CROP CLASSIFICATION BASED ON SPECTRAL STANDARD CURVES

Center for Remote Sensing of Land Surfaces, Bonn, 28-30 September 2006

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CROP CLASSIFICATION BASED ON SPECTRAL STANDARD CURVES

Sibylle Itzerott1and Klaus Kaden2

1. GeoForschungsZentrum Potsdam, Section 1.4 Remote Sensing, Potsdam, Germany; [email protected]

2. University of Potsdam, Institute of Geoecology, Potsdam, Germany; [email protected]

ABSTRACT In order to achieve a higher efficiency with remote sensing-based monitoring approaches, classification instructions have to be comparable to allow a transfer to other regions and frac-tions of time. It is shown that on the basis of the standardisation of the radiometric, spectral, geometrical and temporal information of remote sensing data a standardised classification was developed, which fulfil these requirements. For twelve typical agricultural crop types spectral standard curves were produced and a hierarchically structured algorithm was de-signed. This procedure, targeted on the identification of crops on the field level, takes up the differentiation between the phenology of the crops, illustrated in the standard curves, and converts it into a hierarchical classification strategy. The algorithm covers three basics proc-essing units. In the pre-processing the available Landsat data of a cultivation year are stan-dardized, masked and joined to a multi-temporal NDVI dataset of the agricultural lots. To ob-tain the classification instruction the parameters describing the crops must be extracted from the standard curves for the acquisition dates. The hierarchical classification covers four pixel-based image analyses using the Parallelepiped method and a final majority analysis of the lots.

The determining advantage of the procedure consists in the way of building the classification instruction. While this working step in conventional procedures requires the largest time ex-tent, the expenditure in the presented approach reduces to fitting the points of recording time in into the standard year of phonological development of each culture. According to this ad-justment the culture specific spectral description can be extract from the standard curve di-rectly.

A quality evaluation on the basis of confusion matrices for 1620ha cultivated area on 144 parcels for the cultivation year 1995 resulted in an overall accuracy of 65,7%. In comparison to it a conventional Maximum Likelihood classification of the same database reached a result of 72,8%. That shows that the approach without quality loss is justified in principle. However the time-saving design of classification instruction and the easily repeated application of the procedure are the essential advantages.

Spectral standard curves in their present level of development can be regarded as basis of the method. Their quality will continuously increase by steady adding of new information. They can be regarded as continuously expandable information memory and should take up all classification-relevant information to the appropriate culture in standardized form. This will lead to a quality increase with the application of the procedure. The perfection of the standard curves is thus a central starting point for the advancement of the presented procedure. By the use of the NDVI this method is expandable on other, also new sensor systems.

INTRODUCTION The necessity of the integration of land use dynamics (i) into process oriented geoscientific investigations (ii) is beyond all questions (iii). In the presented study it was required for the modelling of water quality in a river catchment area in the sense of the Water Framework Di-rective (WFD). In this context the land use in the entire catchment plays a substantial role in

Proceedings of the 2nd Workshop of the EARSeL SIG on Land Use and Land Cover

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particular for the chemical quality parameters. The essential none point nutrient entries origi-nate from agriculture, that occupies large surfaces. That fact demands to analyse the land use in strong spatial differentiation and high temporal resolution.

At present data for land use are added as static surface description into the modelling (CORINE land cover, classification of land use based on airborne CIR-images). In some cases a rotation of crop types according to coincidence principle is simulated on the fields. That is unsatisfactorily considering the very different fertilisation quantities and dates for the different crop types.

Classifications of crop types using remote sensing data often were done for different smaller test sites and single years based on ground trouth information for this specific area and time. Multi-temporal applications (iv) rank in a high position in the field of crop distribution (v) clas-sification in agriculture (vi). But a solution for larger regions without using information of test fields was still not found, because the problem of a universal class description for crops, unin-fluenced by special weather and soil conditions, is still unresolved.

The authors are committed that apart from the geometric and atmospheric correction of the data highest importance has to be attached to the temporal correction of the satellite image contents, according to the phenological status of the shown crops. Depending on the re-quirements of the different crops regarding soil quality and water availability, the weather conditions during the crop growth before the acquisition of an image result in an unequal de-lay or lead compared to the long-time phenological development of the crops. That fact has to be considered in new approaches.

CONCEPTION OF SPECTRAL STANDARD CURVES The presented procedure is dedicated to that problem by designing spectral standard curves of the plant behaviour, which link the phenological status with the spectral value of crops (vii). The potentials of remote sensing with the recurrent simultaneous mapping of wide landscape cut outs in a high spatial resolution offer the chance to analyse a large number of satellite images showing the same area over a long period and in varying phases of phenological development. The spectral behaviour of the typical crop types of federal state Brandenburg in each phenological status was evaluated using a time series of 35 Landsat-TM scenes taken from 1987 to 2002 and cultivation information of six agrarian companies with about 400 parcels of acre land (about 3600 cultivation information). The spectral information of a sample parcel with one of the crops issued from an image was temporal corrected applying the results of the phenological observation network of DWD (1951 – 2003). This way the acquisition date was specified into the mapped phenological datum of the crop (Table 1).

Table 1: Conversion of acquisition date into mapped phenological datum of crops

Day of the year

Used phenological day according to the development status of the crop at ac-quisition date based on phenological observation network of DWD

Acquisition

date of

satellite

image acco

rdin

g to

ac

-

quis

ition

dat

e

Fal

low

Win

ter

Rye

Win

ter W

heat

Win

ter B

arle

y

Win

ter R

ape

Gra

ss p

eren

nial

Sum

mer

Cer

eal

Sug

ar B

eets

Sila

ge M

aize

Fla

x an

d P

eas

Pot

atoe

s

Gra

ss fi

rst y

ear

08.06.1996 159 154 151 154 147 147 154 154 166 159 154 166 154


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The NDVI was calculated and the single values were combined to a curve. The final spectral signature curves for the typical field crops transformed on the phenological standard year associate every phenological status during the growth of the crop type with a typical spectrometric information (Fig. 1).

Figure 1: Synthesis of the Winter Wheat NDVI standard curve from NDVI values of sample parcels

In figure 2 the standard curves of the 12 typical crop types in Brandenburg are plotted in Divi-sion of three groups: winter crops, summer crops and permanent crops. The similar curve shape of a group of crop types is determined by the similar phenology. Winter crops grow up in the autumn after the sowing and show starting from this time a rising NDVI value. During the winter break it changes only slightly (tendentious rather dropping). In spring the values rise fast and reach in the early summer their maximum values. Afterwards a relatively rapid decline is noticeable during the ripe phase, in which the harvesting and ploughing are visible. Already in the summer the cycle is final. For summer crops an increasing of the NDVI values is registered only in later spring. The phenological cycle begins deferred and lasts in ap-proximately from April to October. After a very rapid rise of the NDVI values in the late spring different decreases follow to the late summer (summer cereals) to autumn (sugar beets). Permanent crops as fallow and field grass have because of its over years continuous use (management) an phonological cycle adapted to the full phenological year (January - Decem-ber). Since the field is not completely cut, reflection characteristics of the chlorophyll are pre-sent at each time and the usual phenological yearly rhythm of grass and herb-like nature plants is visible. Field grass has however a sequence of several phenological cycles within a yearly. The number results from the number of the cuts. The vitality weakens itself thereby from cut to cut, so that the complete yearly cycle of a nature plant stands out nevertheless. This grouping creates the first starting point for a hierarchically structured classification. As a function of the characteristic phenological cycles for an optimal recognition of these groups specific classification instructions and satellite scenes of different times are needed. This is to be considered in the algorithm.

On the following hierarchic level differentiations are noticeable more clearly within the groups. If one regards the distinction within the winter and within the summer crops, then winter crops possess good separation possibilities particularly at the beginning of the winter break. In addi-tion, to other sections sufficient distinction possibilities are given between individual cultures. Winter rape stands out during the bloom clearly against the other cultures. Winter wheat has a time-delayed grow up in the spring. Winter barley possesses a temporal lead during the ripe process in relation to all remaining crops. Winter rye differs particularly by the kind of growing


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up. Here a more moderate rise of the NDVI values can be determined also in the winter, compared to the other winter crops. Summer cereal is beside grass in the first year the earli-est summer crop. Both reach their maximum values fast. From this time on summer cereal possesses obvious similarities to the winter wheat, so that the beginning of grow up offers the best distinction possibility after seed. The field grass in first year behaves at first similarly, occurs later the rhythm of perennial grass marked by repeated cuts. This can be used for distinction. For oil fruits and legumes (flax and peas) the phenological cycle begins somewhat later. The middle position within the summer crops, connected with smaller maximum values (aspects of flowering), marks this group. The remaining three summer crops start their phono-logical cycle late. Potatoes have an intensive, but relatively short cycle and particularly distin-guish from the remaining crops during dying of the plant leaves in the ripe process. Silage maize possesses best distinction possibilities for nearly all cultures by its late flowering and ripe phase starting from the late summer. Here only certain similarities arise to sugar beets. These keep their vitality up to the harvest and stand out against silage maize in ripe process increasingly better with progressive time.

Within the permanent crops perennial field grass differentiate from the fallow by its mowing rhythm.

Figure 2: Designed NDVI standard curves of dominant crop types in Brandenburg divided into three groups (winter crops – top, summer crops – middle, permanent – bottom).


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NEW ALGORITHM FOR THE CLASSIFICATION OF CROP DISTRIBUTION A new algorithm for the use of the standard curves in a remote sensing-based classification of crop types was developed (viii). This procedure, targeted on the identification of crops on the field level, takes up the differentiation between the phenology of the crops, illustrated in the standard curves, and converts it into a hierarchical classification strategy. The algorithm covers three basics processing units. In the pre-processing the available Landsat data of a cultivation year are standardised, masked and joined to a multi-temporal NDVI dataset of the agricultural parcels. To obtain the classification instruction, the parameters describing the crops must be extracted from the standard curves. The third step, the hierarchical classifica-tion, covers four pixel-based image analyses using the Parallelepiped method and a final ma-jority analysis of the parcels.

Pre-processing According to the classification method based on a standardized classifica-tion instruction, the data records which will be used are to be standardized too. A radiometric standardisation (atmosphere correction and producing reflectance values), a geo-referencing (geometric correction) and the time correction (evaluation of the acquisition date concerning the phenological standard year) are required. The latter is however not accomplished to the image dataset, but effected fruit-specifically according to the phenological observation in the creation of the classification instruction. From the corrected image data an multi-temporal data set of the NDVI values is generated. In order to separate the areas of arable land from the remaining land use types, a agricultural land use mask is provided and applied to the data set. In the case that no suitable land use mapping is present, it can be revert to satellite data. The use of at least two data sets is recommended from the winter period with a temporal dis-tance of some years, in order to ensure on the one hand the separation of the acre land from grassland surfaces and on the other hand the registration of land use changes too. Using this field mask the NDVI data set reduced to areas of arable land. With the demand of a classifi-cation result on parcel level the need to a data set of the parcel borders itself is connected. Since in the rarest cases a mapping is present (the register of InVeKoS solves only into field blocks, not into parcels), again the use of satellite data take place. Modern object-oriented classification procedures (eCognition) allow the extraction of the parcel borders from a multi-temporal data set. The reality proximity is reached by the use of the different dates during the parcel demarcation. The parcel borders are stored as vector data record.

Build classification instruction The NDVI standard curves are the basis for the classifica-tion instruction. The statistic values necessary for the classification can be extracted from them. It has to be noted that the time correction has to be realised here. Not the acquisition date of the satellite image is to be used, but the appropriate day according to the develop-ment status of the crop. With the help of phenological observation data (e.g. the DWD) the development states are to be detected in the used satellite data to determine the pertinent crop-specific standard days and to integrate their statistic values (NDVI) in the classification instruction. These standard NDVI values are the only information, which is used for the classi-fication. There is no sample information taken out of image content.

In the example of cultivation year 1995 (Fig. 3) four scenes were available for the classifica-tion of crop types. The crop-specific phenological status in the images was investigated and the associated NDVI was derived from the standard curves to build the classification instruc-tion.

Hierarchical classification The hierarchy considers the different phenology of the three groups of crops. Their optimal detection is ensured only by the consideration of different time windows. The first level aims at recognizing the permanent crop. They can be defined surely due to the permanent vegetation cover in all image data sets. The complete NDVI data set is analysed by the class description of all crop types using the Parallelepiped method. The raster data set of the result image is stored as intermediate result for the distribution of the permanent crops (fallow, perennial field grass) and is submitted in addition to a parcel-referred majority computation using the parcel borders available as vector data. This result is


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Figure 3: Processing unit 2 – Build classification instruction for the example of cultivation year 1995.

stored as permanent crop mask. The second level separates the winter crop from summer crop areas. The permanent-masked NDVI data set is reduced to two times now. One scene (winters/early spring) has to illustrate the winter crops (including the winter intercrops), the other one (late spring/summer) separates the summer from the winter crops. This way the permanent phenological development of the winter main crops can be recognised separately from the interrupted development (winter intercrop as pre-crop of the summer main crop) or development starting with shift (open area of arable land in the winter, followed by summer main crop). In the classification all winter and summer crop classes are considered first. After the classification the result is generalized on the two evaluations winter crop or summer crop. Finally a parcel-referred majority computation is accomplished again. In the result a winter crop and a summer crop mask are developed. On the third level a separate classification of the winter crops (rape, rye, barley, wheat) and the summer crops (summer cereal, silage maize, potatoes, sugar beets, flax and peas, grass first year) takes place using a specific classification instruction in each case. For the summer crops the time window reduces to the span from April to October, for winter crops images from October to August are included. The two instructions are applied to the accordingly masked data sets. The result images of the summer crop and winter crops are joined with the result image of the permanent crops to a sum image. A final majority computation produces an image of the parcel-referred crop distri-bution in the cultivation year.

RESULTS The 1995 application of this algorithm for the example of the cultivated lots (1600 ha) of an agrarian cooperative in Lower Havel region reach a comparable result quality to conventional procedures as Maximum Likelihood assessed by error matrices (Fig. 4). The main error in the result image computed by standard curves is the misclassification of potatoes instead of si-lage maize. In 1995 the chance to differ potatoes, sugar beet and maize is low according to the unfavourable scene composition. To reach a partition, a scene from end of September is needed. Starting with the harvest season the before similar NDVI development of all these crops ends. A special problem of the year 1995 is the permanent negative water balance. Especially on sites without groundwater supply maize plants dry up in august and lose their photosynthetically active chlorophyll. The resulting spectral behaviour is similar to ripening of potatoes at that time. Maize plants growing in groundwater supplies sites is still green and classified right as silage maize.


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Figure 4: Comparison of classification results for the two meth-ods with cultivation information of agrarian cooperative.

Using the example of sample parcels (figure 5) with different soil quality and water capacity it can be demonstrated, that the classification success depends also on this factors. The weather condition affect to the parcels in a different way, poor soil nutrient and water avail-ability on sandy sites lead to spectral aspects of the stressed plants, which is not fixed in the standard curve. In the comparison of classification quality for six sample parcels (outlined in colours in the map) between real and detected crop rotation it is remarkable, that gross errors (red outline in table) and minor errors (green outline) originate from unfavourable scene com-position in some years (1998 - separation of crops is not possible in existing images) and from adverse soil water conditions in particular on sites with deep sand (parcels no. 5 an 6).

Figure 5: Comparison of computed crop rotation with real rotation for sample parcels with different soil, nutrient and water supply described by the map of landscape types (NRT).


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Figure 6: Distribution of acre land in River Havel catchment (left in brown colour) and of culti-vated crop types in 2000 (right, legend compared to figure 4)

Also in the whole Havel test site (Fig. 6) the division into winter, summer and permanent crops provide mostly good results, only in the case of intercrop cultivation on coming summer crop fields these parcels were classified incorrect as winter crop. Depending on the amount of scenes in the cultivation year the quality of the classification varies. Difficulties to distinguish between few winter and few summer crops exist, especially for sugar beets, maize and pota-toes. Even having these complications in the mind it has to be pointed out, that the new method allows the crop classifications of an area such as River Havel catchment with about 532.000 ha of acre land with a very low expenditure of time.

The new procedure allows the identification of crop rotations too. Figure 7 shows this parcel-referred result for the sample site of Lower Havel region (green box in figure 5) between 1994 and 2000. The utilization of the remote sensing potentials using the classification by standard curves is much easier because of omission of the exhausting search for sample sites in the image to build up the classification rule. The resulting rotation information can be combined with modelling parameters regarding ecological and economical aspects in agriculture. By

Figure 7: Classification results of Lower Havel region for the years from 1994 to 2000 show-ing the parcel-referred crop rotation


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combining the classification result for crop type with recommended fertiliser impacts accord-ing to crop type information about spatial distribution of nutrient entries is extractable for every year. Together with the knowledge about the best fertilising dates per crop in agricultural practice the method allows a realistic implementation of long term none point entries in time and space into the nutrient model.

CONCLUSION AND OUTLOOK

Finally it can be concluded that without quality loss the new procedure can be used for the detection of the agricultural land use from remote sensing data. Spatial and temporal informa-tion about agriculture can be extracted based on the new method in an effective and objective way. The procedure can be considered as an essential progress in comparison to the usual classification based on sample points taken in the image. In the Havel region it should work for other years too without any new ground trouth information about crop distribution, entirely an exact phenological dating of the new scenes is necessary. In other regions (with variation in precipitation and soil compared to Havel) the adaptation of the curve shape is required (start, length and end of phenological cycle). Usage of Fuzzy information like the relationship between soil quality and crop ( cultivation suitability for crop), the availability of water (de-pending on water capacity of the soil, groundwater connection and precipitation) and the sum of precipitation during the season until the date of image (dryness indicator) in the classifica-tion will improve the quality of results furthermore. For the distinction of all crops was found as optimum the combination of images date Early/Mid April – Mid May – Early July – Mid August – Mid September.

ACKNOWLEDGEMENTS Theses investigations (ix) were carried out in a project “Management in the Havel River Ba-sin” and funded by the BMBF (FKZ 0330227).

REFERENCES i Oenema O, P C M Boers, M M van Eerdt, B Fraters, H G van der Meer & C W J Roeste,

1998. Leaching of nitrate from agriculture to groundwater: the effect of policies and measures in the Netherlands. Environmental Pollution, 102: S1, 471-478

ii Van der Vlist M J, 1998. Land use planning in the Netherlands: finding a balance be-tween rural development and protection of the environment. Landscape and Urban Plan-ning, 41: 135-144

iii Ekholm P, K Kallio, S Salo, O P Pietilaeinen, S Rekolainen, Y Laine & M Joukola, 2000. Relationship between catchment characteristics and nutrient concentrations in an agricul-tural river system. Water Resource, 34:15, 3709-3716

iv Panigrahy S &S A Sharma, 1997. Mapping of crop rotation using multidate Indian Re-mote Sensing Satellite digital data. ISPRS J. Photogrammet. Remote Sens., 52: 85-91

v Udelhoven T, P Hostert, P Jarmer & J Hill, 2003. Klassifikation von Getreideflächen mit hyperspektralen Bilddaten des HyMap-Sensors. Photogrammetrie-Fernerkundung-Geoinformation, 1: 35-42

vi Martinez-Casasnovas J A , A Martin-Monteroa & M A Casterad, 2005. Mapping multi-year cropping pattern in small irrigation districts from time-series analysis of Landsat TM images. Europ. J. Agronomy, 23: 159-169


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vii Itzerott S & K Kaden, 2006. Spektrale Normkurven – eine notwendige Voraussetzung für

die Klassifizierung der Fruchtartenverteilung aus Fernerkundungsdaten. Photogrammetrie-Fernerkundung-Geoinformation, 3: 205-216

viii Itzerott S & K Kaden, 2005: Bereitstellung raum-zeit-bezogener Modellierungsparameter unter Einbeziehung von Fernerkundungsdaten. Final Report of Subprojekt 6 in BMBF funded project „Bewirtschaftungsmöglichkeiten im Einzugsgebiet der Havel“., GeoForschungsZentrum Potsdam und Universität Potsdam. www.havelmanagement.net

ix Bronstert A & S Itzerott, 2006: Bewirtschaftungsmöglichkeiten im Einzugsgebiet der Havel – Final Report of BMBF funded project. Brandenburgische Umweltberichte 18, Potsdam

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CROP CLASSIFICATION BASED ON SPECTRAL STANDARD CURVES