present a comprehensive overview of satellite sensors used
in different spheres (land, water, and air), differentiating sensor types (optical/multispec-
tral, radar (active and passive), thermal), giving each sensor’s spatial and temporal
resolution, and illustrating the major application domains the data are used for. The
figures should be read and understood in the following way.
●The X-axis represents spatial resolution (from better than 1 m to over 50 km).
●The Y-axis represents temporal resolution (revisit time) (from better than daily to
less than monthly).
●Dots represent sensors (with the sensor name next to the dot); lines present the
spatial range, if a sensor offers data at more than one spatial resolution. The dot is
located at the highest resolution the sensor has to offer.
●Yellow dots represent optical/multispectral sensors, red dots represent thermal
sensors, blue dots represent radar sensors (passive radar sensors in italics).
●Coloured ellipses represent preferred applications (see legend).
●Arrows within the individual figures represent gaps (requiring the development of
sensors with higher spatial or temporal resolution).
●Tables at the end of this article list all relevant sensors in detail.
Figure 6 and Table 1 show that a large fleet of optical and radar sensors is available for
land applications (thermal sensors are identified separately in Figure 8). While one group
of optical/multispectral sensors is characterized by low-to-medium spatial resolutions
(1 km to 100 m) but near global coverage at weekly to daily temporal resolutions
(green ellipse), other radar and optical sensors have spatial resolutions between 30 m
and better than 1 m. However, this group of sensors (yellow ellipse) does not allow daily
global coverage, not even monthly or annual global coverage. While sensors such as
AVHRR and MODIS acquire almost all data frames and thus deliver global coverage,
sensors such as Landsat, Aster, TerraSAR-X, Radarsat, and WorldView only acquire data
when tasked. Thus, it is rare to achieve daily, weekly, monthly, or even annual coverage
with data from these sensors. The Landsat science team has published global coverage
mosaics composed of data sets acquired during time spans covering up to five years.
Other global products based on this higher spatial resolution data –for example, the
global urban footprint (Esch et al. 2010) –are also a conglomerate mosaic of scenes from
multiple years.
For land-related biodiversity applications, it is the ‘green ellipse sensors’which are
suitable for mapping national to global scale habitat boundary conditions via physical,
6614 C. Kuenzer et al.

index, and thematic variables. As the sensors allow daily coverage, which is provided for
the past 10 to 40 years, it is possible to undertake time-series analysis and derive means,
deviations, anomalies, variability, and possibly even climate-change-related trends
(Cracknell 1997). For example, Dietz (2013) used 30 years of daily available AVHRR-
derived snow cover information to show that the snow season in Central Asia has shifted
during the last decades: in Central Asia over the past 30 years, it can be observed that the
first snowfall comes about two weeks later, and that there is also a shift in the start of
snowmelt, which takes place about two weeks earlier. Such climate change-related shifts
can only be extracted from data that are available daily.
Sensors in the yellow ellipse allow detailed mapping and multitemporal monitoring at
the local scale. These sensors definitely allow mapping down to the species level if the
species covers a large enough area (a few pixels), and also allow the derivation of texture-
related variables of image segments (objects), which is also important information for
species discrimination. While the highest resolution optical sensors such as GeoEye,
WorldView, QuickBird, or IKONOS theoretically also allow the detection of individual
animals and the counting of populations, this is still a niche application, and obtaining
good data for mammal herds or flocks of migratory birds is rather a product of chance
than something that can be planned.
The black arrow indicates that there is still an opportunity to improve the spatial
resolution of daily available global coverage sensor data.
Figure 7 and Table 2 identify sensors predominantly used for the remote sensing of
oceans, lakes, and other waterbodies. Whereas passive radiometers (in italics) as well as
lowest resolution active radars are mainly used to derive wind speed and direction, wave
height and direction, sea ice, and coastal moisture (right, blue ellipse), medium-resolution
Figure 7. Optical/infrared (yellow) and radar (blue) sensors for ocean/waterbody applications.
International Journal of Remote Sensing 6615

optical sensors mainly contribute to water colour mapping, the derivation of chlorophyll
and sediment load, or the identification of large algae carpets (yellow ellipse). The highest
resolution SAR sensors listed in the lower left turquoise ellipse are suitable for local
derivation of waterbody extent, inundation and flood dynamics, as well as the detection of
objects on water surfaces that might threaten marine or limnic ecosystems (ships, oil
spills, floating garbage), whereas the highest resolution optical sensors are used for
shallow water bathymetry derivation and local water colour analysis.
Thermal sensors for the derivation of LST, SST, and thermal anomalies are identified
in Figure 8 and listed in Table 3. As these are usually mounted on the same platforms as
their multispectral counterparts (e.g. AVHRR, MODIS, Landsat, etc.), they also have
similar revisit times. A large number of sensors exist that acquire data daily with near
global coverage (yellow ellipse). These data at spatial resolutions between 50 km and
1 km enable the derivation of large-area LST and SST patterns (cloud cover permitting),
as well as the extraction of large thermal anomalies, such as caused by extensive forest
fires. Thermal sensors on Landsat, Aster, CBERS, or Sentinel-2 are more suitable for local
studies of temperature patterns or local hot spot extraction. They are also suitable for the
detection of thermal water pollution in rivers and lakes.
As the black arrows in Figure 8 indicate, there is still space for the development of
thermal sensors with a daily revisit time and higher spatial resolution.
Figure 9 and Table 4 identify sensors mostly used to derive atmospheric parameters.
Data on cloud cover, water vapour, ozone, nitrous and sulphur oxides, as well as wind
speed are rarely exploited outside the meteorological community. However, due to daily
global coverage, such data might be interesting when combined with global tracking data
Figure 8. Thermal sensors and the opportunities they offer for biodiversity-related applications.
6616 C. Kuenzer et al.

on migratory birds covering large distances. Combining track data with wind speed data,
or combining mammal or bird track with data reflecting air pollution, might give us new
insight into the capability of animals as bio-sensors.
Digital elevation information is usually considered base data, which does not need to
be updated frequently. In the 1990, it was common to use the GTOPO30 global elevation
model at 1 km resolution as a backdrop for many maps, or as the data set for deriving
topographic variables such as height, slope, aspect, and surface roughness. At the turn of
the century, a DEM based on the Shuttle Radar Topography Mission, SRTM, was
released, and until today researchers and cartographers worldwide have used this 90 m
resolution DEM. An even higher resolution 30 m global coverage DEM was released by
the Aster science team in 2009 (GDEM version 1) and 2011 (GDEM version 2) (Hirano,
Welch, and Lang 2003). Soon, a so-called TanDEM-X, based on data collected from two
TerraSAR-X satellites orbiting in parallel, will be released by the German Aerospace
Center, DLR. This 12 m resolution DEM offers near global coverage and a height
resolution better than 2 m.
Theoretically speaking, all sensors –optical (with varying acquisition angles) and
radar alike –allow the generation of DEMs at local scales (marked in green). Spot 5 HRS,
IKONOS, WorldView, and others presented all allow the derivation of highest resolution
DEMs if stereo data are available. Available products as well as sensors used for product
generation are identified in Figure 10 and Table 5.
All the radar sensors identified in Figure 10 also allow derivation of surface displace-
ment based on differential SAR interferometry, DifInSAR. While in most places of the
world such surface movement might not have a large impact on biodiversity, in some
Figure 9. Atmosphere-related sensors and products of interest for biodiversity applications.
International Journal of Remote Sensing 6617

selected cases such data can be relevant. Sinking surfaces might indicate depleting water
levels and thus less water available for plants. In the coastal zone, surface subsidence can
lead to the submersion of land and changes in the coastal ecosystems. Sudden collapse of
surfaces in karst, underground mining, or coal fire landscapes leads to the destruction of
the ecosystem formerly covering the collapsed area. However, DifInSAR is definitely a
niche-field in the remote sensing of biodiversity.
6. Data access and availability
The number of Earth observation sensors and the amount of archived remote-sensing data
have steadily increased over the past decades. Figures 3 to 10 present the large fleet of
optical, thermal, and radar sensors in orbit, suggesting that there is a huge amount of data
available to the global community, including biologists, conservationists, and
stakeholders.
However, data set access and availability is limited. Even for data sets that are
available for download free of charge, it is often difficult for non-remote-sensing scientists
or stakeholders to access them. Ways of data access are not known to everyone; many data
sets are provided in crude formats; projections need to be adjusted, and many data sets
require thorough pre-processing (sensor calibration, atmospheric correction, geocorrec-
tion/orthocorrection) before the data can be displayed as reflectance (%) or temperature
(°C) values. There is no centralized global portal for downloading remote-sensing data,
and depending on the data amount required (global coverage or extensive stacks of time
series), it is also not possible for everyone to download and process the data (hardware
restrictions, network speed restrictions, no programming skills). A large variety of data
Figure 10. Sensor data from which digital elevation models (DEMs) have been and can be derived.
6618 C. Kuenzer et al.

portals exist (e.g. earthexplorer.usgs.gov, landcover.org, glovis.usgs.org), but many are
neither intuitive nor user friendly. Non-remote-sensing scientists can furthermore often not
judge the quality of the provided data sets, as metadata, quality flag information, or even a
crowd-sourcing-based rating (like for books in Amazon) is missing. Although some
informative websites aim to provide insightful lists of available data download portals,
they are usually not very comprehensive and far from exhaustive (e.g. remote-sensing-
biodiversity.org/remote sensing/resources).
The main political and economic challenge to date is still to provide free and open
access to remotely sensed data and products in order to support the application of such
data for many different disciplines (Turner et al. 2013), including for the biodiversity
and conservation community. The move of the USA towards open access to nearly all
remote-sensing data collected from US platforms, and the future free and open access to
ESA (Sentinels) as well as CNES data, will foster the use of remote sensing for
biodiversity applications. This pathway should be continued in the future. Most
remote-sensing sensors have been designed and even built in national research labora-
tories (in joint partnerships with industry) and have often been financed by taxpayers. A
large part of remote-sensing science globally –no matter whether funding comes
national governments, the World Bank, or other development banks –is also paid
with taxpayers’money. Therefore, the data, derived products, and knowledge gained
therefrom should be made publicly available –in an easily accessible manner. This was
also the call of Turner et al. (2013) in a short correspondence note to Nature titled:
‘Satellites: make data freely available’. However, oftentimes, the budgets are just large
enough to launch a sensor into orbit, but maybe not to fund data storage or dissemina-
tion. Many space agencies and other institutions had the funding to launch a sensor –
but were under-equipped to downlink, store, archive, preserve, and distribute the data –
endeavours, whose costs can outweigh satellite design and launch costs by far. We
simply cannot assume that –because an instrument was operating at a certain time –the
data have automatically been kept and are available. The data may not have been
collected, or it may have been received and not archived or it may have been archived
but the data have become unreadable. However, costs of data storage, archiving,
preservation, and dissemination should be considered, when sensor missions are eval-
uated for funding support.
Furthermore, sensor development has for many decades not exclusively been
driven by the needs of scientific and ‘real-world’decision-making users (although a
‘user requirements document’can surely be presented by each space agency for each
sensor development), and also not by global cooperation only. A strong emphasis has
been national independence and the very reasonable desire to develop indigenous
space technology (‘We are building our own optical/radar/thermal sensor so that we
can be independent of others’or ‘We should advance our space sector and industry’),
national pride (‘There are already tens of optical sensors in orbit, but we will show the
world that we can also do it’), a strong drive for ever improving technology (‘The data
from sensor X has not even been fully exploited yet, however, since we are able to
create a higher resolution satellite, let’slaunchitintoorbit’), and obstacles to
cooperation between space nations (‘We cannot permit researchers from that country
to attend our conferences or visit our labs’).
All these reasons, paired with shifts in the political leadership and priorities of
countries and in decisions about how generous or restricted budgets are for space and
Earth science activities, have led to the imbalance depicted in Figure 11; the former have
been at the cost of, first, sensor and data continuity enabling long time series, and, second,
International Journal of Remote Sensing 6619

free and easy data accessibility. This is reflected, for example, in delays in the launch of
successor missions, which cause unfortunate data gaps that hinder needed research, and in
duplication of efforts while other important fields are neglected.
Of the more than 140 sensors reviewed for this article, only a few sensors exist which
really allow continuous time-series monitoring exceeding 20 years, and only data from
very few sensors are truly freely available (AVHRR, MODIS, MERIS, Landsat, Aster,
ASAR, ATSR, AATSR, SCAT, ASCAT, Meteosat, and some others). Freely available we
understand as being able to ‘open a website and download as much data as one likes, for
as much spatial coverage as there is available’. Some sensor data are available in a ‘nearly
freely available mode’. But to access this data, one has to write (sometimes short,
sometimes lengthy) data proposals, fill out forms, and then be granted access to only a
relatively small amount of data of the desired spatial or temporal coverage –so one cannot
obtain as many data sets one needs to work with (for example, the very limited amount of
TerraSAR-X, TanDEM-X, RapidEye, Radarsat, ALOS, TET, or SPOT data that can be
accessed in practice). The reasons are: while possibly satisfactory for small individual
studies or a master’s thesis, this situation is a nuisance for any remote-sensing scientist
who aims for national, continental, or global coverage for his/her product or study,
requires multiple data sources, and cannot understand why it is necessary to invest
weeks and weeks in writing data proposals. Also, most data are only available at some-
times very high prices per square metre or scene (WorldView, QuickBird, IKONOS,
Cartosat, GeoEye, Formosat, Radarsat). Much data cannot be purchased by the global
community, but is only accessible to selected institutes or cooperation programmes. The
unique and well-received European–Chinese Dragon Programme, coordinated by ESA
and the National Remote Sensing Centre of China, is an example (access to selected
Chinese sensor data sets, such as FY or CBERS, is allowed for European cooperation
partners). Such situations may improve with time, when the benefits of cooperation are
appreciated, and after crucial publications appear.
Figure 11. Foci of sensor development and policy impacting data availability and continuity.
6620 C. Kuenzer et al.

Although access to data from China, India, and other emerging global economies is still
limited and the call for opening up the archives also applies to them, we should also
understand that they do not want to share data with the global community that is not yet
well calibrated, tested, and evaluated. It is illusory to assume that access to novel sensor data
from emerging nations can be granted to the world within months after launch. After all, it
also took the USA and Europe many years to open up their archives to the public. Emerging
economies need to feel confident about the data they acquire before sharing them globally.
Distributing badly calibrated or distorted sensor data would hold the potential of ‘face loss’
or professional criticism, and countries may not be able to have the infrastructure and
administrative capacity in place to deal with global data requests. A good example has been
given by the consortium around Gong et al. (2013) enabling access to global Landsat-
derived land-cover data. Nevertheless, we consider the following appeal (Figure 12) worth
making to data provider and remote-sensing community globally.
The biodiversity community and conservationists –as well as most remote-sensing
scientists –usually only tap the sensor data that are, first, easily accessible and mostly free
of charge, and, second, have been sufficiently applied and described in literature, and,
third, can be handled by a natural scientist without too much background in remote
sensing. Complex data formats or unnecessarily complex demands of data pre-processing
all hamper data spread and use. Currently, most biodiversity and conservation experts,
who are not from the remote-sensing field, use data from sensors such as Landsat,
MODIS, ASAR, or –if funds or support programmes are available –from high-resolution
sensors such as IKONOS, QuickBird, GeoEye, WorldView, etc. Additionally, the con-
venience and influence of Google Earth should not be underestimated. In non-remote-
sensing fields, it is quite common to work with single-band georectified screenshots from
Google Earth, with manually digitized products derived on the basis of Google Earth
products, and even with multitemporal information provided via this platform. Even
within the remote-sensing field, Google Earth is nowadays often used for the collection
of high-resolution sample points for training and indirect validation or the derivation of
Figure 12. Appeal to governments, space agencies, and data providers.
International Journal of Remote Sensing 6621

areal cover densities (Cracknell et al. 2013; Gong et al. 2013). Furthermore, most
biologists, ecologists, and conservationists have a relatively local focus area or habitat
of interest, and local studies based on a few Landsat scenes are often sufficient. Especially
managers, decision-makers, and stakeholders cannot be expected to spend months with
data pre-processing, and several months with data processing to finally obtain a highly
scientific map, which the donors, politicians, and public authorities he/she addresses have
trouble understanding.
An incredible value also lies in more complex time-series data sets, which can
probably only be tapped by remote-sensing experts. Leyequien et al. (2007) underline
the value of long-term data availability from sensors such as AVHRR and MODIS and
believe that the ‘analysis of multiannual land-cover data potentially provides a key to
understanding the influence of climate variability on shaping ecosystems –which form the
overarching hierarchical layer in biodiversity assessment’.
Numerous biologists and conservationists are using species distribution models
(SDMs) to predict the distribution of species as a function of environmental variables –
and here especially climate variables (Cord et al. 2014). However, oftentimes the set of
input variables is limited and information on terrain, land cover, land use, phenologic
metrics, or proximities is not incorporated (Cord et al. 2014). However, with suitable sets
of medium-resolution global data and derived products, it would be possible to create a
global ‘Species Distribution Visualizer and Modeller based on Earth Observation Data’.
Our idea is –in a simplified way –presented in Figure 13. The backbone of such a tool
Figure 13. Our concept of an ideal “Species Distribution Visualizer and Modeller”based on
remote-sensing data. If a near unlimited number of daily globally available variables could be
provided as time series up to 30–40 years back into the past (plus granting future continuity), it
would be possible to model selected past, current, and possibly future habitats of animals or
vegetation species after simply moving a slider to define the crucial variables. Of course, additional
biodiversity data including GPS migration and tracking data, field data (ground truth), and further
ancillary data would increase the value of such a tool.
6622 C. Kuenzer et al.

would be a database that contains all the ‘boundary conditions’for selected species
(describing the temperature, precipitation, and elevation ranges, and the land-cover/land-
use and vegetation types usually associated with the species, and which proximity
parameters such as maximum distances to coasts, lakes, etc. need to be ensured).
Global time series of physical, index, thematic, topographic, and texture variables avail-
able for the past 30+ years and into the future would then allow past, present, and even
future species extent to be modelled when considering future climate change or urban
sprawl scenarios (see Figure 13). Our current judgment is that it will be data from sensors
such as AVHRR and/or MODIS, supported by Landsat and the Sentinels, which will –in
the long run –help to realize this idea.
In combination with a global database of biodiversity information (in situ abundance
and species mapping, track data, etc.), such a tool would be of immeasurable value for a
global overview –despite the fact that highly specific local or regional data will yield
better results for the individual case. The biodiversity community makes very similar
requests to the current remote-sensing community. Of course, the realization of this idea
would require a large collaborative effort of remote-sensing scientists and biologists,
ecologists, and conservationists alike. Technically, a realization is possible, but would
depend on strong, cross-sectorial networks and sufficient funding.
7. Conclusion
Cross-sectorial dialogue between disciplines, such as the remote-sensing community and
the community of biologists, ecologists, and conservationists, is essential for creating an
improved understanding of each discipline’s assets and challenges. Biologists, ecologists,
and conservationists are an important user group for information that can be derived from
remote-sensing data. This group has an urgent need to map and quantify animal and
vegetation biodiversity at local, national, regional, and global scales, considering different
hierarchical levels of biodiversity at the species, habitat, and landscape levels. However,
many knowledge gaps exist in both communities.
Remote-sensing experts might not be familiar with terms such as ‘taxa’,‘inverte-
brates’,or‘conservation gap analyses’. Biodiversity experts might not be aware of the
many satellite sensors that exist and can be tapped for remote-sensing-based support of
biodiversity-related mapping activities. Remote-sensing scientists might not know that
habitat boundaries and land-cover classes do not always –or only rarely –correlate.
Biodiversity experts might need to realize that much more information than just land
cover or a vegetation index can be derived from remote-sensing data. Remote-sensing
experts need to understand that managers and decision-makers from the field of biodi-
versity conservation need to be provided with maps and information products that can be
understood by donors and public authorities, who might not be versed in the natural
sciences. And the biodiversity community needs to provide information on habitat
boundary conditions for animal and vegetation species whose habitats might be suitable
for remote-sensing-based analysis.
To contribute to this dialogue and knowledge sharing, in this article we have provided
an overview outlining how remote sensing has so far contributed to animal, vegetation,
and habitat mapping, given a comprehensive overview of all currently existing optical,
multispectral, thermal, and radar sensors in orbit, as well as the availability of long sensor
lines, have presented details of the spatial and temporal resolutions of these sensors, as
well as their most suitable fields of application with respect to biodiversity mapping, and
elaborated on the complexities of data availability, access, and provision schemes. In
International Journal of Remote Sensing 6623

general, the assessment of species biodiversity for animals is restricted, due to the non-
stationary nature of most animals. To capture large animal herds with highest resolution
satellite imagery is rather a product of chance, and cost efficient, reliable, and repeatable
monitoring of animals themselves can hardly be undertaken. Individual animals are
usually too small to be extracted from highest resolution data. The mapping of stationary
vegetation species, which are often (but not always) defining an animal’s habitat, is easier
and many more studies assess vegetation biodiversity from space than animal biodiversity.
But even for vegetation species diversity assessments, remote sensing has limitations.
Species that are much smaller than the spatial resolution of the sensor or that occur only
scattered and alone-standing as well as species that exhibit similar reflectance character-
istics as other species cannot be extracted or differentiated. For mapping requests at the
local to site level, it has therefore to be taken into account that remote-sensing data might
just provide a general outline, land-cover, and land-use information of the site, but that in
situ mapping will yield cost-effective results at much higher quality.
This overview has furthermore elucidated the fact that it is however often also not
sensor availability or spatial resolution that hinders us from providing the right data to the
biodiversity community. A large variety of high-resolution optical, multispectral, and
radar sensors exist. Currently, far over 100 imaging sensors are in orbit, all with their
own spectral, spatial, and temporal characteristics and with spatial resolutions ranging
from centimetres to kilometres, and with revisit times from two weeks up to several times
per day.
A larger challenge than spatial resolution is temporal resolution –revisit time –and
especially the available monitoring period: meaning, for how many years data of one
sensor are available. For many sensors, data continuity is not given. Time series cannot be
generated, multitemporal analyses only work for selected years, and intercomparability
with products derived from other sensors is difficult. Also, maps generated once, with no
capacity for updating, are problematic. We therefore consider long-term governmental
support and programmes to ensure data continuity and stable sensor lines to be the most
pressing need.
Next to long-term continuity, the major bottleneck is obtaining access to Earth
observation data. It is a great asset that many US and European data providers have
opened up their archives and that nowadays data sets from many important sensors are
available free of charge. However, much data of high value for the biodiversity commu-
nity only come at a high cost or are of very limited availability, as global monitoring is not
ensured. It has been underlined by numerous authors (Nagendra et al. 2013; Leyequien
et al. 2007) that it is especially the available choice of data that will determine the type of
information that can be derived for mapping complex, fine-scale, and structurally variable
habitats to the degree of accuracy sought by stakeholders and decision-makers. Facing the
urgent need to monitor our planet, we would hurt ourselves if not all emphasis is put on
making sensor data rapidly available in a free and effective manner. We therefore under-
line recent calls such as that of Turner et al. (2013) to make satellite data freely available.
Last but not least, we currently see large chances for cooperation between the
biodiversity and remote-sensing communities in the field of direct, or proxy-driven
time-series analysis of habitats and habitat changes. Regional or even global time series
of physical, index, thematic, topographic, and texture variables –if analysed jointly –can
support long-term understanding of climate change- and human-induced habitat shifts and
losses. When combined with knowledge of species boundary conditions, as well as with a
global collection of in situ biodiversity data and tracking data, this approach could tap a
wealth of new information.
6624 C. Kuenzer et al.

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Table 1. “Land”application-related sensors.
Sensor/
instrument Sensor type Spatial resolution
Minimum local
revisit time
(days)
Maximum swath
width (km) Platform Agency Launch date
QuickBird Optical 0.6–2.4 m 2.5–5.6 18 QuickBird Digital Globe 2001
IKONOS Optical 1–4 m 3 11 IKONOS GeoEye 1999
GeoEye-1 Optical 0.41–1.65 m 2.1–8.3 15.2 GeoEye-1 GeoEye 2008
WorldView 2 Optical 0.5–1.8 m 1.1–3.7 16.4 WorldView-2 Digital Globe 2009
HiRI Optical 0.7–2.8 m 2 20–100 Pleiades 1A/1B CNES 2011/2012
HRC (ZY-3A) Optical 2.1 (nadir)–3.5 m
(forward/
backward)
59 (4–5 in special
cases)
52 ZY-3A CRESDA 2012
HRC
(ZY1-02 C)
Optical 2.36 m 26 54 ZY1-02C CRESDA 2011
Naomi Optical 2–8 m 3 (daily revisit
capability
SPOT 6/7)
120 SPOT 6/7 EADS Astrium 2012/2014
SPOT-HRG Optical 2.5 m/5 m/10 m/
20 m
2–3 60 SPOT 5 EADS Astrium 2002
Cartosat-1 Optical 2.5 m 5 30 CARTOSAT 1 ISRO 2005
Cartosat-2 Optical 1 m 4 9.6 CARTOSAT 2/2A/2B ISRO 2007/2008/
2010
RapidEye Optical 5 m 1–5.5 77 RapidEye 1/2/3/4/5 RapidEye 2008
Formosat Optical 2–8 m 1 24 Formosat-2 NSPO 2004
LISS-IV Optical 5.8 m 5 23–70 Resourcesat-1/2 ISRO 2003/2011
PAN/MS
(ZY1-02 C)
Optical 5–10 m 26 60 ZY1-02C CRESDA 2011
MSC (ZY-3A) Optical 6 m 59 (4–5 in special
cases)
51 ZY-3A CRESDA 2012
PANMUX Optical 5–10 m 5 60 CBERS-4 CRESDA/INPE 2014
(Continued)
International Journal of Remote Sensing 6633

Table 1. (Continued ).
Sensor/
instrument Sensor type Spatial resolution
Minimum local
revisit time
(days)
Maximum swath
width (km) Platform Agency Launch date
MSI Optical 10 m/20 m/60 m 10 (5 with two
satellites)
290 Sentinel-2A ESA 2014
HR CCD Optical 20 m 3–26 113 CBERS-1/2/2B CRESDA/INPE 1999/2003/
2007
SPOT-HRVIR Optical 10 m/20 m 2–3 60 SPOT 4 EADS Astrium 1998
SPOT-HRV Optical 10 m/20 m 2–3 60 SPOT 1–3 EADS Astrium 1986/1990/
1993
MUXCAM Optical 20 m 5 120 CBERS-4 CRESDA/INPE 2014
ASTER Optical 15 m/30 m/90 m 16 60 Terra NASA 1999
LISS-III Optical 23,5 m 24 141 Resourcesat-1/2 ISRO 2003/2011
AVNIR-2 Optical 10 m 2 70 ALOS NASDA 2006
ALI Optical 10 m/30 m 16 37 NMP EO-1 NASA 2000
OLI Optical 15 m/30 m 16 185 Landsat 8 USGS/NASA 2013
TM Optical 30 m/120 m 16 185 Landsat 5 USGS/NASA 1984
ETM+ Optical 30 m/60 m 16 185 Landsat 7 USGS/NASA 1999
WVC Optical 30 m 4 700 HJ-1A, HJ-1B CAST 2008
SLIM6 Optical 32 m 5 320/600 UK-DMC-1/UK-DMC-2 BNSC 2003/2009
AWiFS Optical 56 m 5 740 Resourcesat-1/2 ISRO 2003/2011
WFI2 Optical 64 m 5 866 CBERS-4 CRESDA/INPE 2014
IRMSS
(HJ-1 B)
Optical 150–300 m 4 720 HJ-1B VAST 2008
(Continued)
6634 C. Kuenzer et al.

Table 1. (Continued ).
Sensor/
instrument Sensor type Spatial resolution
Minimum local
revisit time
(days)
Maximum swath
width (km) Platform Agency Launch date
MERSI Optical 250 m–1.1 km 1 2800 FY-3A/3B/3C CMA/CNSA 2008/2010/
2013
WFI Optical 258 m 5 890 CBERS-1/2/2B CRESDA/INPE 1999/2003/
2007
MODIS Optical 250–1000 m 1–2 2330 Terra NASA 1999
VIIRS Optical 375–750 m 2 3000 Suomi NPP NASA/NOAA 2011
AATSR Optical 1 km 3 512 Envisat ESA 2002
ATSR-1 Optical 1 km 3 500 ERS-1 ESA 1991
ATSR-2 Optical 1 km 3 512 ERS-2 ESA 1995
AVHRR/1 Optical 1.1 km <1 2600 TIROS-N, NOAA-6/8/10 NOAA 1978–1986
AVHRR/2 Optical 1.1 km <1 3000 NOAA-
7/9/11/12/14
NOAA 1981–1994
AVHRR/3 Optical 1.1 km <1 3000 NOAA-15/16/17/18/19, MetOp
A/B
NOAA,
EUMETSAT
1998–2012
MVISR Optical 1.1 km 3–4 3200 FY-1D CMA/NRSCC 2002
SPOT-
Vegetation
Optical 1 km 1 2250 SPOT 4, SPOT 5 EADS Astrium 1998/2002
Imager Optical 1–4 km Geostationary,
every 30 min
Full Earth disk MTSAT-2 JMA 2006
RadarSat-1 Radar 8–100 m 24 45–500 Radarsat 1 CSA 1995
RadarSat-2 Radar 3–100 m 24 50–500 Radarsat 2 CSA 2007
TerraSAR-X Radar 1–18 m 11 10–100 TerraSAR-X DLR 2007
Tandem-X Radar 1–18 m 11 10–100 Tandem-X DLR 2010
ASAR Radar 30–1000 m 2–75–405 Envisat ESA 2002
SAR 2000 Radar 1–100 m 5 (0.5 in full
constellation
with SkyMed
1–4)
10–200 Cosmo-SkyMed
1/2/3/4
ASI 2007/2007/
2008/2010
(Continued)
International Journal of Remote Sensing 6635

Table 1. (Continued ).
Sensor/
instrument Sensor type Spatial resolution
Minimum local
revisit time
(days)
Maximum swath
width (km) Platform Agency Launch date
ALOS-PALSAR Radar 10–100 m 46 70–350 ALOS NASDA 2006
AMI-SAR Radar 30–1000 m 35 100 ERS-1/2 ESA 1991/1995
RISAT-1 Radar 3–50 m 25 30–240 RISAT-1 ISRO 2012
RISAT-2 Radar 3–8 m 25 10–650 RISAT-2 ISRO 2009
SEASAT Radar 25 m 105 100 SEASAT-1 NASA 1978
RORSAT Radar n.a. n.a. n.a. KOSMOS
102–1932
n.a. 1967–1988
HSI Optical 100 m 4–31 50–500 HJ-1A CRESDA 2008
Hyperion Optical 30 m 16 7.5 NMP EO-1 NASA 2000
HRG, High Resolution Geometrical; IMC, Infrared Multispectral Camera; LISS, Linear Imaging Self Scanner; MERSI, Medium-Resolution Spectral Imager; HRVIR, High-Resolution
Visible; WFI2, Wide-Field Imager Camera 2; HRV, High Resolution Visible; MVISR, Multispectral Visible and Infrared Scan Radiometer; ASTER, Advanced Spaceborne Thermal
Emission and Reflection Radiometer; ASAR, Advanced Synthetic Aperture Radar; ALI, Advanced Land Imager; LISS-IV, Linear Imaging Self Scanner III; OLI, Operational Land
Imager; AVHRR, Advanced Very High-Resolution Radiometer; TM, Thematic Mapper; HRC, High-Resolution Camera; ETM, Enhanced Thematic Mapper; HR Pan, High-Resolution
Panchromatic; AWFI, Advanced Wide-Field Imaging Camera; PAN/MS, Panchromatic/Multispectral; AWiFS, Advanced Wide-Field Imager; MUX, Multispectral; MODIS, Moderate-
Resolution Imaging Spectroradiometer; PANMUX, Panchromatic and Multispectral; AATSR, Advanced Along-Track Scanning Radiometer; HR CCD, High Resolution CCD;
ATSR-1, Along-Track Scanning Radiometer-1; MUXCAM, Multispectral Camera; ATSR-2, Along-Track Scanning Radiometer-2; MVISR, Multispectral Visible and Infrared Scan
Radiometer; VHRR, Very High-Resolution Radiometer; VIIRS, Visible/Infrared Imager Radiometer Suite; WFI, Wide-Field Imager Camera; HSI, Hyperspectral Imager; WVC, Wide-
View CCD Camera; HiRI, High-Resolution Imager; SLIM6, Surrey Linear Imager Multispectral 6 channels.
6636 C. Kuenzer et al.

Table 2. “Ocean and inland waterbodies”application-related sensors.
Sensor/
instrument Sensor type
Spatial
resolution
Minimum local revisit
time (days)
Maximum
swath width
(km) Platform Agency Launch date
Ocean and Inland Waterbodies
MODIS Optical 250–1000 m 1–2 2330 Terra, Aqua NASA 1999/2002
CZI Optical 250 m 7 500 HY-1A/1
B/1 C/1D
CAST 2002/2007/2014/
2014
MERIS Optical 300 m 3 575–1150 Envisat ESA 2001
OLCI Optical 300 m 2.2 (1.9 with two
satellites)
1270 Sentinel-3A ESA 2014
OCM Optical 236 m 2 1420 Oceansat-2 ISRO 2009
GOCI Optical 500 m Geostationary, 3 per
day
1440 COMS-1 Astrium/KARI/
KORDI
2010
OLS Optical 560 m Geostationary,<1 2960 DMSP-F01-19 NOAA 1976–2014
MISR Optical 275 m/1.1 km 2–9 360 Terra NASA 1999
MERSI Optical 250–1.1 km 1 2800 FY-3A/3 B/3 C CMA/CNSA 2008/2010/2013
CZCS Optical 825 m 1 1600 Nimbis-7 NASA 1978
SeaWIFS Optical 1.1 km 1 2800 OrbView-2 NASA 1997
MVISR Optical 1.1 km 3–4 3200 FY-1 C/1D CMA/NRSCC 1999/2002
SLIM-6 Optical 32 m 5 320/600 UK-DMC-1/UK-
DMC-2
BNSC 2003/2009
Radarsat-1 Radar 8–100 m 24 45–500 Radarsat-1 CSA 1995
Radarsat-2 Radar 3–100 m 24 50–500 Radarsat-2 CSA 2007
ASAR Radar 30–1000 m 2–7d 5–405 Envisat ESA 2002
RA-2 Radar 10 km 2–7d 10 Envisat ESA 2002
(Continued)
International Journal of Remote Sensing 6637

Table 2. (Continued ).
Sensor/
instrument Sensor type
Spatial
resolution
Minimum local revisit
time (days)
Maximum
swath width
(km) Platform Agency Launch date
Poseidon-3 Radar 600 m-6 km 10 300 Jason 2 NASA/NOAA/
CNES/
EUMETSAT
2008
ALT Radar 16 km 14 16 HY-2A NSOAS, CAST 2011
TerraSAR-X/
Tandem-X
Radar 1–16 m 11 10–100 TerraSAR-X/
Tandem-X
DLR 2007/2011
MIRAS Passive
microwave
33–50 km 1–2 1000 SMOS ESA 2009
SCAT Radar 50 km 14 1300 HY-2A NSOAS, CAST 2011
AMI/
Scatterometer
Radar 50 km 35 1000 ERS-1/ERS-2 ESA 1991/1995
AMI/SAR Radar 30–1000 m 35 100 ERS-1/ERS-2 ESA 1991/1995
ASCAT Radar 25/50 km 4 500 Metop-A/B EUMETSAT 2006/2012
Seawinds Radar 25 km 1–2 1600 QuikSCAT, ADEOS-
II
NASA 1999/2002
AMSU-A Passive
microwave
50 km 1 2100 NOAA 15/16/17/18,
MetopA/B
NOAA,
EUMETSAT
1998–2012
MWRI Passive
microwave
15–85 km 1 1400 FY-3A/3 B/3 C CMA/CNSA 2008/2010/2013
VIIRS Optical 375 m–1.6 km 1 3000 Suomi NPP NASA/NOAA 2011
AVHRR/1 Optical 1.1 km <1 2600 TIROS-N, NOAA-6/
8/10
NOAA 1978–1986
AVHRR/2 Optical 1.1 km <1 3000 NOAA-7,9,11,12,14 NOAA 1981–1994
(Continued)
6638 C. Kuenzer et al.

Table 2. (Continued ).
Sensor/
instrument Sensor type
Spatial
resolution
Minimum local revisit
time (days)
Maximum
swath width
(km) Platform Agency Launch date
AVHRR/3 Optical 1.1 km <1 3000 NOAA-15/16/17/18/
19, MetOp A/B
NOAA,
EUMETSAT
1998–2012
COCTS Optical 1.1 km 7 1400 HY-1A/1 B/1 C/1D CAST 2002/2007/2014/
2014
TMI Passive
microwave
5 km 0.5–1 790 TRMM JAXA/NASA 1997
AMSR-2 Passive
microwave
5–50 km 1–2 1450 GCOM-W1 JAXA 2008
RISAT-1 Radar 3–50 m 25 30–240 RISAT-1 ISRO 2012
RISAT-2 Radar 3–8 m 25 30–-650 RISAT-2 ISRO 2009
PALSAR-2 Radar 1–100 m 14 25–350 ALOS-2 JAXA 2012
PALSAR Radar 10–100 m 46 70–350 ALOS JAXA 2006
AVHRR, Advanced Very High-Resolution Radiometer; MVISR, Multispectral Visible and Infrared Scan Radiometer; MODIS, Moderate-Resolution Imaging Spectroradiometer;
ASAR, Advanced Synthetic Aperture Radar; MERIS, Medium-Resolution Imaging Spectrometer; RA-2, Radar Altimeter-2; OCM, Ocean Colour Monitor; ALT, Radar Altimeter;
GOCI, Geostationary Ocean Colour Imager; MIRAS, Microwave Imaging Radiometer with Aperture Synthesis; MISR, Multi-Angle Imaging SpectroRadiometer; Ascat, Advanced
Scatterometer; MERSI, Medium Resolution Spectral Imager; AMI, Active Microwave Instrumentation; SeaWiFS, Sea-Viewing Wide Field-of-View Sensor; TMI, TRMM Microwave
Imager; Scat, Scatterometer; AMSR-2, Advanced Microwave Scanning Radiometer-2; AMSU-A, Advanced Microwave Sounding Unit-A; RISAT, Radar Imaging Satellite; MWRI,
Microwave Radiometer Imager; PALSAR, Phased Array type L-band Synthetic Aperture Radar; VIIRS, Visible/Infrared Imager Radiometer Suite; CZI, Coastal Zone Imager; COCTS,
China Ocean Colour and Temperature Scanner; CZCS, Coastal Zone Colour Scanner.
International Journal of Remote Sensing 6639

Table 3. “Thermal”application-related sensors.
Sensor/
instrument
Sensor
type Spatial resolution
Minimum local
revisit
time (days)
Maximum swath
width (km) Platform Agency Launch date
Surface Temperature
ETM+ Optical 60 m 16 185 Landsat 7 USGS/NASA 1999
MSI Optical 60 m 10 (5 with two
satellites)
290 Sentinel-2A ESA 2014
TM Optical 120 m 16 185 Landsat 5 USGS/NASA 1984
ASTER Optical 90 m 16 60 Terra NASA 1999
IRMSS-2 Optical 80 m 26 120 CBERS-4 CRESDA/INPE 2014
TIRS Optical 100 m 16 185 Landsat 8 USGS/NASA 2013
IRMSS Optical 160 m 26 120 CBERS-1/2 CRESDA/INPE 1999/2003
CIRC Optical 200 m 14 128 ALOS-2 JAXA 2014
MERSI Optical 250 m 1 2800 FY-3A/3 B/3 C CMA/CNSA 2008/2010/2013
IRMSS
(HJ-1B)
Optical 300 m 4 720 HJ-1B CRESDA/CAST/
NRSCC
2008
TET-1 Optical 356 m 10 180 TET-1 DLR 2012
NIRST Optical 351 m <1–2 182–1000 Aquarius NASA/CONAE 2011
OLS Optical 560 m Geostationary, <1 2960 DMSP F01-19 NOAA 1976–2014
CZCS Optical 825 m 1 1600 Nimbis-7 NASA 1978
ATSR-1 Optical 1 km 3 500 ERS-1 ESA 1991
ATSR-2 Optical 1 km 3 512 ERS-2 ESA 1995
AATSR Optical 1 km 3 500 Envisat ESA 2002
SLSTR Optical 1 km 1.8 (0.9 with two
satellites)
1420 Sentinel-3A ESA 2014
IIR Optical 1 km 16 64 CALIPSO CNES 2006
MODIS Optical 1 km 1–2 2330 Terra, Aqua NASA 1999/2002
AVHRR/1 Optical 1.1 km <1 2600 TIROS-N,
NOAA-6/8/10
NOAA 1978–1986
AVHRR/2 Optical 1.1 km <1 3000 NOAA-7/9/
11/12/14
NOAA 1981–1994
(Continued)
6640 C. Kuenzer et al.

Table 3. (Continued ).
Sensor/
instrument
Sensor
type Spatial resolution
Minimum local
revisit
time (days)
Maximum swath
width (km) Platform Agency Launch date
AVHRR/3 Optical 1.1 km <1 3000 NOAA15/16/17/18/19,
Metop
A/B
NOAA/
EUMETSAT
1998–2012
MSG-
SEVIRI
Optical 1–3 km Geostationary,
<1
Full Earth disk Meteosat-8/9/19 ESA/EUMETSAT 2002/2005/2012
VIRR Optical 1.1 km 1 2800 FY-3A/3B/3C CMA/CNSA 2008/2010/2013
MVISR Optical 1.1 km 3–4 3200 FY-1C/1D CMA/NRSCC 1999/2002
VIIRS Optical 1.6 km <1 3000 Suomi NPP NASA/NOAA 2011
VIRS Optical 2 km <1 720 TRMM NASA 1997
IVISSR Optical 5 km Geostationary,
<1
Full Earth disk FY-2B/2C/2D/2E/2F/2G NRSCC/CAST/
NSMC-CMA
2000/2004/2006/2008/
2012/2014
MVIRI Optical 5 km Geostationary,
every 30 min
Full Earth disk Meteosat-
1/2/3/4/5/6/7
EUMETSAT, ESA 1977/81/88/89/91/93/97/
98/ 2006/2007
MSU-MR Optical 1 km 37 3000 Meteor 3M, Meteor-M N1 ROSHHYROMET
a.o.
2001/2009
MSU-GS Optical 4 km Geostationary
<1
Full Earth disk Elektro-L N1 ROSHHYROMET
a.o.
2011
MI-COMS Optical 4 km Geostationary,
every 30 min
Full Earth disk COMS KARI/ITT 2010
JAMI Optical 4 km Geostationary, <1 Full Earth disk MTSAT-1R JMA 2005
IMAGER Optical 4 km Geostationary, <1 Full Earth disk MTSAT-2 JMA 2006
VISSR Optical 5 km Geostationary,
every 30 min
Full Earth disk GMS-1/2/3/4/5 JMA 1977/1981/1984/1989/
1995
VHRR Optical 8 km Geostationary,
every 30 min
Full Earth disk Insat-1A/1B/1C/1D/2A/2B/
2C/2D, Kalpana-1,
Insat-3A
ISRO 1982/83/86/90/92/93/97/
99, 2002/2003
GOES
Imager
Optical 1–4 km Geostationary,
every 30 min
Full Earth disk GOES 12/13/14/15 NOAA 2001/2006/2099/2010
(Continued)
International Journal of Remote Sensing 6641

Table 3. (Continued ).
Sensor/
instrument
Sensor
type Spatial resolution
Minimum local
revisit
time (days)
Maximum swath
width (km) Platform Agency Launch date
Tanso-FTS Optical 10.5 km 3 160 GOSAT JAXA 2009
IRAS Optical 14 km 1 952 FY-3A/3B/3C NRSCC/CAST/
NSMC-CMA
2008/2010/2013
CrIS Optical 14 km <1 2200 Suomi NPP NASA/NOAA 2011
CERES Optical 20 km <1 3000 Suomi NPP NASA/NOAA 2011
IASI Optical 25 km 0.5/twice daily 2052 MetOP-A/B EUMETSAT 2006/2012
HIRS/3 Optical 20.3 km <1 2240 NOAA-15/16/17 NOAA 1998/2000/2002
HIRS/4 Optical 20.3 km <1 2240 NOAA 18, MetOp A,
NOAA 19, MetOp B
NOAA/
EUMETSAT
2005/2006/2009/2012
GERB Optical 40 km Geostationary,
every 5 min
Full Earth disk Meteosat-8/9/10 EUMETSAT/ESA/
RAL
2002/2005/2012
ScaRaB Optical 40 km 1 2200 Megha Tropiques CNES 2011
ETM+, Enhanced Thematic Mapper; VIIRS, Visible/Infrared Imager Radiometer Suite; TM, Thematic Mapper; AATSR, Advanced Along-Track Scanning Radiometer; ASTER,
Advanced Spaceborne Thermal Emission and Reflection Radiometer; AVHRR, Advanced Very High-Resolution Radiometer; IRSCAM, Infrared Medium Resolution Camera; MSG-
SEVIRI, Spinning Enhanced Visible and InfraRed Imager; TIRS, Thermal infrared sensor; VIRR, Visible and InfraRed Radiometer; IRMSS, InfraRed Multispectral Sensor; MVISR,
Multispectral Visible and Infrared Scan Radiometer; MODIS, Moderate Resolution Imaging Spectroradiometer; IVISSR, Improved Multispectral Visible and Infrared Scan Radiometer;
MERSI, Medium Resolution Spectral Imager; VHRR, Very High-Resolution Rdaiometer; NIRST, New Infrared Sensor Technology; IASI, Infrared Atmospheric Sounding
Interferometer; VIRS, Visible and Infrared Scanner; MI, Meteorological Imager; OLS, Operational Linescan System; JAMI, Japanese Advanced Meteorological Imager; GERB,
Geostationary Earth Radiation Budget; SLSTR, Sea and Land Surface Temperature Radiometer; VISSR, Visible-Infrared Spin Scan Radiometer.
6642 C. Kuenzer et al.

Table 4. “Atmosphere”application-related sensors.
Sensor/
instrument Sensor type
Spatial
resolution
Minimum local
revisit time
(days)
Maximum
swath width
(km) Platform Agency Launch date
Atmosphere
AVHRR/1 Optical 1.1 km <1 2600 TIROS-N, NOAA-6/
8/10
NOAA 1978–1986
AVHRR/2 Optical 1.1 km <1 3000 NOAA-7/9/11/12/14 NOAA 1981–1994
AVHRR/3 Optical 1.1 km <1 3000 NOAA-15/16/17/18/
19, MetOp A/B
NOAA/EUMETSAT 1998–2012
MVISR Optical 1.1 km 3–4 3200 FY-1D CMA/NRSCC 2002
GOES Sounder Optical 8 km Full Earth disk
in 8 h
Full Earth disk GOES-12/13/14/15 NOAA 2001/2006/
2009/2010
OCM Optical 236 m 2 1440 Oceansat-2 ISRO 2009
VIIRS Optical 375 m–
1.6 km
<1 3000 Suomi NPP NASA/NOAA 2011
OMPS Optical 50 km <1 250–2800 Suomi NPP NASA/NOAA 2011
MVIRI Optical 2.5 km/5 km Full Earth disk
every 30 min
Full Earth disk Meteosat-7 ESA/EUMETSAT 1997
HIRS/3 Optical 20.3 km <1 2240 NOAA-15/16/17 NOAA 1998/2000/2002
HIRS/4 Optical 20.3 km <1 2240 NOAA-18, Metop-A,
NOAA-19
NOAA 2005/2006/2009
MODIS Optical 250 m–1km 1–2 2330 Terra, Aqua NASA 1999/2002
OLS Optical 560 m Geostationary,
<1
2960 DMSP F01-19 NOAA 1976–2014
MSG-SEVIRI Optical 1–3 km Every 15 min Full Earth disk Meteosat-8/9/10 ESA/EUMETSAT 2002/2005/2012
GOES Imager Optical 1–4 km Every 30 min Full Earth disk GOES-12/13/14/15 NOAA 2001/2006/
2009/2010
(Continued)
International Journal of Remote Sensing 6643

Table 4. (Continued).
Sensor/
instrument Sensor type
Spatial
resolution
Minimum local
revisit time
(days)
Maximum
swath width
(km) Platform Agency Launch date
VISSR Optical 1.25–5 km Geostationary,
every 30 min
Full Earth disk GMS-1/2/3/4/5 JMA 1977/1981/
1984/1989/
1995
IRAS Optical 14 km 1 952 FY-3A/3B/3C NRSCC, CAST, NSMC-CMA 2008/2010/2013
MI-COMS Optical 4 km Every 30 min Full Earth disk COMS-1 Kari/ITT 2010
IVISSR Optical 5 km <1 Full Earth disk FY-2D/2E/2F NRSCC/CAST/NSMC-CMA 2006/2008/2012
MISR Optical 275 m/
1.1 km
2–9 360 Terra NASA 1999
IMAGER Optical 1–4 km <1 Full Earth disk MTSAT-2 JMA 2006
PR Radar 4.3 km Intertropical
coverage
one to two
times/day
215 TRMM JAXA/NASA 1997
TMI Passive microwave 5 km Intertropical
coverage
0.5–1
790 TRMM JAXA/NASA 1997
AMSU-A Passive microwave 50 km 0.5–1 2100 NOAA-15/16/17,
Metop,A/B
NOAA/EUMETSAT 1998/2000/
2002/2005/
2006/2012
CPR Radar 500 m 16 2 CloudSat NASA 2006
VHRR Optical 2–8 km Every 30 min Full Earth disk Kaplana-1, Insat-3A ISRO 2002/2003
MWRI Passive microwave 15–85 km 1 1400 FY-3A/3 B CMA/CNSA 2008/2010
ATMS Passive microwave 15–75 km 1 2300 Suomi NPP NASA/NOAA 2011
MWR Passive microwave 54 km 7 380 Aquarius/SAC-D NASA/CONAE 2011
(Continued)
6644 C. Kuenzer et al.

Table 4. (Continued).
Sensor/
instrument Sensor type
Spatial
resolution
Minimum local
revisit time
(days)
Maximum
swath width
(km) Platform Agency Launch date
AMSR-2 Passive microwave 5–50 km 1–2 1450 GCOM-W1 JAXA 2012
AIRS Optical 13.5 km 0.5/twice daily 1800 Aqua NASA 2002
IASI Optical 25 km 0.5/twice daily 2052 Metop-A/B EUMETSAT 2006/2012
GOME-2 Optical 40 km 1 120–1920 Metop-A/B EUMETSAT 2006/2012
AVHRR, Advanced Very High Resolution Radiometer; IVISSR, Improved Multispectral Visible and Infrared Scan Radiometer; MODIS, Moderate-Resolution Imaging Spectroradiometer;
MISR, Multi-Angle Imaging SpectroRadiometer; MVISR, Multispectral Visible and Infrared Scan Radiometer; PR, Precipitation Radar; OCM, Ocean Colour Monitor; TMI, TRMM
Microwave Imager; VIIRS, Visible/Infrared Imager Radiometer Suite; AMSU-A, Advanced Microwave Sounding Unit-A; OMPS, Ozone Mapping and Profiler Suite; ATMS, Advanced
Technology Mircowave Sounder; MVIRI, Meteosat Visible and Infrared Imager; MWR, Microwave Radiometer; HIRS 3/4, High Resolution Infrared Sounder ¾; AMSR-2, Advanced
Microwave Scanning Radiometer −2; MSG-SEVIRI, Meteosat Second Generation –Spinning Enhanced Visible and Infrared Imager; AIRS, Atmospheric Infrared Sounder; IRAS, Infrared
Atmospheric Sounder; CrIS, Cross-track Infrared Sounder; MI COMS, Meteorological Imager; GOME-2, Global Ozone Monitoring Experiment; MHS, Microwave Humidity Sounder;
CPR, Cloud Profiling Radar.
International Journal of Remote Sensing 6645

Table 5. “Topography”-related sensors.
Sensor name Sensor type
Spatial
resolution
Minimum local revisit time
(days)
Maximum swath
width (km) Platform Agency Launch date
Topography
SRTM Radar 90 m - Global coverage Space Shuttle NASA(NGA 2001
TerraSAR-X/
TanDEM-X
Radar 1–16 m
(DEM
12 m)
11 10–100 TerraSAR-X/
TanDem-X
DLR 2007/2011
Radarsat-1 Radar 8–100 m 24 20–500 Radarsat-1 CSA 1995
Radarsat-2 Radar 3–100 m 24 20–500 Radarsat-2 CSA 2007
WorldView-2 Optical 0.5–1.8 m 2.2–7.2d 16.4 WorldView-2 Digital Globe 2009
ASTER Optical 30 m 16 60 Terra NASA 1999
SPOT-HRS Optical 5–10 m 4–6 120 SPOT 5 EADS Astrium 2002
IKONOS Optical 1–4 m 6 11 IKONOS GeoEye 1999
HRC (ZY-3) Optical 3.5 m 52 ZY-3A CRESDA 2012
PALSAR-2 Radar 1–100 m 14 25–350 ALOS-2 JAXA 2014
PALSAR Radar 30 km/
100 km
46 70–350 ALOS JAXA 2006
ASAR Radar 30–1000 m 2–75–405 Envisat ESA 2002
PRISM Optical 2.5 m 46/96 for stereoscopy 35–70 ALOS NASDA 2006
CARTOSAT-
PAN
Optical 1 m 4 10 CartoSat-2/2A/
2B
ISRO 2007/2008/
2010
SAR-C Radar 5–80 m 12 80–400 Sentinel-1A ESA 2014
SAR-S Radar 5–25 m 4 40–100 HJ-1C CRESDA 2012
GLAS Lidar 66 m 91 Airborne ICESAT NASA 2003
AMI-SAR Radar 30–1000 m 35 100 ERS-1/2 ESA 1991/1995
JERS-1 SAR Radar 18 m 44 75 JERS-1 JAXA 1992
SAR-C Radar 3–50 m 7–30 30–240 RISAT-1 ISRO 2012
SAR-X Radar 3–8m 5 10–650 RISAT-2 ISRO 2009
SEASAT Radar 25 m 105 100 SEASAT-1 NASA 1978
RORSAT Radar n.a. n.a. n.a. KOSMOS
102–1932
n.a. 1967–1988
(Continued)
6646 C. Kuenzer et al.

Table 5. (Continued ).
Sensor name Sensor type
Spatial
resolution
Minimum local revisit time
(days)
Maximum swath
width (km) Platform Agency Launch date
Poseidon 3 Radar 30 km 10 days–1 month 30–100 Jason 2 NASA/NOAA/
CNES/
EUMETSAT
2008
SIRAL Radar 250 m 369/30 15 Cryosat-2 ESA 2008
SRAL Radar 300 m 10–30d 20 Sentinel-3A ESA 2015
ALT Radar 16 km 14 16 HY-2A NSOAS, CAST 2011
RA Radar 16–20 km 35 16–20 ERS-1/2 ESA 1991/1995
RA-2 Radar 10 km 2–7 10 Envisat ESA 2002
SAR 2000 Radar 8 m Global coverage in 14; (0.5 in
full constellation)
10–200 Cosmo-
SkyMed
1–4
ASI 2007/2008/
2010
GTOPO 30 derived raster and
vector sources
1km ———USGS 1996
SRTM, Shuttle Radar Topography Mapper; AMI-SAR, Active Microwave Instrumentation –Image Mode; ASTER, Advanced Spaceborne Thermal Emission and Reflection
Radiometer; PR, Precipitation Radar; SPOT-HRS, SPOT- High-Resolution Stereoscope; SIRAL, SAR Interferometric Radar Altimeter; PALSAR, Phased Array type L-band
Synthetic Aperture Radar; RA-2, Radar Altimeter-2; PRISM, Panchromatic Remote-Sensing Instrument for Stereo Mapping; PRSIM, Panchromatic Remote-Sensing Instrument for
Stereo Mapping; Lidar, Light Detection and Ranging; AMI-SAR, Active Microwave Instrumentation –Image Mode.
International Journal of Remote Sensing 6647