In Detail: Recommended Practice drought monitoring using the Vegetation Condition Index (VCI)

Abstract: 

Spectral vegetation indices are among the most commonly used satellite data products for evaluation, monitoring, and measurement of vegetation cover, condition, biophysical processes, and changes [5]. This recommended practice shows how to apply a multi-temporal analysis of MODIS-based Vegetation Condition Index (VCI) to support drought monitoring and early warning. The method was applied in Iran.

Furthermore, there is in addition implemented an updated recommended practice on drought monitoring and early warning including a cloud mask, which had not been considered before. This practice was tested in Central America.

Background:

Introduction

Drought is a recurring phenomenon that can lead to significant losses to societies affecting different aspects of human life such as agriculture, food security, and the environment. Since 1967 droughts have affected 50% of the 2.8 billion people who suffered from all natural disasters [1].

Traditional methods of drought assessment and monitoring rely on rainfall data. However, this approach has two main disadvantages: rainfall data are limited to the region, and they are often inaccurate and difficult to obtain in near-real time. In contrast, satellite-sensor data is continuously available and can be used to detect the onset of a drought, its duration and magnitude [2]. The purpose of this recommended practice is to monitor impacts of meteorological droughts on natural vegetation (rain fed, range land & forest). Availability, simplicity, free of charge data, good research literature and citation, minimum requirements of inputs are main criteria, which have been considered to define the methodology.

Drought definition

Meteorological drought is often defined by a period of substantially diminished precipitation duration and/or intensity. The commonly used definition of meteorological drought is an interval of time, generally on the order of months or years, during which the actual moisture supply at a given place falls below the climatically appropriate moisture supply.

Agricultural drought occurs when there is inadequate soil moisture to meet the needs of a particular crop at a particular time. Agricultural drought usually occurs after or during meteorological drought but before hydrological drought.

Hydrological drought refers to deficiencies in surface and subsurface water supplies. It is measured as stream flow, snowpack, and as lake and groundwater levels. There is usually a delay between lack of rain or snow and less measurable water in streams, lakes and reservoirs. Therefore, hydrological measurements tend to lag other drought indicators.

Socioeconomic drought occurs when physical water shortages start to affect the health, well-being, and quality of life of the people, or when the drought starts to affect the supply and demand of an economic product.

The figure below shows the relations between these types of drought:

Country Background: Iran

Iran is located in an arid and semi-arid region, between 44º 02 ́ and 63º 20 ́ eastern longitude and 25º 03 ́ to 39º 46 ́ northern latitudes. The country covers an area of about 1.648 million km2. Iran is bordered on the north by Armenia, Azerbaijan, Caspian Sea and Turkmenistan; on the east by Afghanistan and Pakistan; on the south by Oman Sea, Strait of Hormuz and Persian Gulf, and on the west by Iraq and Turkey (cf. figure below).

Iran - location

The test site includes the following five provinces in Iran: Alborz, Tehran, Semnan, Qom, Isfahan covering about 250,000km2. The topographic height in the area ranges from 270m to 4,390m. The below images show details of the test site: location of the area within Iran (areas marked purple in the top left image), a Landsat mosaic of the area (top right image), digital elevation model (bottom left) and a land cover map (bottom right).

 

Drought impact in Iran

  • According to United Nations reports, the cumulative effect of droughts from 1999 to 2001 has seriously affected Iran’s agriculture and livestock production [3].
  • In Iran, a three-year drought from 1999 to 2001 has severely affected ten of the country's 28 provinces, leaving an estimated 37 million people (over half the country's population) vulnerable to food and water insecurity.
  • Twenty provinces have experienced precipitation shortfalls during winter and spring 2001. According to the statistics of the Ministry of the Interior, water reserves in the country were down by 45% in July 2001.
  • In the agricultural sector, Iranian farmers have sold roughly 80% of their livestock, and an estimated 800,000 livestock were lost in 2000 as a result of the drought. An estimated 2.6 million hectares of irrigated lands and 4 million hectares of rain-fed agriculture have experienced the drought’s impact in 2001.
  • The most severe drought across the country occurred between 1998 and 2001, with approximately 80% of the country experiencing an exceptional drought.

Requirements: 

Input data:

  • Two weeks composites of NDVI (MVC) from MODIS Satellite Imagery

In the test site, highest vegetation growth is during May and June. Thus, two weeks maximum value composites (MVC) of NDVI from MODIS Imagery (year 2000 to 2013) of May and June were used. Note that in other areas of the world the choice of months will vary.

MODIS is the primary sensor for monitoring the terrestrial ecosystem in the NASA (EOS) program [2]. Time series of MODIS imagery provide near real-time and continuous data with a high temporal resolution. The MODIS sensor acquires data in 36 spectral bands, with variable spatial resolution of 250–1000 meters (depending on band), in narrow bandwidths recorded in 12-bit format. MODIS bands are a compromise for atmospheric, land and ocean studies. Seven bands are considered optimal for land applications [4]. MODIS data and products are all available since year 2000.

From MODIS multi-temporal 2-weeks NDVI composites of the year 2012 and topographic and ecological parameters, a Land Cover (LC) Map of the study area was generated in ENVI software. A knowledge-based classification method was used to classify the region to 18 LC classes as follows:

Dense forest, medium forest, irrigated lands, Orchard, rain-fed, dense range, medium range, poor range, sand dune, marne, salin land, bare land, outcrop, seasonal lake, island, wetland, water body, snow & ice.

Since, the main objective of current study is to monitor meteorological drought impacts on natural vegetation (rain fed, range land & forest), these three classes were masked from the available Landcover map. All NDVI composites were masked based on rain fed and range land classes separately [see more details in "Step-by-Step"]. Note that the classification is not described in the step-by-step procedures; it is assumed that a land cover map is available to the user.

Applications: 

The results of this study can be used for the development of a regional drought monitoring and risk assessment system. Considering the spread and frequency of droughts in the region on the one hand, and the lack of ground climate observations and technical capacity in the countries of the region to deal with droughts on the other, such a system could play an invaluable role for drought preparedness.

The results can be used as a drought-monitoring tool and as a tool for decision support in regional drought assessment and management.

Strengths and Limitations: 

Strengths:

The availability of MODIS data is guaranteed at least until 2018, with continuity missions planned with its successors NPP and NPOESS.

Tests showed that NDVI curve findings and VCI trends are similar to Standard Precipitation Index (SPI) results.

Limitations:

The limitations of NDVI mean curves are that the deviation from the mean does not take into account the standard deviation, and hence can be misinterpreted when the variability in vegetation conditions in a region is very high in a given year [6].

In order to get more objective information, it is recommended to combine remote sensing data and meteorological operational data [7]. However, simultaneous meteorological data are often lacking. Besides, primary processing for meteorological data is time-consuming.

Drought monitoring methodologies for low resolution data require historical NDVI records extending longer than MODIS and Spot VEGETATION operational times. (It was found that NDVI data for one sensor could be predicted from NDVI data collected by another sensor with considerable accuracy. Consequently, MODIS and Spot VEGETATION historical NDVI records could be extended based on past AVHRR data, and applications could benefit by interchanging sensors for provision of NDVI data in the event of a sensor failure).

As with all methodologies involving optical data, clouds are limiting the analysis.

Workflow: 

Bibliography: 

1. Jain, SK, Keshri , R, Goswami , A, Sarkar , A, Chaudhry , A (2009). Identification of drought vulnerable areas using NOAA AVHRR data, International Journal of Remote Sensing, 30(10).

2. Thiruvengadachari,s; Gopalkrishna.H.R,(1993). An integrated PC environment for assessment of drought. International journal of RS 14:3201-3208.

3. Mokhtari, M.H., (2005). Agricultural Drought Impact Assessment Using Remote sensing: A case study Borkhar district –Iran, M.sc Thesis, ITC University.

4. Justice et al, Remote Sensing of environment, (2002). The MODIS fire products.

5. Jesslyn Brown et al, (2008).Using eMODIS Vegetation Indices for Operational Drought Monitoring.

6. Thenkabail, P. S., Gamage, M. S. D. N. and Smakhtin, V. U, The Use of Remote Sensing Data for Drought Assessment and Monitoring in Southwest Asia International Water Management Institute.

7. HONGRUI ZHAO et al. Agriculture Drought and Forest Fire Monitoring in Chonqing City with MODIS and Meteorological Observations.

8. W.T.LIU, F.N.KOGAN, 1996, Monitoring regional drought using the Vegetation Condition Index. INT.J. Remote Sensing, vol. 17, NO. 14,2761-2782.

Zircon - This is a contributing Drupal Theme
Design by WeebPal.