**Strengthening Regional Capacities for Providing Remote Sensing Decision Support in Drylands in the Context of Climate Variability and Change**

Humberto A. Barbosa and T. V. Lakshmi Kumar

*1Laboratory of Analysis and Processing of Satellite Images (LAPIS), Universidade Federal de Alagoas (UFAL), 2Atmospheric Science Research Laboratory, SRM University, Kattankulathur, 1Brazil 2India* 

#### **1. Introduction**

398 International Perspectives on Global Environmental Change

Sachez-Arcilla, A.; Jimrnez, J.; Valdemoro, H. & Gracia, V. (2008). Implications of Climatic

Seni, Α. & Karibalis, Ε. (2007). *Identifying the vulnerability of coastal areas by sea level rise using* 

Skourtos, M.; Kontogianni, A.; Georgiou, S. & Turner, R. K. (2005). Valuing Coastal Systems,

Sonderquist, Τ. & Hasselstroom, L. (2008). *The economic value of ecosystem services provided by* 

Sterr, H. (2008). Assessment of Vulnerability and Adaptation to Sea-Level Rise for the Coastal Zone of Germany, *Journal of Coastal Research,* 24, 2, pp. 380-393 Stergiou, Ν. & Doukakis, Ε. (2003). *The impacts of sea level rise on the coastal zone of saltmarsh in* 

Tompkins, E.; Nicholson-Cole, S.; Hurlston, L.; Boyd, E.; Brooks Hodge, G.; Clarke, J.; Gray,

Turner, R.K.; Bateman, I.J. & Adger, W.N. (2001). *Economics of Coastal and Water Resources: Valuing Environmental Functions*, Dordrech: Kluwer Academic Publishers UNEP (1999), Strategic action programme to address pollution from land-based activities.

Velegrakis, A.F. (2010). *Coastal systems*, unpublished paper, Department of Marine Sciences,

Velegrakis, A.F.; Vousdoukas, M.; Andreadis, O.; Pasakalidou, E.; Adamakis, G. &

Vafeidis, A.T.; Nicholls, R.J.; McFadden, L.; Tol, R.S.J.; Hinkel, J.; Spencer, T.; Grashoff, P.S.;

Vött, A. (2007). Relative sea level changes and regional tectonic evolution of seven coastal areas in NW Greece since the mid-Holocene, *Quaternary Science Reviews*, 26, pp. 894-919 Wigley, T.M.L. (1995). Global-Mean Temperature and Sea Level Consequences of Greenhouse Gas Concentration Stabilization, *Geophysical Research Letters*, 22, pp. 45-48 Zanou B.,A. Kontogianni, M.Skourtos (2003) A classification approach of cost effective

Meligonitis, R. (2008). Impacts of dams on their downstream beaches: A case study from Eresos coastal basin, Island of Lesvos, Greece, *Marine Georesources and* 

Boot, G. & Klein, R.J.T. (2008). A new global coastal database for impact and vulnerability analysis to sea-level rise, *Journal of Coastal Research*, 24, 4, pp. 917–924 Velegrakis, A.F.; Vousdoukas, M.I. & Meligonitis, R. (2005). Beach erosion: Phenomenology

and causes of the degradation of the greatest natural resource of the Greek Archipelago In: Tsaltas, The Greek Archipelago in the 21st Century, Vol. I, Sideris

management measures for the improvement of watershed quality. Ocean and

*Guidebook*, Tyndall center for Climate Change Research, UK

Publications, pp. 243-262 (In Greek with English Abstract)

Slovic, P.; Fischof, B. & Lichtenstein, S. (1979). Rating the Risks, *Environment,* 21, 3

*of Coastal Research*, 24, 2, pp. 306-316

Environmental Protection Agency

Department of Topography, (in greek) Tipping Points Report (2009) WWF and Allianz SE

UNEP/MAP Athens

University of Aegean

*Geotechnology*, 26, pp. 350-371

Coastal Management 46, pp. 957-983

Geography and Area management", (in greek)

Present and Future, Springer Verlag, pp. 119-136

Change on Spanish Mediterranean Low-Lying Coasts: The Ebro Delta Case, *Journal* 

*GIS. The case study of Porto Heli and Ermioni (Peloponnese)*, Dissertation, Harokopion University, Department of Geography, Postgraduate Program: "Applied

In: R.K. Turner, W. Salomons, J. Vermaat (Ed), Managing European Coasts: Past,

*the Baltic Sea and Skagerrak.* Existing information and gaps of knowledge, Swedish

*Kitros Pierias*, Disseration, NTUA, School of Rural and Surveying Engineering,

G.; Trotz, N. & Varlack, L. (2005). *Surviving Climate Change in Small Islands - A* 

Dryland ecosystems cover one third of the earth's total land surface, comprise areas with a ratio of average annual rainfall to evapotranspiration of less than 0.65 (MEA, 2005). These regions are fragile environments characterized by unreliable rainfall patterns and support livelihoods of over 2.5 billion people (Reynolds et al., 2007). Widespread episodes of drought, heavy precipitation and heat waves have been reported as a consequence of global sea level increase (Verdin et al., 2005). However, the projections of the impacts of global warming on regional climate are largely uncertain due to the complex and site-specific interdependencies among landscape properties, environmental traits and policy decisions (Boulanger et al., 2005). The predictions of climate changes and their impacts in those dry lands are important because of their characteristics affecting economic activity based on agriculture and the role of natural ecosystems in carbon sequestration and water budget, which could lessen or mitigate the impacts of global changes in the weather system of these regions.

Climate variability and change play a significant role in dryland decision making, at various time scales. Decisions affected by climate considerations include both dryland hardware (infrastructure and technology) and software (management, policies, laws, governance arrangements). Strategic (decadal scale) and tactical (seasonal or interannual scale) decisions regarding such matters as infrastructure for storing water and dryland conservation measures must be made in the face of uncertainty about interdecadal, intraseasonal and interannual flows.

There is a need to understand changes that have occurred in the resources in dryland ecosystems that contain a variety of plant species that have developed special strategies to cope with the low and sporadic rainfall and extreme variability in temperatures A better understanding of various dynamics at work in drylands will put us in a better position to predict the future of the ecosystems. Sustainable land use under climate change requires

Strengthening Regional Capacities for Providing Remote

**2.1 Normalized Difference Vegetation Index (NDVI)** 

change and climate variability.

**studies** 

cover is given below.

Sensing Decision Support in Drylands in the Context of Climate Variability and Change 401

Nina barely showed that in many cases, La Nina had positive impact and El Nino, a negative one. Due to climate change and variability, the disasters like droughts became frequent in the above said regions. Semi arid Asia is experiencing an increase in the frequency of severity of wild fires. African rainfall changed substantially over last 60 years due to land cover changes and forest destruction. Though, India is not showing any significant trend in its annual rainfall, an increase in extreme weather events are evidenced (Lakshmi Kumar et al., 2011). So it is must to address these issues from the remote sensing perspective, that too in assessing and monitoring droughts. The present chapter aimed to study the land surface and vegetation and their response to climate in the context of climate

**2. Monitoring the ground vegetation – Soil wetness by satellites – Previous** 

The study of vegetation cover over a region which can be formed either by native or by cultivation attained a great significance. Barbosa et al. (2006), reported that the NDVI is a reliable index to study the ground vegetal cover and to monitor the changes occur in the vegetation due to climatic abnormalities. Study of spatiotemporal variations of NDVI is of great importance now a days in the context of increased greenhouse gases that modulate the global climate systems in terms of short term climate signals such as El Nino and La Nina. The NDVI variations on both space and time scales not only important in view of varying crop stages but also prominent in vegetation-climate feedback mechanism thus giving a challenge to policy makers in proactive and reactive measurements of risk Cihlar et al., 1991; Davenport & Nicholson, 1993; Al-Bakri & Suleiman, 2004; Kazuo & Yasuo, 2005, Ma & Frank, 2006 & Nagai et al., 2007. Global scientific community focused on NDVI as the indicator of agricultural droughts where in crop growth is known by NDVI value and found that the NOAA Advanced Very High Resolution Radiometer (AVHRR) NDVI is one of the best among the other vegetation indices derived from the other satellites. The NDVI can be defined as the ratio of difference between Channel 1 (red) and Channel 2 (near Infrared) which is based on the more absorbance for healthy vegetation and more reflectance for the poor vegetation. In other way, NDVI measures the changes chlorophyl content (via absorption of visible red radiation) and is sponzy mesophyll (via reflected NIR radiation)

with in the vegetation canopy, thus NDVI from AVHRR can be written as

NDVI = (ρ857 – ρ645 )/ (ρ857 + ρ645 ) This NDVI varies from -1 to + 1 and the category in classifying the density of vegetation

NDVI < 0.2 -------------- Low vegetation

NDVI < 0.4 -------------- Medium vegetation

NDVI > 0.4 ------------- High vegetation Relevant research in changes in vegetation cover in the Sahel demonstrates that the NDVI happens to correlate particularly closely with rainfall, as high as 0.84. The cause of this

detailed knowledge of the system dynamics. This is particularly pertinent in the management of domestic livestock in semiarid and arid grazing systems, where the risk of degradation is high and likely climate change may have a strong impact. Although these drylands are of environmental and socio-economic importance, they are faced with serious management challenges. Hence, their sustainable management requires an evaluation of the magnitude, pattern, and type of land-use/cover changes and the projection of the consequences of these changes to their conservation. It is also important to precisely describe and classify land cover changes in order to define sustainable land-use systems that are best suited for each place (FAO 1998). Monitoring the locations and distributions of land cover changes is important for establishing links between policy decisions, regulatory actions and subsequent land-use activities. In this regard, there is a need to consider both the socio-economic environment (Giannecchini et al., 2007) and other environmental factors. In this context, there is a need for agriculture administrators and policy makers to better understand the intraseasonal-to-interannual variability of climate and its effects on the landscape properties. The comprehension of interactions of weather variability and those landscape properties could lead to improved understanding of those landscape vulnerability to global changes, enhanced natural-resource management and to a better emergency planning to withstand the effects of extreme episodes on the natural and agricultural systems at regional scale (Rosenzweig et al., 1994). Nonetheless, the climatic data, at adequate spatio-temporal resolution at the regional level is scarce, representing an obstacle to researchers.

Prognostic numerical models are one of the main research tools used to predict past and future states of the Earth system (Cramer et al., 2001), yet persistent problems limit their acceptance in ecological and global change research. Aber (1997) posed the question "Why have models failed to penetrate the heart of ecological sciences?" and found that all too often model predictions are made prior to parameterization, validation, sensitivity analysis, and description of model structure. While today models are more accepted in a wide variety of fields, these issues are still prevalent and still ignored too often. With the advent of global monitoring systems based on satellites, it became possible to understand the nature and response of these ecosystems and drylands to day- to-day fluctuations in weather. In particular, the spatial-temporal analysis of vegetation dynamics (i.e., the response of vegetation to climatic conditions) in the semi-arid tropical region is important in the context of climate change where these dynamics show quicker response in short term climate indices such as the Southern Oscillation Index (SOI) and the Nino Sea Surface Temperatures (Fischer, 1996). The present study aims to cover certain regions across the tropics of arid (R/PE is 0.05 to 0.20) and semi-arid (R/PE is 0.20 to 0.50) nature (where R represents Rainfall and PE, Potential Evapotranspiration). Such regions of this type are Northeastern Brazil, West Sahel in Africa and Andhra Pradesh in India. The rainfall in India is mainly by south-west monsoon (June to September). In Sahel, it is primarily from June to August and to a lesser extent in September. During the positive phase of El Nino Southern Oscillation (ENSO) which is the sudden rise of Pacific Sea Surface Temperatures, an increase is observed the intensity of drought in Northeastern Brazil, and the Sahel (Africa) rainfall changes are also found due to global ocean circulation and patterns of SSTs. This ENSO phases are explicitly seen in inter-annual variability of south-west monsoon in India and play a major role in the agricultural sector of the country. The evaluation of El Nino and La Nina barely showed that in many cases, La Nina had positive impact and El Nino, a negative one. Due to climate change and variability, the disasters like droughts became frequent in the above said regions. Semi arid Asia is experiencing an increase in the frequency of severity of wild fires. African rainfall changed substantially over last 60 years due to land cover changes and forest destruction. Though, India is not showing any significant trend in its annual rainfall, an increase in extreme weather events are evidenced (Lakshmi Kumar et al., 2011). So it is must to address these issues from the remote sensing perspective, that too in assessing and monitoring droughts. The present chapter aimed to study the land surface and vegetation and their response to climate in the context of climate change and climate variability.

#### **2. Monitoring the ground vegetation – Soil wetness by satellites – Previous studies**

#### **2.1 Normalized Difference Vegetation Index (NDVI)**

400 International Perspectives on Global Environmental Change

detailed knowledge of the system dynamics. This is particularly pertinent in the management of domestic livestock in semiarid and arid grazing systems, where the risk of degradation is high and likely climate change may have a strong impact. Although these drylands are of environmental and socio-economic importance, they are faced with serious management challenges. Hence, their sustainable management requires an evaluation of the magnitude, pattern, and type of land-use/cover changes and the projection of the consequences of these changes to their conservation. It is also important to precisely describe and classify land cover changes in order to define sustainable land-use systems that are best suited for each place (FAO 1998). Monitoring the locations and distributions of land cover changes is important for establishing links between policy decisions, regulatory actions and subsequent land-use activities. In this regard, there is a need to consider both the socio-economic environment (Giannecchini et al., 2007) and other environmental factors. In this context, there is a need for agriculture administrators and policy makers to better understand the intraseasonal-to-interannual variability of climate and its effects on the landscape properties. The comprehension of interactions of weather variability and those landscape properties could lead to improved understanding of those landscape vulnerability to global changes, enhanced natural-resource management and to a better emergency planning to withstand the effects of extreme episodes on the natural and agricultural systems at regional scale (Rosenzweig et al., 1994). Nonetheless, the climatic data, at adequate spatio-temporal resolution at the regional level is scarce, representing an

Prognostic numerical models are one of the main research tools used to predict past and future states of the Earth system (Cramer et al., 2001), yet persistent problems limit their acceptance in ecological and global change research. Aber (1997) posed the question "Why have models failed to penetrate the heart of ecological sciences?" and found that all too often model predictions are made prior to parameterization, validation, sensitivity analysis, and description of model structure. While today models are more accepted in a wide variety of fields, these issues are still prevalent and still ignored too often. With the advent of global monitoring systems based on satellites, it became possible to understand the nature and response of these ecosystems and drylands to day- to-day fluctuations in weather. In particular, the spatial-temporal analysis of vegetation dynamics (i.e., the response of vegetation to climatic conditions) in the semi-arid tropical region is important in the context of climate change where these dynamics show quicker response in short term climate indices such as the Southern Oscillation Index (SOI) and the Nino Sea Surface Temperatures (Fischer, 1996). The present study aims to cover certain regions across the tropics of arid (R/PE is 0.05 to 0.20) and semi-arid (R/PE is 0.20 to 0.50) nature (where R represents Rainfall and PE, Potential Evapotranspiration). Such regions of this type are Northeastern Brazil, West Sahel in Africa and Andhra Pradesh in India. The rainfall in India is mainly by south-west monsoon (June to September). In Sahel, it is primarily from June to August and to a lesser extent in September. During the positive phase of El Nino Southern Oscillation (ENSO) which is the sudden rise of Pacific Sea Surface Temperatures, an increase is observed the intensity of drought in Northeastern Brazil, and the Sahel (Africa) rainfall changes are also found due to global ocean circulation and patterns of SSTs. This ENSO phases are explicitly seen in inter-annual variability of south-west monsoon in India and play a major role in the agricultural sector of the country. The evaluation of El Nino and La

obstacle to researchers.

The study of vegetation cover over a region which can be formed either by native or by cultivation attained a great significance. Barbosa et al. (2006), reported that the NDVI is a reliable index to study the ground vegetal cover and to monitor the changes occur in the vegetation due to climatic abnormalities. Study of spatiotemporal variations of NDVI is of great importance now a days in the context of increased greenhouse gases that modulate the global climate systems in terms of short term climate signals such as El Nino and La Nina. The NDVI variations on both space and time scales not only important in view of varying crop stages but also prominent in vegetation-climate feedback mechanism thus giving a challenge to policy makers in proactive and reactive measurements of risk Cihlar et al., 1991; Davenport & Nicholson, 1993; Al-Bakri & Suleiman, 2004; Kazuo & Yasuo, 2005, Ma & Frank, 2006 & Nagai et al., 2007. Global scientific community focused on NDVI as the indicator of agricultural droughts where in crop growth is known by NDVI value and found that the NOAA Advanced Very High Resolution Radiometer (AVHRR) NDVI is one of the best among the other vegetation indices derived from the other satellites. The NDVI can be defined as the ratio of difference between Channel 1 (red) and Channel 2 (near Infrared) which is based on the more absorbance for healthy vegetation and more reflectance for the poor vegetation. In other way, NDVI measures the changes chlorophyl content (via absorption of visible red radiation) and is sponzy mesophyll (via reflected NIR radiation) with in the vegetation canopy, thus NDVI from AVHRR can be written as

$$\text{NDVI} = (\mathfrak{p}\_{857} - \mathfrak{p}\_{645}) / \text{ ( $\mathfrak{p}\_{857} + \mathfrak{p}\_{645}$ )}$$

This NDVI varies from -1 to + 1 and the category in classifying the density of vegetation cover is given below.

> NDVI < 0.2 -------------- Low vegetation NDVI < 0.4 -------------- Medium vegetation NDVI > 0.4 ------------- High vegetation

Relevant research in changes in vegetation cover in the Sahel demonstrates that the NDVI happens to correlate particularly closely with rainfall, as high as 0.84. The cause of this

Strengthening Regional Capacities for Providing Remote

**3. Case studies, methodologies and findings** 

**Caatinga and African Western Sahel** 

grassland (11004'N; 39047'E) (Figure.1).

ground in conjunction with NDVI data.

Sensing Decision Support in Drylands in the Context of Climate Variability and Change 403

The Caatinga and Savanna vegetation covers are likely the most sensitive to changes in climate. Satellite observations show that changes in vegetation greenness follow rainfall variability. Because water availability is a key factor in the abundance of vegetation, changes in precipitation are most critical for continued biodiversity and human livelihood opportunities in arid and semi-arid environments. In earlier studies (Barbosa 1998; Nicholson and Farar, 1994) mostly held in the atmospheric dynamics context have incorporated long time series of NDVI data taken by the National Oceanic and Atmospheric Administration (NOAA) AVHRR to monitor the dynamics of the temporal structures of vegetation responses to climatic fluctuations across the Northeastern Brazil and the West African Sahel's landscapes. These investigations have found clear and positive linear relationships between NDVI and rainfall thanks to different analyses across the semi-arid tropical ecosystems where rainfall is below an absolute amount of rainfall of 50-100 mm/month. In this study we have the objective to investigate the NDVI responses to

**3.1 Analysis of the NDVI temporal dynamics in semi-arid ecosystems: Brazilian** 

rainfall oscillations at seasonal scale over the last two decades of the 20th century.

were extracted from NDVI images with a resolution spatial of 7.6 km.

Temporal analyses performed in this research were based on the monthly NDVI imagery from the Goddard Distributed Active Archive Center (GDAAC) for the 1982 to 2000 period. The NDVI images were originally in the Goode's Interrupted Homolosine projection, and they were geo-referenced to a geographical coordinate system (latitude and longitude). The 20-year series of monthly NDVI data for Brazilian semi-arid and West African Sahel regions

Aiming to characterize the seasonal variability of land cover types in Caatinga and Savanna Biomes to the understanding of their responses to the seasonal rainfall variability, we verified how available GDAAC NDVI are able to capture the climatic variability, and how it could be used in ecological studies, at the local level. Based on the vegetation map published by the Brazilian Institute for Geography and Statistics (IBGE, 1993) and by author's local knowledge, as a basis, four homogeneous vegetation sites covering semi-arid Caatinga in Northeastern Brazil were selected from vegetation classes, and located by ground meteorological stations (sites): site#1-caatinga arbórea aberta (open arboreous shrubbery) (4031'S; 40012'W), site#2**-**caatinga arbustiva densa (dense shrubbery) (4037'S; 4207'W), site #3-caatinga arbórea densa (dense arboreous shrubbery) (8037'S; 4207'W), and site #4-caatinga arbustiva aberta (open shrubbery) (9025'S; 4107'W). For the semi-arid Sahelian region, four vegetation classes were conducted over the UNESCO map produced by White (1983). Representatives from the following land cover types dominated in this classification: site#1**-**woodland (10055'N; 14019'W), site#2 woodland (110026'S; 7025'W), site#3**-**woodland (11004'N; 7042'E), and site#4-wooded

The 20-year integrated series of monthly NDVI data were extracted by averaging the NDVI values for a window of 3 by 3 pixel arrays at selected locations within each land cover type in order to characterize the seasonal variability in land cover type for each series. The database consisted of land cover classes from the vegetation maps (1:5,000,000) that were used to guide site locations by using the geo-referenced meteorological stations on the

significant correlation is two-fold: first, it is commonly known that vegetation growth is limited by water; second, the climate in Sahel, rainfall in particular, is very sensitive to changes in vegetation Charney et al., 1977. Sarma and Lakshmi Kumar,2006 derived the NDVI from NOAA AVHRR for the state Andhra Pradesh and saw how it varies in accordance with the crop growing periods such as moist, humid, moderate dry and dry as suggested by Higgins and Kassam (1981) and found a good agreement i.e prevalence of good NDVI is subjected to moist/humid periods. Barbosa et al, 2011 studied the vegetation indices such as NDVI and Enhanced Vegetation Index to understand the underlying mechanism of vegetation dynamics in Amazon forests.

#### **2.2 Brightness Temperature (BT)**

Studies on the direct measurement of soil moisture are few. Remotely sensed data in terms of brightness temperature is useful in the study of spatiotemporal variability as well as verifying land surface processes (Rao et al., 2001). Soil moisture can be retrieved by making use of remote sensing observations. Pathak et al, 1993 reported the estimation of soil moisture using land surface temperature retrieved from the INSAT – VHRR data. Microwave sensors provide a great opportunity to measure soil moisture because these microwave radiations can penetrate the clouds and vegetation over the land surface. Microwave brightness temperature can be used to measure soil wetness under different surface roughness and vegetation cover conditions (Ahmed, 1995). Thapliyal et al. (2003) reported soil moisture over India using microwave brightness temperature of IRS-P4. Sarma and Lakshmi Kumar (2007), explained the variations in brightness temperature of different soil types in Andhra Pradesh. The data retrieved from the Multichannel Scanning Microwave Radiometer (MSMR) carried by Indian Remote Sensing (IRS) – P4 satellite, is made use in understanding the nature of relation between soil moisture and BTD. The BTD of 6.6GHz frequency channel of MSMR, taken at 1830hrs Indian Standard Time (IST) for the horizontal polarization is used for the estimation of soil wetness which in turn portrays the drought prevailing conditions over that place.

The brightness temperature depends on the angle of incidence and the plane of polarization, vertical as well as horizontal. It is reported that the horizontal polarization is more sensitive to soil moisture and hence the same is used here. The microwave polarized temperature (MPT) is defined as

#### MPT = TB (1,H)

Here 1 refers to wavelength and is related apart from other factors to moisture content of the soil horizon.

Brightness Temperature (BT) at the microwave frequencies can be written as

BT = eTS

Where e is the emissivity of the surface and TS is the surface temperature.

As BT is a function of emissivity and surface temperature, the lands having less emissivity (wet lands) exhibit low signatures and is a good indicator of soil wetness status. Similarly, the lands having high emissivity (dry soils) give high BT signals showing low soil wetness status from which one can assess the moisture condition over the soil to estimate the drought condition (Sarma and Lakshmi Kumar, 2006).
