Experiences with Applied DSM: Protocol, Availability, Quality and Capacity Building
10.4 Availability of Suitable Input Data for DSM
Input data required for DSM can be differentiated into field-obtained training data needed to establish how soils or soil properties vary across the landscape and pre-dictor data sets required to support application of predictions of that variation across entire areas (Fig. 10.1). Both of these may be in short supply in areas of sparse spa-tial data but, of the two, geo-referenced point samples or detailed soil-landform field observations are likely to be the most limiting (see also Sections 1.3, 9.1 and 20.1).
Carr´e et al., (2007) and Minasny and McBratney (2007) provide some guidelines for methods of selecting geo-located point observations to support prediction of indi-vidual soil properties. Odgers et al. (2008) have proposed a random catena method
10 Experiences with Applied DSM 117 of obtaining soil class observations along a topographic profile from the top to the base of a hillslope in order to collect data suitable for establishing relations between soil classes and landform controls. Field samples and observations are expensive and difficult to collect and may be entirely lacking in many less-developed areas.
In some cases, it may be necessary to infer the nature of differences in soil classes or soil properties based on theoretical or empirical understanding of soil forming processes only, with no recourse to local field observations to confirm or correct these assumptions.
Spatially extensive data sets that completely cover entire areas of interest are necessary to support predictions of the spatial pattern of distribution of soil classes or soil properties. Most protocols for predicting soil classes or properties have iden-tified and used more or less similar sets of input data layers consisting of measures extracted from DEMs, imagery of varying spatial, spectral and, more recently, tem-poral resolution and what may be termed ancillary environmental maps depicting regional or local variation in controls such as geology or bedrock lithology, parent material type and texture, climate or vegetation (see also Sections 1.2, 2.2 and 3.3.1).
The scale and level of detail of these input predictor layers have varied in response to both availability of input data and the nature and scale of the target features to be predicted. A limited number of discrete scales for which predictive mapping has been attempted can be recognized and associated with specific grid resolutions of input data layers (Table 10.1).
Many potential practitioners of DSM in regions with sparse spatial data infras-tructures have expressed frustration with the lack of availability and lack of quality of predictor data layers, particularly DEMs, for their areas of interest. Clearly, many developing regions lack access to fine (5–10 m) to medium (25–50 m) resolution DEM data layers, but efforts such as those of Moran and Bui (2002) and Bui et al.
(2002) have demonstrated that useful and effective regional predictive maps can be produced using DEM data with a grid resolution as course as 250 m. Their key re-alization was that local 3× 3 window variables (slope, aspect, curvatures) were not very meaningful or useful predictors when derived from course resolution DEM grids but that measures of regional hydrological context and of variation in elevation, slope and morphology within larger search windows of varying size did prove to be useful.
The point here is that it is important to compute and use derived variables or predictive measures that are matched, or suited, to the dimensions of the DEM that is available, the size and scale of the output entities to be predicted and the intended use of the resulting map. Most areas now have access to free 90 m SRTM DEM data (Fig. 10.2d,e) and should also be able to order 30 m ASTER DEM data at a reason-able cost (for examples see Sections 2.2.1, 4.2 and 16.1). Admittedly these DEM data sets have limitations in that they often portray a mixture of the bare ground surface and a canopy surface in areas of dense tree cover. However, they can still provide valid and useful measures of regional context, relative drainage conditions and local surface form and orientation. With proper care not to assume the surfaces portrayed by these data are exact representations of the true topographic surface, these medium to moderately coarse resolution DEM data sets can be highly useful for making predictions at scales as fine as 1:50,000 to 1:100,000. Finally, increasing
118R.A.MacMillan Table 10.1 Major types and sources of input data for DSM at different scales and grid resolutions
Grid Size(m) <5m 5–10 m 25–50 m 100–250 m >250m (1000m)
Scale 1:1,000–1:5:000 1:5,000–1:20,000 1:20,000–1:100,000 1:100,000–1:250,000 1:1–1.2 M Application areas Site-level operational
management
Site-level operational management to detailed planning
Non site-specific regional planning, Management of minimum sized areas
Regional planning and policy development
State to national planning and policy development
DEM Data sources LiDAR, DGPS, Photogrammetric, lkonos 2–5 m
USGS 10 m from contours, LiDAR, Photogrammetric SPOT 5–10 m
Photogrametric SRTM 30 m, ASTER 30 m, SPOT 20 m
SRTM 90 m, DTED Level 1 90 m, National DEMs from contours
SRTM 90 m generalized GTOPO30, DTED Level 0
Image Data Sources SPOT 5 2–5 m, EROS 2–10 m, IKONOS 1–4 m, Quickbird 1–2.5 m
IRS 5–20 m, SPOT 4 10–20 m, ASTER 15 m, Landsat7 15 m
Landsat 7 15–30 m, SPOT MS, ASTER 30 m
MODIS 250 m, Landsat 4 80 m,
AVHRR, MODIS 500–1000 m
Ancillary Data Sources On-site field sensing such as EM, GPR
On-site field sensing such as EM, GPR
Airbome geophysics for parent material depth and texture
Regional Bedrock and Surficial geology maps
State to national bedrock and surficial geology maps Site specific direct field
measurements
Airbome thermal or multi-temporal to detect local climate
Airbome thermal of multi-temporal to detect local climate
Manually prepared maps of vegetation or land system
Manually prepared maps of
physiographic regions
∗Some useful sites for downloading SRTM and ASTER DEM data and satellite imagery Corrected SRTM: http://srtm.csi.cgiar.org
Corrected SRTM: http://www.ambiotek.com/topoview Hydro-corrected SRTM: http://hydrosheds.cr.usgs.gov/
ASTER DEM: http://edcimswww.cr.usgs.gov/pub/imswelcome/
USGS Seamless SRTM and Image Data: http://seamless.usgs.gov/
Canada Toporama Satellite Imagery: http://toporama.cits.rncan.gc.ca/toporama en.html UK LandMap DEMs and Satellite Imagery: http://www.landmap.ac.uk/
US NOAA/NGDC GLOBE DEM: http://www.ngdc.noaa.gov/mgg/topo/gltiles.html US National Map, DEMs, Imagery, vectors: http://nmviewogc.cr.usgs.gov/viewer.htm
10 Experiences with Applied DSM 119
Fig. 10.2 Illustration of access to and use of free spatial data that is widely available for most areas, even those considered to have sparse spatial data availability (See also Plate 13 in the Colour Plate Section)
availability and lower costs for LIDAR DEMs of very fine spatial resolution may rapidly turn the problem from one of insufficient amounts of data to one of more data than can be handled effectively.
Even in areas of sparse spatial data infrastructures, the quantity and quality of remotely sensed image data is rapidly improving and free data at resolutions of 30–90 m is becoming widely available (Fig. 10.2a,b,c). These data are adequate to support initial regional scale (1:25,000–1:100,000) predictive mapping efforts (see Fig. 10.2e) (for examples see also Chapters 26, 30 and 34). The author has found
120 R.A. MacMillan that ancillary information describing regional scale variation in environmental con-ditions such as bedrock and surficial geology, vegetation, climate, and physiography can usually be extracted from available secondary source thematic maps or can, if necessary, be interpreted manually using manual visual analysis of available im-agery and DEM derivatives. However, at finer resolutions, improved predictive abil-ities will require more reliable and spatially precise information on parent material texture and depth, perhaps obtained from analysis of airborne radiometric data (see Chapters 14 and 15), on local variation in climate, perhaps obtained from analysis of thermal or multi-temporal imagery and on other subsurface conditions, such as soil depth, salinity or moisture content, that may be detected and mapped using proximal field sensing tools (see Chapters 2 and 13 for examples). Ancillary data sources can be vital inputs for predictive mapping and they can often be approxi-mated using manual interpretation, if necessary or obtained directly using additional sensing technologies, if available.
Of the two main types of data required to support DSM, it is therefore the author’s opinion (supported by comments in Sections 1.2 and 20.1) that the most limiting is that which permits description and elaboration of rules that describe the spatial arrangement of soils in the landscape and the conditions or criteria that control this distribution. Field observations of soil-landscape relationships or well devel-oped tacit knowledge of these relationships are essential to support construction, application and review of classification rules for predictive maps. In areas of sparse spatial data, the most important requirement may well be to find ways to collect and assimilate information on soil-landscape patterns so that this can be related to available input data layers in digital format to create predictive rules. (examples of approaches to building knowledge of soil-landscape relationships are presented in Chapters 9, 20 and 25)