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Assessing the impact of fresh water scarcity on semiarid vegetation by remote sensing, case study: Karbala, Iraq
Abstract
Climate change is one of the main factors that caused scarcity of fresh water phenomenon all over the world. The lack of water in major parts of Iraq affected all sectors that use water and cause obvious damages to ecosystems. Karbala province suffers from frequent water scarcity due to water scarcity and abnormally high temperatures. In the present study, remote sensing and GIS were applied to quantify water scarcity and evaluate its effects on vegetation in this fragile semiarid ecosystem. Analysis of hydrological data of the study area was carried out during 2013 to 2022 to compute water availability and shortage based on the criteria and requirements of water sector and environmental management in Iraq. Remotely sensed Landsat 8 images data were applied to measure changes on vegetation and the effects of water scarcity. Soil Adjusted Vegetation Index (SAVI) was employed to identify vegetation and detect its change. Results showed that the area witnessed decreasing in water availability compared to the reference year. Maximum available water reached 1977.535 million
in 2013, while the minimum of 859.227 million was observed in 2022. The maximum and minimum vegetation area reached 535.610
and 430.605
in 2013 and 2022, respectively. Results indicated that all the years posterior to the reference year experienced water scarcity and vegetation damage, where the maximum and minimum water scarcity rates were 56% and 8% in 2022 and 2016, respectively. The maximum impact of water scarcity rate on vegetation was ca. 20% in both years 2015 and 2022. Water scarcity is constantly increasing over time, thus evaluating its impacts and forecasting its future specification will support decision-makers to take the necessary measures to mitigate its effects.
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Drought is a common event in Iraq’s climate, and the country has severely suffered from drought episodes in the last two decades. The Kurdistan Region of Iraq (KRI) is geographically situated in the semi-arid zone in Iraq, whose water resources have been limited in the last decades and mostly shared with other neighboring countries. To analyze drought impacts on the vegetation cover and the land surface temperature in the KRI for a span of 20 years from 1998 to 2017, remote sensing (RS) and Geographical Information Systems (GIS) have been adopted in this study. For this study, 120 Landsat satellite images were downloaded and utilized, whereas six images covering the entire study area were used for each year of the study period. The Normalized Difference Vegetation Index (NDVI) and Land Surfaces Temperature Index (LST) were applied to produce multi-temporal classified drought maps. Changes in the area and values of the classified NDVI and LST were calculated and mapped. Mann–Kendall and Sen’s Slope statistical tests were used to assess the variability of drought indices variation in 60 locations in the study area. The results revealed increases in severity and frequency of drought over the study period, particularly in the years 2000 and 2008, which were characterized by an increase in land surface temperatures, a decrease in vegetation area cover, and a lack of precipitation averages. Climate conditions affect the increase/decrease of the vegetated cover area, and geographical variability is also one factor that significantly influences the distribution of vegetation. It can be concluded that the southeast and southwestern parts of the KRI were subjected to the most severe droughts over the past 20 years.
Despite the growing interest in predicting global and regional trends in vegetation productivity in response to a changing climate, changes in water constraint on vegetation productivity (i.e., water limitations on vegetation growth) remain poorly understood. Here we conduct a comprehensive evaluation of changes in water constraint on vegetation growth in the extratropical Northern Hemisphere between 1982 and 2015. We document a significant increase in vegetation water constraint over this period. Remarkably divergent trends were found with vegetation water deficit areas significantly expanding, and water surplus areas significantly shrinking. The increase in water constraints associated with water deficit was also consistent with a decreasing response time to water scarcity, suggesting a stronger susceptibility of vegetation to drought. We also observed shortened water surplus period for water surplus areas, suggesting a shortened exposure to water surplus associated with humid conditions. These observed changes were found to be attributable to trends in temperature, solar radiation, precipitation, and atmospheric CO 2 . Our findings highlight the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate.
Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past. The Iraqi Kurdistan Region (IKR) is located in the north of Iraq, which has also suffered from extreme drought. In this study, the drought severity status in Sulaimaniyah Province, one of four provinces of the IKR, was investigated for the years from 1998 to 2017. Thus, Landsat time series dataset, including 40 images, were downloaded and used in this study. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) were utilized as spectral-based drought indices and the Standardized Precipitation Index (SPI) was employed as a meteorological-based drought index, to assess the drought severity and analyse the changes of vegetative cover and water bodies. The study area experienced precipitation deficiency and severe drought in 1999, 2000, 2008, 2009, and 2012. Study findings also revealed a drop in the vegetative cover by 33.3% in the year 2000. Furthermore, the most significant shrinkage in water bodies was observed in the Lake Darbandikhan (LDK), which lost 40.5% of its total surface area in 2009. The statistical analyses revealed that precipitation was significantly positively correlated with the SPI and the surface area of the LDK (correlation coefficients of 0.92 and 0.72, respectively). The relationship between SPI and NDVI-based vegetation cover was positive but not significant. Low precipitation did not always correspond to vegetative drought; the delay of the effect of precipitation on NDVI was one year.
http://jal.xjegi.com/EN/10.1007/s40333-021-0062-9
Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.
Climate change is defined as the shift in climate patterns mainly caused by greenhouse gas emissions from natural systems and human activities. So far, anthropogenic activities have caused about 1.0 °C of global warming above the pre-industrial level and this is likely to reach 1.5 °C between 2030 and 2052 if the current emission rates persist. In 2018, the world encountered 315 cases of natural disasters which are mainly related to the climate. Approximately 68.5 million people were affected, and economic losses amounted to $131.7 billion, of which storms, floods, wildfires and droughts accounted for approximately 93%. Economic losses attributed to wildfires in 2018 alone are almost equal to the collective losses from wildfires incurred over the past decade, which is quite alarming. Furthermore, food, water, health, ecosystem, human habitat and infrastructure have been identified as the most vulnerable sectors under climate attack. In 2015, the Paris agreement was introduced with the main objective of limiting global temperature increase to 2 °C by 2100 and pursuing efforts to limit the increase to 1.5 °C. This article reviews the main strategies for climate change abatement, namely conventional mitigation, negative emissions and radiative forcing geoengineering. Conventional mitigation technologies focus on reducing fossil-based CO2 emissions. Negative emissions technologies are aiming to capture and sequester atmospheric carbon to reduce carbon dioxide levels. Finally, geoengineering techniques of radiative forcing alter the earth’s radiative energy budget to stabilize or reduce global temperatures. It is evident that conventional mitigation efforts alone are not sufficient to meet the targets stipulated by the Paris agreement; therefore, the utilization of alternative routes appears inevitable. While various technologies presented may still be at an early stage of development, biogenic-based sequestration techniques are to a certain extent mature and can be deployed immediately.
Iraq, including Erbil Province in the Kurdistan region, has severely suffered drought events in the past, particularly during the last two decades. This study incorporated Geoinformation technology into assessing and monitoring drought’s severity and impacts that affected Erbil province in a span from 1998 to 2017. Twenty mosaics of two Landsat scenes for 20 years have accomplished for this study. Two spectral drought indices, the Normalized Difference Vegetation Index (NDVI) and the Temperature Condition Index (TCI), were extracted using 20 Landsat images mosaics to assess the drought’s severity and impacts. Statistical correlation analyses computed between the NDVI, TCI, and the study variables, such as climatic factors, field crop area, and field crop yield. The study findings clarified that Erbil province experienced severe to extreme severe agricultural droughts, which caused significant reductions in crop yields, particularly in 2000, 2008, and 2012. The results also revealed significant declines in the vegetative cover in the years 2000, 2008 and 2012, by 43.8%, 16.3%, and 10.9%, respectively. Furthermore, a drop in precipitation averages has occurred in those three drought years, as well as a significant reduction in the TCI values. The study results clarified that the southern and western parts of Erbil Province were the most parts exposed to the severe to exceptional agricultural drought episodes throughout the twenty years of this study.
This paper focuses on the testing and comparison of the sensitivity of vegetation indices to determine the changes occurred in forest areas of the Cluj Forest District during the post-socialist period, between 1986 and 2009. Concurrently, the paper pursues to assess the way in which the socio-economic and social changes have caused alterations of the forest areas. These have been significantly affected by the changes occurred over the past decades, as well as by the increase of the anthropization degree of the natural areas. Remote sensing applications are very efficient tools in detecting and assessing the changes both in space and over time. Two vegetation indices were used -NDVI and SAVI. The results show the spatio-temporal dynamics of the vegetation cover for approximately one third of the area, as the used indices presented decreasing values of up to 35% (NDVI 27%, SAVI 32%). The accuracy of classifying indices in decreasing, increasing and no -change areas is 88.20%, while the Kappa coefficient is 0.82. In addition, user’s and producer’s accuracy was calculated for each class.
This article presents an enhanced Change Detection method for the analysis of Satellite image based on Normalized Difference Vegetation Index (NDVI). NDVI employs the Multi-Spectral Remote Sensing data technique to find Vegetation Index, land cover classification, vegetation, water bodies, open area, scrub area, hilly areas, agricultural area, thick forest, thin forest with few band combinations of the remote sensed data. Land Resources are easily interpreted by computing their Normalized Difference Vegetation Index for Land Cover classification. Remote Sensing data from Landsat TM image along with NDVI and DEM data layers have been used to perform multi-source classification. The Change Detection method used was NDVI differencing. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4 and 0.5. The Simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for policy makers in decision making. The Vegetation analysis can be helpful in predicting the unfortunate natural disasters to provide humanitarian aid, damage assessment and furthermore to device new protection strategies. From the empirical study, the forest or shrub land and Barren land cover types have decreased by about 6% and 23% from 2001 to 2006 respectively, while agricultural land, built-up and water areas have increased by about 19%, 4% and 7% respectively. Curvature, Plan curvature, Profile curvature and Wetness Index areas are also estimated.
Monitoring broad-scale ecological responses to disturbance can be facilitated by automated change-detection approaches using remotely sensed data. This study evaluated the effectiveness of five unsupervised change-detection techniques using multispectral, multitemporal SPOT High Resolution Visible (HRV) data for identifying vegetation responses to extensive flooding of a forested ecosystem associated with Tropical Storm Alberto in July 1994. Standard statistical techniques, logistic multiple regression, and a probability vector model were used to quantitatively and visually assess classification accuracy. The change-detection techniques were (1) spectral-temporal change classification, (2) temporal change classification based on the Normalized Difference Vegetation Index (NDVI), (3) principal components analysis (PCA) of spectral data, (4) PCA of NDVI data, and (5) NDVI image differencing. Spectral-temporal change classification was the least effective of the techniques evaluated. Classification accuracy improved when temporal change classification was based on NDVI data. Both PCA methods were more sensitive to flood-affected vegetation than the temporal change classifications based on spectral and NDVI data. Vegetation changes were most accurately identified by image differencing of NDVI data. Logistic multiple regression and a probability vector model were especially useful for relating spectral responses to vegetation changes observed during field surveys and identifying areas of agreement and disagreement among the different classification methods.
Vegetation indices [m) have long been used in remote sens-ing for monitoring temporal changes associated with vegeta-tion. In this study, seven vegetation indices were compared for their value in vegetation and land-cover change detection in part of the State of Chiapas, Mexico. VI values were devel-oped from three different dates of Landsat Multispectral Scanner [MSS) data. The study suggested that (1) if normali-zation techniques were used, then all seven vegetation indi-ces could be grouped into three categories according to their computational procedures; (2) vegetation indices in different categories had significantly different statistical characteris-tics, and only NDVI showed normal distribution histograms; and [3), of the three vegetation index groups, the NDVI group was least affected b y topographic factors i n this study. Com-parisons of these techniques found that the NDVI difference technique demonstrated the best vegetation change detection as judged b y laboratory and field results.
Beginning with a discussion of drought definitions, this review paper attempts to provide a review of fundamental concepts of drought, classification of droughts, drought indices, and the role of remote sensing and geographic information systems for drought evaluation. Owing to the rise in water demand and looming climate change, recent years have witnessed much focus on global drought scenarios. As a natural hazard, drought is best characterized by multiple climatological and hydrological parameters. An understanding of the relationships between these two sets of parameters is necessary to develop measures for mitigating the impacts of droughts. Droughts are recognized as an environmental disaster and have attracted the attention of environmentalists, ecologists, hydrologists, meteorologists, geologists, and agricultural scientists. Temperatures; high winds; low relative humidity; and timing and characteristics of rains, including distribution of rainy days during crop growing seasons, intensity, and duration of rain, and onset and termination, play a significant role in the occurrence of droughts. In contrast to aridity, which is a permanent feature of climate and is restricted to low rainfall areas, a drought is a temporary aberration. Often, there is confusion between a heat wave and a drought, and the distinction is emphasized between heat wave and drought, noting that a typical time scale associated with a heat wave is on the order of a week, while a drought may persist for months or even years. The combination of a heat wave and a drought has direct socio-economic consequences. Drought risk is a product of a region’s exposure to the natural hazard and its vulnerability to extended periods of water shortage. If nations and regions are to make progress in reducing the serious consequences of drought, they must improve their under-standing of the hazard and the factors that influence vulnerability. It is critical for drought-prone regions to better understand their drought climatology (i.e., the probability of drought at different levels of intensity and duration) and establish comprehensive and integrated drought information system that incorporates climate, soil, and water supply factors such as precipitation, temperature, soil moisture, snow pack, reservoir and lake levels, ground water levels, and stream flow. All drought-prone nations should develop national drought policies and preparedness plans that place emphasis on risk management rather than following the traditional approach of crisis management, where the emphasis is on reactive, emergency response measures. Crisis management decreases self-reliance and increases dependence on government and donors.
Remote sensing has demonstrated wide applicability in the area of estimating and mapping forest physical and structural features. Focus in recent years has been directed towards measuring the biophysical/physiological character of forest ecosystems in order to estimate and predict forest ecosystem health and sustainability. The following reviews the relationship between forest condition and reflectance; remote-sensing measurements (and derivatives) that provide biophysical/physiological information; and the potential of hyperspectral sensors in the measurement of these parameters.
Drought years are a very frequent phenomenon in Israel. Between the years 1994/1995 and 2001/2002, Israel experienced four (non-consecutive) years of drought. Consequently the Yatir forest, a pine forest located in the desert fringe, suffered from a notable water shortage. The aim of this research is to detect and assess seasonal/phenological changes and inter-annual changes in the forest trees with respect to the drought effect. The use of a spectral vegetation index, namely the Normalized Difference Vegetation Index (NDVI) to detect stress conditions was implemented by using eight Landsat-TM and ETM+ images. In addition, the change detection NDVI Image Differencing technique was applied for assessing seasonal and inter-annual variations in vegetation. The results indicate similarity between the photosynthetic activity and the NDVI dynamics along the growing season. Considerable NDVI decline was observed between 1995 and 2000 due to the drought events during these years, enabling assessment of the spatial and temporal effects of such a disaster. The NDVI measured from the forest trees was found to be inversely related to the age of the trees due to strong effect of soil background in the younger forest sections that are characterized by lower vegetation density. Topographic attributes such as slope orientation (aspect) were found to affect NDVI only at the year that was not under stress. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics.
Multitemporal Landsat multispectral scanner data were analyzed to test various computer-aided analysis techniques for detecting significant forest canopy alteration. Three data transformations - differencing, ratioing, and a vegetative index difference - were tested to determine which best delineated gypsy moth defoliation. Response surface analyses were conducted to determine optimal threshold levels for the individual transformed bands and band combinations. Results indicate that, of the three transformations investigated, a vegetative index difference (VID) transformation most accurately delineates forest canopy change. Band 5 (0.6 to 0.7 micron ratioed data did nearly as well. However, other single bands and band combinations did not improve upon the band 5 ratio and VID results.
In recent years, the rapidly urbanization and industrialization has caused
dramatic changes in land cover. The study on change detection of land cover plays
a signification role in making strategy of the managers. Approaches in change
detection research using remote sensing and GIS can be categorized into two main
groups: post-classification change detection analysis and pre-classification
changing spectral determination. The pre-classification approach is further divided
into different methods such as: Image Differencing, Multi-date Principal
Component Analysis (MPCA); Change Vector Analysis (CVA). Among these, the
CVA method is based on two important indices in order to reveal the primary
feature of land cover which can be named as vegetation index (NDVI) and barren
land index (BI). The ability of applying method of CVA has been indicated by many
specialists (Corey Baker et al., 2007; Ding Yuan et al., 1998; Eric F. Lambin et al.,
1994). However, the NDVI indices selection and variability threshold haven’t been
applied in change detection assessment. This paper proposes application to solve
these two problems.
Vegetation is profoundly impacted by anthropogenic activities, particularly trampling. The ultimate effect of trampling is a reduction in amount of vegetation, often resulting in complete loss of vegetation cover. Satellite remote sensed data for land cover, landuse and its changes is a key to many diverse applications. Methods for monitoring vegetation change range from intensive field sampling with plot inventories to extensive analysis of remotely sensed data which has proven to be more cost effective for large regions, small site assessment and analysis. Remotely sensed change detection based on artificial neural networks presents a new technique using training algorithm. The trained neural network detected changes on a pixel-by-pixel basis in real time applications. The trained four-layered neural network (for example, decreased, some decrease, some increase and increased) provided complete categorical information about the nature of changes and detected complete land cover change "from-to" information, which is desirable in most change detection applications. This paper presents an application of the use of Landsat ETM+ images and MODIS EVI/NDVI time-series vegetation phenology algorithms of Faisalabad and Multan districts for evaluation of soil productivity and comparison of temporal change detection. The proposed method is successfully applied to actual multi-temporal and multi-spectral images.
Many rivers in Australia and elsewhere around the world are flow stressed. There is now considerable activity to restore crucial components of the natural flow regime of such rivers—known as environmental flows, but methods underpinning the decision-making often lack rigour and transparency. The restoration of environmental flow regimes is usually determined using historic flow data, available expertise, sometimes ad-hoc compromise between conflicting and spatio-temporally variable water needs, and then implemented through special environmental allocations or water sharing plans. However, ecological flow requirements are poorly integrated into the decision-making process. This paper reports an eco-hydrological Bayesian network model developed to assist in risk assessment and decision-making on improving the flow conditions for Australian Grayling (Prototroctes maraena) in the Yarra River in south-eastern Australia. The Yarra environmental flow model was adapted from previous work on Australian Grayling in the Latrobe River system, and contained the same ecological model as for the Latrobe River, but with a new hydrological sub-model developed specifically for the Yarra River. The model was used to assess the relative ecological benefits (to Australian Grayling) of restoring specific components of the natural flow regime, in particular autumn freshes and spring freshes.
Information-based decision-making during drought, often brings out some of the excellent practices that are prevalent in society / individuals. This book is designed to provide information on the drought process, meteorological, hydrological, agriculture, socio-economic aspects and available technologies such as satellite remote sensing data analysis and Geographical Information system for assessment. Assessment procedures utilising the various parameters of importance from various sources for micro level management that would enhance the effectiveness of management practice are dealt in detail. Resource availability and affected group determine the relief assistance for the present event and information that would help them in their realisation and preparedness for the forthcoming years by select countries is highlighted. This would help in the formulation of schemes for event mitigation and area development plans. The readers would gain complete knowledge on drought. This book is expected to act as a guide in preparing people as effective natural resource utilizationist under drought situations.
Normalized difference vegetation index (NDVI) derived from SPOT HRV multispectral data was used to study the changing environmental quality of Hong Kong from 1987, 1991 and 1993 to 1995. Conventional change detection techniques such as image differencing or principal components analysis helped to highlight salient changes. These techniques, however, were less effective in identifying subtle changes, in particular the amount and quality of green space. Integrating the mean NDVI values at the Tertiary Planning Unit (TPU) level with census and land-cover data showed that the NDVI values were related to woodland, tall scrubland and high-density urban areas. It was also related to the level of crowding as depicted from a factor analysis of census data. Tracing the changing pattern of mean NDVI values revealed that areas with continuous increases in NDVI values are scattered around old urban districts experiencing improved landscaping. Areas of continuous decrease in NDVI values covered a large part of rural New Territories and western Hong Kong Island revealing the urban expansion process. This provided valuable information for the assessment of environmental quality for planning and management of the environment.
The new millennium has started with a series of large-area droughts
which have had grim consequences for the affected countries and regions.
The enormous impacts of the 2000 and 2001 droughts were experienced on
both physical and psychological levels, in part because no large-scale
drought occurred in 1999. In some regions, severe drought continued for
two years in a row.
This paper reports the development and application of two Bayesian Network models to assist decision making on the environmental flows required to maintain the ecological health of the Daly River (Northern Territory, Australia). Currently, the Daly River is unregulated, with only a small volume of water extracted annually for agriculture. However, there is considerable pressure for further agricultural development in the catchment, particularly with demand for extra water extraction during the dry season (May–November).
The abundances of two fish species—barramundi ( Lates calcarifer ) and sooty grunter ( Hephaestus fuliginosus )—were chosen as the ecological endpoints for the models, which linked dry season flows to key aspects of the biology of each species. Where available, data were used to define flow–fish habitat relationships, but most of the relationships were defined by expert opinion because of a lack of quantified ecological knowledge.
Recent field data on fish abundances were used to validate the models and gave prediction errors of 20–30%. The barramundi model indicated that the adult sub‐population was key to overall fish abundance, with this sub‐population particularly impacted by the timing of abstraction (early vs. late dry season). The sooty grunter model indicated that the juvenile sub‐population dominated the overall abundance and that this was primarily due to the amount of hydraulically suitable riffle habitat. If current extraction entitlements were fully utilized, the models showed there would be significant impacts on the populations of these two fish species, with the probability of unacceptable abundances increasing to 43% from 25% for sooty grunter and from 36% for barramundi under natural conditions. Copyright © 2010 John Wiley & Sons, Ltd.
1] An aggregate drought index (ADI) has been developed, and evaluated within three diverse climate divisions in California. The ADI comprehensively considers all physical forms of drought (meteorological, hydrological, and agricultural) through selection of variables that are related to each drought type. Water stored in large surface water reservoirs was also included. Hydroclimatic monthly data for each climate division underwent correlation-based principal component analysis (PCA), and the first principal component was deseasonalized to arrive at a single ADI value for each month. ADI time series were compared against the Palmer Drought Severity Index (PDSI) to describe two important droughts in California, the 1976–1977 and 1987–1992 events, from a hydroclimatological perspective. The ADI methodology provides a clear, objective approach for describing the intensity of drought and can be readily adapted to characterize drought on an operational basis. Citation: Keyantash, J. A., and J. A. Dracup (2004), An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage, Water Resour. Res., 40, W09304, doi:10.1029/2003WR002610.
The hard-rock hilly Aravalli terrain of Rajasthan province of India suffers with frequent drought due to poor and delayed monsoon, abnormally high summer-temperature and insufficient water resources. In the present study, detailed analysis of meteorological and hydrological data of the Aravalli region has been carried out for the years 1984–2003. Standardised Precipitation Index (SPI) has been used to quantify the precipitation deficit. Standardised Water-Level Index (SWI) has been developed to assess ground-water recharge-deficit. Vegetative drought indices like Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) and Vegetation Health Index (VHI) have been computed using NDVI values obtained from Global Vegetation Index (GVI) and thermal channel data of NOAA AVHRR satellite. Detailed analyses of spatial and temporal drought dynamics during monsoon and non-monsoon seasons have been carried out through drought index maps generated in Geographic Information Systems (GIS) environment. Analysis and interpretation of these maps reveal that negative SPI anomalies not always correspond to drought. In the Aravalli region, aquifer-stress shifts its position time to time, and in certain pockets it is more frequent. In comparison to hydrological stress, vegetative stress in the Aravalli region is found to be slower to begin but quicker to withdraw.
To effectively deal with the spring drought facing South Korea periodically since 2000, a monitoring system to keep tracing the drought is critical, along with the index indicating the severity of the drought in a quantified value. To conduct the in-depth evaluation of the behavior and intensity of the drought by region, a spatial analysis by area unit is needed. Satellite imagery is an effective tool that provides spatial information regularly in a timely manner and the vegetation index developed using band combination of satellite imagery has been used for monitoring the drought since the mid-1990s. In this study, VCI and SVI were produced, using MODIS NDVI to evaluate the droughts that occurred during the period 2000 to 2007, investigating the years, severity and the regions and times where and when the drought occurred the most. Moreover, comparing with existing monitoring tool was conducted, thereby presenting the efficient method for spring drought monitoring using vegetation index.
A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton (Gossypium hirsutum L. var DPI-70) and range grass (Eragrosticslehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple °lobal” that can describe dynamic soil-vegetation systems from remotely sensed data.
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Landsat 7 ETM+ provides an opportunity to extend the area and frequency with which we are able to monitor the Earth's surface with fine spatial resolution data. To take advantage of this opportunity it is necessary to move beyond the traditional image-by-image approach to data analysis. A new approach to monitoring large areas is to extend the application of a trained image classifier to data beyond its original temporal, spatial, and sensor domains. A map of forest change in the Cascade Range of Oregon developed with methods based on such generalization shows accuracies comparable to a map produced with current state-of-the-art methods. A test of generalization across sensors to monitor forest change in the Rocky Mountains indicates that Landsat 7 ETM+ data can be combined with earlier Landsat 5 TM data without retraining the classifier. Methods based on generalization require less time and effort than conventional methods and as a result may allow monitoring of larger areas or more frequent monitoring at reduced cost. One key component to achieving this goal is the improved availability and affordability of Landsat 7 imagery. These results highlight the value of the existing Landsat archive and the importance for continuity in the Landsat Program.
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