Análisis de los déficit de precipitación y su impacto sobre la vegetación de Oltenia 

Illano usando imágenes de satélite durante el período 2000-2009. Este investigación 

pretende evaluar el déficit de precipitaciones en temporada de verano y su impacto sobre 

la vegetación. Disminución de las precipitaciones en comparación con el valor medio 

representa el primer firme que precede el sequía. El análisis de los resultados de el Índice 

de Precipitación Estandarizado calculado por tres meses (SPI-3) y el mes (SPI-1), reveló 

que durante 2000-2009 en la Oltenia Illano tuvimos 5-6 años deficientes de precipitation 

de los cuales 2 años fueron calificados como los años secos y muy secos. Este déficit se 

observa en el análisis de satélite productos, NDVI y LAI obtenido por procesamiento de 

imágenes Spot-Vegetation y MODIS en la tercera década de cada mes de verano. Las 

imágenes de satélite revelan las diferencias temporales y espaciales de esta parametro 

meteorologico y el impacto que tiene en la vegetación de la llanura de Oltenia

IRINA ONTEL

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      Romanian National Forest Agency Special Award The 19th National Geographic Student Symposium, Bucharest 30 March-1 April 2012  ANALYSIS OF RAINFALL DEFICIT AND ITS IMPACT ON THE OLTENIA PLAIN VEGETATION USING SATELLITE IMAGES FROM THE PERIOD 2000-2009  Irina ONŢEL        1.  INTRODUCTION ......................................................................................................................................................... 53 2.  DATA AND METHODS ............................................................................................................................................ 54 3.  RESULTS AND DISCUSSION ................................................................................................................................ 56 4.  NORMALIZED DIFFERENCE VEGETATION INDEX ............................................................................... 59 5.  LEAF AREA INDEX .................................................................................................................................................... 62 6.  CONCLUSIONS ............................................................................................................................................................. 64 7.  REFERENCES ............................................................................................................................................................... 65   Cite this document: Onțel, I., 2012. Analysis of rainfall deficit and its impact on the Oltenia Plain vegetation using satellite images from the period 2000-2009. Cinq Continents 2 (1): 51-65 [Available online]| URL: http://www.cinqcontinents.uv.ro/2/2_1_Ontel.pdf Volume 2 / Numéro 1 (4) Printemps 2012 ISSN: 2247 - 2290 p. 51-65 Master Degree in Hydrology and Climatology   Faculty of Geography, University of Bucharest  ontel.irina@yahoo.com   Contents:   Cinq Continents Volume 2, Numéro 1, 2012, p. 51-65 [52]  Analysis of rainfall deficit and its impact on the Oltenia Plain vegetation using satellite images from the period 2000-2009  Irina Onțel  Análisis de los déficit de precipitación y su impacto sobre la vegetación de Oltenia Illano usando imágenes de satélite durante el período 2000-2009. Este investigación pretende evaluar el déficit de precipitaciones en temporada de verano y su impacto sobre la  vegetación.  Disminución  de  las  precipitaciones  en  comparación  con  el  valor  medio representa el primer firme que precede el sequía. El análisis de los resultados de el Índice de Precipitación Estandarizado calculado por tres meses (SPI-3) y el mes (SPI-1), reveló que durante 2000-2009 en la Oltenia Illano tuvimos 5-6 años deficientes de precipitation de los cuales 2 años fueron calificados como los años secos y muy secos. Este déficit se observa en el análisis de satélite productos, NDVI y LAI obtenido por procesamiento de imágenes  Spot-Vegetation  y  MODIS  en  la  tercera  década  de  cada  mes  de  verano.  Las imágenes de satélite revelan las diferencias temporales y espaciales de esta parametro meteorologico y el impacto que tiene en la vegetación de la llanura de Oltenia.  Palabras clave: sequía, la vegetación, SPI-3, SPI-1, NDVI, LAI, Oltenia Plain.  Analiza  deficitului  de  precipitaţii  şi  impactul  acestuia  asupra  vegetaţiei  Câmpiei Olteniei  cu  ajutorul  imaginilor  satelitare  în  perioada  2000-2009.  Lucrarea  are  ca obiectiv evaluarea deficitului de precipitaţii din anotimpul de vară şi impactul său asupra vegetaţiei.  Scăderea  cantităţii  de  precipitaţii  raportată  la  o  valoare  medie  reprezintă primul  semn  ce  precede  fenomenul  de  secetă.  Din  analiza  valorilor  rezultate  în  urma calculării  Indicelui  Standardizat  de  Precipitaţii  pe  trei  luni  (SPI-3)  şi  o  lună  (SPI-1),  a rezultat faptul că în perioada 2000-2009 în Câmpia Olteniei am avut 5-6 ani cu deficit de precipiataţii dintre care 2 ani s-au încadrat în categoria anilor secetoşi şi foarte secetoşi. Acest deficit se observă şi prin analiza produselor satelitare NDVI şi LAI, obţinute prin prelucrarea imaginilor Spot-Vegetation şi MODIS din a treia decadă a fiecărei luni de vară. Imaginile  satelitare  pun  în  evidenţă  atât  diferenţieri  temporale  cât  şi  spaţiale  ale impactului  pe  care  acest  parametru  meteorologic  îl  are  asupra  vegetaţiei  din  Câmpia Olteniei.  Cuvinte cheie: secetă, vegetaţie, SPI-3, SPI-1, NDVI, LAI, Câmpia Olteniei. I. ONȚEL 1. INTRODUCTION The decrease in rainfall relative to the mean, is the first sign preceding droughts. This decrease correlated with temperatures increased and poor irrigation system, leading to a temporary inability to meet agricultural water consumption generating food insecurity. This is today an important issue in the World. Meteorological drought is a short lived, recurring natural disaster, which originate from the lack of precipitation and can bring significant economic losses [1]. It is not possible to avoid meteorological droughts, but it can be monitored, and their adverse impacts can be alleviated [2]. The success of the drought prediction depends on how well it is defined and identified.  Because  drought is  intimately related with food security, therefore, study on drought hazards especially drought monitoring are essential for implementing mitigation to reduce drought impact in Oltenia Plain[3]. The purpose of this article is to evaluate the precipitation deficit in summer season and observing the  impact it has  this parameter on  vegetation  within  Oltenia Plain. The drought  estimation  is  conventionally  based  on  indices  for  the  identification  of  drought characteristics, such as its intensity or severity, duration and its extent surface. The study area is Oltenia Plain which is located in South-West Romania, between the Danube to the South and West, Olt river to the East and Getic Plateau in the North (Figure 1). This sector is well individualized as compared to the other sectors of the Romanian Plain because of a series of morphostructural elements. It is a typically piedmont-like plain, where all the valleys, including the Danube’s, display well-developed systems of terraces. Its main feature is the presence of sand dunes, which covers almost half of the surface of the plain. The presence of the dunes is extremely important from the climatic point of view as sandy soils impose a series of restrictions. Within these areas, the precipitation amount is relatively  low  and  the  temperatures  quite  high,  especially  in  summer;  thus,  due  to  the physical-chemical features of the soil the water deficit must be covered through irrigation [4].  Cinq Continents Volume 2, Numéro 1, 2012, p. 51-65 [54]    2. DATA AND METHODS The  analysis  of  precipitation  deficit  was  performed  by  calculating  Standardized Precipitation  Index for three months and one  month, using data  from weather stations within Oltenia Plain. The SPI developed by McKee et al. (1993) is based on the concept of standardized precipitation, which is the difference between total precipitation for some period of time and  historical  mean  total  precipitation  for  the  same  period  divided  by  the  standard deviation.  The  use  of  flexible  time  scales,  which  can  be  obtained  only  by  accumulating precipitation  for  a  desired  period  of  time,  makes  this  index  very  important  because different time scales are applied to different types of drought.  An  analyst  with  a  time  series  of  monthly  precipitation  data  for  a  location  can calculate the SPI for any month in the record for the previous i months, where i=1, 2, 3, 12... 24... 48 ... depending upon the time scale of interest. Hence, the SPI can be computed from precipitation for 1 month to 48 month length [3]. SPI has been selected by the World Meteorological Organization as a key indicator for monitoring drought. As a result it is used by drought monitoring centres worldwide. There are many examples of its successful application in research oriented literature. I. ONȚEL  ;    ; Pi = rainfall from current period;  Pm = the average annual precipitation from period i;  = standard deviation; n = number of values. The negative values correspond of the periods with rainfall deficiency while the positive values correspond the periods with rainfall excess (Table 1).  SPI Value  Class  Colour SPI > 2,00  Extreme wet   1,50 < SPI ≤ 2,00  Severe wet   1,00 < SPI ≤ 1,50  Moderate wet   -1,00 < SPI ≤ 1,00  Normal   -1,50 < SPI  ≤ -1,00  Moderate dry   -2,00 < SPI ≤ -1,50  Severe dry   SPI < -2,00  Extreme dry    Impact  on  vegetation  is  observed  used  satellite  data,  SPOT  VEGETATION (Normalized Difference Vegetation Index, 10 days synthesis, 1km spatial resolution) and TERRA/MODIS  (Leaf  Area  Index,  8  days  synthesis,  1  km  spatial  resolution).  These products are provided free by the French Space Agency [5] and NASA [6]. Normalized Difference Vegetation Index (NDVI) is one of the most successful of many attempts to simply and quickly identify vegetated areas and their "condition," and it remains the most well-known and used index to detect live green plant canopies in multispectral remote sensing data. The NDVI is calculated from these individual measurements as follows:    Where: NIR and RED stand for the spectral reflectance measurements acquired in the visible (red) and near-infrared regions, respectively. NDVI was introduced in literature by Rouse (1973), for vegetation mapping from U.S. plains using ERTS images, the first generation of Landsat images (MSS). The purpose of the first formula was to highlight the spectral signatures of the vegetation in near-infrared (the spectral area reflectance in the chlorophyll is highest), in relation to the Cinq Continents Volume 2, Numéro 1, 2012, p. 51-65 [56]  area of red (chlorophyll absorbs light radiation which impose the green color to the healthy vegetation, located in high season) [7]. This formula is based on the fact that chlorophyll absorbs in the red part of the spectrum  whereas  the  mesophyll  leaf  structure  scatters  NIR  radiation.  NDVI  values ranges from -1 to +1, where negative values correspond to an absence of vegetation e.g. water surface, clouds [8]. The  Leaf  Area  Index  (LAI),  defined  as  half  the  total  leaf  area  per unit  ground surface  area  [9]  is  a  key  biophysical  canopy  descriptor,  which  play  a  major  role  in vegetation physiological processes and ecosystem functioning. Crop LAI estimation and its  spatial  distribution  has  grat  importance  for  crop  growth  monitoring,  vegetation stress, crop forecasting, yield predictions and management practices.The LAI estimation algorithms  from  satellite  data  are  based  on  the  analysis  of  multispectral  and multidirectional surface reflectance signatures of vegetation elements [10]. LAI=(NDVI*1.71)+0.48  3. RESULTS AND DISCUSSION According to the value to the Standardized Precipitation Index in three months (SPI-3), in 2000 and 2003 we had moderate dry summers in most of the Oltenia Plain and in the Caracal Plain we had severe dry (Figure 2). The summers from 2005 and 2006 were moderate wet, severe wet and extremele wet and the rest of the years we have had relatively normal summers. From rainfall point of view, June was moderate dry in 2000, 2002,  2003  and  2007,  the  July  was  in  2007  and  the  August  was  in  2002  and  2003 (Figure  3).  Standardized  Precipitation  Index  values  is  fluctuates  in  time  and  space acording to the movement of the air masses circulation across the continent.  I. ONȚEL        Cinq Continents Volume 2, Numéro 1, 2012, p. 51-65 [58]                            I. ONȚEL Even if the most years has been normals from the pluviometric point of view, this normality did not had asured enought water that the plant to develop. This thing can be obseved on the satellite images.  4. NORMALIZED DIFFERENCE VEGETATION INDEX For June, a very dry year was 2000 and a result of that was the  act that the vegetation density did not exceed 0.5 and in some areas was recorded values of 0.3 (Figure 4). Another remarkable year in terms of rainfall was 2002 and the value of the vegetation index was below 0.3. Although the June 2003 data recorded at stations in Oltenia Plain had indicated that this was a very dry month, the vegetation density was very high (Figure 5).    Cinq Continents Volume 2, Numéro 1, 2012, p. 51-65 [60]    In July 2000 can be seen a spatial differentiation of NDVI in witch recorded values are lower to the Est of Jiu river, and slightly higher to the West of it. In 2002 the higher values has been recorded to the West and the lower ones of the Est, which it is opposite of 2000. Due to low rainfall and because of the high temperature which was recorded in 2007, the NDVI values are lower to the entire study area (Figure 6).  I. ONȚEL   In August, 2000 shared the same space as noted in previous months, with low values to the East than West Jiu (Figure 7). Another year with sparse vegetation was 2001. In 2003 they are higher than previous years, and the same thing is applied in the years 2008 and 2009. According to the National Statistics Institute, in 2000, 2002 and 2007 years, was recorded the lowest crop production. Cinq Continents Volume 2, Numéro 1, 2012, p. 51-65 [62]    5. LEAF AREA INDEX  Spatial and temporal distribution of the Leaf Area Index kept almost the same trends as in the case of the NDVI's. In June 2000, the value of Leaf Area Index is reduced East of the Jiu (Figure 8), in 2002 values between 0.3 and 0.5 are located in the central part of the Oltenia Plain (Figure 9) and in 2003 is the same exception as the index values above, the foliar surface is very high.  I. ONȚEL     Cinq Continents Volume 2, Numéro 1, 2012, p. 51-65 [64]  In July the situation remains the same because of the phenological plant stage. A representative year for low values of index is 2007 (Figure 10), the predominant surface does not exceed 1 value, and highlighting the intensity of drought. In August, the index show low values on most images.    6. CONCLUSION Standardized Precipitation Index revealed dry periods of the years 2000-2009, the Oltenia Plain, emphasizing the summers of the years 2000, 2002, 2003 and 2007. In East, Oltenia Plain was more affected by drought than in West of its. Spot-Vegetation images and Terra/MODIS were an important source of data for analysis of temporal and spatial evolution of vegetation, identifying the dry years. This images  represents  one  of  the  most  important  type  of  satellite  data  available  free  of charge which can be successfully used in determining the vegetation status at one point or to predict the changes that may appear in plants activity. The correlations between multispectral information provided by VEGETATION and  MODIS  sensors  and  the  measurements  of  Oltenia  Plain  stations,  highlighted  the influence that rainfall have on vegetation. I. ONȚEL The  importance  of  such  analysis  is  given  of  the  use  of  land,  which  is predominantly agricultural, which requires constant monitoring of agricultural crops.  7. REFERENCES  [1] PAL J. S., SMALL E.E., ELTAHIR E. A. B. Simulation of regionalscale water and energy Budgets:  Representation  of  subgrid  cloud  and  precipitation  processes  within RegCM,  J.  Geophys.  Res.,  vol.  105  (D24);  2000,  pp.  29,579-29,594,  doi: 10.1029/2000JD900415. [2] GOMMES R., PETRASSI F. Rainfall variability and drought in Sub-Saharan Africa since 1960, FAO Agrometeorology Series Working Paper No. 9, FAO, Rome, Italy, 1994. [3]  RAFIUDDIN  M.,  DASH  B.  K.,  KHANAM  F.,  ISLAM  M.  N.  Diagnosis  of  Drought  in Bangladesh  using  Standardized  Precipitation  Index,  International  Conference  on Environment Science and Engineering IPCBEE vol.8 IACSIT Press, Singapore; 2011. [4] VLĂDUŢ A. The pluviometric and thermal regime within the Oltenia Plain. Analele Universităţii  din  Craiova.  Seria  Geografie,  vol.  X,  Serie  Nouă,  Craiova:  Editura Universitaria, pp. 21-28; 2007. [5] http://www.free.vgt.vito.be/ [6] http://www.daac.ornl.gov/MODIS/ [7] MIHAI B. Teledetacţie. Introducere în procesarea digitală a imaginilor, Vol. 1, Editura Universităţii din Bucureşti, 2007; [8] MYNENI R.B., HALL F.G., SELLERS P.J., MARSHAK A.L. The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Rem. Sens. 33, p. 481-486; 1995. [9]  CHEN  J.,  BLACK  T.A.  Defining  leaf  area  index  for  non-flat  leaves.  Plant  Cell Environment 15; p. 421-429; 1992 [10]  STANCALIE    G.,  ARGENTINA  N.,  TOULIOS  L.  Satellite  based  methods  for  the estimation  of  Leaf  Area  Index,  COST  Action  734.  Impact  of  Climate  Change  and Variability on European Agriculture; 2010, p. 49-74. 

Analysis of rainfall deficit and its impact on the Oltenia Plain vegetation using satellite images from the period 2000-2009

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