The vegetation data sets of National Oceanic and Atmospheric Administration (NOAA) AVHRR, popularly known as NormalizedDifferenceVegetationIndex (NDVI) are of useful in studying the ground vegetal cover. It is reported that NDVI is commonly used to monitor the seasonal and annual vegetation (Jackson et al., 1983; Tucker et al., 1991). Several studies have conducted over the world on different applications of NDVI in studying the climate feedback mechanism (Lu et al., 2001; Mabuchi and Sato, 2005), crop growing pe riods (Sarma and Kumar, 2006) and drought assessment (Savin and Flueraru, 2006; Sarma and Kumar, 2007). The strong relationshipbetween natural vegetation and climatic elements has been described in wide range of research (Anyamba and Tucker, 2005; Fabricante et al., 2009; Lei and Peters 2004; Yang et al., 1998). Remote sensing plays important role in and provides an effective tool for monitoring different parameters of complex ecosystem (Zhong et al.,2010) in big countries like Ethiopia. Using remote sensing technology, different indices have been developed to study the properties of vegetation and vegetation dynamics. Moreover, the availability of high spatial and temporal resolution precipitation data increases interest in hydrology, meteorology and ecology research (Shaofeng, 2011). Rainfall plays a crucial role in the vegetation growing cycle and is a determining factor for agriculture and food security. Therefore, it is very important to study the relationshipbetweenrainfall and their impact on the general ecosystem and particularly on the vegetation.
In Africa, meaningful direct relationships have been found between NDVI, rainfall and vegetation cover in many studies carried out in the Sahel Zone (Tucker et al. 1985; Hielkema et al., 1986, Malo and Nicholson 1990), Botswana (Prince and Tucker 1986), East Africa (Boutton and Tieszen 1983, Davenport and Nicholson 1993) and Tunisia (Kennedy 1989). However, findings for these locations were still highly variable. Nonetheless, it is often concluded from this body of work that NDVI and precipitation have a strong linear (Malo and Nicholson 1990) or log-linear (Davenport and Nicholoson 1993) relationship, when monthly and annual precipitation is within a range that makes rainfall a limiting factor on vegetation growth. This last condition, confines the strongest relationships between NDVI and precipitation to regions where annual rainfall is within a specific range, identified as between 300 and 900 mm in South Africa (Richard and Poccard 1998), between 500 and
The study area is in Klang-Langat Valley, Malaysia, that located central districts (viz. Kuala Lumpur, Petaling and Klang) northern districts (viz. Kuala Selangor, Hulu Selangor and Gombak) and southern districts (viz. Part of Selangor and Putrajaya). These areas is located between longitude 101 o 12’ to 102 o 00’ E and latitude 2 o 35’ to 3 o 25’ N. The Klang-Langat Valley was chosen as the study area because it locates several urban centres such as Shah Alam, Petaling Jaya, Subang Jaya, Bandar Baru Bangi, Kajang, Kuala Lumpur and Putrajaya. In addition, this area also experiences rapid population growth and urbanization, resulting in drastic land use changes and severe environmental problems. Petaling recorded the highest population and Hulu Selangor was the biggest in term of area. Being a tropical climate, its daily temperature ranges from 23°C to 33°C. Humidity usually exceeds 80% and annual rainfall is 2,700 mm. Rain falls every month throughout the year, but December, January and February are the wettest months, over the years, brought by the Northeast Monsoon, vegetation cover has reduced tremendously in the Klang-Langat Valley as economic development and urbanisation proceed rapidly. Landsat TM satellite data from 1989, 1990, 1991, 1993, 1996, 1998 and 2001 were used in this study. The data were selected based on having the least cloud cover so that only minimum cloud masking needed to be done , . Initially, the Landsat data were geometrically rectified based on topographic maps. The other type of data used in this study were land use data and the population data for various districts and periods. Multi-temporal analysis technique was used to identify land use changes. All processing tasks were done using Erdas Imagine software. Initially, land use change was analysed visually by assigning bands 4, 3 and 2 to red, green and blue. By doing so, the land use pattern and trend, linkage between places and location of the land use changes can be determined. The Landsat has seven bands where band 3 records visible red measurement while band 4 records near infrared measurement; hence the NDVI was be determined using:
sidered as the most rainfall-dependent of all human ac- tivities [6,7]. This vulnerability is enhanced for the less economically developed countries in the tropics that, in many cases, are exposed to high climate variability at different spatial-temporal scales. Of particular impor- tance and relevance to Kenya is the El Niño Southern Oscillation (ENSO) phenomenon that has been linked to climate variability in many parts of Sub-Saharan Africa where unique and persistent anomaly patterns have been detected in the rainfall over parts of southern Africa, eastern Africa, the Sahel region during periods of strong and persistent ENSO events [8-12]. The Sub-Saharan Africa is the only region world-wide where food produc- tion per capita has decreased over the last twenty years . Staple crop production occupies an important place in government policies, and one of the top priorities has become the stabilization of crop yields  in the con- text of the long-term drought of the last decades  and the uncertainties of the global climate change . With increased capability to forecast ENSO events well in advance [17-19], there has emerged a growing convic- tion and interest in using climatic information in deci- sion-making process, especially during crop production [20,21]. The assumption we explore here is that the
the rainfall of the preceding two months was also includ- ed. Quantitative variations of satellite derived vegetationindex over Africa were also provided by Adeyewa . His analysis of anomalies in the NDVI showed that sig- nificant reductions in the vegetative activities are in the western, eastern and southernpart of Africa, while the western and central part witnessed increased activities for different time period between the years 1981-1999. NDVI is a good indicator of the ability for vegetation to absorb photosynthetically active radiation and has been widely used by researchers to estimate green biomass , leaf area index  and patterns of productivity  because the internal mesophyll structure of healthy green leaves strongly reflects NIR radiation, and leaf chlorophyll and other pigments absorb a large proportion of the red VIS radiation [8,9].
For a certain study area, in addition to rainfall and vegetation, the other im- pact factors can be considered as constants within a certain period of time. Therefore, accurate analysis of temporal matching relationship of rainfall and vegetation during the year is of great significance for assessing soil erosion risk and optimizing soil and water conservation. Langbein & Schumm (1958) reveal a complex relationship that the rainfall directly result in soil erosion and simul- taneously facilitate vegetation growth, as an indirect way to reduce the effects of surface erosion. Vrieling et al. (2008) analyzed the severe erosion periods of Cerrados regions in Brazil using moderate resolution imaging spectroradiometer (MODIS) normalizeddifferencevegetationindex (NDVI) time series data and high-intensity rainfall showed by Tropical Rainfall Measuring Mission (TRMM) three-hourly data. The purpose of this paper is to find a good way for choosing remote sensing images which is used to assess soil erosion risk, that is, to identi- fy optimal vegetation cover for annual soil erosion risk assessment. TRMM-3B43 monthly rainfall data and MODIS-NDVI data are used to study the temporal matching relationship of rainfall and vegetation in the Huangfuchuan basin and an indicator RV is developed to aid remote sensing image selection in practical applications.
Current methods of applying nitrogen (N) fertilizer do not treat small scale variability that is known to exist. Variations in corn grain yield can be found from one plant to the next. With knowledge that yield potential can be predicted by-plant, this in turn can be used to adjust fertilizer N rates for individual plants. This study was conducted in 2010 and 2011 to evaluate by- plant sidedress N using plant height and normalizeddifferencevegetationindex (NDVI) sensor readings. Treatments included preplant N rates of 0, 45, 90, and 180 kg N ha -1 with 180 kg N ha -1
The visual interpretation of residuals (Fig. 9 ) as well as the detailed statistical residual analy- ses expects that an ordinary linear regression of NDVI time series can be an appropriate instru- ment of trend detection. However, both analyses also demonstrate that some parts (segments) of the analyzed time series may show quite different behavior. Therefore, it could be suggested that a decomposition of the time series into different temporal segments will lead to an improvement of linear trend modeling. The chosen method is evaluated using several criteria and underlined the significance and necessity of considering structural breaks from a purely statistical point of view. Furthermore, this evaluation can be completed by considering context-related changes in addition to statistical measures. This is particularly reflected in comparison of locations [a]–[d]. Changes in the phenological condition take place at approximately the same time and order. The changes itself are of varied nature and thus usable as indicators for the characteristic of the chosen locations. The amount of break as well as the direction of change (positive or negative slope) gives valuable information about the condition of vegetation and orientation of vegetation development.
Since the development efforts of Cubesat missions are typically done on a much smaller budget compared to larger satellites, the payload design frequently incorporates COTS components with the use of open-source software and modeling tools where possible to reduce costs. Using COTS microcontrollers on Cubesats allows for higher data processing capabilities compared to space qualified components. However, there is a higher risk for susceptibility to space radiation since COTS microcontrollers typically are not manufactured with radiation hardened electrical components and materials. As of 2011, the standard bus system in Cubesat missions was I2C, despite the sporadic use of USB and CAN buses . Typical on-board data storage systems for Cubesats range between 32 kB and 8 MB, though additional flash memory of up to 8 GB has been employed in some payload designs. For a VGA camera with a 640 x 480 pixel resolution, 8 bit/pixel and no data compression, 8 GB equates to about 3500 images. However, the data storage capacity is irrelevant if higher data rates cannot be achieved. “In fact, it is trivial to show that there is a linear relationshipbetween storage capacity and data rate if the constraint is enforced to have enough storage capability to store all the images that can be downloaded in one access to the ground station”. This relationship is shown in Equation (4):
In this paper, we explore the feasibility of offering index insurance contracts to Zimbabwe smallholders that are based on two distinct indices: rainfall measurements taken at established meteorological stations, and remotely-sensed NormalizedDifferenceVegetationIndex (NDVI) measurements provided by orbiting National Oceanic and Atmospheric Administration (NOAA) satellites. 2 Both indices meet, prima facie, the most important necessary conditions for use as an insurance index: they are objectively and reliably measureable and are not subject to manipulation by either the insurer or the insured. To be determined is whether a specific contract design can be found that meets all the other conditions for economic viability as an insurance contract (Skees et al., 1999). These conditions include the following: (i) the contract must be affordable and accessible to the majority of the farmers, including poor smallholders in Zimbabwe; (ii) the contract should compensate for catastrophic income losses and protect subsistence consumption; (iii) the contract ought to be provided either by the private sector or public sector with few or no government subsidies; and (iv) there should be sufficient data to allow the contract to be actuarially rated with few opportunities for adverse selection problems to arise.
Figure 11: Yearly spatial correlation betweenRainfall and Vegetation for 2005 (a), 2010 (b) and 2015 (c) The annual spatial correlation analysis produced results suggesting very high positive correlation betweenrainfall and vegetation in the northern part of Ghana. The northern part of the country recorded correlation coefficients between 0.6 and 0.8. This relationshipbetweenRainfall and Vegetation is evident throughout the study period (from 2005 to 2015) as represented in Figure 11 a, b and c. This strong positive correlation betweenrainfall and vegetation in the northern part of Ghana can be attributed to the dominant savannah vegetation found in that part of the country. The savannah vegetation is highly responsive to water (rainfall), therefore considering that part of the country as an arid region, vegetation greenness is greatly dependent on water (rainfall).
Risks due to natural disasters faced by humankind such as landslides can be escalated by unfavorable variations in land cover conditions and unplanned construction. This is particularly an issue with landslides induced by human activities such as deforestation. Absence of vegetation is a major promoting factor for landslide occurrence in mountainous areas, since the presence of vegetation reduces the erodibility of a slope. Thus, effective land cover classification methods that can be updated regularly such as those based on imagery have been employed in landslide risk assessment. In developing a reliable land cover classification for landslide risk assessment, facility for updated assessment of the vegetation density should be an important requirement. The NDVI derived from satellite imagery provides a convenient method for quantifying the vegetation density in a timely manner. Furthermore, the NDVI’s ability to distinguish betweenvegetation densities would provide the ability for timely detection of sudden changes in land cover due to deforestation and construction. Of the existing methods of landslide risk assessment, several methods consider NDVI and land cover class as two separate parameters [ 10 , 11 ]. However, this study employed the NDVI itself as the land cover classification parameter, thereby combining the above mentioned two parameters into a stand-alone parameter. Therefore, the NDVI-based land cover classification method would also eliminate the redundancy in some current landslide risk assessment methods.
The usage of NormalizedDifferenceVegetationIndex study is numerous. But it is frequently used for monitoring drought, monitor and predict the agricultural production, vegetation monitoring from year-to-year and predicting hazardous fire zones. The present study deals about the vegetationindex assessment for predicting the status of vegetation in salem district. The red and infrared bands enable to monitor density and intensity of green vegetation growth using spectral reflectivity of solar radiation. Greenness of the leaves generally illustrates better reflection in the NIR wavelength range than in visible wavelength ranges. When the leaves affected by water scarcity, diseases and dead, they turn into yellowish color, and it reflect less in the NIR range. The NDVI mapped depicts the single band data set for greenery. Peter H et al. (2004) revealed that NDVI method support to the Land- use change modeling. Daman Winter (2003) stated that a relative method of using NDVI has been developed for monitoring the presence and spread of cheat grass. Bran (1996) revealed that the NDVI illustrates the patterns of plant growth from green-up to senescence by indicating the quantity of actively photosynthesizing biomass on a landscape.
Previous study had shown that the trend of LST could be explained by a simple linear regression model (Stroppiana et al., 2014). Nonetheless, the linear model might not be the appropriate way to estimate trends for shorter periods. A polynomial pattern of temperature was identified in Australia from 1970 to 2012 (Wanishsakpong and McNeil 2016) where the 6 th degree polynomial regression model had been used because the data were for more than 40 years. It could be seen that, within 40 years, a 15 year periodic temperature pattern were well explained and hence in our study, having shorter time frame data, a polynomial with lower degree (2 nd ) was used. Hence, the annual temperature patterns illustrated in 15 groups are more interesting in this study. The patterns suggested the local variation of temperature change in the study area, that might be due to the effect of different natural (vegetation, altitude and topography) and human factors (land use and urban activities). The southern and the central sub regions have accelerating pattern. Both of these regions had lower altitude and dense population. The north and west area, both having higher altitude, had ‘non accelerating’ pattern. The literatures had showed that the analysis of regional or global scale data is very common to determine trends (Hughes et al., 2006; Devkota, 2014) and patterns (Zhou et al., 2009; Wanishasakpong and McNeil, 2016) of temperature but the results do not illustrate the pattern at a particular locality in a huge study area. At the expense of macro-level spatial analysis, often, the local level changes have been overlooked. However, this study not only improves the understanding of temperature patterns in Kathmandu but also provides a basis for urban planning and environmental management at local level.
Remotely sensed data are suitable to assess large areas, and considerable effort has been made to characterize vegetation using satellite data. Basic relationships exist between spectral reflectance and vegetative characteristics. These relationships allow the use of spectral transforms to define biophysical parameters for plants. The normalizeddifferencevegetationindex (NDVI) has been shown to be related to plant canopy variables, which also relate to ET.
These OLI images are calibrated radiometrically and corrected from atmos- pheric effects and then resampled at a spatial resolution of 15 m. The NDVI ve- getation index of each image is calculated to consolidate and create a time series . The spectral profiles of each culture were extracted from the image indexes on the basis of the field data. For a good accuracy of our classification, we hid the occupation of the elements of the soil that will not show interest in this study (Figure 2).
Up to now, several spatiotemporal fusion models have been proposed. Gao et al.  proposed a spatial and temporal adaptive reflectance fusion model (STARFM) to blend MODIS and Landsat image to produce a synthetic surface reflectance product at 30 m spatial resolution. Based the STARFM, Zhu et al.  developed an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), introducing conversion coefficient between pixels and improving the prediction accuracy. Zhu et al.  proposed the flexible spatiotemporal data fusion model (FSDAF) which performs better in predicting abrupt land cover changes. Liao et al.  developed a spatiotemporal vegetationindex image fu- sion model (STVIFM) to generate NDVI time series images with high spatial and temporal resolution in heterogeneous regions. In this study, we made a comparation between STARFM, ESTARFM, FSDAF, and STVIFM methods, tested by Landsat and MODIS data acquired in same site and quantitatively as- sess the accuracy of predicted image generated from each fusion model.
Abstract. The Apuan Alps region is one of the rainiest ar- eas in Italy (more than 3000 mm/year), in which frequently heavy and concentrated rainfall occurs. This is particularly due to its geographical position and conformation: the Apuan chain is located along the northern Tuscan coast, close to the Ligurian Sea, and the main peaks reach almost 2000 m. In several cases, the storms that hit the area have triggered many shallow landslides (soil slip-debris flows), which exposed the population to serious risks (during the 19 June 1996 rain- storm about 1000 landslides were triggered and 14 people died). The assessment of the rainfall thresholds is very im- portant in order to prepare efficient alarm systems in a region particularly dedicated to tourism and marble activities.
period of June 22, 2015, to June 9, 2016 (Fig. 5) for each feature class showed distinct profiles, especially between crop and noncrop classes. Noncrop classes, i.e., woody vegetation, bare soil, water, and pasture, showed more even profiles, since these objects tend to be static throughout time. On the other hand, crop classes experienced a dynamic life-cycle growth, starting from planting, peak season, and senescence, which was then reflected in their time-series profiles. The crop profiles formed a curved shape, which started from a planting season, then increased until reaching a maximum reflectance value (peak), and finally decreased to a period of sen- escence where the crops were harvested. In addition, each crop grows in a specific period depending on its characteristics, climate, and water availability. As a result, different crops may have different or similar time-series profiles. In this study, mung bean and corn profiles were almost similar, with the difference apparent in planting time (start of the season). Mung bean was planted at the beginning of January 2016, whereas corn was planted in the middle of December 2015. The profile of peanut crops was slightly different from those two previously mentioned crops. Due to its longer growing period, i.e., 110 to 170 days (16 to 24 weeks), 2 peanut planting and harvesting (end of the season) times were totally different from other crops, i.e., November 2015 and May 2016, respectively. Among all crops, sorghum presented a different profile, with significant difference in its peak NDVI value, which was the lowest among others. The peak NDVI value for the other three crops was almost similar. The harvesting times for mung bean, sorghum, and corn were relatively similar; however, the NDVI value of mung bean was different from the other two crops. The figure also illustrated that during crop-growing seasons, there was a limited number of NDVI imagery. It was also observed that spectral responses outside of the crop growth period, i.e., June to October 2015, tend to be static, indicating that crops were not planted during this time.
2.1 Main characteristics of rainfalls in Calabria In Calabria, average yearly rainfalls vary between 1000 and 2000 mm y −1 in mountainous and internal areas, and be- tween 600 and 900 mm y −1 in coastal areas, with a mean re- gional value of about 1150 mm y −1 . As confirmed in a recent review of the general frame of storm conditions in Calabria, heavy rains are by far more frequent on the Jonian side of the region (Terranova, 2004). Over 70% of the yearly precipita- tion occurs from October to March, with negligible monthly values from June to September. Orography strongly influ- ences the precipitation regime (Bellecci et al., 2002), due to fronts transversally approaching the Calabrian peninsula, and convective cells climbing up the steep sea-side slopes of the mountain chains.