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Vegetation cover degradation assessment in Madagascar savanna based on trend analysis of MODIS NDVI time series
International Journal of Applied Earth Observation and Geoinformation 12S (2010) S3–S10
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Vegetation cover degradation assessment in Madagascar savanna based on trend analysis of MODIS NDVI time series Anne Jacquin a,*, David Sheeren b, Jean-Paul Lacombe a,b a b
Universite´ de Toulouse; Ecole d’Inge´nieurs de PURPAN, BP 57611 - 75 voie du TOEC, 31076 Toulouse Cedex 3, France Universite´ de Toulouse; INPT-ENSAT; UMR1202 DYNAFOR, BP 32607, 31326 Castanet-Tolosan Cedex, France
A R T I C L E I N F O
A B S T R A C T
Article history: Received 17 October 2008 Accepted 9 November 2009
Like other African countries, Madagascar is concerned by vegetation cover degradation especially in savanna ecosystems. In this article, we describe an approach to quantify and localise savanna vegetation cover degradation. To this end, we analyse using STL decomposition method the trends measured between 2000 and 2007 of two phenological indicators which are derived from NDVI MODIS time series and characterizing vegetation activity during the growing season. Three types of trend were observed – null, positive or negative – over the study period with which we can associate a state of vegetation cover degradation. Future work will provide validation of this result. Next a comparison between the spatial variations of vegetation cover degradation and fire pressure for the same period should improve knowledge on the effect of fire on savanna vegetation activity. This information will be useful for local managers in order to implement savanna management strategies. ß 2009 Elsevier B.V. All rights reserved.
Keywords: MODIS Time series STL Change Trend Savanna Phenological indicator
1. Introduction Vegetation cover degradation in African savanna ecosystem is leading to the desertification of some areas, with the disappearance of the vegetation strata (regressive vegetation dynamics), or to an encroachment and a landscape closure, with the development of shrub-tree strata (progressive vegetation dynamics). In both cases, the equilibrium that maintains the savanna ecosystem is modified (Anyamba and Tucker, 2005; Olsson et al., 2005). In African countries, savanna is an essential ecosystem for the local population because of its agricultural, environmental and economic importance. Due to recent increases in the degradation of the vegetation cover in this ecosystem, there is a pressing need for a quantitative and reproducible assessment of the phenomenon to support policy development for food security and resource conservation (Harrison and Pearce, 2000). Changes in ecosystems can be classified in three groups, seasonal, gradual and abrupt change (Verbesselt et al., 2009). The phenomenon of vegetation cover degradation results from a modification in the level of vegetation activity over time that can be measured through the study of trends in the vegetation index or
* Corresponding author. Tel.: +33 561153087; fax: +33 561153060. E-mail addresses: [email protected] (A. Jacquin), [email protected] (D. Sheeren). 0303-2434/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2009.11.004
phenological indicators (Reed et al., 2003). Vegetation cover degradation belongs to the class of gradual change, which is why, in this article, we assume that multi-year trends are smooth and change slowly; they can be modelled by a 1st degree polynomial. Two requirements were defined to select the method for analysing image time series: - To be able to identify the specific phenology cycle of the local savanna, from which phenological indicators are derived. - To allow the characterisation of gradual change in savanna ecosystems by deriving the direction of change within the trend of the time series and without the need to define the change trajectory or to identify breakpoints. Among recent methods proposed for analysing image time series, some were only developed to identify seasonal change such as functions fitted to time series data (Jo¨nsson and Eklundh, 2002), or abrupt change by discriminating noise from the signal by its temporal characteristics (Millward et al., 2006), or seasonal and gradual changes using the breaks for additive seasonal and trend (BFAST) method (Verbesselt et al., 2009). In this paper, we chose seasonal decomposition of time series by Loess (STL) because it provides an accurate and robust estimation of trend and seasonal components thanks to its capacity to deal with outliers or missing values within the time series. The Loess method was also used to decompose the 16,886 time series (composed of 178 values) to
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cover the savanna study area because, according to Johnson et al. (2008) and Lu et al. (2001), STL method proved to be flexible, computationally efficient and simple. STL is a non-parametric method which, using an additive model (Eq. (1)), flexibly decomposes time series data into three separate components – trend (Zt), seasonal (St) and remainder (et) – for each pixel. This type of model implies that the magnitude of fluctuations in the original series resulting from the seasonal pattern and the residual component is not affected by the level of the trend. X t ¼ Z t þ St þ et
(1)
STL is an iterative procedure that repeatedly uses different types of LOcally wEighted regreSion Smoother (LOESS) (Cleveland et al., 1990). To evaluate the Loess fit g(x) at a given x, all data points in the neighbourhood of x are assigned tricubic weights, so that the closer a point is to x, the larger its weight. Weighted least squares are then used to fit a polynomial though the points and g(x) equals the value of the polynomial at x. The parameters to be defined are the size of the neighbourhood and the degree of the polynomial (constant, linear or quadratic). The seasonal component provides the phenology cycle of the local vegetation for the study period while the trend component, modelled by a piecewise linear function, enables determination of the direction of change during the study period by analysing the slope sign of the trend. Satellite remote sensing has long been a source of data to detect and monitor vegetation cover dynamics over time (Coppin et al., 2004). Coarse and medium spatial resolution satellite images are well-suited for this task. They provide consistent, valuable and repeatable measurements at a spatial scale which is appropriate for detecting the effects of many processes that cause degradation of the vegetation cover. Changes in vegetation cover can be measured by remote sensing of the normalised difference vegetation index (NDVI) as a strong correlation has been established between NDVI and vegetation cover. Time series of MODIS NDVI data have been successfully applied to quantify vegetation activity and to measure vegetation dynamics (Ahl et al., 2006; Zhang et al., 2003). The purpose of this paper is to describe and compare two approaches to quantify vegetation cover degradation in savanna ecosystems using remote sensing time series NDVI data. The first approach consists in measuring the trend of a phenological indicator time series characterising vegetation cover degradation over time (Bai et al., 2005; Coppin et al., 2004). Characteristics of the phenological indicator are derived from the analysis of the NDVI profile given by the STL seasonal component. Then the
phenological indicator is calculated for each growing season from the NDVI time series. The second approach is based on the measurement of the trends of NDVI time series using the STL trend component. This change detection technique enables gradual change to be isolated from seasonal and abrupt changes (Eklundh and Olsson, 2003). In both approaches, NDVI time series and phenological indicators represent an integrated measure of vegetation activity. A deviance of its values from a local reference value is assumed to be a measure of vegetation cover degradation. 2. Material 2.1. Study area The Marovoay watershed is located on the north-west coast of Madagascar, in the province of Mahajanga, on the banks of the river Betsiboka. It covers an area of approximately 1200 km2 (Fig. 1). The population of the Marovoay district was estimated to be 124,739 in 2001 with a density of around 32.8 km2 (INSTAT, 2005). Marovoay is one of the seven pilot sites of the PLAE project started in 1998 to study and control soil erosion (PLAE, 2004). Since Marovoay is the second largest rice-producing region in Madagascar, soil conservation is a major challenge in this area. The climate is semi-arid and subtropical with average annual rainfall of 1600 mm. Rains are concentrated in a single rainy season lasting from November to March. The long dry season (April– October) is characterized by an average temperature of 25 8C. Farming is the main activity. Land-use depends on the topography of the zone concerned. The plain is exclusively occupied by paddy fields cultivated by a tribe of rice-growers. This zone has been equipped with irrigation infrastructure enabling rice production all year round. The upstream area consists of a hilly relief characterised by cuestas in the north, east and west of the plain and by plateaux in the south. Human settlements are concentrated around the irrigated plain and in the main valleys (Fig. 1). Different types of soils are observed, most of which are highly sensitive to erosion. In these areas, savanna formations prevail. The plant cover is characterised by a grassy layer, dominated by Aristida barbicollis and to a lesser degree by Hyparrhenia rufa, Heteropogon contortus and Themeda triandra. The tree cover is limited. The most frequent species are Acridocarpus excelsus, Hyphaene shatan, Ziziphus mauritiana and Strychnos spinosa. These species are mainly devoted to grazing and are maintained in a state of unprotected open access area by a tribe of stockbreeders.
Fig. 1. Localisation of study site and delimitation of savanna area.
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Fire is the main tool for managing the grass resources in the savanna (Kull, 2000). This practice is partially responsible for the degradation phenomenon, both of the soils and of the vegetation cover. Downstream the phenomenon takes the form of silting up of the irrigation channels and a reduction in the land’s productive potential. Between 1960 and 1997, the rice yield is reported to have fallen from 2.1 t/ha to 1.1 t/ha (Roubaud, 1997), a situation which is creating tension between the tribes of stockbreeders and rice-growers. Originally, the whole region was covered by a dense dry forest of broad-leaved trees (Dalbergia spp., Albizia spp., Tamarindus indica), and part of this forest still exists to the south of the study site. It is a protected zone and is managed by the Ankarafantsika National Park. In this article, our study zone is limited to the savanna area. Its extent was delimited by means of a vegetation map established on the basis of a 10 m SPOT-5 satellite image in the framework of the PLAE programme (Gay and Jacquin, 2005). 2.2. Image data We used 16-day MODIS NDVI composites with a 250 m spatial resolution (MOD13Q1 collection 5) mainly for their capacity to detect human-driven land cover changes. The composited VI product is generated using a constrained-view angle – maximum value compositing (CV-MVC) method (Huete et al., 2002) in order to limit residual cloud and atmospheric effects and to constrain view angles. The MOD13Q1 images were acquired between February 24th, 2000, to the end of 2007 (178 images) and were used to derive eight years of NDVI temporal profiles. They were resampled to Laborde Madagascar projection. In addition to image data, a binary Quality Assurance number is available for each pixel. Among the different groups which describe different properties of the pixel, we chose the ‘VI Usefulness’ index to select only could-free data of highest quality (Huete et al., 1999). All pixels presenting a value between 7 and 15 were rejected. As composited VI product may still contain perturbations, we also used the ‘Best Index Slope Extraction’ (BISE) algorithm (Viovy et al., 1992) to detect any remaining irregular NDVI values. Missing values were replaced by linear interpolation considering neighbouring values within the NDVI time series (Verbesselt et al., 2006) (Fig. 2). 2.3. Savanna vegetation data According to the definition given by the Yangambi classification (Aubreville, 1957), the ‘savanna’ ecosystem corresponds to open herbaceous formations composed of annual or perennial grass species where shrub-tree strata do not exceed 30% of total vegetation cover. Within this ecosystem, a vegetation continuum exists, from continuous high grassy formation, called savanna, to discontinuous low grassy formation, called steppe or pseudosteppe. Differences are observed in the physiognomy of the vegetation (mainly height of the grass formation and ratio of bare soil to herbaceous cover). In our study, a vegetation map was used to geographically delimit the extent of the savanna ecosystem at the study site and to describe the vegetation continuum that characterises this environment. The vegetation map was established on the basis of a 10 m SPOT-5 satellite image (acquired on June 10th, 2005) and field data. The classification procedure, which has already been used for other types of ecosystem (Jacquin et al., 2005), combines a per-pixel spectral algorithm (maximum likelihood classifier) and an aggregation-based object method (Robbez-Masson, 1994) providing a final product of good accuracy (with a Kappa index value of 0.80) (Gay and Jacquin, 2005). The first method enables identification of the main land cover types – i.e. bare soil and the three vegetation strata (herbaceous, shrub and
Fig. 2. 16-day MODIS NDVI time series analysis for one savanna pixel: (at the top) NDVI profiles established for the period 2000–2007 with raw data and corrected data (application of MODIS QA quality mask and BISE smoothing) – (in the center) STL decomposition of a 16-day MODIS NDVI time series into seasonal, trend and remainder components – (at the bottom) Vegetation NDVI profile and definition of phenology indicators.
tree) – whereas the second procedure analyses the spatial organisation of land cover types and classifies pixels into five pre-defined physiognomy vegetation classes (from steppe formations to savanna formations): (1) Discontinuous grass cover with dominance of bare soil, (2) Discontinuous grass cover with presence of bare soil, (3) Continuous grass cover, (4) Discontinuous grass cover with presence of shrub-tree strata and (5) Discontinuous grass cover with presence of bare soil and shrub-tree strata. 3. Method The objective is to quantify vegetation cover degradation in Madagascar savanna ecosystem by analysing NDVI time series of Terra-MODIS images for the period 2000–2007. The method consists in localizing savanna pixels that present no change or a significant decrease or increase in vegetation activity during the
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study period by analyzing the trend component of time series data. This happens in two steps: first, characterisation of the vegetation activity parameter and, second, estimation of vegetation activity trends. 3.1. Characterisation of the vegetation activity parameter In this article, two approaches are presented for the characterisation of the vegetation activity parameter: (1) using the NDVI time series directly, as it was demonstrated that NDVI is strongly correlated to biophysical parameters such as biomass or Leaf Area Index, (2) using time series of a phenological indicator (sumNDVI) corresponding to the accumulated NDVI measured during the vegetation’s active growth phase, as a linear relation has been established between NDVI seasonal accumulation and biophysical parameters linked to photosynthetic activity of the plant cover (Tucker and Sellers, 1986). 3.1.1. NDVI time series trend analysis using the STL TREND component We applied the STL procedure to the 16-day MODIS NDVI time series for the period 2000–2007 (23 images/year except for 2000 i.e. a total of 178 images). In the STL Trend component, for each of the 16,886 savanna pixels, the NDVI trend is modeled by a 1st degree polynomial (Fig. 2). The direction of change is determined through the analysis of the slope value (See Section 3.2). 3.1.2. Phenological indicator time series analysis using the STL seasonal component We applied the STL procedure to the 16-day MODIS NDVI time series for the period 2000–2007 (23 images/year except for 2000 i.e. a total of 178 images). In the STL Seasonal component, for each of the 16,886 savanna pixels, the savanna vegetation growing season is modeled by a NDVI profile (Fig. 2). Using the STL procedure, effects linked to external factors are attenuated in the resulting NDVI profile. They are assumed to be in the Remainder component. But this profile is only valid for the period covered by the time series. Analysis of NDVI profile enables the two phenological stages (start and end of the active growth phase) required to compute the sumNDVI indicator given by Eq. (2) to be pinpointed (Fig. 2).
sumNDVI ¼
Date ofX maxNDVI
16 day MODIS NDVI
(2)
3.2. Estimation of the vegetation activity trends By analyzing the year-to-year fluctuations of a time series, it is possible to reveal and quantify variations in these data over the observation period. Thus, the data series can be modelled by a trend line indicating the variation between two limits: upwards or downwards trend or stability. For each direction of change, it is possible to associate hypotheses with these trends according to the known elements of the data dynamics concerned (Reed, 2006). In our study, NDVI time series and sumNDVIYear of growing season indicator represent an integrated measure of vegetation activity. Trends significantly different from a null trend are assumed to be a measure of degradation of the vegetation cover: trends with non significant slope values represent savanna areas under stable vegetation dynamics whereas trends with significant positive or negative slope values are respectively associated with savanna areas under progressive or regressive vegetation dynamics. Trend estimation was based on two steps: - Step 1: Determination of the trend function parameters. For each pixel, the parameters of the polynomial used to model the trend of NDVI and sumNDVIYear of growing season indicator time series were extracted: the slope (a) and the degree of freedom. - Step 2: Characterisation of change direction. For each pixel, we identified those for which the slope value of the observed trend (from the NDVI and sumNDVIYear of growing season indicator time series) was significantly different to a trend presenting a null slope. To this end, we compared the values of the trend slope (an) with the value of a null trend slope (a0). We applied a Student t test (Eq. (3)). an a0 ffi t ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 1 ðxxÞ ¯ 2 n1 pffiffiffiffiffiffiffi
(3)
n2
where an is the slope of the trend for the studied pixel, a0 is the slope of a null trend (equal to 0), n is the number of observations, x represents the observed values of the NDVI or the phenological indicator and x¯ is the mean for one pixel of the observed values of the NDVI or the phenological indicator within the time series. Finally, we grouped areas of change into three classes: Null trend, Positive trend and Negative trend and mapped it over the savanna area of the study site.
Date of minNDVI
where Date of minNDVI and Date of maxNDVI correspond respectively to the start and the end of the active growth phase during the savanna vegetation growing season. Then, the sumNDVI indicator is calculated for the NDVI profile provided by the STL Seasonal component (sumNDVIReference) and for the NDVI profiles from each growing season covered by the time series (sumNDVIYear of growing season). At this stage, two analyses are conducted: - For each savanna pixel, year-to-year fluctuations in the vegetation activity can be revealed by comparing the sumNDVIReference value with the sumNDVIYear of growing season value for each growing season studied. Based on this information, a connection can be made between these inter-annual variations and the existence of vegetation pressure factors (climatic conditions or human factors such as fire and grazing intensity) (Tucker et al., 1991). For each savanna pixel, sumNDVIYear of growing trend is modeled by a 1st degree polynomial. The direction of change is determined by analysis of the slope value (See Section 3.2).
4. Results 4.1. Savanna NDVI profile from the STL seasonal component For each of the 16,886 savanna pixels, the STL seasonal component provides the NDVI profile for the 2000–2007 period from which we identified the phenological keys stages to compute the sumNDVI indicator (Fig. 3). The active growth phase starts in November and reaches its peak photosynthesis activity level in March (maximum NDVI observed). Then, the NDVI values decreases from April until October (minimum NDVI observed), characterising the senescent phase. The sumNDVI indicator corresponds to the accumulated NDVI values measured between the beginning of November until the end of March. Like in other tropical regions, the savanna vegetation phenology in Madagascar is influenced by the alternation of the dry season (April to October) and rainy season (November to March). 4.2. Year-to-year variations of the savanna vegetation activity We calculated the variations of the annual sumNDVI indicator (sumNDVIyear of growing season) for all the savanna pixels between 2000
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Fig. 3. NDVI profiles of a savanna pixel established for each growing season between 2000 and 2007 from the 16-day MODIS NDVI time series and from STL seasonal component – Identification of phenological key stages.
and 2007. Values are distributed between 2% and +2% in reference of the mean of the sumNDVI indicator for the studied period (Fig. 4), showing a global stability of the savanna vegetation activity between 2000 and 2007. But, since the 2003/2004 growing season, the values of the annual sumNDVI indicator are systematically lower than the mean of the sumNDVI indicator. It revealed a durable disorder of the savanna vegetation activity after this season. Several authors demonstrated that the inter-annual variations of the savanna vegetation activity are correlated with annual fluctuations of rainfall (Camberlin et al., 2007; Nicholson et al., 1990; Zhang et al., 2005). On the study site, a weather station localized close to the irrigated plain (Fig. 1) provides monthly rainfall for the 2000–2007 period. We calculated a climatic indicator (sumRainfall) corresponding to the accumulated rainfall measured between November and March given by Eq. (4) for each growing season. sumRainfall ¼
Date of end of theX active growth phase
Monthly rainfall
(4)
Date of start of the active growth phase
Analysis of the annual variations of the sumRainfall indicator between 2000 and 2007 revealed that accumulated rainfall measured during the active growth phase are around 1600 mm except for the 2001/2002 growing season which is drier than the others (Fig. 4). The 2003/2004 growing season is particular because of the passage of the cyclone ELITA in December 2003 and February 2004 on the study site. Even if the sumRainfall indicator for this growing season is not different from the mean measured for the studied period, the value of the sumNDVI indicator for this growing season is lower than the mean. We compared values of the the sumNDVI indicator and the sumRainfall indicator measured between 2000 and 2007 by calculating a correlation coefficient on two periods [2000–2003]
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Fig. 4. (At the top) Annual variations of the sumNDVI indicator for all the savanna pixels between 2000 and 2007 – (at the bottom) Annual variations of the sumRainfall indicator between 2000 and 2007.
and [2004–2007] in order to isolate the 2003/2004 growing season. Vegetation activity indicator and rainfall indicator are positively correlated (0.987 [2000–2003] for and 0.972 for [2004–2007]). This result confirmed the existence of the relation between savanna vegetation activity and rainfall. In this article, the decrease of the savanna vegetation activity observed after the 2003/2004 growing season can be a consequence of the disorder induced by the passage of the cyclone. 4.3. Savanna vegetation activity change Even if vegetation activity is relatively stable for the savanna area between 2000 and 2007 (Fig. 4), analysis of the trends of vegetation activity indicators for each savanna pixel enables to quantify and localise areas characterized by significant vegetation activity changes. The increase and decrease of the vegetal activity indicators (NDVI or sumNDVI), measured between 2000 and 2007 and statistically significant (for a = 5%), represent an indicator of ‘vegetation cover degradation’ (Fig. 5). From the analysis of the vegetation activity changes based on the sumNDVI indicator time series trends, areas characterised in 2007 by a decrease, an increase or a stability of vegetation activity cover respectively 20%, 1% and 79% of the savanna extent. Areas not affected by vegetation cover degradation between 2000 and 2007 are dominating. However, analysis of the spatial distribution of the three classes of the trends highlighted the existence of a vegetation activity gradient on the scale of the study area (Fig. 5). The savanna vegetation activity tends to increase, on the right bank of the Betsiboka, in the south-east (area under the management of the Ankarafantsika National Park) and to decrease all around the irrigated plain on both sides of the Betsiboka river. For this last class (negative trend), even if savanna concerned by vegetation cover degradation are not affected large areas, their spatial
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Fig. 5. Estimation of vegetation cover degradation between 2000 and 2007: (on the right), trend estimation of vegetation activity changes is based on the analysis of the sum NDVI indicator time series; (on the left), estimation of vegetation activity changes is based on the analysis of the NDVI time series; (at the bottom), fire pressure map measured between 2000 and 2007 from a 8-day MODIS surface reflectance time series.
proximity to the irrigated plain represents a danger due to the sensitivity of the soil to erosion in these places. Analysis of the vegetation activity changes based on the NDVI time series trends shows that 30% of savanna pixels is characterised by in 2007 a significant decrease of the vegetal activity. Areas characterised by an increase of the vegetation activity cover 52% of the savanna. From the analysis of the spatial distribution of the trend classes, we conclude that most of the savanna area localized closed to the irrigated plain is under vegetation cover degradation.
5. Discussion and future work 5.1. Estimation of vegetal activity using phenological indicators In this article, savanna vegetation activity is estimated (1) through NDVI time series directly and (2) through the sumNDVI phenological indicator time series. The main difference between the two approaches is that savanna areas with a significant decrease or increase of NDVI between 2000 and 2007 are characterised by a
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significant stability of the sumNDVI indicator for the same period (Fig. 5). For the estimation of the vegetation activity change with the NDVI time series, all the growing season is considered whereas, using the sumNDVI indicator, only data of the active growth phase are used to measure vegetation activity change. Factors that can explain the inter-annual variations of the indicators are different. As vegetation cover degradation is a complex phenomenon, several phenological indicators should be combined to quantify it. For example, Bai and Dent (2006), into their approach of land degradation assessment, used successfully a set of seven phenological indicators extracted from the NDVI profile (Minimum, Maximum, Difference between maximum and minimum, Mean, Sum, Standard-deviation and Covariance). 5.2. Validation issue of the savanna vegetation cover degradation indicator A comparison of the sumNDVI indicator map for the 2004/2005 growing season, divided into four classes of vegetation activity (very low, low, medium and high) with the savanna vegetation map derived from the SPOT-5 satellite image acquired in 2005 was realised. It emerges that each class of the sumNDVI indicator is effectively characterised by one or two dominant physiognomic types of vegetation. The classes with low and very low sumNDVI values are mainly associated with discontinuous grass cover type whereas the classes with high sumNDVI values essentially correspond to continuous grass cover type. This result tends to prove the relevance of the sumNDVI phenological indicator to represent the savanna vegetation activity. But, validation of the savanna areas characterised by vegetation cover degradation from the MODIS NDVI time series analysis is still missing. Future work will consist on the production of a new vegetation map, from a SPOT-5 image (acquired in June 2009), to quantify and localise areas characterised by vegetation physiognomic changes. 6. Conclusion and perspectives In this study, we described a method for vegetation cover degradation assessment based on a time series trend analysis. Based on the results obtained, three conclusions are identified: - A combination of several phenological indicators is required to quantify vegetation cover degradation in relation to external pressure factors; - The STL procedure is an adapted method to determine simultaneously savanna seasonal change and gradual change; - Trend estimation of phenological indicators time series is a promising indicator of vegetation cover degradation. Further research will be dedicated to the explanation of spatial variation of vegetation cover degradation. Two distinct situations could be observed in Fig. 5. For the areas included in the Ankarafantsika National Park, the trend of the sumNDVI indicator is stable in the large stretches of savanna and is positive in the forest areas. Everywhere else in the catchment basin, however, the trend of the sumNDVI indicator is negative, representing the degradation in the plant cover of the savannas. In our approach, we consider that the anthropic pressures represent the main factor explaining the plant cover change trends. In fact, we put forward the hypothesis that rainfall is distributed evenly over the whole of our study area and, consequently, it could not explain the observed spatial variations in the studied indicator. Since the year-to-year fluctuations in the climatic conditions and, in particular, in the rainfall do not contain any irregularities (Fig. 4), we will consider that they are not at the origin of the vegetation activity change trends that we observed. The anthropic pressure factors should
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therefore play a preponderant role in vegetation dynamics. The degradation of the savannas is thought to be due to the use of fire and to the intensity of grazing (Gillon, 1983; Skarpe, 1992). In Madagascar, the use of fire is an integral part of the practices used to manage natural resources (Kull, 2000). Several works have shown contradictions concerning the effects of fire on the vegetation dynamics of savannas. Some people consider it can constitute a tool for the sustainable management of savannas (Bloesch, 1999). Others, see it as a factor in the degradation of those same lands (Thonicke et al., 2001). On the Marovoay site, from the analysis of a 8-day MODIS surface reflectance time series covering the 2000–2007 period, an indicator that localises annual burned surfaces was determined and has allowed to estimate and localise fire pressure between 2000 and 2007 in the savanna area (PLAE, 2008) (Fig. 5). Results revealed that the zones whose trend of the sumNDVI indicator is stable are just as much concerned by fire pressure as the areas whose the trend of the sumNDVI indicator is positive or negative. This is why the hypothesis whereby fire, according to the way it is used, is a factor in the stability or degradation of savannas still has to be developed. A study of the interactions between the fire utilisation practices and the evolution of the environments would present a two-fold interest. Each year, it would make it possible to provide quantifiable and upto-date spatialized information on the use of fire (frequency and season) within the areas concerned. Lastly, it would provide the managers of those areas with part of the data they need and expect to elaborate an operational system for monitoring and managing fire-use. This system does not exist at present, except in areas that benefit from significant financial resources, such as the National Parks or Integral Nature Reserves (Frost, 1999; Parr and Brockett, 1999). Acknowledgements This study was supported by the Programme de Lutte AntiErosive (PLAE), a Madagascar national program of rural development, financed by the Federal Minister of Development and Cooperation (BMZ) through the KFW. References Ahl, D.E., Gower, S.T., Burrows, S.N., Shabanov, N.V., Myneni, R.B., Knyazikhin, Y., 2006. Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sensing of Environment 104, 88–95. Anyamba, A., Tucker, C.J., 2005. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981 to 2003. Journal of Arid Environments 63, 596–614. Aubreville, A., 1957. Accord a` Yangambi sur la nomenclature des types africains de ve´ge´tation. Bois et Foreˆts des Tropiques 51, 23–27. Bai, Z.A., Dent, D.L., Schaepman, M.E. 2005. Quantitative global assessment of land degradation and improvement: pilot study in North China. ISRIC Rep 2005/06, Wageningen. Bai, Z.A., Dent, D.L. 2006. Global assessment of land degradatin and improvement: pilot study in Kenya. ISRIC World Soil Information Rep 2006/01, Wageningen. Bloesch, U., 1999. Fire as a tool in the management of a savanna/dry forest reserve in Madagascar. Applied Vegetation Science 2 (1), 117–124. Camberlin, P., Martiny, N., Philippon, N., Richard, Y., 2007. Determinants of the inter-annual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sensing of Environment 106, 199–216. Cleveland, R.B., Cleveland, W.S., MacRae, J.E., Terpenning, I., 1990. STL: a seasonaltrend decomposition procedure based on loess. Journal of Official Statistics 6 (1), 3–73. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 25 (9), 1565–1596. Eklundh, L., Olsson, L., 2003. Vegetation index trends for the African Sahel 1982– 1999. Geophysical Research Letters 30, 1430–1433. Frost, P.G.H., 1999. Fire in southern African woodlands: origins, impacts, effects, and control. FAO Forestry Paper 138, 181–205. Gay, M., Jacquin, A. 2005. Programme Lutte Anti-Erosive (PLAE) – Rapport annuel d’activite´. EI Purpan, Toulouse, France, 7. Gillon, D., 1983. The fire problem in tropical savannas. In: Bourlie`re, F. (Ed.), Tropical savannas. Ecosystems of the World Elsevier, Amsterdam, pp. 617–641.
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Harrison, P., Pearce, F. 2000. AAAS Atlas of Population and Environment Washington: American Association for the Advancement of Science and the University of California Press. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213. Huete, A., Justice, C.O., van Leeuwen, W. 1999. MODIS Vegetation Index (MOD13). Algorithm theoretical basis document. Version 3. Jacquin, A., Che´ret, V., Denux, J.-P., Gay, M., Xofis, P., Mitchley, J., 2005. Habitat suitability modelling of Capercaillie (Tetrao urogallus) using earth observation data. Journal for Nature Conservation 13, 161–169. Johnson, L., Cooke, B., Ramos, V.H., Easson, G. 2008. Use of NASA Satellite Assets for Predicting Wildfire Potential for Forest Environments in Guatemala. Preliminary Report. University of Mississippi Geoinformatics Center. Jo¨nsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing 40 (8), 1824–1832. Kull, C.A., 2000. Isle of Fire: The Political Ecology of Landscape Burning in Madagascar. The University of Chicago Press, Chicago. Lu, H., Raupach, M.R., McVicar, T.R. 2001. Decomposition of Vegetation Cover into Woody and Herbaceous Components Using AVHRR NDVI Time Series. Technical Report 35/01, CSIRO Land and Water, PO Box 1666, Canberra, 2601, Australia. Millward, A.A., Piwowar, J.M., Howarth, P.J., 2006. Time series analysis of mediumresolution, multisensor satellite data for identifying landscape change. Photogrammetric Engineering and Remote Sensing 72. (4). Nicholson, S.E., Davenport, M.L., Malo, A.R., 1990. A comparison of the vegetation response to rainfall in the Sahel and Esat Africa, using normalized vegetation index from NOAA AVHRR. Climate Change 17, 209–241. Olsson, L., Eklundh, L., Ardo, J., 2005. A recent greening of the Sahel-trends, patterns and potential causes. Journal of Arid Environments 63, 556–566. Parr, C.L, Brockett, B.H., 1999. Patch-mosaic burning. A new paradigm for Savanna fire management in protected areas? Koedoe 42, 117–130. PLAE 2004. Programme de Lutte anti-e´rosive - Pre´sentation ge´ne´rale. PLAE, Marovoay, 25. PLAE, 2008. Programme de Lutte anti-e´rosive – Rapport annuel d’activite´. PLAE, Marovoay, 56.
Reed, B.C., 2006. Trend analysis of time series phenology of North America derived from satellite data. GIScience and Remote Sensing 43 (1), 1–15. Reed, B.C., White, M.A., Brown, J.F., 2003. Remote Sensing Phenology. In: Schwartz, M.D. (Ed.), Phenology: An Integrative Science. Kluwer Academic Publishing, Dordrecht, The Netherlands. Robbez-Masson, J.M. 1994. Reconnaissance et de´limitation de motifs d’organisation spatiale. Application a` la cartographie des pe´dopaysages. Ecole Nationale Supe´rieure d’Agronomie de Montpellier, Montpellier, 161. Roubaud 1997. La question rizicole a` Madagascar: Les re´sultats d’une de´cennie de libe´ralisation. Economie de Madagascar, 2, 37–62. Skarpe, C., 1992. Dynamics of Savanna ecosystems. Journal of Vegetation Science 3, 292–300. Thonicke, K., Venevsky, S., Sitch, S., Cramer, W., 2001. The role of fire disturbance for global vegetation dynamics: coupling fire into a dynamic global vegetation model. Global Ecology and Biogeography 10, 661–677. Tucker, C.J., Newcomb, W.W., Los, S.O., Prince, S.D., 1991. Mean and inter-annual variation of growing season normalized difference vegetation index for the Sahel 1981–1989. International Journal of Remote Sensing 12, 1133–1135. Tucker, C.J., Sellers, P.J., 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing 7, 1395–1417. Verbesselt, J., Jo¨nsson, P., Lhermitte, S., van Aardt, J., Coppin, P., 2006. Evaluating satellite and climate data derived indices as fire risk indicators in savanna ecosystems. IEEE Transactions on Geoscience and Remote Sensing 44 (6), 1622– 1632. Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D., 2009. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment 114, 106–115. Viovy, N., Arino, O., Belward, A.S., 1992. The best index slope extraction (BISE): A method for reducting noise in NDVI time series. International Journal of Remote Sensing 13 (8), 1585–1590. Zhang, X., Friedl, M.A., Schaaf, C.B. and Strahler, A.H. 2005. Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research, 110 (D12103). Zhang, X., Schaaf, C.B., Strahler, A.H., Friedl, M.A., Hodges, J.C.F., Gao, F., Reed, B.C., Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84, 471–475.
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International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag
Vegetation cover degradation assessment in Madagascar savanna based on trend analysis of MODIS NDVI time series Anne Jacquin a,*, David Sheeren b, Jean-Paul Lacombe a,b a b
Universite´ de Toulouse; Ecole d’Inge´nieurs de PURPAN, BP 57611 - 75 voie du TOEC, 31076 Toulouse Cedex 3, France Universite´ de Toulouse; INPT-ENSAT; UMR1202 DYNAFOR, BP 32607, 31326 Castanet-Tolosan Cedex, France
A R T I C L E I N F O
A B S T R A C T
Article history: Received 17 October 2008 Accepted 9 November 2009
Like other African countries, Madagascar is concerned by vegetation cover degradation especially in savanna ecosystems. In this article, we describe an approach to quantify and localise savanna vegetation cover degradation. To this end, we analyse using STL decomposition method the trends measured between 2000 and 2007 of two phenological indicators which are derived from NDVI MODIS time series and characterizing vegetation activity during the growing season. Three types of trend were observed – null, positive or negative – over the study period with which we can associate a state of vegetation cover degradation. Future work will provide validation of this result. Next a comparison between the spatial variations of vegetation cover degradation and fire pressure for the same period should improve knowledge on the effect of fire on savanna vegetation activity. This information will be useful for local managers in order to implement savanna management strategies. ß 2009 Elsevier B.V. All rights reserved.
Keywords: MODIS Time series STL Change Trend Savanna Phenological indicator
1. Introduction Vegetation cover degradation in African savanna ecosystem is leading to the desertification of some areas, with the disappearance of the vegetation strata (regressive vegetation dynamics), or to an encroachment and a landscape closure, with the development of shrub-tree strata (progressive vegetation dynamics). In both cases, the equilibrium that maintains the savanna ecosystem is modified (Anyamba and Tucker, 2005; Olsson et al., 2005). In African countries, savanna is an essential ecosystem for the local population because of its agricultural, environmental and economic importance. Due to recent increases in the degradation of the vegetation cover in this ecosystem, there is a pressing need for a quantitative and reproducible assessment of the phenomenon to support policy development for food security and resource conservation (Harrison and Pearce, 2000). Changes in ecosystems can be classified in three groups, seasonal, gradual and abrupt change (Verbesselt et al., 2009). The phenomenon of vegetation cover degradation results from a modification in the level of vegetation activity over time that can be measured through the study of trends in the vegetation index or
* Corresponding author. Tel.: +33 561153087; fax: +33 561153060. E-mail addresses: [email protected] (A. Jacquin), [email protected] (D. Sheeren). 0303-2434/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2009.11.004
phenological indicators (Reed et al., 2003). Vegetation cover degradation belongs to the class of gradual change, which is why, in this article, we assume that multi-year trends are smooth and change slowly; they can be modelled by a 1st degree polynomial. Two requirements were defined to select the method for analysing image time series: - To be able to identify the specific phenology cycle of the local savanna, from which phenological indicators are derived. - To allow the characterisation of gradual change in savanna ecosystems by deriving the direction of change within the trend of the time series and without the need to define the change trajectory or to identify breakpoints. Among recent methods proposed for analysing image time series, some were only developed to identify seasonal change such as functions fitted to time series data (Jo¨nsson and Eklundh, 2002), or abrupt change by discriminating noise from the signal by its temporal characteristics (Millward et al., 2006), or seasonal and gradual changes using the breaks for additive seasonal and trend (BFAST) method (Verbesselt et al., 2009). In this paper, we chose seasonal decomposition of time series by Loess (STL) because it provides an accurate and robust estimation of trend and seasonal components thanks to its capacity to deal with outliers or missing values within the time series. The Loess method was also used to decompose the 16,886 time series (composed of 178 values) to
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cover the savanna study area because, according to Johnson et al. (2008) and Lu et al. (2001), STL method proved to be flexible, computationally efficient and simple. STL is a non-parametric method which, using an additive model (Eq. (1)), flexibly decomposes time series data into three separate components – trend (Zt), seasonal (St) and remainder (et) – for each pixel. This type of model implies that the magnitude of fluctuations in the original series resulting from the seasonal pattern and the residual component is not affected by the level of the trend. X t ¼ Z t þ St þ et
(1)
STL is an iterative procedure that repeatedly uses different types of LOcally wEighted regreSion Smoother (LOESS) (Cleveland et al., 1990). To evaluate the Loess fit g(x) at a given x, all data points in the neighbourhood of x are assigned tricubic weights, so that the closer a point is to x, the larger its weight. Weighted least squares are then used to fit a polynomial though the points and g(x) equals the value of the polynomial at x. The parameters to be defined are the size of the neighbourhood and the degree of the polynomial (constant, linear or quadratic). The seasonal component provides the phenology cycle of the local vegetation for the study period while the trend component, modelled by a piecewise linear function, enables determination of the direction of change during the study period by analysing the slope sign of the trend. Satellite remote sensing has long been a source of data to detect and monitor vegetation cover dynamics over time (Coppin et al., 2004). Coarse and medium spatial resolution satellite images are well-suited for this task. They provide consistent, valuable and repeatable measurements at a spatial scale which is appropriate for detecting the effects of many processes that cause degradation of the vegetation cover. Changes in vegetation cover can be measured by remote sensing of the normalised difference vegetation index (NDVI) as a strong correlation has been established between NDVI and vegetation cover. Time series of MODIS NDVI data have been successfully applied to quantify vegetation activity and to measure vegetation dynamics (Ahl et al., 2006; Zhang et al., 2003). The purpose of this paper is to describe and compare two approaches to quantify vegetation cover degradation in savanna ecosystems using remote sensing time series NDVI data. The first approach consists in measuring the trend of a phenological indicator time series characterising vegetation cover degradation over time (Bai et al., 2005; Coppin et al., 2004). Characteristics of the phenological indicator are derived from the analysis of the NDVI profile given by the STL seasonal component. Then the
phenological indicator is calculated for each growing season from the NDVI time series. The second approach is based on the measurement of the trends of NDVI time series using the STL trend component. This change detection technique enables gradual change to be isolated from seasonal and abrupt changes (Eklundh and Olsson, 2003). In both approaches, NDVI time series and phenological indicators represent an integrated measure of vegetation activity. A deviance of its values from a local reference value is assumed to be a measure of vegetation cover degradation. 2. Material 2.1. Study area The Marovoay watershed is located on the north-west coast of Madagascar, in the province of Mahajanga, on the banks of the river Betsiboka. It covers an area of approximately 1200 km2 (Fig. 1). The population of the Marovoay district was estimated to be 124,739 in 2001 with a density of around 32.8 km2 (INSTAT, 2005). Marovoay is one of the seven pilot sites of the PLAE project started in 1998 to study and control soil erosion (PLAE, 2004). Since Marovoay is the second largest rice-producing region in Madagascar, soil conservation is a major challenge in this area. The climate is semi-arid and subtropical with average annual rainfall of 1600 mm. Rains are concentrated in a single rainy season lasting from November to March. The long dry season (April– October) is characterized by an average temperature of 25 8C. Farming is the main activity. Land-use depends on the topography of the zone concerned. The plain is exclusively occupied by paddy fields cultivated by a tribe of rice-growers. This zone has been equipped with irrigation infrastructure enabling rice production all year round. The upstream area consists of a hilly relief characterised by cuestas in the north, east and west of the plain and by plateaux in the south. Human settlements are concentrated around the irrigated plain and in the main valleys (Fig. 1). Different types of soils are observed, most of which are highly sensitive to erosion. In these areas, savanna formations prevail. The plant cover is characterised by a grassy layer, dominated by Aristida barbicollis and to a lesser degree by Hyparrhenia rufa, Heteropogon contortus and Themeda triandra. The tree cover is limited. The most frequent species are Acridocarpus excelsus, Hyphaene shatan, Ziziphus mauritiana and Strychnos spinosa. These species are mainly devoted to grazing and are maintained in a state of unprotected open access area by a tribe of stockbreeders.
Fig. 1. Localisation of study site and delimitation of savanna area.
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Fire is the main tool for managing the grass resources in the savanna (Kull, 2000). This practice is partially responsible for the degradation phenomenon, both of the soils and of the vegetation cover. Downstream the phenomenon takes the form of silting up of the irrigation channels and a reduction in the land’s productive potential. Between 1960 and 1997, the rice yield is reported to have fallen from 2.1 t/ha to 1.1 t/ha (Roubaud, 1997), a situation which is creating tension between the tribes of stockbreeders and rice-growers. Originally, the whole region was covered by a dense dry forest of broad-leaved trees (Dalbergia spp., Albizia spp., Tamarindus indica), and part of this forest still exists to the south of the study site. It is a protected zone and is managed by the Ankarafantsika National Park. In this article, our study zone is limited to the savanna area. Its extent was delimited by means of a vegetation map established on the basis of a 10 m SPOT-5 satellite image in the framework of the PLAE programme (Gay and Jacquin, 2005). 2.2. Image data We used 16-day MODIS NDVI composites with a 250 m spatial resolution (MOD13Q1 collection 5) mainly for their capacity to detect human-driven land cover changes. The composited VI product is generated using a constrained-view angle – maximum value compositing (CV-MVC) method (Huete et al., 2002) in order to limit residual cloud and atmospheric effects and to constrain view angles. The MOD13Q1 images were acquired between February 24th, 2000, to the end of 2007 (178 images) and were used to derive eight years of NDVI temporal profiles. They were resampled to Laborde Madagascar projection. In addition to image data, a binary Quality Assurance number is available for each pixel. Among the different groups which describe different properties of the pixel, we chose the ‘VI Usefulness’ index to select only could-free data of highest quality (Huete et al., 1999). All pixels presenting a value between 7 and 15 were rejected. As composited VI product may still contain perturbations, we also used the ‘Best Index Slope Extraction’ (BISE) algorithm (Viovy et al., 1992) to detect any remaining irregular NDVI values. Missing values were replaced by linear interpolation considering neighbouring values within the NDVI time series (Verbesselt et al., 2006) (Fig. 2). 2.3. Savanna vegetation data According to the definition given by the Yangambi classification (Aubreville, 1957), the ‘savanna’ ecosystem corresponds to open herbaceous formations composed of annual or perennial grass species where shrub-tree strata do not exceed 30% of total vegetation cover. Within this ecosystem, a vegetation continuum exists, from continuous high grassy formation, called savanna, to discontinuous low grassy formation, called steppe or pseudosteppe. Differences are observed in the physiognomy of the vegetation (mainly height of the grass formation and ratio of bare soil to herbaceous cover). In our study, a vegetation map was used to geographically delimit the extent of the savanna ecosystem at the study site and to describe the vegetation continuum that characterises this environment. The vegetation map was established on the basis of a 10 m SPOT-5 satellite image (acquired on June 10th, 2005) and field data. The classification procedure, which has already been used for other types of ecosystem (Jacquin et al., 2005), combines a per-pixel spectral algorithm (maximum likelihood classifier) and an aggregation-based object method (Robbez-Masson, 1994) providing a final product of good accuracy (with a Kappa index value of 0.80) (Gay and Jacquin, 2005). The first method enables identification of the main land cover types – i.e. bare soil and the three vegetation strata (herbaceous, shrub and
Fig. 2. 16-day MODIS NDVI time series analysis for one savanna pixel: (at the top) NDVI profiles established for the period 2000–2007 with raw data and corrected data (application of MODIS QA quality mask and BISE smoothing) – (in the center) STL decomposition of a 16-day MODIS NDVI time series into seasonal, trend and remainder components – (at the bottom) Vegetation NDVI profile and definition of phenology indicators.
tree) – whereas the second procedure analyses the spatial organisation of land cover types and classifies pixels into five pre-defined physiognomy vegetation classes (from steppe formations to savanna formations): (1) Discontinuous grass cover with dominance of bare soil, (2) Discontinuous grass cover with presence of bare soil, (3) Continuous grass cover, (4) Discontinuous grass cover with presence of shrub-tree strata and (5) Discontinuous grass cover with presence of bare soil and shrub-tree strata. 3. Method The objective is to quantify vegetation cover degradation in Madagascar savanna ecosystem by analysing NDVI time series of Terra-MODIS images for the period 2000–2007. The method consists in localizing savanna pixels that present no change or a significant decrease or increase in vegetation activity during the
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study period by analyzing the trend component of time series data. This happens in two steps: first, characterisation of the vegetation activity parameter and, second, estimation of vegetation activity trends. 3.1. Characterisation of the vegetation activity parameter In this article, two approaches are presented for the characterisation of the vegetation activity parameter: (1) using the NDVI time series directly, as it was demonstrated that NDVI is strongly correlated to biophysical parameters such as biomass or Leaf Area Index, (2) using time series of a phenological indicator (sumNDVI) corresponding to the accumulated NDVI measured during the vegetation’s active growth phase, as a linear relation has been established between NDVI seasonal accumulation and biophysical parameters linked to photosynthetic activity of the plant cover (Tucker and Sellers, 1986). 3.1.1. NDVI time series trend analysis using the STL TREND component We applied the STL procedure to the 16-day MODIS NDVI time series for the period 2000–2007 (23 images/year except for 2000 i.e. a total of 178 images). In the STL Trend component, for each of the 16,886 savanna pixels, the NDVI trend is modeled by a 1st degree polynomial (Fig. 2). The direction of change is determined through the analysis of the slope value (See Section 3.2). 3.1.2. Phenological indicator time series analysis using the STL seasonal component We applied the STL procedure to the 16-day MODIS NDVI time series for the period 2000–2007 (23 images/year except for 2000 i.e. a total of 178 images). In the STL Seasonal component, for each of the 16,886 savanna pixels, the savanna vegetation growing season is modeled by a NDVI profile (Fig. 2). Using the STL procedure, effects linked to external factors are attenuated in the resulting NDVI profile. They are assumed to be in the Remainder component. But this profile is only valid for the period covered by the time series. Analysis of NDVI profile enables the two phenological stages (start and end of the active growth phase) required to compute the sumNDVI indicator given by Eq. (2) to be pinpointed (Fig. 2).
sumNDVI ¼
Date ofX maxNDVI
16 day MODIS NDVI
(2)
3.2. Estimation of the vegetation activity trends By analyzing the year-to-year fluctuations of a time series, it is possible to reveal and quantify variations in these data over the observation period. Thus, the data series can be modelled by a trend line indicating the variation between two limits: upwards or downwards trend or stability. For each direction of change, it is possible to associate hypotheses with these trends according to the known elements of the data dynamics concerned (Reed, 2006). In our study, NDVI time series and sumNDVIYear of growing season indicator represent an integrated measure of vegetation activity. Trends significantly different from a null trend are assumed to be a measure of degradation of the vegetation cover: trends with non significant slope values represent savanna areas under stable vegetation dynamics whereas trends with significant positive or negative slope values are respectively associated with savanna areas under progressive or regressive vegetation dynamics. Trend estimation was based on two steps: - Step 1: Determination of the trend function parameters. For each pixel, the parameters of the polynomial used to model the trend of NDVI and sumNDVIYear of growing season indicator time series were extracted: the slope (a) and the degree of freedom. - Step 2: Characterisation of change direction. For each pixel, we identified those for which the slope value of the observed trend (from the NDVI and sumNDVIYear of growing season indicator time series) was significantly different to a trend presenting a null slope. To this end, we compared the values of the trend slope (an) with the value of a null trend slope (a0). We applied a Student t test (Eq. (3)). an a0 ffi t ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 1 ðxxÞ ¯ 2 n1 pffiffiffiffiffiffiffi
(3)
n2
where an is the slope of the trend for the studied pixel, a0 is the slope of a null trend (equal to 0), n is the number of observations, x represents the observed values of the NDVI or the phenological indicator and x¯ is the mean for one pixel of the observed values of the NDVI or the phenological indicator within the time series. Finally, we grouped areas of change into three classes: Null trend, Positive trend and Negative trend and mapped it over the savanna area of the study site.
Date of minNDVI
where Date of minNDVI and Date of maxNDVI correspond respectively to the start and the end of the active growth phase during the savanna vegetation growing season. Then, the sumNDVI indicator is calculated for the NDVI profile provided by the STL Seasonal component (sumNDVIReference) and for the NDVI profiles from each growing season covered by the time series (sumNDVIYear of growing season). At this stage, two analyses are conducted: - For each savanna pixel, year-to-year fluctuations in the vegetation activity can be revealed by comparing the sumNDVIReference value with the sumNDVIYear of growing season value for each growing season studied. Based on this information, a connection can be made between these inter-annual variations and the existence of vegetation pressure factors (climatic conditions or human factors such as fire and grazing intensity) (Tucker et al., 1991). For each savanna pixel, sumNDVIYear of growing trend is modeled by a 1st degree polynomial. The direction of change is determined by analysis of the slope value (See Section 3.2).
4. Results 4.1. Savanna NDVI profile from the STL seasonal component For each of the 16,886 savanna pixels, the STL seasonal component provides the NDVI profile for the 2000–2007 period from which we identified the phenological keys stages to compute the sumNDVI indicator (Fig. 3). The active growth phase starts in November and reaches its peak photosynthesis activity level in March (maximum NDVI observed). Then, the NDVI values decreases from April until October (minimum NDVI observed), characterising the senescent phase. The sumNDVI indicator corresponds to the accumulated NDVI values measured between the beginning of November until the end of March. Like in other tropical regions, the savanna vegetation phenology in Madagascar is influenced by the alternation of the dry season (April to October) and rainy season (November to March). 4.2. Year-to-year variations of the savanna vegetation activity We calculated the variations of the annual sumNDVI indicator (sumNDVIyear of growing season) for all the savanna pixels between 2000
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Fig. 3. NDVI profiles of a savanna pixel established for each growing season between 2000 and 2007 from the 16-day MODIS NDVI time series and from STL seasonal component – Identification of phenological key stages.
and 2007. Values are distributed between 2% and +2% in reference of the mean of the sumNDVI indicator for the studied period (Fig. 4), showing a global stability of the savanna vegetation activity between 2000 and 2007. But, since the 2003/2004 growing season, the values of the annual sumNDVI indicator are systematically lower than the mean of the sumNDVI indicator. It revealed a durable disorder of the savanna vegetation activity after this season. Several authors demonstrated that the inter-annual variations of the savanna vegetation activity are correlated with annual fluctuations of rainfall (Camberlin et al., 2007; Nicholson et al., 1990; Zhang et al., 2005). On the study site, a weather station localized close to the irrigated plain (Fig. 1) provides monthly rainfall for the 2000–2007 period. We calculated a climatic indicator (sumRainfall) corresponding to the accumulated rainfall measured between November and March given by Eq. (4) for each growing season. sumRainfall ¼
Date of end of theX active growth phase
Monthly rainfall
(4)
Date of start of the active growth phase
Analysis of the annual variations of the sumRainfall indicator between 2000 and 2007 revealed that accumulated rainfall measured during the active growth phase are around 1600 mm except for the 2001/2002 growing season which is drier than the others (Fig. 4). The 2003/2004 growing season is particular because of the passage of the cyclone ELITA in December 2003 and February 2004 on the study site. Even if the sumRainfall indicator for this growing season is not different from the mean measured for the studied period, the value of the sumNDVI indicator for this growing season is lower than the mean. We compared values of the the sumNDVI indicator and the sumRainfall indicator measured between 2000 and 2007 by calculating a correlation coefficient on two periods [2000–2003]
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Fig. 4. (At the top) Annual variations of the sumNDVI indicator for all the savanna pixels between 2000 and 2007 – (at the bottom) Annual variations of the sumRainfall indicator between 2000 and 2007.
and [2004–2007] in order to isolate the 2003/2004 growing season. Vegetation activity indicator and rainfall indicator are positively correlated (0.987 [2000–2003] for and 0.972 for [2004–2007]). This result confirmed the existence of the relation between savanna vegetation activity and rainfall. In this article, the decrease of the savanna vegetation activity observed after the 2003/2004 growing season can be a consequence of the disorder induced by the passage of the cyclone. 4.3. Savanna vegetation activity change Even if vegetation activity is relatively stable for the savanna area between 2000 and 2007 (Fig. 4), analysis of the trends of vegetation activity indicators for each savanna pixel enables to quantify and localise areas characterized by significant vegetation activity changes. The increase and decrease of the vegetal activity indicators (NDVI or sumNDVI), measured between 2000 and 2007 and statistically significant (for a = 5%), represent an indicator of ‘vegetation cover degradation’ (Fig. 5). From the analysis of the vegetation activity changes based on the sumNDVI indicator time series trends, areas characterised in 2007 by a decrease, an increase or a stability of vegetation activity cover respectively 20%, 1% and 79% of the savanna extent. Areas not affected by vegetation cover degradation between 2000 and 2007 are dominating. However, analysis of the spatial distribution of the three classes of the trends highlighted the existence of a vegetation activity gradient on the scale of the study area (Fig. 5). The savanna vegetation activity tends to increase, on the right bank of the Betsiboka, in the south-east (area under the management of the Ankarafantsika National Park) and to decrease all around the irrigated plain on both sides of the Betsiboka river. For this last class (negative trend), even if savanna concerned by vegetation cover degradation are not affected large areas, their spatial
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Fig. 5. Estimation of vegetation cover degradation between 2000 and 2007: (on the right), trend estimation of vegetation activity changes is based on the analysis of the sum NDVI indicator time series; (on the left), estimation of vegetation activity changes is based on the analysis of the NDVI time series; (at the bottom), fire pressure map measured between 2000 and 2007 from a 8-day MODIS surface reflectance time series.
proximity to the irrigated plain represents a danger due to the sensitivity of the soil to erosion in these places. Analysis of the vegetation activity changes based on the NDVI time series trends shows that 30% of savanna pixels is characterised by in 2007 a significant decrease of the vegetal activity. Areas characterised by an increase of the vegetation activity cover 52% of the savanna. From the analysis of the spatial distribution of the trend classes, we conclude that most of the savanna area localized closed to the irrigated plain is under vegetation cover degradation.
5. Discussion and future work 5.1. Estimation of vegetal activity using phenological indicators In this article, savanna vegetation activity is estimated (1) through NDVI time series directly and (2) through the sumNDVI phenological indicator time series. The main difference between the two approaches is that savanna areas with a significant decrease or increase of NDVI between 2000 and 2007 are characterised by a
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significant stability of the sumNDVI indicator for the same period (Fig. 5). For the estimation of the vegetation activity change with the NDVI time series, all the growing season is considered whereas, using the sumNDVI indicator, only data of the active growth phase are used to measure vegetation activity change. Factors that can explain the inter-annual variations of the indicators are different. As vegetation cover degradation is a complex phenomenon, several phenological indicators should be combined to quantify it. For example, Bai and Dent (2006), into their approach of land degradation assessment, used successfully a set of seven phenological indicators extracted from the NDVI profile (Minimum, Maximum, Difference between maximum and minimum, Mean, Sum, Standard-deviation and Covariance). 5.2. Validation issue of the savanna vegetation cover degradation indicator A comparison of the sumNDVI indicator map for the 2004/2005 growing season, divided into four classes of vegetation activity (very low, low, medium and high) with the savanna vegetation map derived from the SPOT-5 satellite image acquired in 2005 was realised. It emerges that each class of the sumNDVI indicator is effectively characterised by one or two dominant physiognomic types of vegetation. The classes with low and very low sumNDVI values are mainly associated with discontinuous grass cover type whereas the classes with high sumNDVI values essentially correspond to continuous grass cover type. This result tends to prove the relevance of the sumNDVI phenological indicator to represent the savanna vegetation activity. But, validation of the savanna areas characterised by vegetation cover degradation from the MODIS NDVI time series analysis is still missing. Future work will consist on the production of a new vegetation map, from a SPOT-5 image (acquired in June 2009), to quantify and localise areas characterised by vegetation physiognomic changes. 6. Conclusion and perspectives In this study, we described a method for vegetation cover degradation assessment based on a time series trend analysis. Based on the results obtained, three conclusions are identified: - A combination of several phenological indicators is required to quantify vegetation cover degradation in relation to external pressure factors; - The STL procedure is an adapted method to determine simultaneously savanna seasonal change and gradual change; - Trend estimation of phenological indicators time series is a promising indicator of vegetation cover degradation. Further research will be dedicated to the explanation of spatial variation of vegetation cover degradation. Two distinct situations could be observed in Fig. 5. For the areas included in the Ankarafantsika National Park, the trend of the sumNDVI indicator is stable in the large stretches of savanna and is positive in the forest areas. Everywhere else in the catchment basin, however, the trend of the sumNDVI indicator is negative, representing the degradation in the plant cover of the savannas. In our approach, we consider that the anthropic pressures represent the main factor explaining the plant cover change trends. In fact, we put forward the hypothesis that rainfall is distributed evenly over the whole of our study area and, consequently, it could not explain the observed spatial variations in the studied indicator. Since the year-to-year fluctuations in the climatic conditions and, in particular, in the rainfall do not contain any irregularities (Fig. 4), we will consider that they are not at the origin of the vegetation activity change trends that we observed. The anthropic pressure factors should
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therefore play a preponderant role in vegetation dynamics. The degradation of the savannas is thought to be due to the use of fire and to the intensity of grazing (Gillon, 1983; Skarpe, 1992). In Madagascar, the use of fire is an integral part of the practices used to manage natural resources (Kull, 2000). Several works have shown contradictions concerning the effects of fire on the vegetation dynamics of savannas. Some people consider it can constitute a tool for the sustainable management of savannas (Bloesch, 1999). Others, see it as a factor in the degradation of those same lands (Thonicke et al., 2001). On the Marovoay site, from the analysis of a 8-day MODIS surface reflectance time series covering the 2000–2007 period, an indicator that localises annual burned surfaces was determined and has allowed to estimate and localise fire pressure between 2000 and 2007 in the savanna area (PLAE, 2008) (Fig. 5). Results revealed that the zones whose trend of the sumNDVI indicator is stable are just as much concerned by fire pressure as the areas whose the trend of the sumNDVI indicator is positive or negative. This is why the hypothesis whereby fire, according to the way it is used, is a factor in the stability or degradation of savannas still has to be developed. A study of the interactions between the fire utilisation practices and the evolution of the environments would present a two-fold interest. Each year, it would make it possible to provide quantifiable and upto-date spatialized information on the use of fire (frequency and season) within the areas concerned. Lastly, it would provide the managers of those areas with part of the data they need and expect to elaborate an operational system for monitoring and managing fire-use. This system does not exist at present, except in areas that benefit from significant financial resources, such as the National Parks or Integral Nature Reserves (Frost, 1999; Parr and Brockett, 1999). Acknowledgements This study was supported by the Programme de Lutte AntiErosive (PLAE), a Madagascar national program of rural development, financed by the Federal Minister of Development and Cooperation (BMZ) through the KFW. References Ahl, D.E., Gower, S.T., Burrows, S.N., Shabanov, N.V., Myneni, R.B., Knyazikhin, Y., 2006. Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sensing of Environment 104, 88–95. Anyamba, A., Tucker, C.J., 2005. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981 to 2003. Journal of Arid Environments 63, 596–614. Aubreville, A., 1957. Accord a` Yangambi sur la nomenclature des types africains de ve´ge´tation. Bois et Foreˆts des Tropiques 51, 23–27. Bai, Z.A., Dent, D.L., Schaepman, M.E. 2005. Quantitative global assessment of land degradation and improvement: pilot study in North China. ISRIC Rep 2005/06, Wageningen. Bai, Z.A., Dent, D.L. 2006. Global assessment of land degradatin and improvement: pilot study in Kenya. ISRIC World Soil Information Rep 2006/01, Wageningen. Bloesch, U., 1999. Fire as a tool in the management of a savanna/dry forest reserve in Madagascar. Applied Vegetation Science 2 (1), 117–124. Camberlin, P., Martiny, N., Philippon, N., Richard, Y., 2007. Determinants of the inter-annual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sensing of Environment 106, 199–216. Cleveland, R.B., Cleveland, W.S., MacRae, J.E., Terpenning, I., 1990. STL: a seasonaltrend decomposition procedure based on loess. Journal of Official Statistics 6 (1), 3–73. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 25 (9), 1565–1596. Eklundh, L., Olsson, L., 2003. Vegetation index trends for the African Sahel 1982– 1999. Geophysical Research Letters 30, 1430–1433. Frost, P.G.H., 1999. Fire in southern African woodlands: origins, impacts, effects, and control. FAO Forestry Paper 138, 181–205. Gay, M., Jacquin, A. 2005. Programme Lutte Anti-Erosive (PLAE) – Rapport annuel d’activite´. EI Purpan, Toulouse, France, 7. Gillon, D., 1983. The fire problem in tropical savannas. In: Bourlie`re, F. (Ed.), Tropical savannas. Ecosystems of the World Elsevier, Amsterdam, pp. 617–641.
S10
A. Jacquin et al. / International Journal of Applied Earth Observation and Geoinformation 12S (2010) S3–S10
Harrison, P., Pearce, F. 2000. AAAS Atlas of Population and Environment Washington: American Association for the Advancement of Science and the University of California Press. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213. Huete, A., Justice, C.O., van Leeuwen, W. 1999. MODIS Vegetation Index (MOD13). Algorithm theoretical basis document. Version 3. Jacquin, A., Che´ret, V., Denux, J.-P., Gay, M., Xofis, P., Mitchley, J., 2005. Habitat suitability modelling of Capercaillie (Tetrao urogallus) using earth observation data. Journal for Nature Conservation 13, 161–169. Johnson, L., Cooke, B., Ramos, V.H., Easson, G. 2008. Use of NASA Satellite Assets for Predicting Wildfire Potential for Forest Environments in Guatemala. Preliminary Report. University of Mississippi Geoinformatics Center. Jo¨nsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing 40 (8), 1824–1832. Kull, C.A., 2000. Isle of Fire: The Political Ecology of Landscape Burning in Madagascar. The University of Chicago Press, Chicago. Lu, H., Raupach, M.R., McVicar, T.R. 2001. Decomposition of Vegetation Cover into Woody and Herbaceous Components Using AVHRR NDVI Time Series. Technical Report 35/01, CSIRO Land and Water, PO Box 1666, Canberra, 2601, Australia. Millward, A.A., Piwowar, J.M., Howarth, P.J., 2006. Time series analysis of mediumresolution, multisensor satellite data for identifying landscape change. Photogrammetric Engineering and Remote Sensing 72. (4). Nicholson, S.E., Davenport, M.L., Malo, A.R., 1990. A comparison of the vegetation response to rainfall in the Sahel and Esat Africa, using normalized vegetation index from NOAA AVHRR. Climate Change 17, 209–241. Olsson, L., Eklundh, L., Ardo, J., 2005. A recent greening of the Sahel-trends, patterns and potential causes. Journal of Arid Environments 63, 556–566. Parr, C.L, Brockett, B.H., 1999. Patch-mosaic burning. A new paradigm for Savanna fire management in protected areas? Koedoe 42, 117–130. PLAE 2004. Programme de Lutte anti-e´rosive - Pre´sentation ge´ne´rale. PLAE, Marovoay, 25. PLAE, 2008. Programme de Lutte anti-e´rosive – Rapport annuel d’activite´. PLAE, Marovoay, 56.
Reed, B.C., 2006. Trend analysis of time series phenology of North America derived from satellite data. GIScience and Remote Sensing 43 (1), 1–15. Reed, B.C., White, M.A., Brown, J.F., 2003. Remote Sensing Phenology. In: Schwartz, M.D. (Ed.), Phenology: An Integrative Science. Kluwer Academic Publishing, Dordrecht, The Netherlands. Robbez-Masson, J.M. 1994. Reconnaissance et de´limitation de motifs d’organisation spatiale. Application a` la cartographie des pe´dopaysages. Ecole Nationale Supe´rieure d’Agronomie de Montpellier, Montpellier, 161. Roubaud 1997. La question rizicole a` Madagascar: Les re´sultats d’une de´cennie de libe´ralisation. Economie de Madagascar, 2, 37–62. Skarpe, C., 1992. Dynamics of Savanna ecosystems. Journal of Vegetation Science 3, 292–300. Thonicke, K., Venevsky, S., Sitch, S., Cramer, W., 2001. The role of fire disturbance for global vegetation dynamics: coupling fire into a dynamic global vegetation model. Global Ecology and Biogeography 10, 661–677. Tucker, C.J., Newcomb, W.W., Los, S.O., Prince, S.D., 1991. Mean and inter-annual variation of growing season normalized difference vegetation index for the Sahel 1981–1989. International Journal of Remote Sensing 12, 1133–1135. Tucker, C.J., Sellers, P.J., 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing 7, 1395–1417. Verbesselt, J., Jo¨nsson, P., Lhermitte, S., van Aardt, J., Coppin, P., 2006. Evaluating satellite and climate data derived indices as fire risk indicators in savanna ecosystems. IEEE Transactions on Geoscience and Remote Sensing 44 (6), 1622– 1632. Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D., 2009. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment 114, 106–115. Viovy, N., Arino, O., Belward, A.S., 1992. The best index slope extraction (BISE): A method for reducting noise in NDVI time series. International Journal of Remote Sensing 13 (8), 1585–1590. Zhang, X., Friedl, M.A., Schaaf, C.B. and Strahler, A.H. 2005. Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research, 110 (D12103). Zhang, X., Schaaf, C.B., Strahler, A.H., Friedl, M.A., Hodges, J.C.F., Gao, F., Reed, B.C., Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84, 471–475.