Towards an integrated land monitoring system

- spatial aspects - Knowledge on the actual cover of the earth surface and its evolution is a key element of many environmental and agro-economical model-calculations. Different uses however require different kinds of information and it is probably impossible to compress all necessary information potentially needed into a single Land Cover (LC) layer. Some of the applications would need purely statistical information integrated to a specific area, while others need information on the distribution of land cover elements on the earth surface. Some of the applications need information only on some basic land cover elements, but in a very fine scale (e.g. High Resolution databases), while other applications need a landscape view of many thematic classes including land use (LU) information. Some of the applications need the knowledge of the static land cover, others need information about seasonal or even more frequent changes. To satisfy all these needs, we have to build up a structure of shared land cover / land use information layers based on the principles of the Shared Environmental Information System (SEIS): Information should be managed as close as possible to its source;

collected once and shared with others for many purposes;

readily available and easy accessible;

accessible to enable users to make comparisons at the appropriate geographical scale;

fully available to the general public at national level in the relevant national language(s);

supported through common, free open software standards.

1 Building blocks of the proposed Land Cover / Land Use Information System The proposed blocks mean groups of layers containing LC/LU information. Layers in one group have similar characteristics and are based on existing European and/or national initiatives. The 5 major group would be: Statistical area frame surveys (point samples and transects),

High resolution (raster) LC databases,

Land cover maps (vector),

Layers of specific LC/LU elements (mainly vector data),

Remotely Sensed data.

1.1 Statistical area frame surveys There exist several national (e.g. France, Switzerland) and one European system (LUCAS). These systems are based on point sampling, which gives the opportunity of frequent field checking (the locations are usually documented with field photographs as well) and the evaluation is based on known statistical backgrounds – representativeness of the LU/LC statistics can be calculated. The methodology allows the clear separation of LC and LU information (the LUCAS survey uses two separate nomenclatures) and the collection of several attributes for each sample locations. Additional to the point sampling specific features (e.g. transects) are also used in order to collect information on the landscape structure. Representativeness: The representativeness of a point sampling depends basically on the number of samples collected. Because the results show the percentage of area occupied by the given LC/LU class, the same number of samples are required for different size areas in order to reach the same representativeness. An appropriate stratification (the use of a priori information of the landscape) can increase the representativeness of a point sampling. Collecting information about LU/LC changes: LU/LC changes can be considered a specific class occupying a certain (usually very small) fraction of the total area. Usually the point sampling that is representative for larger LC/LU classes does not give enough information for neither small “status” classes nor changes. Drastically increasing the number of samples, or an appropriate stratification can help to get representative results on LC/LU changes.

1.2 High Resolution layers A set of high-resolution (HR) land cover layers have been proposed in the frames of GMES program. High-resolution is understood here in spatial terms; these layers are based on the (semi-)automatic processing of high-resolution (20m) satellite imagery. Currently five HR layers are under preparation: Soil Sealing: Seamless raster layer containing the degree of

imperviousness ranging from 0 to 100% based on IMAGE2006; available for all Europe;

Forest layer: Seamless raster layer containing the degree of tree density ranging from 0 to 100%;

Grassland layer: Mapped as a thematic class;

Water layer: Mapped as a thematic class;

Wetland layer: Mapped as a thematic class based on existing status data.

The last four HR layers are under preparation by GEOLAND2 project, being produced for the area of an “Alpine transect” (from Munich via Innsbruck to Verona).

Figure 1: Alpine transect: The target area for the initial creation of new HR layers

Some of the HR layers correspond to or are very similar to the definitions of the major land cover elements as derived by the EAGL (EIONET Working Group on Data Model for Land Monitoring), namely: Soil sealing (= artificial built-up areas), forest layer (=woody areas), water (=water). The grassland and wetland layers represent more complex information, not corresponding to a pure LC class.

Another difference between HR layer concepts is that while the pixels of sealing and forest layers measure percentages, the other three layers are defined as thematic classes. (However, if we aggregate the 20x20m pixels to larger areas, even to 100x100m cells, percentage values can be calculated). From spatial point of view, there is a basic difference between using percentage values or thematic classes, as demonstrated below on the example of the SSE-Hungary (enhanced soil sealing) database.

The soil sealing layer is a seamless raster layer containing the degree of imperviousness ranging from 0 to 100%. This corresponds to the percentage of sealed area within the original 20x20m pixel. If we want to calculate the amount of the sealed area inside a pixel with 1% degree we should write:

2401,02020 mAreasealed

Correspondingly, we can calculate the amount of sealed area for larger area by summing the sealed area of the “n” number of inherent pixels:

n

iipixelpixelipixel

n

iipixelsealed elsealinglevareaelsealinglevareaArea

1__

1_

because the pixel area is the same all over the database.

Applying this equation we calculated the amount of sealed area for Hungary based on the revised 2006 soil sealing database. The result is 294.306 ha (appr. 3,2% of the country area). Now, we aggregated the soil sealing database to 40, 60, 80, 100, … 1000m cells and calculated the sealed area of the country from each of the databases, as shown on Figure 2.

Figure 2: Amount of sealed area calculated for Hungary at different cell sizes

There is certainly a fluctuation in the values, but the calculated amount of sealed area is surprisingly stable the standard deviation around the mean value being only 83 ha (less than 0,03%)!

The next experiment was to define LC classes based on sealing levels defined by thresholds. 3 classes were defined this way. Figure 3 shows that the country-level estimation of an LC class strongly depends on the aggregation level.

Figure 3: Built-up class area calculated for Hungary at different cell sizes

Proposal for HR layers: The ideal structure of HR LC layers would be a system of pure LC elements that are in correspondence with EAGL recommendations (based on the LCCS logic) and can be derived regularly by mostly automated method from remotely sensed data. For each unit (rectangular cells or larger regions) these elements should complete each other to 100%.

Potential major uses of HR layers:

Statistics / modelling: Calculating the amount of sealed (also forested, water covered…) area for a land unit.

Landscape modelling: using the advantages of the high resolution and the good geometry, recognizing landscape patterns, linear elements.

Determining LC (e.g. built-up, forest…) classes at several specified scales.

Accuracy: Accuracy of a HR layer (e.g. soil sealing) is measured currently via comparison of the sealing level aggregated to 100m cells with the percentage of sealed areas for sample cell areas estimated using VHR imagery. Evaluation of the results can be done either by analysing sealing level differences or defining built-up classes based on sealing level classes (defined by thresholds) and calculating commission / omission errors.

Specific uses require different kinds of accuracy parameters. Therefore we propose to develop in the future different ways of measurement for the potential major uses of the data.

Changes: We do not have experience with HR change maps yet. Generally speaking, if a status HR layer has a certain level of random errors (e.g. commission / omission) the difference of two status layers will have a larger error level than the error levels of the status layers. Therefore we do not expect a very high accuracy of HR change maps created as differences of two HR status layers in terms of commission / omission errors.

Still, HR LC change patterns will be a useful information. Additionally, HR change maps would probably give rather reliable results if changes of the amount of LC content (e.g. sealed, forested…) are calculated aggregated for larger land units.

1.3 Vector LC layers Although raster data can be vectorized and vice-versa, the classical vector-based LC databases (like CLC databases) are dominantly the results of manual delineation based on visual interpretation of remotely sensed imagery and other ancillary data. This methodology allows derivation of complex LC/LU information from the available data by delineating LC/LU objects. Characteristics of vector-based LC layers of general use are:

Dominantly the results of visual interpretation and manual delineation, which require expert knowledge;

Need for generalization: a Minimum Mapping Unit (MMU) and a Minimum Width of Linear Elements have to be defined, to prevent too detailed delineation and to define a base of harmonisation;

The nomenclature is usually a mixture of clean LC classes (like water surfaces or sand), clean LU classes (like industry), mixed LC/LU classes (like forests or airports) or complex patterns of these.

The delineated landscape objects are attributed primarily with a single LC code, defining a class in the nomenclature. Nomenclatures usually apply a hierarchical system of LC/LU classes. It is also possible to give additional attributes to LC objects (like the presence of irrigation in an agricultural area), and it is also possible to estimate the presence of basic LC components within an object (polygon).

The scale effect

Spatially, the use of a single MMU for a LC database seems to be a good solution for spatial harmonization, but this kind of generalization makes questionable the statistical usability of vector LC data. The LC/LU elements represented by different LC classes have sometimes very different spatial behaviour. A test to illustrate this is shown below.

To test the effect of generalization we have rasterized the CLC2006-Hungary vector database using different cell sizes (50, 100, 150, …. 2300 m). We applied the most general rules of rasterization: if more than one polygons are covering the area of the raster cell, the cell will get the code of the polygon that occupies the largest area of that cell. For each new raster database (all created by rasterizing the original vector data) we calculated the areal statistics for the different CLC classes. Figure 4 shows these values compared to the statistics calculated from the original vector database.

Figure 4: Effect of rasterization of CLC2006 class areas. Values represent percentages of the total area of a specific CLC class compared to CLC class areas in the vector database.

At 100 m cell size we do not see any significant difference between statistics calculated from the original vector database and the rasterized version. At larger cell sizes however we see that areal statistics of CLC classes behave very differently:

There is a large random fluctuation for rare CLC classes having characteristically small areas (123, 111);

There is a rapid decrease of sum areas for classes featuring characteristically linear elements or small patches (511, 122, 133);

There is a slower decrease for most of the classes having medium size patches and a larger occupation of the total area (112, 121, 313, 324, 411, 512,. …);

The statistics is more or less stable for 321, 213 and 221;

There is an increase for classes occupying large areas (211, 311).

This behaviour is not very surprising, the large classes “eat” the small ones linear features rapidly disappearing. The message of this experiment is that LC classes do not have equal weight and can have very different spatial characteristics. The use of a large single MMU biases the area statistics differently.

In a second experiment we compared the statistics of two CLC databases of different scale. One was the national 1:50.000 scale CLC database (CLC-50) created using SPOT4 satellite imagery taken in 1998-99, the second being a “side product” of the CLC2000-Hungary project, a generalized version of CLC50 (called CLC-100 here). Thus we had two CLC databases for same reference date (Table 1).

Table 1 Comparison of main parameters of CLC-100 and CLC-50

Parameter CLC-100 Hungary CLC-50 Nomenclature standard EU level-3 extended level-4/5

Area resolution 25 ha for all categories 4 ha

1 ha for lakes Linear resolution 100 m 50 m

No. of classes 27 (out of 44) 79

No. of polygons 24.000 174.000

Final product topologically structured database in ArcInfo format

The generalization of CLC-50 data was performed on a semiautomatic way by respecting the parameters of standard CLC (level-3 nomenclature, 25 ha area limit, 100 meter limit for the width of linear elements, generalisation rules to yield CLC-100).

In theory, comparison of the statistics of CLC-50 (1998) and CLC-100 (1998) databases is rather easy as the nomenclature of CLC-50 is based on the traditional CLC nomenclature. Thus taking the first 3 digits of the CLC-50 code in the most cases CLC-100 codes can be directly derived.

However, there are cases when generalization cannot be done automatically, thus are not directly comparable in the statistics: Intelligent generalization of narrow features;

Intelligent generalization of small gardens of villages (242) (heterogeneous agricultural areas) into 112 (urban fabric) class;

Intelligent generalization of complex CLC classes (242, 243) from smaller LC objects;

Differences in the definition of CLC classes 231 and 321 at national and European level.

Table 2: Comparison of CLC-100 and CLC-50 statistics (ref. year 1998)

Differences

(CLC-50 – CLC-100)

CLC code

CLC-50

ha

CLC-100

ha

ha Percent of CLC-50 class area

111 3 493 3 352 141 4,03% 112 354 904 414 216 -59 312 -16,71% 121 108 926 49 653 59 273 54,42% 122 16 103 4 668 11 435 71,01% 123 595 385 210 35,28% 124 9 097 6 400 2 696 29,64% 131 9 285 6 939 2 346 25,27% 132 8 005 4 986 3 019 37,71% 133 3 458 1 819 1 639 47,40% 141 8 881 5 627 3 254 36,64% 142 31 368 31 477 -109 -0,35% 211 4 866 625 4 981 469 -114 844 -2,36% 212 28 476 0 28 476 100,00% 213 10 270 10 710 -441 -4,29% 221 139 355 139 589 -234 -0,17% 222 71 964 61 231 10 733 14,91% 231 401 537 569 317 -167 780 -41,78% 242 245 607 303 723 -58 116 -23,66% 243 82 882 179 754 -96 873 -116,88% 311 1 555 435 1 496 354 59 081 3,80% 312 162 138 102 816 59 322 36,59% 313 80 253 151 557 -71 304 -88,85% 321 546 557 290 645 255 912 46,82% 324 223 608 201 976 21 632 9,67% 331 158 155 3 1,85% 332 172 115 57 33,16% 333 9 164 4 318 4 846 52,89% 334 69 26 43 61,86% 411 116 183 93 063 23 120 19,90% 412 11 311 12 719 -1 408 -12,44% 511 61 897 46 896 15 000 24,23% 512 133 331 125 149 8 182 6,14%

Sum 9 301 106 9 301 106

Table 2 shows significant differences in CLC statistics for many LC classes. These differences in statistics are partly caused by differences of CLC class definitions and intelligent generalization methods mentioned above. However, most others seem to be mainly the consequence of the scale effect.

Our assumption is that it is possible to find a common denominator in the statistics via aggregation, so we rasterized both CLC databases with different cell sizes, similarly as we did with the CLC2006 database.

Figure 5: Effect of rasterization of CLC-50 – CLC-100 differences. Values represent percentages of the total area of a specific CLC class compared to the CLC-50 class areas in the vector database.

Figure 5 shows that aggregation via rasterization reduces the differences in the statistics, but the use of large cell sizes is not enough to remove all differences. Around 500m cell size (25 ha cell area) the differences in statistics were significantly reduced for most of the classes.

For complex classes (243, 313, 242) and in case of CLC classes with changed definition (231 and 321) the relative differences in statistics between CLC-50 and CLC-100 data are still around 50% in large cell sizes as well.

LC Changes: In CLC databases land cover changes are mapped as change objects. The MMU is reduced to 5 ha for the European CLC-change mapping, because only a few LC change objects would reach the 25ha area. The consequence of the reduced MMU is a scale change. The CLC-change databases contain more details than the CLC status layers and thus cannot be produced as the difference of two CLC status layers.

Conclusions:

Scale effect studies showed that vector LC databases with a single MMU do not give the same estimation of the reality in a statistical sense. Still, we

assume that the estimation given by a harmonized European dataset such as CLC is comparable in a European level;

To approximate the reality by mapping LC objects would require better definition of LC objects and the use of characteristic MMU-s for each LC object type. There are national examples for the using different MMU-s in a status layer (e.g. Spanish SIOSE database),

Mapping LC changes requires usually higher level of detail than mapping LC status. Thus additional efforts are needed to produce a proper LC-change database (cannot be calculated from the difference of two LC status maps). The use of different MMU-s in LC status layers introduces a complex problem to determine proper MMU(s) to be used for LC-changes.

Until now CLC-changes have always been derived for between two dates. A robust concept is needed to establish a harmonised, multi-date change database.

1.4 Layers of specific LC/LU elements In most countries, a high diversity of layers exist at national level that concentrate to specific LC/LU elements and give a country-wide coverage. Many of these layers represent INSPIRE themes, some of them existing already at a harmonized European level as well. Examples of these layers are:

Hydrography (INSPIRE ANNEX I.),

Transport network (INSPIRE ANNEX I.),

Cadastral parcels (INSPIRE ANNEX I.),

Administrative units (INSPIRE ANNEX I.),

Protected sites (INSPIRE ANNEX I.),

Buildings (INSPIRE ANNEX III.),

LPIS blocks,

Forestry blocks,

Vineyard and grape cadastre,

Ecological regions, botanical databases,

Topography in general,

…

The above layers can have different resolution, reliability, update cycle and age, but since most of these layers are mapped at a high precision and are updated more or less regularly, these can be considered foot-stones or anchors of any LC/LU information systems. If relevant and up-to-date high-precision data exist for a region, the elementary logic says that a less precise LC/LU layer should not contradict to this “bottom-up” information. On the other hand, remotely sensed “top-down” information could help to find errors in these datasets.

Changes: Many of these layers are already stored in a “living” database, which is edited continuously according to administrative notifications and/or remote

sensing data. Changes can be derived by intersecting status layers. For this “snapshots” of the status should be created on yearly, or seasonal bases as needed.

1.5 Remotely Sensed data Finally, remotely sensed data should be a part of the proposed LC/LU information system. RS imagery is a primary source data for deriving LC information for many of the above mentioned LC/LU layers. As part of the proposed information system, RS data do not provide LC/LU information directly, but it has an important role as spatial reference and gap filler being an additional background information source. Currently, in lack of a common European HR optical satellite system, it is rather difficult to ensure the legal background for a commonly available satellite image mosaic (like IMAGE2006). Google Earth acts currently as a gap-filler in some applications, but GE imagery does not fulfil many of the information requirements. Hopefully, the imagery of the planned new European Sentinel satellite system will be a stable background in these terms.

2 Potential major uses of the shared LC/LU information system Specific applications require different kinds of LC/LU information. It is hopeless to specify all potential uses of LC/LU information; therefore we have to determine the potential main use types.

2.1 Areal statistics Probably all GIS based LC/LU data are used to calculate areal statistics, but in most of the cases the user is not aware of the limits of the usability of these data. Random point sampling gives the opportunity to estimate the reliability of the calculated statistics (e.g. confidence intervals). Even in case of point sampling stratification, which is often and ad-hoc applied make nearly impossible to puzzle out the meaning of the final statistical results.

HR raster layers are produced mainly by statistical processing of remotely-sensed images (image processing and classification algorithms). Knowing the applied processing steps makes possible a primary estimation of accuracy However, an extended validation activity is needed to derive reliability values of statistics obtained from HR layers. In the practice most of the HR layers are produced not purely by image processing, because the current definition of GMES HR layers requires the use of other ancillary data and visual interpretation.

Land cover maps produced mainly by visual interpretation supply essential information that would be hard to get otherwise, but the definition of a general land cover map covering larger territories contains many compromises both in thematic and spatial sense. The effects of a relatively large uniform MMU and the use of complex LC/LU classes on the areal statistics are not clarified. Currently, we cannot estimate the reliability of the statistics calculated from usual vector LC databases, and do not know the influence of changing the MMU or changes of the nomenclature on the statistics.

The layers of specific LC/LU elements might give us the most exact data in statistical and geometric sense – restricted to the specific LC/LU elements. When mapping eg. buildings or vineyards it is easy to define a characteristic MMU valid to these features, as artificial features with regular shapes are easier to map than irregular natural LC elements.

When creating a general LC/LU statistics a complex model is needed for the integration of pieces of information. In spatial terms probably the most general way would be the rasterization of all the datasets at a specified scale and the weighting of the information sources. An example: let us imagine a paved road going through a smaller forest patch inside a large agricultural area. Neither the thin road nor the small forest (less than 25 ha) will not be represented in the CLC database. The CLC database will inform us about the surroundings of an e.g. 100m cell in a general term The HR database will show tree cover inside a cell, but we have a precise road database which will have in this case the most weight. So, we know that e.g. 65% of the 100m cell is covered with a sealed surface (paved road), the rest is forest.

2.2 GIS modelling GIS modelling often requires the knowledge of LC/LU elements that occupy only small and/or narrow areas, but have significant impact to their environment. Livestock units or pipelines are usually not shown on LC maps, but are significant (potential) sources of pollution. Other linear elements, like roads, streams or alleys are usually too narrow and not represented on LC maps, but have major influence to wildlife. The combination of raster and vector data model and the complex use of HR layers, vector LC maps and layers of specific LC elements mean the solution for most of the environmental GIS modelling needs.

3 Conclusions and proposals Main conclusions:

A new conceptual model is needed to integrate and harmonize the land monitoring activities on national and European level,

One LC layer cannot integrate all available and necessary LC/LU information,

The proposed solution is a structured system of LC/LU information layers according to the principles pronounced for SEIS,

Basic layer types (building blocks) of LC information have to be identified, the collection of the necessary information has to be ensured at national and European level by harmonizing efforts for data collection and optimizing resources,

Recommendations for optimal aggregation and processing rules have to be collected and published,

Accuracy information and recommendations on major potential uses – including limitations of applicability - should be part of data description (metadata),

Gaps in LC/LU information structure have to be identified and filled to get harmonized results at national / European level,

…

Budapest, 2010.05.03

References Jacques, P., Gallego, J.: The LUCAS 2006 project – A new methodology.

http://mars.jrc.it/mars/content/download/567/4122/file/Lucas%20new%20methodology.pdf

Büttner, G., M. Bíró, M. Maucha, O.Petrik: Land Cover mapping at scale 1:50.000 in Hungary: Lessons learnt from the European CORINE programme, 20th EARSeL Symposium, 14-16 June 2000, In: A decade of Trans-European Remote Sensing Cooperation, pp. 25-31.

Büttner, G., G. Maucha, G. Taracsák: Inter-Change: A software support for interpreting land cover changes, In: Proceedings of the 22nd EARSeL Symposium, Prague, 4-6 June 2002; pp. 93-98, Millpress, 2003.

Maucha, G., Taracsák, G. Büttner 2003. Methodological questions of CORINE Land Cover change mapping, Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing images, MultiTemp-2003 Workshop, 16-18 July, 2003, Joint Research Centre, Editors: P Smith and L. Bruzzone, pp. 302-313. Series in Remote Sensing. Vol.3. World Scientific Publishing Co., 2004

Maucha, G., Büttner, G ., 2008. Recommendations for quantitative assesment high-resolution soil sealing layer. ETC-LUSI report

Steenmans, C. and Büttner, G: Mapping land cover of Europe for 2006 under GMES. Proceedings of the 2nd workshop of the EARSeL SIG on land use and land cover, Bonn, Germany, 28-30 September, 2006 pp. 202-207.

To be complemented…