Home Articles Vegetation Growth Zones using NOAA-AVHRR data: Comparison between conventional and Satellite based...

Vegetation Growth Zones using NOAA-AVHRR data: Comparison between conventional and Satellite based methods

Lal Muthuwatta
International Water Management Institute
PO Box 2075, Colombo, Sri Lanka
Tel: (94)-1-787404
Fax: (94)-1-786854
Email: [email protected]

Introduction
Since availability of fresh water is limited, managing water among different uses and users is becoming a central issue. A common problem is a lack of information on water utilization. This information should include water consumption through actual evapotranspiration and the relationship between water consumption and biomass production in different parts of agricultural lands. Using this information, water and land use can be linked in order to get optimal and sustainable use of natural resources.

Satellite remote sensing gives opportunities to monitor land surface conditions and the status of water resources on different spatial and temporal resolutions. The NOAA-AVHRR sensor supplies daily data at 1km spatial resolution. This low cost satellite data coupled with physically based models enables the estimation of parameters such as vegetation growth and actual evapotranspiration that are paramount information to water managers.

Since vegetation reflects the climatic, soil and terrain conditions, vegetation growth can be used as a single indicator for land use classification. Conventional classification systems use different types of data in different scales. For example, soil maps give homogeneous soil units, but the climate data on the same regions, based on point data, may not be homogeneously distributed over the whole area. The currently available Sri Lanka agro-ecological zones map based on rainfall, soils and elevation—divides the country into 24 regions (Panabokke 1996). But by looking at vegetation growth patterns it is possible to identify different areas with different potentials of vegetation growth inside a single zone of this map. Therefore, spatial and temporal variation of vegetation growth, which gives the result of all the parameters responsible for vegetation growth, enables a more refined classification rather than one based on few parameters.

This paper aims at discussing the demarcation of Sri Lanka into different zones in terms of spatial and temporal variation of vegetation growth and demonstrates the feasibility of applying such a technique using a case study.

Sri Lanka is situated within latitudes North 90 50’N, South 50 55’N and longitudes East 810 53’E and West 790 42′ E. The aerial extent of the country is 65,610 km2. The mean annual rainfall in Sri Lanka varies from 2,500mm to over 5,000 mm in the southwest of the island. This rainfall is mainly from two monsoons: the southwest monsoon from May to September and the northeast monsoon from December to February. The rainfall during the two inter-monsoon periods, March to April and October to November is mainly convectional and occurs over the whole island. The regional difference in temperature is mainly due to altitude only and shows no dependence on latitude. The mean monthly temperature differs slightly throughout the year depending on the seasonal movement of the sun, with some modifying influence caused by rainfall. In the lowlands, the mean annual temperature is 27.5 0C and at Nuwara Eliya at an altitude of 1800 m above mean sea level, the mean temperature is 15.9 0.C. Sri Lanka is categorized into three elevation classes: up country, mid country and low country. Low country is demarcated as land below an elevation of 300 meters, up country is above 900 meters and mid country is between 300 and 900 meters in elevation.

Materials and Method
In this study, NOAA-AVHRR images acquired and processed by the department of meteorology, Sri Lanka, were used. The study is based on a series of 10-day composites of NOAA images, which cover entire Sri Lanka during a complete annual cycle started in July 1999. The actual evapotranspiration and evaporative fraction were estimated with the surface energy balance algorithm for land (SEBAL) (Bastiaanssen 1998). In addition to that, indicators such as vegetation growth, soil moisture, potential evapotranspiration and surface temperature were derived (Chandrapala 2002). The accumulation of biomass or primary production is according to the Monteith model (1972) related to accumulated absorbed photosynthetic active radiation (APAR). Net primary biomass production was calculated with a minimum of ground data by considering physical models. Soil moisture was estimated using the statistical relationship between evaporative fraction and soil moisture (Bastiaanssen et.al. 2000).

Based on this dataset, monthly vegetation growth was estimated over the country during the study period (Samarasinghe et al. 2002). Using these estimates, the whole country was divided into 91 vegetation growth zones with respect to the spatial and temporal variations of vegetation growth (Muthuwatta et al. 2002). These zones were named and described by the degree of wetness and elevation. Wetness was determined, in addition to soil moisture, using the moisture availability index (MAI) (Heargreaves, 1975). Areas where annual MAI is below 1.0, from 1.0 to 1.4 and above 1.4 were classified as dry (D), intermediate (I) and wet (W) regions, respectively. Of these zones, 60 came under low country, 8 under low country and 7 under up country. In addition to that, 10 zones were distributed between the low country and the mid country, and 6 zones were distributed between the mid country and the up country. Two letters and a number were assigned to each zone: the first letter refers to the MAI zone and the second to the elevation class (U for up country, M for mid country and L for low country), and the number refers to the MAI in ascending order. For example, WL1 gives the area with the lowest MAI in the wet areas of the low country (Muthuwatta et al. 2002).

Case Study
A case study carried out for the DL1 region is indicated in the existing agro-ecological zone map to present the usefulness of the new zoning technique (figure 1). This DL1 zone belongs to the dry part of the country. The dry zone of the country is classified into 5 regions namely DL1, DL2, DL3, DL4 and DL5. DL1 has a 75 percent probability annual rainfall amounting to 775 mm. The soils in DL1 are predominantly reddish brown earths and low humid clay soils. More than 50 percent of the total irrigated lands of the dry zone are situated in this region. Rice is the main crop in the irrigated lowland.


Figure 1 Agro-ecological regions and Vegetation growth zones within the DL1 zone
There are 29 major vegetation growth zones identified inside the DL1 zone. In the existing agro-ecological zone map the whole area is classified as a dry zone region. However, in the new classification system, there are 3 zones identified within the intermediate zone out of these 29 vegetation growth zones (1