PHENOLOGICAL STRUCTURING OF MULTI-TEMPORAL ... - Uni Halle

PHENOLOGICAL STRUCTURING OF MULTI-TEMPORAL RAPIDEYE IMAGERGY M. Möller?∗ ?

S. Müller†

C. Gläßer?

University Halle-Wittenberg, Department of Remote Sensing and Cartography, Von-Seckendorff-Platz 4, 06120 Halle (Saale), Germany † University of Hanover, Institute of Photogrammetry and GeoInformation, Nienburger Str. 1, 30167 Hanover, Germany ∓ Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany

ABSTRACT The simplified phenological model PHASE is presented which enables a quick and and ’on the fly’ Germany-wide prediction of phenological phases. The PHASE model bases on the assumption of linear relations between observed phenological events and phase-specific accumulated temperatures. The model was designed as simple as possible and is adapted to the specific data situation in Germany regarding publicly available information on weather, phenology and elevation. On the example of the agricultural land use classes Grassland and Winter Wheat and a German test site, the model results are used for the detection of relations between optimal acquisition dates and specific phenological phases for the distinction of agricultural land use classes (phenological structuring). Index Terms— Phenology, temporal window, segmentation, SRTM, DeCover 1. INTRODUCTION European initiatives for the establishment of remote sensingbased services aims at the production of up-to-date land cover information [1]. In this context, new satellite systems have been launched in the last few years providing remote sensing data of high temporal and geometric resolution [2]. With it, especially dynamic phenomena of the earth surface should be able to monitored more closely. The German joint project DeCover 21 was initiated to support the GMES land cover monitoring service components (Global Monitoring for Environment and Security)2 and is developing a methodological framework for the spatial and thematic updating of existing land use data sets [1]. The updating is based on RapidEye imagery which are characterized ∗ The

D. Doktor∓

project is funded by the German Ministry of Economics and Technology, managed by the German Aerospace Center (contract no.:FKZ 50EE0915). 1 http://www.decover.info 2 http://www.gmes.info

by a temporal coverage of 5 days, a spatial resolution of 6.5 m and five multi-spectral bands [3]. During the course of the project, a large number of RapidEye images were provided by the RapidEye Science Archive (RESA3 ) which is managed by the German Aerospace Center (DLR4 ). Thus, a phenological structuring of the available RapidEye imagery over the year is needed which aims at the detection of relations between optimal acquisition dates and specific phenological phases for the distinction of specific vegetation classes. In doing so, an efficient phenological assessment of already existing and planned remotely-sensed imagery could be made. In this article, the simplified temperature-vegetation model PHASE is presented which focuses on a quick, automatic large-scale prediction of agricultural phenological phases. PHASE basically aims at the spatial prediction of sparse point-related information regarding temperature and phase-specific events on the total area of Germany in a reproducible manner. The prediction results are the basis for the phenological structuring of multi-temporal RapidEye images which is exemplified for the distinction of the agricultural land use classes Grassland and Winter Wheat. 2. MODEL Plant phenology is the study of periodic events and their dependence on environmental factors, especially temperature changes driven by weather and climate. Phenological events are clearly visible developmental phases like emergence, blossoming or ripening [4, 5]. An often applied concept for the modeling of phenological phases bases on relations between observed phenological events or ‘Days of the Year’ (DOY) and accumulated sums of daily temperatures [6]. The phenological model PHASE represents a simplified temperature-vegetation model which is adapted to the specific data situation regarding weather and phenology in Germany. PHASE bases on the assumption of linear relations between 3 http://resaweb.dlr.de/ 4 http://www.dlr.de

INPUT LOOP

DWDT sT1...365 Point data set

MODEL 1

SRTM DEM Polygon data set

SRTMT,uHU oT1...365 | uoHU1...365 Polygon data set

INPUT LOOP

DWDP psDOYi...j Point data set

MODEL 2

DWDpHU psHUi...j Point data set

Accuracy

MODEL 3

V (T ) | V (poHU ) | V (poDOY ) D | RM SE | rho

SRTMpHU,pDOY poHUi...j | poDOYi...j Polygon data set

Visualization & Mapping

(a) MODEL 1 psDOY1 . . . OPTIMIZATION

MODEL 2

MODEL 3 . . . psDOYmax V (pDOY )opt V (pHU)opt start.ph

MODEL 2 MODEL 3 Accurracy

(b)

Fig. 1. Principle workflow (a) and optimization procedure (b). observed phenological Julian days of the P year pDOY and n phase-specific accumulated temperatures i Ti...j or heat units pHUi...j . Figure 1a illustrates the principle workflow. Accordingly, PHASE consists of four sub-models and is based on point data sets of daily mean temperatures (DWDT ) and phenological events (DWDP ) which are coupled with an object data set of a segmented digital elevation model and natural units (SRTM; Fig. 2). MODEL 1 predicts stationrelated daily temperatures sT1...365 to Germany-wide daily temperatures oT1...365 Pn and adds them up to all possible cases of the expression 1 oT1...365 which we termed as unspecific heat units (uoHU1...365 ). MODEL 2 calculates phase- and station-specific heat units psHUi...max . The end point of the summation corresponds to the observed station- and phase-specific phenological event psDOYmax . The starting DOY (psDOYi ) is crucial for the resulting model fit and accuracy. The Ti values can be set by export knowledge (e.g. the first day of a phase) or

Fig. 2. Input data and location of the test site Bitterfeld/Wolfen by running an optimization procedure (Fig. 1b). The optimization procedure aims at the determination of an optimal starting DOY (start.ph) for the summation of heat units leading to the best model fit. In doing so, the algorithm calculates internal accuracy metrics (V (pHU ), V (pHU )) for all possible starting DOY of the value range psDOY1 . . . max. Then, a minimum criteria is used for the determination of the optimal internal accuracy values. The actual prediction of phase-specific phenological events (poDOYi...j ) is executed in MODEL 3. The prediction bases on statistical relations between phase- and station-specific phenological events (psDOYi...j ) as well as phase-specific heat units (poHUi...j ). poHUi...j is the result of statistical relations between phase- and stationspecific heat units (psHUi...j ) as well as unspecific heat units (uoHU1...365 ). The PHASE workflow encloses three separate predictions (oT , poHU , poDOY ). Each prediction is evaluated by the internal accuracy metric Variance explained (V ). The external metrics Spearman’s rank correlation coefficient (rho) and the Root Mean Square Error (RSM E) compare observed and predicted DOY. The remaining sub-models enable the automatic visualization of accuracy metrics and the automatic map creation of prediction results. 3. APPLICATIONS 3.1. Prediction of phenological phases In this study, PHASE was applied for the Germany-wide prediction of agricultural phases for Grassland and Winter Wheat (Table 1). Figure 3 shows the prediction result and the used phenological training data set of the phase Begin-

Table 1. List of the considered agricultural plants Grassland and Winter Wheat and corresponding phenological phases observed in 2010 Plant name

Phase-ID

Permanent grassland

GL

1, 25, 26

Winter wheat

WW

10, 12, 15, 18, 21, 24

5 – beginning of flowering | 10 – beginning of tilling, sowing, drilling | 12 – emergence | 15 – beginning of shooting | 18 – beginning of heading | 19 – beginning of milk ripeness | 21 – beginning of yellow ripeness | 24 – beginning of harvest | 25 – 1. cut for hay | 26 – 1. cut for silage

ning of Schooting of Winter Wheat for 2010. The map was automatically created in the course of the PHASE model run. The spatial DOY distribution represents typical meteorological and geomorphological conditions in Germany. The phase begins in the south-western part of Germany (Rhine Valley) and in the lowlands in the north of the low mountain ranges (e.g. Ore Mountains, Thuringian Slate Mountains and Forest, Rhenish Slate Mountains, Westerwald). The latest DOY can be found in the high (Alps) and low mountain ranges as well as in the north-western coastal area. 3.2. Phenological structuring

RE 5 − RE 3 (1) RE 5 + RE 3 The acquisition date-specific distributions’ comparison is done by the Kolmogorov Smirnov goodness-of-fit (KS) test [7]. Based on the empirical cumulative distribution function (ECDF), the KS test verifies whether two distributions are the same (null hypothesis) or significantly different from each other. The degree of difference is expressed by the maximal absolute difference D between the cumulative NDVIdistributions of Grassland (GL) and Winter Wheat (WW; Eq. (2)).

Fig. 3. Prediction result for the phenological phase begin of tassel emergence of Winter Wheat for 2010 (EPSG projection 25832; http://www.spatialreference.org) 309000,000000

5732000 ,000000

The structuring of the RapidEye imagery is exemplified on the test site Bitterfeld/Wolfen with an area of 263 km2 . The test site is situated in the south of the German Federal State of Saxony-Anhalt (Fig. 2). Six RapidEye satellite data sets of 2010 made during the whole vegetation period from March to October are taken (22th March, 6th June, 3rd July, 21st August, 7th September, 1st October). The phenological structuring is based on the comparison of RapidEye-N DV I distributions of the land use classes Grassland and Winter Wheat. The N DV I or Normalized Differenced Vegetation Index was calculated according to Equation (1) using the RapidEye bands 5 (near infrared) and 3 (red).

D = max |ECDFGL − ECDFW W |

º

LAND USE CLASSES GRASSLAND WINTER WHEAT 0

2,5

5,0

10 km

5716000 ,000000

N DV I =

321000,000000

Fig. 4. Grassland and Winter Wheat locations within the study site

(2)

The calculated D values were interpolated over the year using a spline function. As shown in Figure 5, the interpolated D values can be coupled with the predicted temporal

windows of specific phenological phases. In doing so, optimal phenological phases for the distinction of the land use classes Grassland and Winter Wheat could be identified. Ac-

1.0

GL | WW 22 | 3 | 2010 GL1

WW15

GL26

5 | 6 | 2010 WW18 GL25

3 | 7 | 2010

21 | 8 | 2010 WW21

7 | 9 | 2010

1 | 10 | 2010 WW10

WW24

0.8

● ● ●

●

●

●

0.6 0.4

● ●

●

● ●

0.0

0.2

NDVI | D

●

74 80 86 92 98 105

113

121

129

137

145

153

161

169

177

185

193

201

209

217

225

233

241

249

257

265

273

281

DOY

Fig. 5. Comparison of N DV I distributions (boxplots) and corresponding Kolomogorov Smirnov-distances for six RapidEye acquisition dates and the land uses classes Grassland (GL; green) and Winter Wheat (MS; red) over the year as well as the periods of the predicted phenological phases GL 1, GL 25, GL 26, WW 10, WW 12, WW 15, WW 18, WW 21, WW 24 (see Table 1). The black colored line indicates the temporally interpolated Kolmogorov Smirnov distances D, the green and red colored lines stand for the temporally interpolated medians of the considered N DV I-distributions. GL.WW ● ● ●

0.6 ● ● ● ●

0.4

phenological prediction results over several years in order to detect stable phases as indicators for the distinction of agricultural land use classes.

● ● ●

● ● ● ● ●

D

● ●

0.2

●

5. REFERENCES

●

0.0

GL1

GL25

GL26

WW15

WW18

WW21

WW24

Fig. 6. Optimal phenological phases for the distinction of the land use classes Grassland and Winter Wheat cordingly, the phases GL 25, GL 26, WW 15 and WW 18 are suitable for the detection of appropriate RapidEye acquisition dates (Fig. 6). Among the available imagery, the acquisition date 6th June 2010 is characterized by the closest temporal coincidence with the phases WW 18 and GL 25. The acquisition date 7th September 2010 is also appropriate for the distinction of both classes (Fig. 5). However, no direct phenological information were available. In such cases, phenological information of plants with similar spectral features (e.g. Winter Rye) could be used as indirect indicator. 4. CONCLUSION Against the background of the GMES project DeCover 2, we introduced the simplified phenological model PHASE that enables an automatic, Germany-wide and "on the fly" prediction of phenological phases. The prediction results were used for the identification of suitable RapidEye scenes for the distinction of the agricultural land use classes Grassland and Winter Wheat. Currently, we are analyzing RapidEye imagery and

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