Generally speaking, high spatial resolution, also called fine spatial resolution, is less than 10 m, and ranges from 0.5–10 m in the commercial domain for environmental research. IKONOS, QuickBird, OrbView-3 and SPOT-5 (Satellite Pour l’Observation de la Terre-5) are the commonly used systems (see [15] for the high-spatial resolution optical sensors). The benefit of high spatial resolution imagery is that it greatly increases the accuracy of identification and characterization of small objects at spatial scales which were previously only available from airborne platforms [3,14]. For example, Gillespie et al. provided several examples of accurately identifying plant species based on the high spatial resolution imagery [14]. Turner et al. have pointed out it is applicable and feasible to directly identify certain species and species assemblages at the scale of high spatial resolution [3]. In addition, high spatial resolution imagery can be employed to assess the accuracy of remote sensing precuts derived from moderate or coarse spatial resolution imagery. For instance, Wabnitz et al. assessed the accuracy of Landsat-based large-scale seagrass mapping against patterns detectable with very high-resolution IKONOS images [16]. However, the high spatial resolution imagery is still expensive to acquire from commercial satellites, at the price of approximately 3,000–5,000 US$ for 10 km² [14], although it has tended to decrease with the emergence of more sensors and the upcoming competition. Moreover, data coverage and security restrictions are still a significant hurdle before easily accessing high spatial resolution satellite data [17].

Due to the large amount of high spatial resolution sensors, the commonly-used IKONOS imagery was selected to display their typical applications in 2008 and 2009. First of all land cover, as the representative of basic landscape information, can be extracted quickly and reliably based on the high spatial resolution data. For example, the object-oriented classification of IKONOS-2 satellite images was utilized to explicitly recognize the transitional areas between tree crowns and tree shades (tree shadows), and then for the quantification of canopy cover [18]. Further, IKONOS imagery can be used to quantify and evaluate the spatial structure of critical habitats and how it affects endemic species, which is essential baseline information for biodiversity monitoring and management (e.g., [19]). In the context of marine applications, areas of coastline, with their fertile soil and unique flora and fauna which need to be highly protected, were planned for in a sustainable way through mapping the changes in land use of the area based on IKONOS imagery in the Cesme Peninsula (Turkey) [20]. Improving the science and conservation of coral reef ecosystems, such as the significant fish-habitat relationship, is often the objective of marine ecology, and also is an important facet in the application of IKONOS imagery [21]. Harborne et al. examined intra-habitat variability in coral-reef fish by mapping habitat heterogeneity, which is always considered a surrogate of biodiversity, in order to aid the design of networks of marine reserves [22]. Although high spatial resolution satellite remote sensing has been hailed as a very useful source of data, Nagendra and Rocchini pointed out that high spatial resolution remotely sensed data are one of the most potentially powerfully yet underutilized sources for tropical research on biodiversity, and stimulating discussion on the applications should be the first step in promoting a more extensive use of such data [17].

Hyperspectral data have the ability to collect ample spectral information across a continuous spectrum generally with 100 or more contiguous spectral bands. It is different from multispectral sensors which detect relatively few discrete bands [17]. Hundreds of spectral bands with 10–20 nm spectral bandwidths offer new possibilities to detect subtle differences between objects of interest. The best example is to discriminate fine-scale, species-specific land cover [3], such as vegetation categories or soil types [11], which make remarkable contribution to the study regarding biodiversity patterns. Moreover, Nagendra and Rocchini summarized that hyperspectral data have been successfully applied in recording information regarding critical plant properties (e.g., leaf pigment, water content and chemical composition), discriminating tree species in landscapes, and fairly accurate identification between different species [17]. What is more, spectral signatures acquired from atmosphere-corrected hyperspectral data can be directly compared to the existing spectral library (e.g., the Jet Propulsion Laboratory Spectral Library) in order to rapidly identify ground information useful in land-cover classification, characterization and change detection [3]. Similar to the situation with high spatial resolution imagery, the hyperspectral imagery encountered the same underutilization, and a high cost which may put it out of research for many ecologists [14], especially those in developing countries who eagerly need the data [17].

Shippert listed the existing hyperspectral sensors acquiring imagery from space, including the Hyperion sensor on NASA’s EO-1 (National Aeronautics and Space Administration’s Earth Observing-1), the CHRIS (Compact High Resolution Imaging Spectrometer) sensor on the European Space Agency’s PROBA (PRoject for On-Board Autonomy) satellite, and the FTHSI (Fourier Transform Hyperspectral Imager) sensor on the U.S. Air Force Research Lab’s MightySat II satellite [23]. Of these sensors, the first-civilian and most commonly used data are derived from the Hyperion, which is operated by the EROS (Earth Resources Observation and Science) at a relatively low cost to the general public [23]. The EO-1, on which the Hyperion sensor is, was launched in November, 2000 as a one-year technology validation and demonstration in support of the LDCM (Landsat Data Continuity Mission; [24]). The Hyperion sensor, an upgrade from the LEWIS Hyperspectral Imaging Instrument (HSI), records visible light and other reflected electromagnetic energy in 220 spectral bands from 0.4 to 2.5 μm at a 30 m resolution [25]. Table 1 lists the Hyperion characteristics.

The recent applications of Hyperion hyperspectral imagery mainly include ecology and biodiversity in forest, grassland [27], agriculture [28], and vegetation [29], fragmented ecosystem and ecosystem succession, coastal environment [30], etc. For example, vegetation types and densities were classified in support of the wildfire management, that is, fire propagation simulation models and fire risk assessment were based on a Hyperion classification map with 93% accuracy [31]. Foster et al. proposed hyperspectral imagery from EO-1 Hyperion is capable of mapping low-lying woody lianas, which are critical to tropical forest dynamics because of their strong influence on forest regeneration, disturbance ecology, and biodiversity [32]. Pignatti et al. analyzed the capability of Hyperion data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000), and the results showed the potential of the imagery up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem [33]. Besides the application of land cover classification, the relationships between LAI and spectral reflectance were studied by [34] using narrowband (EO-1 Hyperion) and broadband (Landsat ETM+ [Enhanced Thematic Mapper Plus]) remotely sensed data in Sulawesi (Indonesia). Nagendra and Rocchini preliminarily discussed the strengths and drawbacks of hyperspatial (i.e., high spatial resolution) and hyperspectral data [17]. Hyperspatial data was considered to be best suited for facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity. However, conversely, hyperspectral data appear capable of identifying features with significantly increasing accuracy. Therefore, the integration of Hyperion and IKONOS imagery was proposed to differentiate the subtle spectral differences of land-use/land-cover types on household farms in the Northern Ecuadorian Amazon with an emphasis on secondary and successional forests, and the promising results supported the integrated use of hyperspectral and hyperspatial data [35].

Thermal remote sensing detects the energy emitted from the Earth’s surface in the thermal infrared (TIR, 3 μm to 15 μm), which can be radiated by all bodies above absolute zero. Theoretically, TIR sensors measure the surface temperature and thermal properties of targets [36], which are essential for developing a better understanding, and more robust models, of land-surface energy balance interactions [37]. Moreover, TIR remote sensing is capable of uncovering the principles of ecological patterns of structure and function due to the development of ecological thermodynamics [37]. A thermal grey level image is generated based on relative radiant temperatures (a thermogram), and light tones correspond to warmer temperatures and dark tones to cooler temperatures [36]. TIR remote sensing plays an important role in observation of Earth surface characteristics, and is very useful for research regarding analysis of biophysical Earth processes, in particular landscape characterization and measurement of land surface processes [37]. The well-known sensors with TIR bands include the Advanced Very High Resolution Radiometer (AVHRR) onboard the Polar Orbiting Environmental Satellites (POES), the Landsat Thematic Mapper (TM) and ETM+, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra Earth observing satellite platform, etc. [37].

TIR remote sensing has been developing since 1880, and has proven to be an integral part of understanding landscape characteristics [37], although it is relatively rarely used by ecologists [2]. However, interests have increasingly focused on the use of TIR remote sensing in EBC. For instance, biophysical variables were derived from thermal and multispectral remote sensing data and coupled with a Soil-Vegetation-Atmosphere-Transfer (SVAT) model [38]. Duro et al. pointed out the TIR region is an important source of information to study environmental disturbance because of the negative relationship between vegetation density and land surface temperatures [13]. Mildrexler et al. proposed a disturbance detection index using Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day Enhanced Vegetation Index (EVI) and 8-day Land Surface Temperature (LST), and it was successfully applied to detect continental-scale disturbance events such as wildfire, irrigated vegetation, precipitation variability, and the incremental process of recovery of disturbed landscapes [39]. Another good use of TIR remote sensing data is to measure evapotranspiration, evaporation, and soil moisture. For example, Crow and Zhan analyzed the continental-scale performance of surface soil moisture retrieval algorithms depending on satellite passive microwave, scatterometer, and thermal remote sensing observations [40]. Petropoulos et al. reviewed Ts/VI (surface temperature/vegetation index) remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture, and suggested one piece of future work should evaluate the accuracy of these methods under diverse environments [41].

A small satellite generally refers to its mass in the range of 1–500 kg and satellite constellation is defined as groups of satellites working in concert [42]. Since 1997, six symposia on small satellites have been organized by the International Academy of Astronautics (IAA) in Berlin, Germany. Kramer and Cracknell reviewed the development of small satellites in remote sensing [43]. With the launch of DMC (Disaster Monitoring Constellation, Table 2), the concept of the Earth-observation constellation of low-cost small satellites has been put into action. It is capable of obtaining multispectral images of any part of the world every day [24]. The DMC was initially proposed in 1996 and led by SSTL (Surrey Satellite Technology Limited), which is a world leader in high performance small satellites [42]. Wang et al. briefly introduced the characteristics of DMC imagery and its potential applications in environmental science [44]. Also, HJ-1 (Huan Jing-1, also called Environment-1, operated by China) is another outstanding constellation system. It is designed mainly for environmental protection and disaster monitoring, and the payload instruments onboard consist of a CCD (Charge-Coupled Device) camera, an infrared camera, a hyperspectral imager and an S-band SAR (Synthetic Aperture Radar, [45]).

Besides the benefits in cost and operation, the constellation of small satellites has two obvious advantages in applications, i.e., global surveying and increased revisit frequency [24]. It is relatively easy to obtain observation data across the world in a short time for constellation systems. The increased revisit frequency can not only satisfy the application of detecting rapid surface changes such as crop-growth monitoring and detecting intraseasonal ecosystem disturbance, but also promotes acquisition of good-quality imagery with limited cloud-contamination. Wang et al. discussed the issue of clouds and cloud shadows in the environmental remote sensing community, and advised looking for good solutions to the unavoidable problem in optical remote sensing [44]. The development of a constellation of low-cost small satellites is believed to make contributions to this issue at the sensor level. Only a few studies of EBC applied the imagery of small-satellite constellation, though Aplin has predicted the bright future of this kind of satellite imagery [11]. Qian et al. demonstrated that simulated HJ-1B satellite data performed better on smaller and cooler fires than MODIS or AVHRR data, and believe it will offer a great opportunity for fire detection [46]. The FRIGBP has started testing the applicability of DMC imagery for wildlife large-area habitat mapping in west-central Alberta (Canada) [44].

Light Detection and Ranging (LIDAR), also called Laser altimetry, is an active remote sensing technology that utilizes a laser to illuminate a target object and a photodiode to register the backscatter radiation [47,48]. The current LIDAR remote sensing can be categorized into two general groups: non-scanning LIDARs, and scanning LIDARs. The non-scanning LIDARs record pulsed ranging that measures the travel time between the transmitted and received signal backscattered from the object surface, and the scanning LIDARs register continuous wave ranging that is produced in a transmitted sinusoidal signal and carried out by modulating the laser light intensity [49]. According to the characteristics of LIDAR technology, it has been proven to provide horizontal and vertical information at high spatial resolutions and vertical accuracies [47]. For example, Miller stated that 5–30 cm range is the typical accuracies for LIDAR-derived vertical information [50]. Airborne LIDAR remote sensing systems such as LVIS (Laser Vegetation Imaging Sensor) have been used for bathymetry, forestry, and other applications [48,51,52]. For instance, Turner et al. briefly discussed the airborne LIDAR remote sensing for biodiversity science and conservation [3]; Lim et al. reviewed the application within forest structure (vertical information) [47], e.g., canopy and tree height, biomass, and volume; Goetz et al. claimed species distribution models have been improved through airborne LIDAR quantifying vegetation structure within a landscape [53]. LIDAR was underlined by [11] as one of the strong interests of the remote sensing community in ecology. Besides airborne LIDAR with the limitations of large data volumes, footprint size and high costs [54], spaceborne LIDAR has come through with the launch of the ICESat/GLAS (Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System), which is the first laser-ranging instrument for continuous global observations. The applications of the GLAS data in EBC, which are seldom reviewed, will be discussed below.

LIDAR focused on the forest vertical structure, especially forest canopy height and aboveground biomass estimation. Lefsky et al. estimated forest canopy height with an RMSE of 5 m (83% of variance explained) in varied forest types including evergreen needle leaf, deciduous broadleaf and mixed stands in temperate North America, and tropical evergreen broadleaf forests in Brazil [55]. Mangrove forests are considered as one of the most biodiverse and productive wetlands on Earth, and the mangrove height and aboveground biomass were measured and mapped based on SRTM (Shuttle Radar Topography Mission) elevation data, GLAS waveforms and field data [56]. Pflugmacher et al. compared GLAS height and biomass estimates with reference data from the Forest Inventory and Analysis (FIA) program of the U.S. Forest Service at a regional scale, and promising results were obtained [57]. Helmer et al. proposed the combination of Landsat time series and the GLAS to estimate the biomass accumulation of the Amazonian secondary forest, and the estimation agreed well with ground-based studies [58]. Duncanson et al. tested simulated GLAS data under tough conditions, e.g., areas with dense forests, high relief, or heterogeneous vegetation cover, and demonstrated the capability of GLAS waveforms as supplemental model input to improve estimates of canopy height [54].

Regardless of the variety of uses for remote sensing images, the first goal is to extract landscape information from the satellite images [59]. Image classification has been recognized as the most effective means to do so since mid-1800s, when humans first identified different types of land-use and land-cover in aerial photography [60]. Jensen discussed in detail the fundamental elements of image interpretation including grayscale tone, color, height and depth, size, shape, texture, pattern shadow, site, association and arrangement [1]. With the widespread of digital computers, special-purpose image classification algorithms have been used to extract land-use/land-cover and biophysical information directly from remotely sensed data [60]. In order to derive more accurate classifications, new approaches have increasingly emerged, and such approaches have made significant contributions to the science of EBC, examples of which would be support vector machines (SVMs), one-class classifier, object-oriented classification, and fuzzy classifications.

SVMs consist of many theoretically superior machine learning algorithms, and make use of optimization algorithms to find an optimal separating hyperplane (OSH) between classes based on training samples [61]. The hyperplane is called support vectors [62]. Foody and Mathur have demonstrated the robustness of SVMs through comparison with artificial neural networks (ANNs) and machine learning decision trees, especially for small training sets [63]. The SVM was selected by [64] to help understand the relationships among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. Ichii et al. applied SVM-based evapotranspiration estimation to refine rooting depths for ecosystem modeling in California [65].

Commonly, only one specific class is the foci of research interest [66]. Due to the fact that conventional multiclass classifier may be suboptimal in terms of the classification accuracy of the class of interest, a one-class-classification approach was suggested to focus tightly on the specific class. For example, Sanchez-Hernandez et al. applied the one-class classifier based on the support-vector data description (SVDD) to map fenland habitat in support of conservation activities [66]. An accuracy of 97.5% and 93.6% from the user’s and producer’s perspectives was obtained, and it performed much better than conventional maximum-likelihood classification. In the same year, the classifier was used to map and monitor coastal saltmarsh habitats of high conservation value under the European Union’s Habitats Directive [67].

With the wide availability of high-spatial resolution satellite data, pixel-based classification algorithms seem not to be ideal to extract information desired from the data exhibiting high frequency components with high contrast and horizontal layover of objects [60]. Therefore, object-oriented classification algorithms have been developed to meet this need, and have established improved classification accuracy when compared with the traditional methods [5,60]. The basic processing units of object-oriented classification are segments, so-called image objects that represent a relatively homogenous unit on the ground [68]. Then classification was performed on image objects, and not on pixels. One of the most popular algorithms was developed to the software of Definiens’ Developer (also called eCognition; [69]). Advantages of object-oriented classification are to make full use of meaningful statistic and texture calculation, uncorrelated shape information (e.g., length-to-width ratio, direction and area of an object, etc.) and topological features (neighbor, super-object, etc.), and the close relation between real-world objects and image objects [68]. Jensen et al. pointed out that the advantages include rapid process, and scale flexibility in which users can select different scale levels according to their images [60]. A variety of studies applied object-oriented classification into the science of EBC. For example, Collingwood et al. classified agricultural areas in Alberta grizzly bear habitats based on one of the object-oriented classification techniques – sequential supervised masking (SSM), in order to help ecologists understand the relationship between crop types and grizzly bear presence [5]. Wang et al. proposed that object-oriented classification may traverse the possible Landsat-gap on applications such as landscape pattern analysis or ecological models [44].

Traditionally, land cover information is assigned into a finite number of non-overlapping classes, and the classes are mutually exclusive [70], which is described as the one-entity-one-class method [71]. However, pixels may contain more than one class because of the heterogeneity and the limitation in spatial resolution of remotely sensed data, especially in medium and coarse spatial resolution imagery [70]. And the presence of mixed pixels could not be removed totally no matter how accurately map classes are defined [71]. Therefore, fuzzy classification, also called subpixel classification, arose in the context of the uncertainty associated to class mixtures. In fuzzy systems, every pixel is supposed to consist of multiple and partial memberships of all candidate classes [70]. Spectral mixture analysis (SMA) is one of the most popular and most effective approaches for dealing with mixed pixel problem [60,70]. For example, Lu and Weng used linear SMA to explore the relationship between urban thermal features and biophysical descriptors based on ASTER images [72]. Plourde et al. estimated species abundance in a northern temperate forest using SMA for better understanding changes in biodiversity, habitat quality, climate, and nutrient cycling [73].

Vegetation indices (VIs) are ‘dimensionless, radiometric relative abundance and activity of green vegetation, including LAI, percentage green cover, chlorophyll content, green biomass, and absorbed photosynthetically active radiation (APAR)’ [1]. Jensen summarized VIs benefit in maximizing sensitivity to biophysical parameters, normalizing or modeling external effects, normalizing internal effects, and assisting validation effort and quality control [1]. Additionally, VIs are simple to understand and implement, easy to quickly calculate, and useful to track temporal characteristics. To date, hundreds of VIs have been used in all kinds of applications of remote sensing. VIs can be roughly categorized into two groups, i.e., biophysical indices and biochemical indices [74]. Biophysical indices represent those designed to link with vegetation biophysical characteristics including structure and condition. They can be grouped into simple ratio-based indices (e.g., Simple Ratio [SR]; [75]), soil-line-related indices (e.g., Soil Adjusted Vegetation Index [SAVI]; [76]), and chlorophyll-corrected indices (Ratio TCARI/OSAVI [Transformed Absorption in Reflectance Index/Optimized Soil Adjusted Vegetation Index]; [77]). Biochemical indices are those mainly employed to estimate vegetation biochemical properties such as Cellulose Absorption Index (CAI) and Lignin-Cellulose Absorption Index (LCAI) [78].

No doubt that NDVI is the most well-known vegetation index. Its use in EBC has been considerably reviewed by [2,13,14], etc. Nonetheless, other indices that are commonly used in the relevant applications are not taken seriously enough in the aforementioned review papers. For example, SR was validated to perform best in early and intermediate forest stages for the assessment of LAI based on ASTER data in East African rainforest ecosystems [79]. The modified soil adjusted vegetation index (MSAVI) was selected as the optimal vegetation index in a linear mixture model to map canopy fractional cover in tropical forests in the Amazonian state of Mato Grosso (Brazil) [80]. Haboudane et al. demonstrated that the existing VIs (e.g., NDVI, SAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI], etc.) were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels, whereas a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2) are proved to be the best predictors of green LAI [77]. Additionally, other recently proposed VIs such as WDRVI (Wide Dynamic Range Vegetation Index; [81]), L-ATSAVI (Litter-corrected Adjusted Transformed Soil Adjusted Vegetation Index; [74]), and VIUPD (Vegetation Index based on a Universal Pattern Decomposition; [82]). However, traditional measures such as the coefficient of determination and root mean square based on regression statistics, are not capable of evaluating the performance of VIs on the estimation of biophysical parameters because the sensitivity of a VI may change substantially with vegetation density [83]. Therefore, a statistical sensitivity function was developed to summarize the overall relationship between VIs and biophysical parameters instead of a constant [83].

Various process-oriented models are developed to characterize Earth environments because traditional methods based on simple statistical relationships are often sensor-dependent, and site-specific [84,85]. These models represent the in-depth understanding of physical processes deriving the Earth system, and are unquestionably useful in Earth observations in support of EBC [85]. Generally speaking, models can be run under two modes, namely inverse mode and forward mode. An inverse mode applies outputs to retrieve inputs that cause them, while a forward mode applies inputs to obtain resulting outputs. For example, Boyd and Danson suggested that a remote sensing model can be used to simulate the reflectance of forest canopies [84]. The forward mode treats data on the forest canopy variables as the inputs and the spectral signature as the output but the inverse mode is converse process, i.e., the spectral signature is the input and estimates of the forest biophysical variables are the outputs. Obviously, the inverse model is more frequently used in remote sensing. The core of inverse model is inversion algorithms, which mostly follow the physical laws and establish cause-and-effect relationships [85]. In order to understand remote sensing signals and develop practical inversion algorithms to estimate land surface variables, physically-based models are advised to discuss the following three areas [86]: atmosphere (atmospheric radiative transfer modeling), land surface (surface radiation modeling), and sensor (sensor modeling). Liang grouped inversion algorithms into four categories: model simulation and statistical analysis, optimization algorithms, look-up table algorithms, and data assimilation [86]. Several recent examples are provided below to display the applications of inversion algorithms in EBC.

In order to monitor and model storm-water pollution, Park and Stenstrom proposed a Bayesian network approach, which falls into the category of model simulation and statistical analysis [87]. A leaf radiative transfer model called the LIBERTY (Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields) was selected and incorporated with three pigments to better understand relationships between leaf biochemical, biophysical, and spectral properties [88]. A look-up table approach was developed to estimate LAI [89]. Migliavacca et al. assimilated remotely sensed vegetation index time series, such as MODIS NDVI, into a process-based model BIOME-BGC (Biome-BioGeochemical Cycles) to estimate the gross primary production (GPP) of agro-forestry ecosystems [90]. However, an intrinsic problem in inverse models is the process from inputs to outputs is often non-invertible, i.e., more than one combination of inputs results in the same output of spectral signature. Liang stated that, because it is still a nonlinear, ill-posed problem to inverse land surface parameters, further research is required to focus on use of regularization [86].

Each kind of imagery has its own benefits and drawbacks, which provide great potential to fully exploit increasingly sophisticated multisource data through data fusion. For example, MODIS imagery has significant advantage in temporal resolution (one day) but is very poor in spatial resolution (250, 500 or 1,000 m) for certain applications, whereas Landsat TM imagery performed very well in spatial resolution (30 m) but with 16-day revisit. Therefore, Hilker et al. developed Spatial Temporal Adaptive Algorithm for Mapping Reflectance Change (STAARCH) model to fuse high spatial- (Landsat) and temporal-resolution (MODIS) for mapping of forest disturbance [91]. A general definition of remotely sensed data (image) fusion is given as ‘the combination of two or more different images to form a new image by using a certain algorithm’ [92]. Since the late 1980s when data fusion emerged as a new topic [93], several comprehensive review papers have been published to review the data fusion techniques, such as [92–95]. In general, the fusion techniques can be categorized into two classes [92]: (1) colour-related techniques, such as colour composites (RGB), intensity-hue-saturation (IHS); (2) Statistical or numerical methods, such as principal component analysis (PCA), band combinations using arithmetic operators and others. Besides typical techniques, wavelet transform, SVM (support vector machine) and ANN (artificial neural network) represent the heart of new data fusion methods (e.g., [96–98]).

Data fusion has matured into a widely used application of EBC. Pan-sharpening technique, which is to integrate a panchromatic (Pan) image with high spatial resolution and a multispectral (MS) image with high spectral resolution [94] to produce a high spatial resolution MS image, is likely to be the first data fusion method to make installing to the commercial remote sensing software such as PANSHARP module in PCI Geomatica software. For example, Wunderle et al. pan-sharpened SPOT-5 imagery to classify stand age of western red cedar in British Columbia (Canada) [99]. Due to the complementary nature of optical and radar imagery, their both fusion is always at the leading edge of remotely sensed data fusion [44]. Huang et al. estimated the quantity and quality of coarse woody debris in Yellowstone post-fire forest ecosystem from fusion of SAR and optical data [100]. Optical (Landsat-5 TM) and SAR (RADARSAT-1 Wide 1) images were fused through the combination of PCA and IHS transforms to map geomorphological and environmental sensitivity index in the Amazonian Mangrove Coast (Brazil) [101].

RS and GIS have a complementary nature and should develop interdependently. RS routinely provides extracted information from remotely sensed data at scales ranging from local to global and the purpose of GIS is to store, analyze and visualize spatial data [102]. Although Hinton has reviewed well the combined use of remotely-sensed data and vector GIS data [103], Merchant and Narumalani claimed the integration of RS and GIS has actually become increasingly apparent since Aronoff [104,105]. Merchant and Narumalani listed key factors to benefit the integration, including development of theory and analytical methods, advances in computing (hardware and software) and global positioning system (GPS) technology [104]. A state-of-the-art definition of the integration is given as ‘the use of each technology to benefit the other, as well as the application of both technologies for modeling and decision support’ [104]. Ehlers et al. proposed a three-level taxonomy of the integration [106]. First-level integration happens in the level of separate but equal data exchange between GIS and image analysis systems, e.g., displaying GIS (usually vector) data and remotely sensed (raster) data simultaneously. Second-level integration permits seamless tandem or combined raster-vector processing based on a common use interface. Certain RS or GIS software has capability of performing the second-level integration. For example, the aforementioned Definiens’ Developer is capable of incorporating GIS data directly into image processing – image segmentation [69]. Third-level integration operates RS and GIS as a unified system, and finally generates an integrated model of the real world, e.g., accommodating raster and vector data in a hierarchical structure. Moreover, Gao pointed out GPS must be involved with the integration to build up seamless RS-GIS-GPS integration for geospatial information analysis [107]. Campbell, and Merchant and Narumalani summarized the contribution of RS to GIS, and GIS to RS [25,104]. The contribution of RS to GIS includes: (1) RS develops thematic layers for GIS, such as surface elevation (Digital Elevation Model [DEM]), land use and land cover mapping, biophysical parameters, feature extraction and landscape change; and (2) RS provides orthoimagery as base data, which plays key role in positioning, registration and geo-referencing. The contribution of GIS to RS consists of (1) mission planning; (2) ancillary data for geometric and radiometric correction, and image classification; and (3) collection, organization and visualization of reference data.

Foody demonstrated many commonly used examples of RS and GIS for biodiversity applications. The following review focuses on the promising applications of the integration in EBC in 2009 [102]. For example, an adaptable method integrating low-cost remote sensing imagery and GIS was developed to assess forest cover change and conversion in support of decision-makers in assessing regional and local land use and planning forest conservation measures [108]. Giriraj et al. applied data generated from RS and GIS to categorize habitats, and then determined the relationship between the habitat categorizations and species-distribution patterns in tropical rain forests of Southern Western Ghats (India) [109]. Dong et al. pointed out that the integration of high-resolution RS images and GIS technique is an effective way to analyze the landscape changes at river basin scale [110]. In the management of water resources, RS and GIS integration techniques were used to design sustainable development plan of area and locale watershed [111], river inundation impact reduction [112], rainwater harvesting for drinking [113].