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ditorial

Statistical sensing of the environment - Space, time, scale: combining

remote sensing and spatial statistics for ecology and the environment

In recent years we have seen a large increase in use and availability of remotely sensed data. This

is partly due to the increased quality of the data, partly to improved analysis procedures and part-

ly to the more proper and specific questions being asked. We have also noticed that spatial sta-

tistics for remote sensing of the environment is increasingly being used in the recent past.

Methodological aspects concern variography and interpolation, image segmentation and classifi-

cation, analysis of issues of scale and optimal sampling on the basis of a remotely sensed image.

Also, the list of recent applications of remote sensing for the environment now includes produc-

tion ecology, changes in land use, quality of ecological structures and geological investigations.

At first sight it might seem that most of the current problems like lack of correspondence between

a remote sensing image and ground cover would have been solved. The increase in spatial and

spectral resolution, however, may lead to new problems. For example, the amount of data has

increased so rapidly, that data mining procedures are now much more needed. Also, classification

procedures have to be improved, as it isan entirely different story to classify a limited number, e.g.

6, more or less independent bands than to classify hundreds of bands that obviously may show a

strong mutual dependence. To combine such data with previously collected data at another scale,

up- and downscaling procedures with a clear statistical signature have to be developed. Therefore,

current procedures need to be adjusted and adapted to the new type, quantity and quality of data.

Ecology and the environment are already on the scientific agenda for several decades. The match

between remote sensing and the environment is in principle a very promising one. In the first place

there is an obvious correspondence in space between some environmental aspects, in particular

those with a spatial extent, and remotely sensed data. In the second place, remote sensing may

help to identify problems and developments in the environment that would otherwise be difficult

to identify, if possible at all. Third, the increase in spectral resolution reveals increasingly detailed

information for ecological purposes. This helps us to understand the complexity of ecological

processes at a variety of different scales. In the fourth place,.the long time series of remote sens-

ing data now available at low or no cost to the general public allows monitoring of dynamic

processes in space and time which inevitably involves spatial statistics. Finally, the advent of both

radiometrically and geometrically well calibrated sensor data allows us to derive high-quality sur-

face compositional information that can be integrated with subsurface information.

At 10 October 2000, an international study day was organized at the International Institute for

Aerospace Survey and Earth Sciences ITC) in Enschede, the Netherlands to study these issues.

Several speakers from around the world gave their views on modern approaches on statistical

methods for remote sensing, in brief on statistical sensing of the environment This special issue

of the International Journal of Applied Earth Observation and Geoinformation mainly collects the

written contributions that accompanied the study day. To this set a small number of additional,

but relevant papers has been added. In total, the full set of papers aims to give an overview of the

current state of the art of statistical methods for remote sensing.

The papers in this issue can be divided into three categories: issues of scale, space-time statistics

and monitoring and new processing strategies.

ISSUESOF SCALE

The first paper in this issue is on scale in remote sensing and GIS. This paper discusses the mean-

ing of scale in particular related to the digital world, and the metrics associated with the spatial

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Editorial JAG Volume 3 - Issue 2 - 2001

meaning. It is shown that some metrics can be better used in the digital world than others. A

dimensionless ratio between two such metrics is suggested that has some useful properties. For

example, the ratio is shown to be relevant to a specific vision for the future of what can be called

the digital earth.

The second paper is on scale issues of images for precision agriculture. It analyses quantitative pat-

terns visible in high-resolution NDVI images obtained on airborne remote sensing images of a sin-

gle farmers field during the growing season. Wavelets are used to distinguish patterns at several

scales. Wavelets reveal patterns that can be used for making decisions by the farmers and filter

out information that is irrelevant for precision agriculture applications. Different wavelet basis

functions are useful for various aspects of decision-making.

SPACE-TIME STATISTICS AND MONITORING

Remote sensing techniques are increasingly used for monitoring, relying on space-time statistics

and modern sampling procedures. Also, for some modern remote sensing images it is not appro-

priate to consider them as generated by continuous data, but a full analysis requires them to be

analysed as count data.

The third paper therefore addresses the Poisson distribution of patterns observed in the field. At

this stage, experience is being gained on images that are collected on individual farmers fields.

The paper hypothesizes that these analysis procedures will become useful in the near future. It is

therefore crucial that methodological experience obtained in that respect becomes available for

the analysis of remotely sensed images.

The fourth paper assesses clustering in wildlife populations in a Kenyan wildlife reserve obtained

through aerial surveys. The role of having more detailed data is discussed, where exact locations

of observed animal groups are recorded. Quantification of clustering is carried out using spatial

regression. The study indicates a relation between species spatial distribution and their dietary

requirements, thereby concluding the usefulness of spatial point pattern analysis in investigating

species spatial distribution. It also provides a technique for explaining and differentiation the dis-

tribution of wildlife species.

Space-time statistics play a role when interpolating data in space and time. The fifth paper, there-

fore, discusses use of thin-plate splines versus various forms of kriging, like cokriging and regres-

sion kriging. It concentrates on meteorological data in a state in Mexico. The use of external infor-

mation, for example obtained from a digital elevation model improves considerably the quality of

the predictions. The study shows that the Gaussian character of the data is important to obtain

the most reliable predictions.

The digital elevation model DEM) is an important part of mapping and is used for several purposes

including orthoimage production, image interpretation, contours derivation, and several

Geographic Information System GIS) applications. Interpolation is often required to create a DEM

from a sparse number of points. In the sixth paper, interpolation accuracy of thin plate spline,

polynomial, local C-function and weighted-distance Shepards) interpolation are tested for com-

parison using two Global Positioning System GPS) derived DEMs. The results of two tests show

that the Shepards technique is most efficient with respect to accuracy as well as surface repre-

sentation.

The seventh paper deals with a high resolution Digital Terrain Model DTM). The probability of

landsliding during a storm in an area in New Zealand is determined for each pixel from GIS layers

of land system, rainfall, vegetation/land cover, and slope. Where landslides occur, they are routed

down the hillside, leaving a trail of sediment about 20 cm thick, on average, until they either reach

a stream or are exhausted by the trail. Sediment delivery ratios are simply calculated for any area.

The paper demonstrates the model by assessing the effects of two afforestation scenarios on soil

loss and sediment delivery for a large simulated storm.

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NEW PROCESSING STRATEGIES

New processing strategies at this stage mainly deal with classification and segmentation proce-

dures. The three papers in this special issue concerning new processing strategies focus on new or

extended classification routines for remote sensing images. The first of these, the eighth paper in

this issue, presents a fractal approach to land degradation monitoring and remote sensing. It dis-

cusses how the assignment of classes on a pixel-by-pixel basis ignores useful reflectance informa-

tion in neighbouring pixels, in contrast to contextual classifiers. The paper proposes the spatial and

spectral classifier that combines the advantages of two classifiers based on spectral information

and contextual information from neighbouring pixels. The method is tested on data from south-

ern France.

The ninth paper discusses how soft classification techniques can be useful to estimate land cover

class composition. The starting point is that ordinary classification routines provide output without

information as to how such classes are distributed spatially. Hopfield neural network techniques

are useful to map the spatial distribution of classes reliably. This technique is applied to map mul-

tiple classes at the sub-pixel scale. It has been used in this paper.on SPOT HRV and LANDSAT TM

agriculture imagery to derive estimates of land cover.

The tenth paper presents work done for oil spill detection along coastal waters. Consumers are pri-

marily interested in automatic detection of oil spills. The study develops an automatic model for

oil spill detection. It includes texture analysis and two algorithms, the Lee and Gamma algorithm.

Texture analysis, such as contrast analysis, discriminates between oil and water. The Lee algorithm

was used to determine the linearity of oil movements, while the Gamma algorithm was used to

determine oil spill spreading. The paper demonstrates how these algorithms can be used on SAR

data for automatic detection of oil spills. Such modules will be useful for rapid detection of oil

spills and is illustrative for educational purposes.

The final contribution is a technical note, that uses the correlation between spectral data derived

from pixel information and libraries as the basis for a new classification routine. This approach

allows the quantification of the uncertainty in the remote sensing classification which is an inte-

gral part of any error analysis of multi-layer geoscience data. The proposed method is a first step

towards integrating various components of surface characteristics with the aim of coupling surface

and subsurface data sets.

The study day built on a previous study day organised in 1996 under auspices of the Netherlands

Society for Earth Observation and Geoinformatics NSEOG) and the Landelijke Studiegroep voor

Statistiek in de Aardwetenschappen LASSA). A paper published in the International Journal of

Remote Sensing Stein et al., 1998) summarises the outcomes of the first study day on Spatial

Statistics for Remote Sensing as well as a book published by Kluwer Academic Publishers in 1999

Stein, et al., 1999).

Finally we wish to acknowledge the support provided by the ITC to organize this day, in particu-

lar contributions by Elisabeth Kosters and Hans Giessen. Last but not least, collaboration with Prof.

Martin Hale as the editor-in-chief and Mrs. Butt-Castro to compile this special issue has been very

pleasant and we are grateful for all the energy that they have put into making this issue of such

quality.

Alfred Stein

Freek van der Meer

Stein A. W. Bastiaansen,5. de Bruin, A. Cracknell P. Curran A. Fabbri B. Gorte J.W. van Groenigen F. van der

Meer and A. Saldaiia. 1998. Integrating spatial statistics and remote sensing. International Journal of Remote

Sensing 19: 1793-l 814.

Stein A, Van der Meer F, Gorte, B.G.H. 1999. Spatial Statistics and Remote Sensing. Dordrecht: Kluwer.

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