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Journal of Biodiversity and Ecological Sciences No.2, Issue1 © ISSN: 2008-9287 (JBES ) Winter 2012 JBES Original Article Impact assessment of climatic condition on rangeland vegetation cover through S.Poormohammadi1* RS and GIS techniques 2 M.H.Rahimian M.Kalantar3 Received Date:May/7/2011 S.Poormohammadi 4 Accepted Date:Mar/23/2011 Abstract In this study an indicator was introduced, namely “impact factor” (I). It shows impacts of long term climatic conditions on rangeland vegetation status. It 1-Islamic Azad University, Yazd, Iran , considers precipitation and temperature as two main climatic parameters and also Young Research Club, MSc. of desert a remotely sensed vegetation index, SAVI. The impact factor was successfully management implemented in this case study through integration of MODIS time series and 2-rrigation and drainage expert, RS & GIS eight meteorological station records. Also digital elevation model (DEM) was dept., National Salinity Research Center employed as an ancillary data for interpolation and generation of temperature (NSRC), Yazd, Iran, and precipitation maps. In the studied region, northern areas which were also the 3-Faculty member, Islamic Azad plain outlet showed the maximum “I” values compared with other areas. These University, Yazd, Iran, areas were found more susceptible to degradation of their resources (vegetation 4-MSc student, Shahid Bahonar cover) as direct impacts of climatic conditions and drought spells. University, Kerman, Iran [email protected] Keywords: MODIS, Vegetation Cover, Climate, Rangeland, Impact Factor INTRODUCTION Rangeland is a type of land which is characterized help decision makers to do necessary by non-forest, native vegetation [NRCS, 1997]. interventions and adopt proper measure against Types of land cover in a rangeland are grasslands, drought spells. shrub lands, and savannas, which are determined Current ground-based methods are not suitable for by climate. Healthy rangelands are a national regional assessments and monitoring of resource that will sustain soil quality, enhance the rangelands, seasonality. These methods invariable availability of clean water, sequester excess consume plenty of time and expense. Conversely, carbon dioxide, maintain plant and animal satellite remote sensing [RS] techniques can diversity, and support a myriad of other non- provide useful information about changes of agricultural uses [Follett et al., 2001]. Generally, rangeland status due to changes in climatic yearly variability of precipitation make rangelands attributes of the region [Lunetta et al, 2006]. This unsuitable for crop production, and livestock technique is significantly promising for the grazing presents a sustainable means of food and development of more reliable and economically fiber production [Hunt et al, 2003]. Droughts, may feasible measures of vegetation status over large drastically affect plant community composition areas. This technique can monitor vegetation and make rangelands more susceptible to diseases, cover status both in time and space [Tueller insect pests, weed invasions, and overgrazing. 1989]. In fact, monitoring of large areas at low Continuous monitoring of rangeland status seems cost and a relatively low time span is the forte of to be necessary and useful in this field. It would remote sensing technique. Journal of Biodiversity and Ecological Sciences JBES IAU of Tonekabon Branch Tonekabon, Iran [email protected] S.Poormohammadi et al Vol.1, Issue1 Successful application of this technique for the al. 1993] have shown that live biomass of a monitoring of rangeland would be attainable with rangeland is correlated to remotely sensed the help of vegetation indices [Bhaskar et al, vegetation indices, particularly NDVI. Also leaf 1994; Hunsaker, 2005]. Vegetation indices [VI] area index [LAI] is related to mass of the foliage; represent different combinations between red [R] the ratio between leaf area and leaf mass is the and near infrared [NIR] portions of the spectrum specific leaf area. Many studies examined the use [bands]. Green vegetative surfaces strongly absorb of remote sensing for estimating LAI [Curran, red light and strongly reflect near-infrared energy. 1983; Hatfield et al., 1985; Price, 1993; Price and No other materials have this spectral signature Bausch, 1995; Qi et al., 2000c]. Absorbed [Lillesand and Keifer, 2000]. Many Red-NIR photosynthetically active radiation [APAR] also combinations have been proposed on this basis was found suitable for estimation of rangeland [Tucker 1979; Lymburner et al. 2000]. biomass [Choudhury 1987]. However, the second Furthermore, drought is a climatic phenomenon approaches are likely to be more successful in that influences green vegetation and in addition, predicting rangeland status across different water is one of the most common limitations that climatic conditions. But some problems became cause this phenomenon. Therefore, remote apparent, which limits the usefulness of these sensing of vegetation water content can play an approaches. One of the major limitations refers to important role in monitoring of drought impacts soil background. It has strong effects on remotely on rangeland vegetation. Besides the thermal sensed data [Ezra et al., 1984; Huete et al, 1985; region of the electromagnetic spectrum [8–14μm], Huete, 1988; Price, 1993; Qi et al., 1994; Price two spectral regions have been found to be and Bausch, 1995] and forces the established suitable for detection of plant water content: the equations to be site specific. However, multi near-infrared region [NIR, 0.7–1.3μm] and the temporal remote sensing is powerful and allows shortwave infrared region [SWIR, 1.3–2.5μm] more mechanistic models of plant production to [Reeves et al., 2002]. be used because, in part, the soil background is NDVI [Normalized Difference Vegetation Index] constant [Hunt et al, 2003]. Data from the Landsat is one of the most frequently used vegetation Thematic Mapper [TM4 and TM5] and the indices for monitoring of moisture-related Enhanced TM plus [Landsat 7] are suitable for vegetation condition. It calculates as [ρNIR - rangeland management [Nouvellon et al., 2000; ρRED]/[ρNIR + ρRED], where ρRED and ρNIR Nouvellon et al., 2001] because the 20-year time are radiances in red and near infra red portions of series of these data can be averaged to overcome the spectrum, respectively. the high year-to-year variation from precipitation Estimation of standing dry mass and yields was [Hunt et al, 2003]. With Landsat Multispectral one of the earliest applications of satellite remote Scanner [Landsat 1 to 5] data, the time series of sensing for agricultural crops, and the developed data extends to 30 years. Long time series of these techniques were applied early on to rangelands data type have been used by Washington-Allen et [Tucker et al., 1975; Richardson et al., 1983; al. [1999] to examine plant community changes in Everitt et al., 1986; Aase et al., 1987; Anderson et relation to climatic variability. Also in most al., 1993b]. At the time, field techniques for applications, remotely sensed imagery has been estimating productivity were based on measuring merged with other images or geospatial data such peak biomass, so estimation of productivity for as Digital Elevation Model [DEM], records of rangelands was based on remote sensing peak meteorological stations, soil maps and field biomass [Tucker et al., 1983; Everitt et al., 1989]. observations of some rangeland attributes. However, two main approaches have been In the present study a time series of Moderate followed for the purpose of rangeland monitoring Resolution Imaging Spectroradiometer [MODIS] through RS data. Establishment of direct data have been used to monitor rangeland status in empirical relationships between spectral ten successive years, and also to investigate the reflectance of land cover and its biomass is one impact of climatic parameters on vegetation cover the approaches that have been accomplished by and its variability in the rangeland. MODIS was some researchers [e.g. Tucker et al. 1983 and successfully deployed by NASA on 18, December Wylie et al. 1995]. In the second approach, 1999. Several decades of improved spectral reflectance data have been used to derive communications, hardware, software, data storage different vegetation indices and also other capacity, and satellite engineering enable the vegetation related parameters. I this regard, MODIS instrument to provide enhanced numerous broad scale studies [Thoma 1998, monitoring capabilities. A suite of satellites has Tucker et al. 1983, Kennedy 1989 and Merril et been used to measure and monitor biophysical 24 Journal of Biodiversity and Ecological Sciences Winter 2012 Impact assessment of climatic condition on rangeland vegetation constituents of the earth’s surface, each exhibiting Accordingly, at least three VI datasets for April, different haracteristics. The MODIS sensor, May and June were prepared in different years. however, is unique because it combines both the Month with maximum values of these VIs was spatial and spectral resolution of several satellites identified and registered as representative month on a single platform [Hunt et al, 2003]. MODIS of that year. Finally, a dataset including mean VI presents greater radiometric resolution than values of representative months for ten successive traditional sensors providing a broader range of years [2000-2009] was generated. measurement and therefore increased sensitivity Identifying relationships between climatic