15arspc Submission 39

8/8/2019 15arspc Submission 39

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CHARACTERIZING LANDSCAPE PROPERTIES INSOUTHEASTERN SPAIN BY COMBINING CLASSIFICATION AND

MULTI-DATE SPECTRAL MIXTURE ANALYSIS

Achim Rder1, Stella Hubert, Marion Stellmes

University of Trier, FB VI Geography/Geosciences,Remote Sensing Department

Campus IID-54286 Trier

Germany+49.651.2014606

[email protected]

Peter Scarth

Remote Sensing Centre, Queensland Department of Environment andResource Management

BrisbaneAustralia

Susana Bautista, Jordi Cortina

Universidad de Alicante, Faculdad de EcologiaAlicanteSpain

Abstract

Mapping land use and land cover is one of the most frequently addressed topicsin remote sensing, owing to the importance of such information for a wide rangeof applications that range from local land management to global climate models.Local to regional scale data, such as those acquired by the Landsat sensors,provide long-term monitoring capacities along with a scale that is consistentwith sub-national land management. These data allow mapping land use andlanding cover for a sequence of dates and comparing results both in terms ofoverall rates as well as in a spatially explicit manner. Yet, it is now accepted thatfor many environmental applications, the modification of ecosystems even if incategorical terms they remain stable may prove equally important thantransformation from one land use type to another. Consequently, adapted dataprocessing and mapping strategies are need to consider both types of change.Also, translating the derived information is essential to comply withassessment frameworks such as ecosystem services and functions, whichmay eventually form the basis for assessing trade-offs associated with land-usedecisions. In this study, we report first steps towards this goal and present acombined approach for enhanced land use and land cover mapping based on a

1 Corresponding author

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rigorous radiometric data processing scheme It incorporates a hybridunsupervised classification approach using available reference data forhistogram based class assignment, and a multi-date spectral unmixingapproach to further differentiate natural and semi-natural vegetation classes.

Results are discussed against the background of major socio-economic trendsand physical processes that are typical for many countries of the EuropeanMediterranean.

Introduction

Assessing the state of ecosystems is one of todays foremost issues; yet,depending on the individual perspective this can be highly subjective. TheMillenium Ecosystem Assessment promoted the concept of EcosystemServices and Functions (Millenium Ecosystem Assessment 2005), which groupsthese into major groups of provisioning, regulating, preserving and culturalservices. This may serve as a conceptual framework to assess the state of

ecosystems with respect to the individual functions and services, while ratingcan then be performed according to the respective individual management ordevelopment goals. Many pressures are being exerted on ecosystem servicesand functions, not the least arising from often competing demands by differentgroups of stakeholders with individual priorities such as conservation, economicdevelopment, food appropriation etc. In this context, land use and its changehave been identified as major driving forces which hence require adequateattention (DeFries et al. 2004).

In the recent past, there have been manifold efforts to provide consistent landuse / land cover maps that cover national, continental or even global scales

(Loveland et al. 2000), and to combine such data with climatological data orinformation on human settlement (Ellis and Ramankutty 2008). Such data setsare essential to provide input to global models and support continental to globalpolicy initiatives. Yet, they are often limited to one particular date or period, orare only available for relatively short time periods (e.g. MODIS land coverproduct). In particular, they are often not suitable to match spatial scalesrelevant for land managers, or at which individual land use decisions are beingtaken and implemented. In this line, the importance of local scale studies tounderstand human-environment systems has been reiterated. In addition to thetraditional concept of assessing land use and land cover change by diachronicanalyses, there is growing evidence that modifications of land cover, although

within a particular land cover class, might be of equal importance (Lambin andGeist 2006). For instance, increasing or decreasing vegetation cover within onebroad vegetation class might have strong implications for fire risk, soil erosionor carbon sequestration, among others.

Therefore, three major prerequisites emerge for land use / land cover changeanalyses in a wider ecosystem perspective: a) they should ideally cover both,transformation and modification processes; b) they should be linked to drivingforces to contribute to understanding of coupled human-environment systems;and c) they should provide results in a way they can be linked to ESF to makethem useful in assessing tradeoffs between certain land use decisions.

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In this study, we focus on the methodologies that might be used to addressmapping land use and land cover transformations and modifications in as muchas possible an operational way.

Test area and data setsA test site in South-Eastern Spain was selected for this task, which correspondsto one full Landsat frame (path 199/row 33) and comprises parts of theprovinces of Valencia, Alicante, Albacete, Cuenca and Murcia. Altogether, it isrepresentative of a wide range of historic and recent change processes typicalfor many countries of the European Mediterranean in general, as well as Spainin particular. Evidently, accession to the European Union in 1986 has been aprimary driver of economic developments ever since.

In general, landscape change in the region is characterized by both,intensification and extensification processes occurring often in close proximity

(Hobbs et al. 1995). On the one hand, there is a strong tendency towardsurbanization, aggravated by increasing touristic uses. Both are concentratedalong the coastal zones and cause a strongly increasing consumption offreshwater (and land). At the same time, an intensive agricultural use is noted insuitable locations, which adds to the problem of water consumption. On theother hand, in less favorable areas land abandonment occurs, oftenaccompanied by ageing rural societies. This process in turn is often followed byshrub encroachment associated with high risk of wildfires. The latter areparticularly evident in the northern part of the test area where, depending on firefrequencies, different states of stability, disturbance and recovery may beobserved, ranging from low, primarily herbaceous cover, to dense mattorrals

and forests. These are mainly composed of pine tree species, which are also asource of timber used commercially. This is contrasted by Stipa steppesoccurring further south, where significantly lower precipitation preventsdevelopment of dense vegetation cover.

A major factor responsible for this land cover pattern is the elevation, rangingfrom sea level to ~1300m a.s.l. in higher mountain ranges and around 750ma.s.l. towards the Meseta higher plains further west. Precipitation shows agradient that is mostly oriented in North-South direction with amounts rangingfrom more than 500 mm in Ayora region to around 300 mm around Alicante. Inthe plain areas, mostly fertile, alluvial soils support agricultural use, while

mountainous regions are commonly characterized by shallow soils forming oncalcareous rocks of the Betic Cordillera.

Individual treatise of transformation and modification processes poses specificrequirements to the data used. Most commonly, land use classificationapproaches rely on multi-temporal data for specific periods, since these allowincorporating seasonality information to discriminate agricultural uses ordifferent vegetation types. On the other hand, assessing modifications requireseither hypertemporal data or, if undertaken at local scale, a time series ofimages acquired at comparable phenological dates. For this reason, we haveset up a data base consisting of 31 Landsat-TM and -ETM+ images and thatcovers the time span from 1984 to 2007. Wherever possible, one image per

year was acquired for the period of maximum photosynthetic activity (preferably

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in a time window between April and June), and complementary images fromother seasons where added for different dates, in particular for 1986 tocharacterize the state prior to accession to the EU, and for 2007 representingthe current situation. In addition, MODIS single day reflectance and SRTM

digital elevation data were used.

Data pre-processing

Geometric processing

For data acquired from Eurimage we adopted a geometric correctionapproach based on cross-correlation windows between an uncorrected Landsatimage and an illuminated digital elevation model (Hill and Mehl 2003). Morethan 2000 ground control points were retrieved this way and enabled a highlyaccurate correction of a master image, including the elevation model to accountfor non-systematic distortions. Subsequently, the same algorithm was employed

to find ground control points for the remaining images. Data provided by USGSwere downloaded as level 1G orthorectified products. Thorough checks showedthese data to be highly consistent with the data rectified as described above,such that they could be incorporated without further processing.

Radiometric processing

Radiometric processing is essential if data from different dates are to be jointlyevaluated and quantitatively analyzed. We have therefore employed a fullradiative transfer model approach based on a modified 5S code (Tanr 1990),which was extended to account for topography-induced illumination effects.Using a digital elevation model, the sun-surface-sensor constellation at the time

of image acquisition is characterized and shaded elements as well as visiblesky fraction are calculated. Then, the information is incorporated into theradiative transfer model; direct irradiation is corrected using a cosine correction,while diffuse irradiation terms are separated into their isotropic and a anisotropiccomponent using the anisotropy index by Hay and McKay (1985). The latter iscorrected using a cosine correction, while the first is corrected making use ofthe visible sky fraction to weight the amount of incoming diffuse irradiance (Hilland Mehl 2003, Rder et al. 2008). Sensor calibration constants were based onin-flight calibration data in the case of Landsat-7 ETM+, or on vicariouscalibration data for Landsat-5 (Helder et al. 2008).

The transfer model is parameterized using Angstrom coefficients to determineaerosol optical depth of the atmosphere, and by characterizing gaseoustransmission based on the Modtran code (Berk et al. 1999) as a function ofwater vapor concentrations. In the absence of specific measurements, anapproach utilizing MODIS single day reflectance data (MOD09GA) wasdeveloped. This draws from the documented high quality of the radiometriccorrection chain implemented for MODIS (Liang et al. 2002). It involves anadjustment of MODIS bands to match TM/ETM+ bandpaths, and application ofa Gaussian smoothing filter and subsequent resampling of the Landsat data, tomatch the low pass effect and lower geometric resolution of the MODIS data.Assuming that after this procedure both data sets are fully congruent,

signatures for areas with different brightness levels were extracted. Using a

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minimization algorithm, the radiative transfer model was used to seek theAngstrom coefficients and water vapour concentrations that allowed to bestmatch corrected Landsat reflectances with MODIS reference reflectances. Theparameter combination found this way was then utilized to correct the Landsat

image in its original resolution and including the information on topography-related illumination effects. In this way, a corrected Landsat master image wasprepared and used to subsequently correct the remaining Landsat scenes usingpseudo-invariant features and the same matching approach. The resulting timeseries was found to be quantitatively consistent, with limitations mainly resultingfrom extreme topographic effects with images acquired at low sun elevationangles, i.e. early or late in the year.

Image classification

Histogram-based classification of non-natural areas

Mapping transformation of land use or land cover classes was based on twomulti-temporal data sets, covering the time frame of 1986 (23/06/1986 and26/08/1986) and 2005-2007 (26/5/2005, 03/07/2007, 04/08/2007). Theclassification strategy was based on the CORINE land cover data (CoordinatedInformation on the European Environment) available from the EuropeanEnvironment Agency (http://www.eea.europa.eu/data-and-maps/data). It isavailable for 1990, 2000 and an update for 2006 is underway. The data sets arebased on photo-interpretation of aerial photographs, Landsat-7 ETM+ andSPOT 4/IRS 6, all with a minimum mapping unit of 25 hectares. These data setswere aggregated to the 8 target classes Urban, Rainfed Agriculture, IrrigatedAgriculture, Permanent Crops, Bare Ground, Herbaceous, Transitional

Woodland, Woodland.

Our approach builds on the assumption that, despite reported deficiencies ofthe CORINE data base, broad land use / land cover categories are adequatelyrepresented. For both multi-temporal stacks, principal components werecalculated and the first 9 and 12 bands were retained. To provide additionaldiscriminative power, NDVI was calculated for every image and added as anadditional band to the respective layer stack. The respective data sets wereused as input for an unsupervised classification using an ISODATA clusteringalgorithm, which yielded 100 spectral classes each. Then, histograms werecalculated for each of these classes, representing the frequencies of thematic

classes of the CORINE reference, using the CORINE 1990 and 2000 layers forboth dates. For each spectral class, the thematic class was assigned if itcontributed more than 60% to the overall pixels. In case of more ambiguousfrequency distributions, classes were manually checked and assigned using acombination of the majority approach described before and contextualinformation. As classification of urban areas poses specific requirements, it wasdecided to include the respective CORINE areas in the classified maps.

While no validation of results was possible for the early date, the recentclassification was validated using a stratified random sampling approach. 1011points were generated in relation to the respective classes (excluding the Urbanclass), exported to Google Earth and labelled with the apparent class.Depending on acquisition conditions, image quality etc., some classes are

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difficult to detect visually. Therefore, validation was carried out at three levels; atthe first, all classes were used; at the second, agricultural classes were mergedwhile natural/semi-natural classes remained unchanged; at the third, onlyagriculture, woody and non-woody cover was evaluated. Considering

uncertainties in mapping validation data, seasonal difference betweenacquisition dates etc., results were considered satisfactory with kappa values of0.65, 0.75 and 0.9, respectively.

Multi-date spectral mixture analysis

Given the relevance of vegetation communities in the context of fire risk andpost-fire dynamics, or nutrient flows in sparsely vegetated Stipa steppes, animportant goal was to further differentiate semi-natural and natural areas. Wehave adopted a multi-date spectral mixture analysis approach (Kuemmerle et al.2006) based on a simple representation of the spectral feature space and itstemporal dimension. Non-natural areas where masked out, and from the

remaining areas every 20th pixel was retained. Then, a composite image wascalculated, incorporating the selected pixels from all images of the respectivetarget period to represent the full spectral dimensionality. Following anorthogonal subspace projection to reduce the number of bands to one less thanthe number of end-members, a simplex-based method was applied to identifythe extremes of the feature space. The method assumes that the volume of asimplex constructed within a data cloud is maximized if the purest endmemberspresent in an image are found. The respective pixels are found by constructingthe volume based on an initial set of randomly selected endmembers, and thenreplacing each endmember with each other pixel (Winter 1999). This way, asimple three endmember set was set up, including spectra forphotosynthetically active green vegetation, a background spectrum, and a zeroreflectance endmember to account for shading effects. The model was appliedto all individual data sets, and a subsequent shade-normalization was carriedout.

To validate the results, aerial photos were employed to map woody cover andthen results were integrated to match the 30 m resolution of the Landsat data.Validation was carried out by comparing unmixing results of the late summerimage on a pixel-per-pixel basis, for a 3x3 window and for the best fitting pixelwithin this window. This way, potential inaccuracies in different pre-processingsteps and inconsistencies between satellite imagery and aerial photos could be

accounted for and an overall estimation of plausibility of the unmixing modelcould be attained. With scatter plots close to the 1:1 line and r2 of .87, .85 and.96, respectively, results were considered satisfactory. The model was thenapplied to all images, and a rule-base was set up to assign each pixel to one ofthe following classes based on seasonal differences: bare ground, herbaceousvegetation, woody cover, dense woody cover. Where seasonal patternsindicated agricultural use, a re-assignment of the respective pixel to thepermanent crop class was carried out. Figures 1 and 2 show the resultingmaps.


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Figure 1: Result of the hybrid classification approach for 1986

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Figure 2: Result of the hybrid classification approach for 2007

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Change analysis and interpretation

The two maps generated according to the methodology described beforeallowed assessing major changes in land use and landing cover for the period

1986 to 2007. While a change matrix was employed to illustrate overall changerates between classes (table 1).

Table 1. Matrix illustrating land use / land cover change in the test area between 1986and 2007, expressed in percent of total area. Changes larger than 1% are highlighted

in red, diagonal elements represent unchanged areas

2007Urb. Arab. Irr. Perm

. Bare Herb. Woody DenseW. Wat.

1986

Urb. 1.46 0.3 0 0.04 0.01 0.02 0.01 0.01 0Arab.

0.18

15.46

0.66

3.97

0.26

2.61

0.33

0.03

0.02

Irr. 0.2 0.5 1.1 0.94 0.01 0.05 0.09 0.04 0Perm. 0.35 7.17 0.69 7.79 0.18 1.87 1.33 0.73 0.03Bare 0.15 2.19 0.12 1.6 0.27 6.17 1.2 0.27 0.04Herb. 0.13 3.35 0.19 3.23 0.08 9.05 6.44 0.77 0.02Woody 0.06 0.39 0.12 1.3 0.02 0.68 4.89 4.47 0.02DenseW. 0.01 0.03 0.06 0.44 0.0 0.07 0.62 3.27 0.02Wat. 0 0.01 0.01 0.02 0.01 0.01 0.02 0.02 0.16

From table 1, two major trends can be discerned that are associated withlargely different change syndromes. The first is the regeneration of previouslyburned areas during the observation period. As fires are frequent in the northernregion of the test area, the first date of image acquisition is likely to showalready different stages of post-fire succession. With the process ofregeneration advancing, these can be perceived as a shift up along a classgradient Bare Herbaceous Woody Dense Woody. Figure 3 showsthe corresponding subset from the classification images.

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Figure 3: Post-fire regeneration apparent from change detection analysis between1986 (top) and 2007 (bottom) in the Northern Ayora area

A second major group of change is attributed to land use transformationsresulting in an overall increase in intensely used land by irrigated agriculture or most importantly - permanent crops. The latter class can be shown toincrease at the expense of arable land as well as natural classes such asBare Ground, Herbaceous and Woody Dominated Cover. The

transformation of natural/semi-natural areas to agricultural use as well astendencies towards intensification are a direct consequence of the notablesocio-economic development in Spain. During the period covered, these arestrongly determined by the accession to the European Union and its benefittingfrom different European funding and subsidy schemes. One area where theseeffects are apparent is the region around the city of Valencia, which is todayrenowned for tree crops of orange, almond and olive trees. While thisdevelopment certainly improved the economic situation of many people, it isevident that the associated increase in water uptake has to be acclaimedcritically, especially given current climate change scenarios for Spain wherewater scarcity is considered a major risk. Figure 4 illustrates this process for the

abundant orange plantations of the Levante, close to the city of Valencia.

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Figure 4: Intensification of agricultural uses and urbanization between 1986 (left) and2007 (right) close to the city of Valencia

While these enlarged subsets of both maps illustrate the processes subsumedin the change matrix, change maps support a spatially explicit evaluation ofchange trajectories. Figures 5 and 6 show these maps for non-natural andnatural areas, respectively.

Besides the examples shown above, another issue typical for many

Mediterranean regions is the process of land abandonment in marginal regions.In the test area, this is coincident with a significant increase in populationfigures along the coast, which result from internal migration from abandonedvillages, but is also connected to European migration patterns of people movingto Mediterranean countries, often after reaching retirement age, and of courseto the touristic attractiveness of seaside resorts. The latter is difficult to assessusing satellite data alone, in particular given that the urban class was directlyincorporated from the respective CORINE data sets with their rather coarseminimum mapping units. Yet, the effect of land abandonment can be attributedto the transformation from Arable Land to the Herbaceous, and to a lesserdegree the Woody class, subsumed in the change map by the arable to

natural class. In the latter cases, the abandonment of regions that had beenused for marginal agricultural use has caused the encroachment of shrubvegetation reflected in the shift to the indicated classes.

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Figure 5: Change from or towards non-natural classes between 1986 and 2007

Opposed to major changes between classes resulting from different factors, thenatural/semi-natural areas in the southern part of the test site remain largelystable. This area is generally characterized by less precipitation and accordinglysparser vegetation, and frequently occupies mountainous ridges unsuitable forintensive use. Therefore, there is no economic appeal to convert these areas,while vegetation cover is too sparse for wildfires to play a major role. Hence, theonly change visible is a shift up of one class classes for some areas (figure 6).

It is also interesting to note that areas classified as Dense Woody Cover arehardly converted to other land uses, which is indicative of the high priorityattributed to maintaining and preserving such areas due to their high ecologicalimportance. Again owing to climatological reasons, these areas occur mainly inthe northern part of the test area and in higher altitudes.

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Figure 6: Change within natural/semi-natural areas between 1986 and 2007

Conclusions and further steps

The approach presented here is based on routinely available Landsat data toaddress land use and land cover in a highly heterogeneous environment. Basedon a dedicated data pre-processing scheme we suggest the combination of aclassification approach with a multi-date spectral unmixing approach is capableof operationally mapping land use and land cover information using availablereference information and image-based endmembers. We have analysedresults for both dates and the resulting change trajectories in relation to drivingfactors responsible for landscape alterations, both physical and socio-economic.Results emerging from the data analysis are congruent with processes such as

land abandonment and associated deterioration of marginal agricultural lands,an increase allocation of land to production of export-oriented cash crops, andtypical succession patterns in areas prone to wildfires. On the other hand,stability is encountered particularly where no economic interests spur land usetransformation, or where no fires occurred for longer periods.

Referring back to the overall research question, it is apparent that theinformation products derived here are more indicative of transformation thanmodification processes with their often transitional character. This issue may beaddressed using time series analysis of densely populated time series usingdata from comparable phenological stages. The potential of this approach has

been demonstrated for a period from 1978 to 2001 for a part of the test area

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(Rder et al. 2008). Further steps therefore include applying the approach to allsemi-natural and natural areas of the full test area, and incorporating derivedtrend parameters into the overall analysis.

The combined analysis of land use / land cover modifications andtransformations will then be analysed in relation to available auxiliaryinformation (e.g. census data, water runoff information etc.), with the aim torelate patterns to landscape change processes beyond the interpretativemanner presented here. This will also form the basis upon which to attempt thetranslation of land use and land cover to ecosystem services and functions,and their subsequent economic and non-economic valuation (Hein et al. 2006)Such spatially explicit, quantitative assessment of ecosystem properties isultimately considered an excellent basis to develop land management orrestoration strategies that consider the trade-off between different ways of usingterrestrial resources.

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