Quantifying environmental attributes from Earth Observation data products by spatial upscaling: Three case studies Keping Chen*a Risk Frontiers - Natural Hazards Research Centre, Macquarie University, North Ryde, NSW 2122, Australia
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ABSTRACT Monitoring and quantifying environmental conditions at various spatial and temporal scales is important, especially as communities around the globe experience serious stress from the rapidly changing global environment such as climate extremes, water scarcity and salinisation. In the current stage of the evolution of Earth system, human-induced perturbations and associated undesirable impacts are clearly on the increase. As for a chronic patient who constantly requires updated health diagnoses, Earth Observation (EO) technologies and related spatial analysis methods provide unique and effective means for auditing physical and socioeconomic attributes and their dynamics. This paper concentrates on spatial upscaling, a powerful data analysis approach, and how it can be used along with validations to reliably extract environmental attributes from EO data products for regional and global impact studies. We stress the importance of defining object-based spatial units of analysis and consider the nature of spatial aggregation in scaling analysis. Three case studies are provided, including rapid estimation of at-risk population in the 12 May 2008 Sichuan Earthquake shaking zones, counting bushfire-prone addresses close to forest in Australia, and quantifying lower-bound world’s population vulnerable to sea-level rise. Finally we advocate object-based spatial scaling based on valid spatial units of analysis, and discuss the importance of acquisition, access and analysis of ever-expanding EO data for global change studies. Keywords: Earth observation, spatial upscaling, population, earthquake, bushfire, sea level rise
1. INTRODUCTION Environment change never ceases. If such change is directly caused by external natural forcings such as increased solar radiation, volcanic eruptions and tectonic movements, humans, as the only intelligent beings occupying this planet, should develop and be equipped with advanced observation technologies to mitigate any suffering. If the change is also partly inflicted by anthropogenic forcings, human beings as a whole now have every reason to accept the responsibility and devise all-around solutions to combat the adversary and benignly manage the environment. In recent few decades, unfortunately, more and more evidence has shown that human-induced perturbations such as excessive industrial developments and large-scale tropical deforestations are causing elevated greenhouse gas concentrations in the atmosphere and subsequently a warming climate (IPCC, 2007). The impact of Global Climate Change is pervasive and it brings about a whole range of other environmental changes such as accelerated mean sea-level rise and prolonged droughts in many parts of the world. To model and manage global environmental changes in either a physical or socioeconomic sense requires the best scientific data available at multiple spatial and temporal scales. In modern era, the case of lacking any baseline data is no longer tolerated and low-quality data often induce confusion and controversies in scientific enquiries and policy-making. It is apparent that the uppermost objective for any environmental change study is to produce and digest high-quality, objective, and quantitative measures about the environment and its dynamics. Over the past five decades, modern Earth Observation (EO) sensors and data products have been pivotal to monitoring and helping understand environmental changes in a unique and cost-effective manner. For example, Landsat MSS/TM/ETM+ satellites have produced landcover changes data series since 1970s; Shuttle Radar Topography Mission (SRTM) in 2000 produced a near-global surface elevation data set; A family of Jason-1, Topex/Poseidon, and OSTM/Jason-2 satellites have been collecting sea surface heights around the world; and the Orbiting Carbon Observatory satellite promises accurate measurements of atmospheric carbon dioxide over the entire globe (first launch failed on 24 February 2009). *
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Chen, K., 2009. Quantifying environmental attributes from Earth Observation data products by spatial upscaling: Three case studies. Proceedings of the 2nd International Conference on Earth Observation for Global Changes (CD-ROM), Pages 1600-1610, 25-29 May 2009, Chengdu, Sichuan Province, China.
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A sample list of some EO-derived global data sources can be found in the Appendix at the end of this paper. As far as data availability and spatial resolution is concerned, we may categorise two situations: (1) high-resolution data exist at the local scale but not at broad regional scales; and (2) medium- or low-resolution data exist at regional and global scales but are almost useless at the local scale. These situations are closely related to the global environmental change studies that often demand investigations to be performed at multiple spatial scales. Spatial scaling (upscaling and downscaling) is a critical approach in this domain. Scaling can be addressed from different perspectives: data or process, statistical or physical, but one of the ultimate goals is to accurately quantify environmental attributes in a spatially explicit manner. Spatial scaling, a major spatial analysis technique, is closely pertinent to the integration of Geographic Information Systems (GIS) and remote sensing (Quattrochi and Goodchild, 1997; Van Gardingen et al., 1997), and has advanced at a rapid pace over the past two decades. In this paper, we concentrate on spatial upscaling and how it can be used along with validations to reliably extract some environmental attributes from local scales to regional or global scales. We present three real-world case studies and demonstrate that, in upscaling analysis, it is important to define true spatial units of analysis and take the nature of spatial aggregation into account. In what follows, we first define true spatial units of analysis for two types of environmental attributes: socioeconomic and physical.
2. SPATIAL UNITS OF ANALYSIS IN SPATIAL SCALING 2.1 Socioeconomic attributes Socioeconomic attributes (e.g., population counts and densities) customarily represented at a hierarchy of areal zones are not suitable for modern spatial analyses since spatial homogeneity across each areal zone is incorrectly assumed. True spatial distributions of socioeconomic attributes should be explored, and what is needed is an approach that is able to transform data from arbitrary areal zones to physically and socially meaningful units with valid spatial representations. Two types of spatial units of analysis (SUAs) can be defined in spatial analysis (Fig. 1): (a) artificial SUAs based on hierarchical areal zones such as census boundaries, and (b) true SUAs based on physically and socially defined objects or entities. Using dasymetric mapping (e.g., Langford, 2003), socioeconomic data can be transformed from arbitrary zones to relevant “landscapes”, such as binary occupied areas (inhabited or not), land uses and even individual dwellings (Fig. 1b). Increasingly, multi-resolution remotely sensed imagery (e.g., MODIS, Landsat ETM+, SPOT-5, and GeoEye-1) with modern object-oriented image processing approaches (e.g., segmentation and classification methods in eCognition), and numerous auxiliary GIS data sets (e.g., land cover maps and street networks) can be used to prescribe true SUAs. A good case in point is the two approaches used for modelling global population distributions. The first approach is to simply map population counts or densities based on census or administrative areal zones. Here it is common to explore statistical inferences of areal zones and apply interpolation algorithms that have no reference to the important, concrete spatial contextures to distribute population counts. The production of earlier versions of the Gridded Population of the World (GPW, http://sedac.ciesin.columbia.edu/gpw/) data product is entirely based on this approach: the smooth pycnophylactic interpolation (Tobler, 1979) was used for GPW V1 and the uniform areal weighting interpolation used for GPW V2. These methods were developed more than three decades ago in the old cartography era and cannot produce population distributions at an acceptable level of accuracy. For example, recent validations show the poor performance of the pycnophylactic interpolation in population mapping (e.g., Nordhaus, 2003; Hay et al., 2005). On the other hand, LandScan global ambient population (http://www.ornl.gov/landscan/) is produced using a “smart” allocation model which incorporates physically-meaningful spatial contextures interpreted from EO imagery, such as nighttime lights and land covers (Dobson et al., 2000). In LandScan, population is modelled in a spatially explicit form based on true SUAs. (In comparison, the former approach described above is not viable to derive any realistic, highresolution population distributions.) For over a decade now the EO-derived LandScan data product has been developed using the same consistent approach and been widely used in global impact and risk assessment. Let’s take an example: Fig. 2 shows population distributions of SE Australia by both artificial and true SUAs. Areal interpolation, a case of spatial scaling, can induce a very large degree of uncertainty in output if population representation is in the form of areal zones (Hawley and Mollering, 2005), but this is not the case for the population represented in the form of true SUAs derived from image classifications (Fisher and Langford, 1996). Different population representation forms will have a significant bearing on estimating at-risk population delineated by hazard footprints, and a case study is provided in Section 3.1.
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Fig. 1. Schematic representation of hierarchical spatial units of analysis (SUAs) for spatial scaling of socioeconomic attributes. (a) Artificial SUAs, and (b) True SUAs. SUAs prescriptions here are not exhaustive and can be extended to all geographical scales, from local to global.
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Fig. 2. An example of representing southeastern Australian population counts by (a) artificial SUA, and (b) true SUA.
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2.2 Physical attributes Quantifying spatially-discrete biophysical attributes, such as leaf area index and net primary productivity on which landsurface process and terrestrial ecosystems models rely (e.g., Chen, 1999; Turner et al., 2004), also demands scaling analyses. Ecologically important features or objects (e.g., individual trees, stands, land covers, waters and eco-regions) can be prescribed as true SUAs, as opposed to artificial SUAs (e.g., regular pixels represented in raw remotely sensed imagery, Fig. 3). For spatially continuous physical attributes such as elevation and surface temperatures, specifying SUAs may not be apparent and always necessary. But if one does not differentiate the representation type (spatially discrete or continuous), the need to classify either artificial or true SUAs will be easily obscured. This is also related to the nature of spatial aggregation. In Sections 3.2 and 3.3, we provide two relevant case studies of spatial upscaling and produce environmental attributes for regional and global studies.
Fig. 3. Schematic representation of hierarchical spatial units of analysis (SUAs) for spatial scaling of physical environmental attributes. (a) Artificial SUAs, and (b) True SUAs (e.g., coloured grids represent various land covers).
3. THREE CASE STUDIES 3.1 Rapidly estimating at-risk population in the 12 May 2008 Sichuan Earthquake shaking zones A magnitude 7.9 earthquake occurred on the northwestern margin of the Sichuan Basin on 12 May 2008 at 2:28pm local time. The earthquake was caused by rupture on a northeast-striking reverse fault beneath and parallel to the Longmen Shan thrust belt, which forms the boundary between the Tibetan Plateau and the Sichuan Basin. Initial observations show that the length of the fault rupture was ~250 km and its width was ~35 km, extending from near the Earth’s surface to a depth of ~20 km. (An updated hazard summary about the event is available through the USGS Earthquake Hazards Program: http://earthquake.usgs.gov/eqcenter/eqinthenews/2008/us2008ryan/) Sichuan Basin is one of the most densely settled regions in China and home to more than 100-million people. The death toll from the event exceeded 70,000, with more than 15 million people displaced, 5 million homes collapsed and another 21 million damaged. By superimposing ground shaking data that were available immediately after the event (from ShakeMaps of the USGS Earthquake Program) with the LandScan world population distribution (Fig. 4a), one can perform a rapid exposure analysis for the affected region: the number of people lived in very strong (Modified Mercalli Intensity level VII), severe (VIII), violent (IX) and extreme (X+) shaking zones is 12.6, 4.1, 0.67 and 0.61 millions, respectively. The majority of reported fatalities are from VIII and above shaking zones.
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This simple example shows how such an exposure analysis can now be performed to rapidly assist disaster response and priority setting. One cannot stress enough that without LandScan global population data represented at true SUA, reliable exposure estimates across regions could not be produced so readily. Conversely, if we employ population statistics based on artificial SUA only (Fig. 4b), the result becomes highly sensitive to the important size/shape of hazard footprints and is not trustworthy. More importantly, this example also demonstrates that LandScan, a very successful EO-derived data product, can indeed greatly aid disaster risk management on the ground. There is a growing demand for more EO-derived data products in an easily accessible form to boost real-world applications. Encouragingly, the past few years have seen more and more such applications in relation to natural disasters and humanitarian crises (NRC, 2007), such as the PAGER system (Prompt Assessment of Global Earthquakes for Response) within the USGS Earthquake Hazards Program, which is able to produce automated, rapid estimates of atrisk population (http://earthquake.usgs.gov/), and the Asia-Pacific Natural Hazards and Vulnerabilities Atlas from the Pacific Disaster Centre, Hawaii (http://www.pdc.org/atlas/html/atlas-init.jsp).
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Fig. 4. Two approaches to estimating at-risk population in the 12 May 2008 Sichuan Earthquake shaking zones. Black star indicates the earthquake epicentre, and black contours show Modified Mercalli Intensity levels from ShakeMaps. (a) LandScan population; and (b) County-level population counts (source: University of Michigan China Data Center).
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3.2 Counting bushfire-prone addresses in Australia Bushfires (wildland fires) play a significant role in the dynamics of forest ecosystems and present large risks to the bushland-urban interface. Massive and uncontrolled fires in tropics and sub-tropics are a major source of trace gases released to the atmosphere and have a large imprint on the global biochemical cycle (FAO, 2001). Nowadays the MODIS Rapid Response System (http://rapidfire.sci.gsfc.nasa.gov/) is able to automatically detect large fires at the global level and efficiently distribute information about fire hotspots and locations. The impact of bushfires on the bushland-urban interface can be dramatic. Bushfires often cause devastating property losses in many parts of the world including southeastern Australia, southern Europe and southern California. One of the popular tasks in bushfire risk assessment is to quantify the number of properties located in bushfire-prone areas. Separation distance between property and nearby bushland is commonly regarded as the most important variable, and prerequisites for such an analysis are accurate location of property addresses and a map of bushland distribution usually interpreted from satellite imagery. If the study is for a large territory or an entire country, fine-resolution imagery such as QuickBird and IKONOS are impractical due to high cost and small swaths. More affordable is medium-resolution imagery such as Landsat TM/ETM+. In Australia we have resorted to this image source to perform vegetation classification, focusing on large, continuous areas of bushland that might allow large fires to develop and, on occasions, get out of control. (USGS released all historical and current Landsat imagery freely for the public in January 2009.) The next question is whether medium-resolution satellite imagery can also attain reliable output. We compared results based on two bushland maps respectively classified from QuickBird and Landsat imagery, for a validation area near the Lane Cove National Park, New South Wales, where high proportions of at-risk addresses exist (Fig. 5). A total of 33,700 property addresses were analysed. Fig. 6 shows cumulative percentages of addresses within each distance group for the test area, and about 45% of addresses are located within 200 m of bushland. Surprisingly, both curves display very similar results and trends: there is very little dependence on classified bushland maps at different resolutions.
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Fig. 5. Validation area (Land Cove National Park, NSW) shown by two image sources: (a) QuickBird imagery (2.4mresolution for multispectral bands), and (b) Landsat imagery (typically 28.5m-resolution for multispectral bands).
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Fig. 7. Schematic representation of bushland boundaries: polygons in blue and red edges indicate bushland classified from high- and medium-resolution imagery, respectively.
Why are the results insensitive to the resolution of bushland map? The answer lies in the aggregate nature of analysis and the fact that bushland, as the focused land cover here, is classified reasonably well from two respective imagery sources despite small discrepancies in boundaries or shapes. In other words, bushland as an object is depicted and retained well from high- to medium-resolutions. Fig. 7 illustrates bushland classified from high-resolution imagery (taken as the ground truth), as well as the bushland based on the medium-resolution imagery, which is less accurate containing both small false positive and false negative regions. For a large number of addresses with calculated distances between address location and nearby bushland, when grouped and summed, both positive and negative errors are cancelled out. The final cumulative result is more than adequate for many applications in insurance and disaster planning. For a larger study area containing millions of addresses, it is reasonable to expect that the difference between the cumulative percentages to become even smaller. We have analysed about 80% of Australian national addresses and found about 500,000 addresses are located within 100 m of bushland (Chen and McAneney, 2005). The use of mediumresolution imagery provides a big cost saving for obtaining such information. The upscaling analysis here is not subject to localities and can be extended to other overseas fire-prone regions.
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3.3 Estimating coastal population vulnerable to significant sea-level rise This has been a hot topic in relation to storm surges, tsunamis and global mean sea-level rise (e.g., Church and White, 2006), and a few impact studies on estimating world’s populations in low-lying coastal areas have been recently reported (e.g., McGranahan et al., 2007; World Bank, 2007). They usually employ some of the best spatial data sets currently available, including LandScan, SRTM surface elevation and GSHHS shorelines (see Appendix), but all lack comprehensive validations over each input. In this paper, we focus on the elevation source. Near-global SRTM has the spatial resolution of 3-arc-second (or 90m at the Equator, SRTM-3"), and its height is specific to land object surfaces (e.g., vegetation canopy and building roofs). SRTM was captured by the same satellite platform and post-processed in a consistent manner, and an absolute vertical accuracy ranges from 5.6 m to 9.0 m on average across continents against a set of ground-truthing observations (Rodriguez et al., 2006). This lends itself as a very attractive data set for quantifying coastal exposures vulnerable to major inundation (e.g., 5 m and above). But how good is the data set for such studies? We examine this now with a validation exercise. We first selected all addresses in the Sydney basin within 3 km of a shoreline, and compared how they were distributed on two independent elevation sources: DEM-5m (a 5m-resolution digital elevation model with height specific to bare land) and the usual SRTM-3". Both elevation data sources display very similar distributions of height (Fig. 8), and statistical results on cumulative numbers of addresses show consistent trends for both inputs (Fig. 9a). Since SRTM data represent the average height of reflecting surfaces (mainly rooftops in this area), we adjusted for this by removing the measured 4.0 m average difference between surface height (SRTM-3") and ground level (DEM-5m), and the result is to shift the original SRTM-based curve leftward to closely match the DEM-5m curve (Fig. 9a). This suggests that for cumulative estimations of vulnerable addresses at larger elevation levels (e.g., 5 m), reliable results can be derived using the SRTM-3" dataset with considerable cost savings over the fine-resolution DEM-5m. We also observe the wide disparity of the absolute numbers of addresses in low SRTM elevations (e.g., less than 9 m, Fig. 9b), but since elevation is a spatially continuous surface variable, like surface temperatures and unlike spatially discrete variables such as land cover and rainfall, errors at low-elevation ranges in aggregate reporting averaged out in the end.
Fig. 8. Validation region (Sydney basin) with two independent elevation sources: (a) SRTM-3", and (b) DEM-5m.
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Fig. 10. Lower-bound estimates of the world’s coastal population (millions) in relation to distance from shoreline and elevation above mean sea level.
The above general findings are supported by empirical tests for many other study areas in Australia (Chen and McAneney, 2005). For more accurate reporting of cumulative results at 5 m and above, we found SRTM elevation adjustments are needed. While such adjustments would vary regionally, it is evident that if no elevation adjustment is undertaken for the direct SRTM-based estimate at higher elevations, the exposure result can be regarded as a plausible lower bound. This was confirmed in our previous validations in the Australian region that the combination of LandScan, SRTM and GSHHS shorelines provides lower-bound estimates of vulnerable coastal population only. Fig. 10 shows the global distribution of population with respect to both elevation and distance from the shoreline. As just one example, within 3 km from the shoreline, at least 50 million people would be inundated by a potential rise in the mean sea level of 5 m. If we consider SRTM elevation adjustments of 2-4m, this estimate ranges 75 and 150 millions. Note that these results are based on the validation of the elevation input alone, and other inputs still need to be examined for more realistic estimates.
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4. DISCUSSION 4.1 Conceptual difference between spatial unit of analysis and scale Since very high-resolution data (e.g., population, vegetation and elevation) usually do not exist at regional and global scales and will not be available in the foreseeable future, validations at local scale in spatial upscaling analysis provide tangible evidence and insights into how one can take advantage of current medium-resolution data for investigating some big questions specific to broad geographical scales. Spatial upscaling along with validation is a very powerful and costeffective spatial analysis technique. Once more empirical studies have been assembled, it may become easier to formalise spatial scaling in a rigorous and intelligent modelling framework. Through the case studies presented here, we have demonstrated the importance of two aspects in spatial upscaling: the use of true spatial units of analysis and the nature of spatial aggregation. Especially we provided classifications and prescriptions of spatial units of analysis (SUAs) and proposed object-based scaling analysis. While some may argue that the concept of SUA is equivalent to scale, we clarify the difference between them with different meanings: (a) Scale is an overloaded, often ambiguous, term across disciplines (e.g., Tate and Atkinson, 2001), but SUAs are intuitive and concrete, and can be clearly perceived by analysts; (b) Scale often refers to size in contemporary usage, but SUAs focus on data integration and spatial analysis - two core themes of GIS; (c) True SUAs are linked to physically-defined “landscapes” or objects, offering a valid reference or constraint for scaling analysis; and (d) True SUAs provide a convergent and formal solution to the spatial incompatibility among heterogeneous data sources. This is important when integrating both physical and socioeconomic environmental attributes in numerous multidisciplinary applications that focus on human-environment interactions, e.g., hazard risk assessment, climate change impact assessment, sustainability science, environmental justice and public health. 4.2 Earth Observation data: Acquisition, access and analysis Our environment is changing: rapid urbanisation and worsening pollution in some developing countries, scarcity of clean water, desertification, tropical deforestation, loss of biodiversity, depletion of natural resources, increased energy consumption, extreme weather, algal blooms, and many others. Heightened global environmental changes require indepth investigations on their causes, processes and consequences. Like any science field, Earth system science demands the acquisition of high-quality data, sound modelling of process and impact, and knowledge presentation and sharing. Earth Observation allows us to monitor the ever-changing environment readily. Initiatives such as the Global Earth Observation System of Systems (GEOSS, http://www.earthobservations.org/) and the NASA Earth System Science Pathfinder Program (ESSP, http://nasascience.nasa.gov/programs/earth-system-science-pathfinder) promise a new datarich era for major scientific progress. EO data contributions have been mainly made by developed countries, predominantly the US, but contributions from the developing world (e.g., China) will become more visible and have an impact even though this is likely in the long run. We should not be complacent about current EO data capture capabilities. First, there is a great divide between expectation and reality. We need to fully acknowledge the limitations of current space-based sensors on spatial, spectral and temporal resolutions. For applications such as rapid disaster response, daily snapshot-like imagery by satellites is not enough, and very high-resolution imagery with up-to-minute or hourly update frequency could be particularly useful. In this regard, there is a need for compact, low-cost multispectral, RADAR and LIDAR aerial mapping systems that can be easily mounted on light helicopters and unmanned aerial vehicles (e.g., ICAROS’s multispectral Digital Mapper, http://www.icaros.us/; hyperspectral sensor AisaEAGLET, http://www.specim.fi/). Secondly, while EO has made some significant progress in monitoring land surfaces and earth atmosphere over the past few decades, challenges remain for acquiring environmental attributes of deep sea waters or below land surface, e.g., subsurface sea temperatures crucial for forecasting tropical cyclone intensity, and any physical and/or chemical precursors or signals before impending major earthquakes. We imagine that in a few decades, hundreds of more sensors will be flying in space and the situation of serious data gaps would change.
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Thirdly, EO concept could be broadened to include all observation means in modern information and telecommunication age, including GPS-enabled digital cameras and mobiles, since monitoring real-world environmental changes often requires a comprehensive solution. Take early warning systems as an example: prompted by mega-tsunami of the 26 December 2004 Sumatra-Andaman earthquake, regional tsunami monitoring networks and systems (e.g., the German Indonesia Tsunami Warning System for the Indian Ocean, http://www.gitews.org/; the Australian Tsunami Warning System, http://www.bom.gov.au/tsunami/about_atws.shtml) have been recently developed, incorporating technologies and infrastructure far more extensive than traditional notion of EO or remote sensing. Acquiring data is the only first step, however. Transforming from raw data to ready-to-use, GIS-compatible data products is urged for wider EO applications. It is not uncommon to see an agency with voluminous EO raw data but few accessible data products. This also highlights the need for significant data pre-processing and advanced imagery analysis techniques. Suitable machine learning algorithms and intelligent statistical inferences (direct or indirect) can be further explored to automatically produce the large number of high-quality data products. At the same time, we recognise that data are not information and knowledge. EO data can be examined using modern geospatial analysis (e.g., scaling analysis), data mining and knowledge discovery techniques, or integrated with data from a multitude of other sources (e.g., those from labs and ground observation stations), to reveal processes and impacts of environmental changes over time in a holistic manner. It is worth mentioning the Earth Trends Modeler in the latest version of IDRISI Taiga (http://www.clarklabs.org/) that includes a suite of methods designed for the time-series analysis of imagery-based environmental attributes (e.g., sea surface temperatures and vegetation indices). As shown by the case studies in this paper, when quantifying some environmental attributes at the regional and global scales, a magnitude of improvement over spatial resolutions of EO data is not always necessary and spatial upscaling is instead useful. Of course, this has further implications for modelling some components of the Earth system, where EO data are routinely major inputs. In numerical hydrological modelling and climate modelling, a mere increase of spatial resolution of data and model is not a panacea (e.g., Beven, 2002; Doron, 2008); in other words, increasing spatial resolutions does not guarantee a significantly better result, and analytics such as spatial scaling should be pursued.
5. CONCLUSIONS Humans are sometimes doomed to be passive victims of environmental changes, irrespective of their causes. But this should not deter our best effort and defer responsibility to manage this changing planet - “our home”. Earth Observation is the best tool humans have ever mastered for this determination, and every new EO data source opens a window for discoveries. When it comes to quantifying regional or global environmental attributes from EO data with inherent resolution limitations, spatial upscaling along with empirical validations seems to offer the only cost-effective solution for the time being. Validation tests should not only establish statistical and spatial correlations between the EO data at different resolutions, but also provide insights into the nature and the process of scaling practice. In this paper, we have demonstrated spatial upscaling approach with a focus on object-based true spatial units of analysis. The conceptual advancement on object-based spatial scaling should excite the interest of scientists who are actively involved in Earth system science research and global change studies.
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APPENDIX: SOME EARTH OBSERVATION-DERIVED DATA SOURCES Earth’s climate system-related data products, imagery about natural hazards Land surface temperature, rainfall, cloud, aerosol, radiation, fires, floods, etc. from NASA Earth Observatory http://earthobservatory.nasa.gov/GlobalMaps/ and http://earthobservatory.nasa.gov/NaturalHazards/ Long-term ocean and climate data products from SSM/I, TMI, AMSR-E, QuikScat, SeaWinds, etc. satellites http://www.ssmi.com/ and http://www.discover-earth.org/ Land cover and land use MERIS-derived GlobCover (resolution 300m), European Space Agency http://ionia1.esrin.esa.int/index.asp AVHRR-derived Global Land Covers (resolution 1 km), Global Land Cover Facility, University of Maryland http://glcf.umiacs.umd.edu/data/landcover/ SPOT4-derived Global Land Cover 2000 (resolution 1 km), JRC http://www-tem.jrc.it/glc2000/defaultGLC2000.htm Global Land Cover Network, UN FAO - http://www.glcn.org/ Elevation Shuttle Radar Topography Mission (SRTM) elevation data (spatial resolution 3-arc-second) NASA JPL - http://www2.jpl.nasa.gov/srtm/ or USGS - http://srtm.usgs.gov/ Shoreline Global Self-Consistent, Hierarchical, High-resolution Shoreline database (GSHHS), scale 1:250K http://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html
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