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\definecolor{GJMediumGrey}{HTML}{6D6E70} \definecolor{GJLightGrey}{HTML}{929497} \renewenvironment{abstract}{% \setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Dr. Farooq Ahmad} \author[2]{Dr. Farooq Ahmad} \affil[1]{ University of the Punjab, New Campus, Lahore, Pakistan.} \renewcommand\Authands{ and } \date{\small \em Received: 9 June 2012 Accepted: 5 July 2012 Published: 15 July 2012} \maketitle \begin{abstract} One of the main factors determining the daily variation of the active surface temperature is the state of the vegetation cover. It can well be characterized by the Normalized Difference Vegetation Index (NDVI). The NDVI has the potential ability to signal the vegetation features of different eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles. The vegetation phenology is the expression of the seasonal cycles of plant processes and contributes vital current information on vegetation conditions and their connections to climate change. The NDVI is computed using near-infrared and red reflectances, and thus has both an accuracy and precision. A gapless time series of MODIS NDVI (MOD13A1) composite raster data from 18th February, 2000 to 16th November, 2012 with a spatial resolution of 500 m was utilized. Time-series terrestrial parameters derived from NDVI have been extensively applied to global climate change, since it analyzes each pixel individually without the setting of thresholds to detect change within a time series. \end{abstract} \keywords{Climate, MODIS, NDVI, remote sensing, time series, vegetation phenology.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{Introduction}]{Introduction}\par ong-term observations of remotely sensed vegetation dynamics have held an increasingly prominent role in the study of terrestrial ecology \hyperref[b16]{(Budde et al., 2004;}\hyperref[b93]{Prasad et al., 2007;}\hyperref[b84]{Ouyang et al., 2012)}. A major limitation of such studies is the limited availability of sufficiently consistent data derived from long-term remote sensing \hyperref[b84]{(Ouyang et al., 2012)}. The benefit obtained from a remote sensing sensor thereby largely depends on its spectral resolution \hyperref[b53]{(Jensen, 2005)}, which determines the sensor's capability to resolve spectral features of land surfaces \hyperref[b35]{(Fontana, 2009)}. One of the key factors in assessing vegetation dynamics and its response to climate change is the ability to make frequent and consistent observations \hyperref[b111]{(Thomas and Leason, 2005;}\hyperref[b84]{Ouyang et al., 2012)}. Ability to assess how environmental changes affect dynamics of vegetation is increasingly important for better predictions of climate change effects \hyperref[b3]{(Aguilar et al., 2012)}. Popularity of the application of the NDVI \hyperref[b98]{(Rouse et al., 1973;}\hyperref[b116]{Tucker, 1979)} in ecological studies has enabled quantification and mapping of green vegetation productivity and other landscape-level fluxes \hyperref[b125]{(Wang et al., 2003;}\hyperref[b88]{Pettorelli et al., 2005;}\hyperref[b3]{Aguilar et al., 2012)}. The evolution of vegetation index exhibits a strong correlation with the typical green vegetation growth stages. The results (temporal curves) can be analyzed to obtain useful information such as the start/end of vegetation growing season {\ref (Gao and Mas, 2008;}\hyperref[b5]{Ahmad, 2012a)}. The NDVI is the most commonly used index {\ref (Ahmad, 2012b)} and serves as a measure of photosynthetic activity within a certain area \hyperref[b35]{(Fontana, 2009)}.\par The NDVI is based on differences in reflectance in the red region and maximum reflectance in the near infrared; it is the most widely used index in remote sensing \hyperref[b3]{(Aguilar et al., 2012)}.\par The NDVI can be a useful tool to couple climate and vegetation distribution and performance at large spatial and temporal scales \hyperref[b88]{(Pettorelli et al., 2005;}\hyperref[b3]{Aguilar et al., 2012)} because vegetation vigor and productivity are related to temperature-precipitation and evapotranspiration. The NDVI serves as a surrogate measure of these factors at the landscape scale \hyperref[b125]{(Wang et al., 2003;} {\ref Groeneveld and Baugh, 2007;}\hyperref[b3]{Aguilar et al., 2012)}. The linear response of vegetation NDVI to rainfall for regions with low vegetation cover and rainfall is well documented \hyperref[b76]{(Malo and Nicholson, 1990} \hyperref[b48]{(Huete et al., 2006;}\hyperref[b19]{Carrão et al., 2008;}\hyperref[b6]{Ahmad, 2012c}). Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon \hyperref[b102]{(Salomonson and Toll, 1991}; GSFC/NASA, 2003; Huete, 2005; Ahmad, 2012c). The MODIS has been applied in diverse applications for two reasons, the advantage of daily data and the possibility for high-quality data covering large land areas \hyperref[b139]{(Zhao et al., 2009;}\hyperref[b84]{Ouyang et al., 2012)} and offers a good opportunity to monitor and analyze regional land surface processes. This is especially true for NDVI vegetation analysis, for which MODIS provides a standard product \hyperref[b90]{(Piao et al., 2006a;}\hyperref[b139]{Zhao et al., 2009;}\hyperref[b84]{Ouyang et al., 2012)}. The MODIS design team gave substantial emphasis to instrument calibration and characterization recognizing these activities as critical for generation of accurate long-term time-series products needed for global change studies \hyperref[b49]{(Huete et al., 1999;}\hyperref[b63]{Justice et al., 2002)}.\par The MODIS, high temporal resolution sensor allow detailed monitoring of the temporal change in vegetation \hyperref[b126]{(Weissteiner et al., 2011;}\hyperref[b84]{Ouyang et al., 2012)}. Higher resolution sensors provide spatial detail while low resolution satellites offer high temporal repetition at the expense of spatial resolution \hyperref[b84]{(Ouyang et al., 2012)}. Over the last two decades, studies of vegetation dynamics have explored several approaches to improve the spatial-temporal resolution of data from remote sensing sources. Integrating medium to high spatial resolution data with high temporal resolution data can provide a means to detect most of the variation in vegetation \hyperref[b55]{(Jin and Steven, 2005;}\hyperref[b84]{Ouyang et al., 2012)}.\par The MODIS NDVI datasets provides unique opportunities for monitoring terrestrial vegetation conditions at regional and global scales {\ref (Yang et} \hyperref[b31]{Dong et al., 2003;}\hyperref[b89]{Piao et al., 2006)}, and phenology \hyperref[b107]{(Reed et al., 1994;}\hyperref[b80]{Moulin et al., 1997;}\hyperref[b89]{Piao et al., 2006)}.\par Multi-year time series of NDVI can reliably measure yearly changes in the timing of the availability of high-quality vegetation. The biological significance of NDVI indices should be assessed in various habitat types before they can be widely used in ecological studies \hyperref[b44]{(Hamel et al., 2009)}. The premise is that the NDVI is an indicator of vegetation health, because degradation of ecosystem vegetation, or a decrease in green, would be reflected in a decrease in NDVI value \hyperref[b44]{(Hamel et al., 2009;}\hyperref[b77]{Meneses-Tovar, 2011)}. The NDVI has the potential ability to signal the vegetation features of different eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles at a regional scale \hyperref[b43]{(Guo, 2003)}. Phenology has emerged recently as an important focus for ecological research \hyperref[b78]{(Menzel et al., 2001;}\hyperref[b45]{Hashemi, 2010)}. \section[{II.}]{II.} \section[{Study Area}]{Study Area}\par The study area lies in the north-eastern Punjab province of Pakistan from 30° 11' 52" to 32° 27' 19" North latitude and 71° 53' 23" to 75° 56' 24" East longitude. The research area is consists of six districts (Figure \hyperref[fig_0]{1}); Lahore, Sheikhupura, Hafizabad, Nankana Sahib, Kasur and Okara of the province. \section[{Research Des?gn and Methods}]{Research Des?gn and Methods}\par The MODIS is a key instrument onboard the Terra satellite and provides images over a given pixel of land specifically designed for measurements of land surface dynamics \hyperref[b56]{(Huete, 2005)}. The MODIS sensors, as a newer generation sensor specifically designed for, inter alia, terrestrial applications \hyperref[b35]{(Fontana, 2009}) and a validation with accurately georeferenced composite data from the MODIS sensor revealed the high accuracy of the orthorectified NDVI composites and emphasized the great importance of orthorectification for data quality in rugged terrain \hyperref[b15]{(Barnes et al., 1998;}\hyperref[b35]{Fontana, 2009)}. The MODIS provides higher radiometric sensitivity \hyperref[b14]{(Barnes and Salomonson, 1993;}\hyperref[b35]{Fontana, 2009)}. In each of the 36 spectral bands, the 12-bit resolution results in 4096 levels of discrimination in measured response \hyperref[b41]{(Guenther et al., 1998;}\hyperref[b35]{Fontana, 2009)}. The bands are sensitive to different portions of the electromagnetic spectrum between 0.46 m and 14.39 m at spatial resolutions of 250 m, 500 m, and 1 km, depending on the spectral band \hyperref[b35]{(Fontana, 2009)}. The output delivered by the MODIS level 1B algorithm includes geolocated at aperture radiances in all 36 spectral bands \hyperref[b50]{(Isaacman et al., 2003;}\hyperref[b134]{Xiong et al., 2003;}\hyperref[b135]{2003a;}\hyperref[b133]{Xiong et al., 2005;}\hyperref[b35]{Fontana, 2009)}.\par Earth location data is available at sub-pixel accuracy. This extraordinary geolocation accuracy is achieved due to several reasons \hyperref[b128]{(Wolfe et al., 1995;}\hyperref[b35]{Fontana, 2009)}: First, the spacecrafts carrying MODIS are very stable and provide highly precise external orientation knowledge. Second, the MODIS instrument was designed to give precise interior orientation knowledge \hyperref[b67]{(Khlopenkov and Trishchenko, 2008;}\hyperref[b35]{Fontana, 2009)}. Third, an accurate global DEM \hyperref[b73]{(Logan, 1999;}\hyperref[b35]{Fontana, 2009}) is used to model and remove relief-induced distortions. Fourth, a global set of GCPs based on Landsat imagery served to determine biases in the sensor orientation, which were finally used to improve geolocation processing \hyperref[b2]{(Ackerman et al., 1998;}\hyperref[b35]{Fontana, 2009)}.\par The MODIS NDVI data products for research area were acquired, in this case MOD13A1 (MODIS TERRA\textunderscore 500 m) data were downloaded from the Land Processes Distributed Active Archive Center (LPDAAC). Tile number covering this area is h24v05, reprojected from the Integerized Sinusoidal projection to a Geographic Lat/Lon projection, and Datum WGS84 (GSFC/NASA, 2003; Ahmad, 2012a). A gapless time series of MODIS NDVI composite raster data from 18 th February, 2000 to 16 th November, 2012 with a spatial resolution of 500 m was utilized (Figure \hyperref[fig_1]{2}) for phenologically-tuned MODIS NDVI-based time series development. The datasets provides frequent information at the spatial scale at which the majority of human-driven land cover changes occur \hyperref[b113]{(Townshend and Justice, 1988;}\hyperref[b121]{Verbesselt et al., 2010)}.\par The NDVI values range from -1 to +1; because of high reflectance in the NIR portion of the EMS, healthy vegetation is represented by high NDVI values between 0.1 and 1 \hyperref[b71]{(Liu and Huete, 1995;} {\ref USGS, 2008;}. Conversely, non-vegetated surfaces such as water bodies yield negative values of NDVI because of the electromagnetic absorption property of water. Bare soil areas represent NDVI values which are closest to 0 due to high reflectance in both the visible and NIR portions of the EMS \hyperref[b112]{(Townshend, 1992)}. The NDVI is related to the absorption of photosynthetically active radiation and basically measures the photosynthetic capability of leaves, which is related to vegetative canopy resistance and water vapour transfer \hyperref[b124]{(Wan, 2003;}\hyperref[b95]{Rahman et al., 2004;}\hyperref[b5]{Ahmad, 2012a)}.\par The NDVI is successful as a vegetation measure is that it is sufficiently stable to permit meaningful comparisons of seasonal and inter-annual changes in vegetation growth and activity \hyperref[b24]{(Choudhury, 1987} The NDVI product works optimally with cloud filtering, radiometric calibration, precise geolocation, and a snow mask. In addition, the product performs best using topof-canopy reflectance inputs, corrected for atmospheric ozone, molecular scattering, aerosol, and water vapour \hyperref[b48]{(Huete et al., 2006)}.\par IV. \section[{Results}]{Results}\par Figure \hyperref[fig_3]{3} shows time-series phenology metrics for Kot Jan Muhammad district Hafizabad. The profile showed that climate was more or less stable. The trend analysis showed slight positive tendency due to increase in precipitation in winter season or western disturbances especially during 2009 to 2012, but severe dryness was observed in January 2003 (NDVI value 0.85) and June 2010 (NDVI value -0.01). Fluctuations were observed at start/end pixel values. The NDVI value on 18 th February, 2000 was 0.72 while the NDVI value on 16 th November, 2012 was 0.28 (Figure \hyperref[fig_3]{3}).\par The phenological studies can be used to evaluate the effects of climate change \hyperref[b20]{(Chandola et al., 2010)}. The vegetation phenology is important for predicting ecosystem carbon, nitrogen, and water fluxes {\ref (Baldocchi et} Figure {\ref 4} shows time-series phenology metrics for Amarkot district Lahore. The profile showed that climate was not stable and the trend analysis showed negative tendency. The fluctuations in the phenological profile were due to variation in the temperatureprecipitation at Amarkot. Fluctuations were observed at start/end pixel values. The NDVI value on 18 th February, 2000 was 0.60 while the NDVI value on 16 th November, 2012 was 0.28 (Figure {\ref 4}). The soil productivity in winter season was higher as compared to summer season. In summer season the temperature raises upto 51 o C or more and in winter the temperature falls below 0 o C. The monsoon occurs in summer season from June till September. Monsoon rains bring much awaited relief from scorching heat. These monsoon rains are quite heavy by nature. The western disturbances generally occur during the winter months and causes moderate rainfall \hyperref[b66]{(Khan, 1993;}\hyperref[b4]{Ahmad, 2012)}.\par Change information of the earth's surface is becoming more and more important in monitoring the local, regional and global resources and environment. The large collection of past and present remote sensing imagery makes it possible to analyze spatio-temporal pattern of environmental elements and impact of human activities in past decades \hyperref[b59]{(Jianya et al., 2008;}\hyperref[b4]{Ahmad, 2012)}.\par Figure {\ref 5} shows time-series phenology metrics for Jodhwala district Kasur. The profile showed that climate was stable and the trend analysis showed slight positive tendency. The NDVI value on 18 th February, 2000 was 0.52 while the NDVI value on 16 th November, 2012 was 0.49 (Figure {\ref 5}). The variation in the vegetation phenological temporal metrics was due to changes in the temperature-precipitation. The green cover fraction and soil productivity in winter season was much higher as compared to summer season. The NDVI has been used for several decades, which is advantageous for studying historical changes \hyperref[b115]{(Trishchenko et al., 2002;}\hyperref[b4]{Ahmad, 2012)}.\par Figure \hyperref[fig_4]{6} shows time-series phenology metrics for Sukka Chak district Okara. The profile showed that climate was stable and the trend analysis showed positive tendency. The NDVI value on 18 th February, 2000 was 0.68 while the NDVI value on 16 th November, 2012 was 0.23 (Figure \hyperref[fig_4]{6}). The fluctuations in the phenological profile were due to variation in the temperature-precipitation. The winter disturbances were stronger as compared to summer monsoon and land degradation or desertification can't be seen at Sukka Chak. The soil productivity in winter season was much higher as compared to summer season. The phenology metrics showed a clear relationship with the seasonality of rainfall, winter and summer growing seasons \hyperref[b127]{(Wessels et al., 2011;}\hyperref[b4]{Ahmad, 2012)}.\par Figure \hyperref[fig_5]{7} shows time-series phenology metrics for Adilgarh district Nankana Sahib. The profile showed that climate was stable and the trend analysis showed gradually positive tendency. The NDVI value on 18 th February, 2000 was 0.64 while the NDVI value on 16 th November, 2012 was 0.36 (Figure \hyperref[fig_5]{7}). The green cover fraction and soil productivity in winter season was much higher as compared to summer season.\par Remote sensing provides a key means of measuring and monitoring phenology at continental to global scales and vegetation indices derived from satellite data are now commonly used for this purpose \hyperref[b94]{(Nightingale et al., 2008;}\hyperref[b110]{Tan et al., 2008;}\hyperref[b4]{Ahmad, 2012)}.\par Figure \hyperref[fig_6]{8} shows time-series phenological variation for Jagganwala district Hafizabad. The profile showed that climate was stable and the trend analysis showed no change during the entire period, but severe dryness was observed in December 2002 and January 2003. Fluctuations were observed at start/end pixel values. The NDVI value on 18 th February, 2000 was 0.69 while the NDVI value on 16 th November, 2012 was 0.35 (Figure \hyperref[fig_6]{8}). The fluctuations in the phenological profile were due to variation in the temperature-precipitation. The winter disturbances were much stronger as compared to summer monsoon and land degradation or desertification can't be seen at Jagganwala. The green cover fraction and soil productivity in winter season was much higher as compared to summer season.\par Variations in vegetation activity have been linked with changes in climates {\ref (Los et} Figure \hyperref[fig_7]{9} shows time-series phenological variation for Qila Deva Singh district Nankana Sahib. The profile showed that climate was stable and the trend analysis showed gradual positive tendency during the entire period, but little dryness was observed in January 2003 (NDVI value 0.13) and July, 2010 (NDVI value 0.09). Fluctuations were observed at start/end pixel values. The NDVI value on 18 th February, 2000 was 0.73 while the NDVI value on 16 th November, 2012 was 0.25 (Figure \hyperref[fig_7]{9}). The NDVI exhibits a strong correlation with the typical green vegetation growth stages \hyperref[b140]{(Zhao et al., 2005;} {\ref Gao and Mas, 2008;}\hyperref[b4]{Ahmad, 2012;}\hyperref[b5]{2012a)}.\par Figure \hyperref[fig_8]{10} shows time-series phenological variation for Ali Raza Abad district Lahore. The profile showed that climate was more or less stable during the entire period. The fluctuations in the phenological profile were due to variation in the temperature-precipitation at Ali Raza Abad. The NDVI value on 18 th February, 2000 was 0.61 while the NDVI value on 16 th November, 2012 was 0.32 (Figure \hyperref[fig_8]{10}).\par To enhance the vegetation signal in remotely sensed data and provide an approximate measure of live green vegetation, a number of spectral vegetation indices have been developed by combining data from multiple spectral bands into single values because they correlate the biophysical characteristics of the vegetation of the land-cover \hyperref[b18]{(Campbell, 1987)} from the satellite spectral signals \hyperref[b138]{(Yang et al., 2008)}.\par Figure \hyperref[fig_9]{11} shows time-series phenological variation for Shakranj Pur district Okara. The profile showed that climate was not stable, severe interannual variation in temperature-precipitation was observed at Shakranj Pur and the trend analysis showed gradual positive tendency during the entire period. Severe dryness was observed in August 2008 (NDVI value 0.004). Fluctuations were observed at start/end pixel values. The NDVI value on 18 th February, 2000 was 0.51 while the NDVI value on 16 th November, 2012 was 0.25 (Figure \hyperref[fig_9]{11}).\par Figure \hyperref[fig_10]{12} shows time-series phenological variation for Titranwali district Nankana Sahib. The profile showed that climate was stable and the trend analysis showed no change. The fluctuations in the phenological profile were due to variation in the temperatureprecipitation during the entire period. The winter disturbances were much stronger as compared to summer monsoon and land degradation or desertification can't be seen at Titranwali. The green cover fraction and soil productivity in winter season was much higher as compared to summer season. Fluctuations were observed at start/end pixel values. The NDVI value on 18 th February, 2000 was 0.67 while the NDVI value on 16 th November, 2012 was 0.27 (Figure \hyperref[fig_10]{12}).\par Figure \hyperref[fig_11]{13} shows time-series variation profile for Ajianwala district Sheikhupura. The profile showed that climate was stable and the trend analysis showed slight positive change, but little dryness was observed in January 2003 (NDVI value 0.05). The fluctuations in the phenological profile were due to variation in the temperature-precipitation during the entire period. The winter disturbances were much stronger as compared to summer monsoon at Ajianwala. Fluctuations were observed at start/end pixel values. The NDVI value on 18 th February, 2000 was 0.72 while the NDVI value on 16 th November, 2012 was 0.35 (Figure \hyperref[fig_11]{13}).\par Time series of acquired multi-spectral image represent characteristics of a landscape and each element represented has a particular spectral response. The time series of such vegetation indices observed over a period can help in further classification of the vegetation as crop and other type of vegetation \hyperref[b81]{(Musande et al., 2012)}.\par Figure \hyperref[fig_12]{14} shows time-series variation profile for Makki Unchi district Sheikhupura. The profile showed that climate was stable and the trend analysis showed no change. The green cover fraction and soil productivity in winter season was much higher as compared to summer season. The NDVI value on 18 th February, 2000 was 0.78 while the NDVI value on 16 th November, 2012 was 0.26 (Figure \hyperref[fig_12]{14}). The NDVI provides useful information for detecting and interpreting vegetation land cover it has been widely used in remote sensing studies \hyperref[b32]{(Dorman and Sellers, 1989;}\hyperref[b82]{Myneni and Asrar, 1994;}\hyperref[b37]{Gao, 1996;}\hyperref[b105]{Sesnie et al., 2008;}\hyperref[b64]{Karaburun, 2010;}\hyperref[b4]{Ahmad, 2012)}.\par Figure \hyperref[fig_13]{15} shows time-series variation profile for Bodal Khaneke district Kasur. The profile showed that climate was not stable; the trend analysis showed negative tendency and severe dryness was observed in August-September 2011 (NDVI value -0.02). The fluctuations in the phenological profile were due to variation in the temperature-precipitation at Bodal Khaneke. Fluctuations were observed at start/end pixel values. The NDVI value on 18 th February, 2000 was 0.23 while the NDVI value on 16 th November, 2012 was 0.17 (Figure \hyperref[fig_13]{15}).\par The ecology of the north-eastern Punjab province is mainly arid to semi arid. Abundant evidence verifies that the natural resources were unwisely overexploited to achieve higher growth rates in agriculture. The country is now suffering from problems like water scarcity for crop cultivation with salinity and sodicity problems, more erratic and low rainfalls for rain-fed agriculture and low carrying capacity of rangelands representing more three-fifth of total geographic area. Agriculture is the only sector which is entirely dependent on the quality of natural resources for sustaining higher productivity \hyperref[b33]{(Farooq et al., 2007;}\hyperref[b4]{Ahmad, 2012)}.\par Figure \hyperref[fig_14]{16} shows time-series variation profile for Asrur district Sheikhupura. The profile showed that climate was not stable and the trend analysis showed gradual positive tendency during the entire period, but little dryness was observed in January 2003 (NDVI value 0.05). The profile also showed gradual increase in precipitation from December 2004 to November 2012. The winter disturbances were much stronger, and the duration also increased from December 2009 to present at Asrur. The NDVI value on 18 th February, 2000 was 0.43 while the NDVI value on 16 th November, 2012 was 0.23 (Figure \hyperref[fig_14]{16}). The healthy and dense vegetation show a large NDVI \hyperref[b36]{(Fuller, 1998;}\hyperref[b4]{Ahmad, 2012)}.\par Figure \hyperref[fig_15]{17} shows time-series variation profile for Katlohi Kallan district Kasur. The profile showed that climate was more or less stable. The trend analysis showed no change, but little dryness was observed in January 2003 (NDVI value 0.12). The NDVI value on 18 th February, 2000 was 0.68 while the NDVI value on 16 th November, 2012 was 0.32 (Figure \hyperref[fig_15]{17}).\par The NDVI can be used not only for accurate description of vegetation classification and vegetation phenology \hyperref[b118]{(Tucker et al., 1982;}\hyperref[b114]{Tarpley et al., 1984;}\hyperref[b60]{Justice et al., 1985;}\hyperref[b72]{Lloyd, 1990;}\hyperref[b99]{Singh et al., 2003;}\hyperref[b74]{Los et al., 2005;}\hyperref[b4]{Ahmad, 2012)} but also effective for monitoring rainfall and drought, estimating net primary production of vegetation, crop growth conditions and crop yields, detecting weather impacts and other events important for agriculture and ecology \hyperref[b68]{(Kogan, 1987;}\hyperref[b28]{Dabrowska-Zielinska et al., 2002;}\hyperref[b99]{Singh et al., 2003;}\hyperref[b25]{Chris and Molly, 2006;}\hyperref[b38]{Glenn et al., 2008;}\hyperref[b4]{Ahmad, 2012)}.\par Figure \hyperref[fig_16]{18} shows phenological temporal comparison for Thatha Khudayar district Sheikhupura. In this profile MODIS NDVI 500 m data products for the period 2007 to 2011 at 16-days interval was evaluated. The findings showed that the impact of summer monsoon was stronger as compared to winter disturbances. The precipitation was received in both the summer and winter season. The climate was stable during the entire period at Thatha Khudayar.\par Figure \hyperref[fig_17]{19} shows phenological temporal comparison for Haveli district Okara using MODIS NDVI 500 m data products at 16-days interval for the period 2007 to 2011. The temporal curves indicate that climate was stable, green cover fraction, and biomass productively increased due to precipitation in summer and winter season. The profile showed maximum soil and biomass productivity in 2009 and minimum in 2010 and land degradation can't be seen at Haveli. Processed by the author. Figure {\ref 4} : Time-series phenology metrics for Amarkot, District Lahore.\par Processed by the author. Figure {\ref 5} : Time-series phenology metrics for Jodhwala, District Kasur.\par Processed by the author. V. \section[{Discussion and Conclusions}]{Discussion and Conclusions}\par As the use of space and computer technology developed, humankind has a great advantage of produce this much important research projects with the help of technology in an easier, more accurate way within less time than other ways. As a result all these can have a very effective role in helping the country to increase the amount and the quality of agricultural products \hyperref[b8]{(Akkartala et al., 2004;} {\ref Ahmad, 2012b)}. Ground cover as estimated did not take into account the physiological status of the vegetation in the sense that we did consider as cover all vegetation whatever its status. The use of NDVI, in general, takes into account mostly the green living vegetation \hyperref[b27]{(Cyr et al., 1995;} {\ref Ahmad, 2012b)}. suitable for monitoring vegetation condition in the Punjab province. The climatic variation is the major contributor to inter-annual NDVI variation. Precipitation has stronger effects on NDVI than temperature \hyperref[b70]{(Li and Guo, 2012)}. Vegetation phenology derived from remote sensing is important for a variety of scientific applications \hyperref[b58]{(Hufkens et al., 2010)}, and can provide a useful signal for classifying vegetated land cover \hyperref[b30]{(Dennison and Roberts, 2003)}. This communication presents a new methodology for studying vegetation phenology using remote sensing \hyperref[b23]{(Charbonneau and Kondolf, 1993)}. The methodology provides a flexible means to monitor vegetation dynamics over large area using remote sensing. Initial results using MODIS NDVI data products for the north-eastern Punjab province of Pakistan demonstrate that the method provides realistic results that are geographically and ecologically consistent with the known behaviour of vegetation. In particular, the MODIS NDVI based estimates of green up onset, maturity onset, and dormancy onset show strong spatiotemporal patterns that also depend on land cover type \hyperref[b13]{(Baret and Guyot, 1991;}\hyperref[b23]{Charbonneau and Kondolf, 1993;}\hyperref[b61]{Justice et al., 1998)}.\par The results of this research are encouraging and the techniques described provide an improved method for estimating the quantity of vegetative cover across large and complex environment of the northeastern Punjab province of Pakistan with MODIS NDVI data products. This study also identified several data acquisition and processing issues that warrant further investigation. Studies are under way to assess the importance of coordinating and timing field data collection and image acquisition dates as a means of improving the strength of the relationships between MODIS NDVI data products and land condition trend analysis \hyperref[b103]{(Senseman et al., 1996;} {\ref Ahmad, 2012b)} ground-truth data. Due to the continuous provision of information to present days, the MODIS data has been used for many other purposes in geophysical, environmental or agricultural sciences and vegetation change \hyperref[b26]{(Chuvieco et al., 2003;}\hyperref[b106]{Stöckli and Vidale, 2004;}\hyperref[b4]{Ahmad, 2012)}.\par The study demonstrates the successful application of MODIS NDVI products on climatic variation. Through the application and analyses of time series data, this research provides valuable understanding of the impact of environmental drivers on the spatial, annual and interannual variation of vegetation dynamics in the north-eastern Punjab province of Pakistan \hyperref[b4]{(Ahmad, 2012)} \begin{figure}[htbp] \noindent\textbf{1}\includegraphics[]{image-2.png} \caption{\label{fig_0}Figure 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2}\includegraphics[]{image-3.png} \caption{\label{fig_1}Figure 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{20}\includegraphics[]{image-4.png} \caption{\label{fig_2}Figure 20 shows}\end{figure} \begin{figure}[htbp] \noindent\textbf{3}\includegraphics[]{image-5.png} \caption{\label{fig_3}Figure 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6}\includegraphics[]{image-6.png} \caption{\label{fig_4}Figure 6 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{7}\includegraphics[]{image-7.png} \caption{\label{fig_5}Figure 7 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{8}\includegraphics[]{image-8.png} \caption{\label{fig_6}Figure 8 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{9}\includegraphics[]{image-9.png} \caption{\label{fig_7}Figure 9 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{10}\includegraphics[]{image-10.png} \caption{\label{fig_8}Figure 10 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{11}\includegraphics[]{image-11.png} \caption{\label{fig_9}Figure 11 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{12}\includegraphics[]{image-12.png} \caption{\label{fig_10}Figure 12 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{13}\includegraphics[]{image-13.png} \caption{\label{fig_11}Figure 13 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{14}\includegraphics[]{image-14.png} \caption{\label{fig_12}Figure 14 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{15}\includegraphics[]{image-15.png} \caption{\label{fig_13}Figure 15 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{16}\includegraphics[]{image-16.png} \caption{\label{fig_14}Figure 16 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{17}\includegraphics[]{image-17.png} \caption{\label{fig_15}Figure 17 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{18}\includegraphics[]{image-18.png} \caption{\label{fig_16}Figure 18 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{19}\includegraphics[]{image-19.png} \caption{\label{fig_17}Figure 19 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{20}\includegraphics[]{image-20.png} \caption{\label{fig_18}Figure 20 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{P{0.6714285714285714\textwidth}P{0.05714285714285714\textwidth}P{0.07142857142857144\textwidth}P{0.049999999999999996\textwidth}} The\tabcellsep Moderate\tabcellsep Resolution\tabcellsep Imaging\\ \multicolumn{4}{l}{Spectroradiometer (MODIS) is a key instrument onboard}\\ \multicolumn{3}{l}{the Terra and Aqua satellite platforms}\tabcellsep \end{longtable} \par {\small\itshape [Note: The NDVI is computed using near-infrared and red reflectances, and thus has both an accuracy and precision. A gapless time series of MODIS NDVI (MOD13A1) composite raster data from 18 th February, 2000 to 16 th November, 2012 with a spatial resolution of 500 m was utilized. Time-series terrestrial parameters derived from NDVI have been extensively applied to global climate change, since it analyzes each pixel individually without the setting of thresholds to detect change within a time series. The required data preparation for the integration of MODIS NDVI data into GIS is described with focus on the projection from the MODIS/Sinusoidal projection to the national coordinate systems. Through the application and analyses of time-series data, this research will provide valuable understanding of the impact of environmental drivers on the spatial, annual and inter-annual variation of climate and vegetation cover dynamics across north-eastern Punjab province of Pakistan. The results have important implications for parameterization of land surface process models using biophysical variables estimated from remotely sensed data.factors are location-dependent, more detailed analyses are needed\hyperref[b125]{(Wang et al., 2003;}\hyperref[b3]{Aguilar et al., 2012)}.]} \caption{\label{tab_0}}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_2}}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_3}}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_4}}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_5}}\end{figure} \footnote{Phenologically-Tuned MODIS NDVI-Based Time Series (2000-2012) for Monitoring of Vegetation and Climate Change in North-Eastern Punjab, Pakistan} \backmatter \par Jodhwala, Sukka Chak, Adilgarh, Jagganwala, Qila Deva Singh, Ali Raza Abad, Shakranj Pur, Titranwali, Ajianwala, Makki Unchi, Bodal Khaneke, Asrur, Katlohi Kallan villages and phenological temporal comparison at 16-days interval for Thatha Khudayar and Haveli villages, year 2007 to 2011. Further, biomass versus NDVI and mean maximum; January 2011 to December 2011 was generated for Bahak Maqaiyaddin village of the research area. \subsection[{Acknowledgements}]{Acknowledgements}\par The author wishes to thank his respected mentor Dr. Robert Bryant, Sheffield Centre for International Drylands Research, Department of Geography, The University of Sheffield for review and providing valuable comments on draft-version of this paper. \begin{bibitemlist}{1} \bibitem[ Journal of Hydrology]{b39}\label{b39} \textit{}, \textit{Journal of Hydrology} 344 (1-2) p. . \bibitem[ Biological Conservation]{b87}\label{b87} \textit{}, \textit{Biological Conservation} 127 p. . \bibitem[ Geocarto International]{b104}\label{b104} \textit{}, \textit{Geocarto International} 11 (3) p. . \bibitem[Reed et al. ()]{b107}\label{b107} \textit{}, B C Reed , J F Brown , D Vanderzee , T R Loveland , J W Merchant , D O Ohlen . 1994. \bibitem[Dabrowska-Zielinska et al. 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