1. By overlaying test data obtained using GPS and field survey.
2. By comparison with limited number of temporal high resolution classified images (Landsat ETM+ available in public domain and IRS LISS III/IV images procured from NRSC – National Remote Sensing Centre, Hyderabad, India).
3. By visual inspection/expert knowledge, and
4. By comparing each of the LC classes with Google Earth historical images (http://earth.google.com) through visual checks.

However, the error matrices have not been shown here because of the large spatial and temporal nature of the data. The producer's accuracies ranged from 67 to 81%, user's accuracies ranged from 69 to 84% and overall accuracies ranged between 65 to 80.5%. Figure 5 shows sample LC maps of January 2003 and December 2012 for northern, central and southern Western Ghats and table 1–3 depicts the LC class statistics. Figure 6–8 shows time-series graph for the LC change (dense vegetation and agriculture) for northern, central and southern Western Ghats.

Figure 5: LC maps of northern, central and southern Western Ghats of January, 2003 and December, 2012.
Table 1: LC change statistics of northern Western Ghats from 2003 to 2012 based on NDVI classification

Table 2: LC change statistics of central Western Ghats from 2003 to 2012 based on NDVI classification

Table 3: LC change statistics of southern Western Ghats from 2003 to 2012 based on NDVI classification

Sometimes, it was difficult to segregate dense vegetation from agriculture/grassland by identifying an exact NDVI threshold, and exposed soil covered with grass created confusion between soil and grassland. Table 1–3 shows that forest area have decreased by ~3% in northern, by ~4.4% in central and by 5.7% in southern Western Ghats. Agriculture/grasslands have increased by 2.23% in northern, by 4.3% in central and 5.8% in southern region respectively. Soil/settlement have increased marginally and the spatial extent of water bodies have decreased in all the three regions of Western Ghats. Figure 6–8 show percentage change in the estimate of dense vegetation and agriculture/grassland in northern, central and southern Western Ghats from 2003 to 2012. Northern and central Western Ghats shows very similar trends. Dense vegetation/forest area increases in September–October–November and decreases in January–February. This season (August–September–October) corresponds from mid to end of the monsoon that brings heavy showers from Arabian Sea. Due to this rain, entire Ghats region is full of thick vegetation. Agriculture/grassland area is maximum in August–September and December–January and minimum in April–May in both northern and central Western Ghats. However, southern Western Ghats shows a slightly different pattern with peak dense vegetation between October through June and lowest in August and September. Agriculture crops occupy maximum spatial extent between August to December and show valleys in the time-series graph between April to June.

The valleys in the three graphs (figure 6 to 8) for all the three climatic/ecological regions for agriculture correspond to April to June when there is no water and the farmlands are generally fallow. The farmers wait for the monsoon in July/August to sow the crops. Crops mature and are ready for harvesting in December/January indicating peak in the graphs with maximum area under this class (agriculture). These crops are popularly known as kharif crops or monsoon crops (July to October) and rabbi crops (October to March). Southern Western Ghats receives the first rainfall of monsoon season in the country every year and therefore agriculture/grassland areas show an early increasing trend in this region. It is to be noted that the data for July month was not available (due to the presence of clouds), therefore the graph suddenly dips down in this month for all the years. Figure 6–8 brings a very important conclusion, i.e. the total dense green area in northern and central Western Ghats are between 20–80% of all the LC classes while dense vegetation constitute around 60–95% of the southern Western Ghats throughout the years of study (2003–2012).

Figure 6: Percentage change (monthly) in dense vegetation and agriculture/grassland in northern Western Ghats from 2003 to 2012.

Figure 7: Percentage change (monthly) in dense vegetation and agriculture/grassland in central Western Ghats from 2003 to 2012.

Figure 8: Percentage change (monthly) in dense vegetation and agriculture/grassland in southern Western Ghats from 2003 to 2012.

Figure 9–11 show changes in dense vegetation/forest (left figure) and agriculture/grassland (right figure) to other LC classes. The changes are represented by different colours for a year. For example, in figure 9, yellow in the legend shows areas that have been converted from dense vegetation to either agriculture or grassland or soil/settlement or water in 2003 (in the left figure). Whereas red pixels in the right figure shows areas that have been converted from either dense vegetation, or soil/settlement or other classes to agriculture/grassland in 2004. Similarly, figures 10 and 11 reflect the changes in central and southern Western Ghats. Table 4 shows area converted from dense vegetation to other three classes, and from other three classes to agriculture/grassland in the three regions of Western Ghats. It indicates the area (in ha) that have changed from dense vegetation class to other LC classes (agriculture, soil or water) from January, 2003 to December, 2003 and then for each subsequent year (2004 to 2012). It may be noted that the total area belonging to dense vegetation class is decreasing every year indicating "forest loss" although there might be pixels belonging to other three classes that would also have changed to dense vegetation class at other pixel locations but may be present in low numbers (counts) in the entire image. Minus sign indicates loss in overall forest area every year. The remaining part of the table shows annual conversion from other classes (dense vegetation, soil/settlement and water) to agriculture/grassland indicated by positive sign. It signifies that overall, agricultural area is increasing at the cost of other three LC classes.

 

Figure 9: Annual change from one LC class to other LC classes in northern Western Ghats between 2003 to 2012 obtained from MODIS NDVI. The base image (background) is 250 m black and white version of MODIS NDVI of January, 2003. Colour palate in the middle represents changes in that year.

Figure 10: Annual change from one LC class to other LC classes in central Western Ghats between 2003 to 2012 obtained from MODIS NDVI. The base image (background) is 250 m black and white version of MODIS NDVI of January, 2003. Colour palate in the middle represents changes in that year.

Figure 11: Annual change from one LC class to other LC classes in southern Western Ghats between 2003 to 2012 obtained from MODIS NDVI. The base image (background) is 250 m black and white version of MODIS NDVI of January, 2003. Colour palate in the middle represents changes in that year.

Table 4: Annual conversion from dense vegetation to other LC classes, and from other LC classes to agricultural/grassland in the three regions of Western Ghats.

Figure 12–14 shows monthly boxplots for dense vegetation, agriculture/grassland, soil/settlement and water for the year 2003 and 2012 for northern, central and southern Western Ghats (X-axis: Month, Y-axis: NDVI values). In general, median of all the boxplots of dense vegetation class (figure 12 and 13 for 2003 and 2012) is different for northern and central Western Ghats. However, the locations of median are closer for a few consecutive months (for example, February–March, September–October) in southern Western Ghats and their trends (figure 14) for 2003 and 2012 are alike with overlapping inter-quartile range. For all the three regions of Western Ghats, median NDVI is low during March to June, which corresponds to the summer season and the leaves have either dried or fallen. In September– November, median NDVI is higher compared to other months; this is also evident from the graphs in figure 6, 7 and 8 showing percentage change in dense vegetation from 2003 to 2012 and from monthly minimum, maximum and mean NDVI values for dense vegetation for northern, central and southern Western Ghats in figure 15.

Agriculture shows a similar trend like dense vegetation; they have different medians for all the months, yet the median follows similar trend across the months in a year in northern and central Western Ghats (see figure 12 and 13). April–May–June have lowest median when the crops are being sown at the beginning of monsoon with smallest box and inter-quartile range and highest median in September to November which is the time before harvesting of crops (matured/grownup state). This observation is also supported by the change in LC statistics in figure 6 and 7.   
Southern Western Ghats presents a slightly different picture with a dissimilar trend as that of other two regions, although they follow a similar yearly trend across months in 2003 and 2012 which are multimodal and non-sinusoidal (figure 15) with overlapping inter-quartile ranges (see figure 14). The peak season for agricultural crops is August to December and have lowest median in March–April–May. This may indicate early summer and early onset of monsoon in this region. LC change statistics in figure 8 further corroborates this trend.

Soil/settlements have minimum NDVI in May/June (summer) and have maximum NDVI (towards positive) in November–January. For southern Western Ghats, minimum NDVI for soil occurs in May and maximum occurs during August to December, earlier than northern and central regions. Water bodies exhibit least NDVI from April to June as they are either dry or have less water and have maximum NDVI in August (monsoon) with plenty of rain water in rivers/streams, water accumulated in lakes and sometimes covered with macrophytes.

         
Figure 12: Boxplots for dense vegetation, agriculture/grassland, soil/settlement and water for the year 2003 and 2012 for northern Western Ghats (X-axis: Month, Y-axis: NDVI values).

Dense vegetation (2003)	Dense vegetation (2012)
Agriculture/grassland (2003)	Agriculture/grassland (2012)
Soil/settlement (2003)	Soil/settlement (2012)
Water (2003)	Water (2012)

Figure 13: Boxplots for dense vegetation, agriculture/grassland, soil/settlement and water for the year 2003 and 2012 for central Western Ghats (X-axis: Month, Y-axis: NDVI values).

Dense vegetation (2003)	Dense vegetation (2012)
Agriculture/grassland (2003)	Agriculture/grassland (2012)
Soil/settlement(2003)	Soil/settlement (2012)
Water (2003)	Water (2012)

Figure 14: Boxplots for dense vegetation, agriculture/grassland, soil/settlement and water for the year 2003 and 2012 for southern Western Ghats (X-axis: Month, Y-axis: NDVI values).

Statistical analysis showed that the above boxplots are not exactly symmetrical about median, hence sample mean does not coincide with sample median. Therefore median does not necessarily represent the trend of LC classes for further analysis, so we explore the utility of minimum-mean-maximum time-series NDVI values of dense vegetation and subsequently study the different LC classes using mean NDVI.
Figure 15 depicts monthly minimum, mean and maximum NDVI time-series values for dense vegetation for the northern, central and southern Western Ghats (top, middle and bottom graphs) from 2003 to 2012. The trend line almost shows a sinusoidal wave pattern with peak NDVI values in August–February and valley in April–June in northern and central Western Ghats. Southern Western Ghats shows a slightly different pattern with maximum NDVI depicted by high peak (close to +1 NDVI values) most of the months in a year. It shows high mean NDVI between September–January and low NDVI values between March to June (in summer months) every year.
A comparison of NDVI values with the spatial extent graphs in figure 6–8 reveal that northern and central Western Ghats show maximum spatial extent under dense vegetation in September–November whereas peak NDVI is present from August to January. The peak NDVI may also be contributed from the agricultural crops which are in their growing stage in September and in matured stage in December. They are harvested in December/January, bringing down the spatial extent in January–February while the reflectance from dense green forest may still exhibit high NDVI values till February.
In southern Western Ghats, spatial extent of dense vegetation is maximum during October to June while the peak NDVI reflectance is from September to January. This indicates that forest and agricultural crops (that also include coconut, rubber, coffee plantation, etc.) mature during September to January exhibiting high mean NDVI, while the geographical extent of combined forest and agricultural crops remain high through October to June. One reason for this could be that, the contribution from vegetation and agricultural crops to high reflectance in NIR band could be least after harvesting (in February), thereby decreasing the overall mean NDVI. On the other hand, agriculture crops have a low geographical extent among green vegetation and the southern Western Ghats is mostly dominated by dense mixed vegetation. So, even though the mean NDVI forms a valley after harvesting (in February), the overall dense vegetation continue to show higher spatial extent till June across years. This phenomenon is observed as a repeated cycle every year during the course of this study. It also indicates that during the growth stage of the plants in September to January, it is difficult to separate the dense agricultural crops from dense vegetation by means of NDVI and high spectral resolution sensors with higher number of spectral bands such as Hyper-spectral data may be required for further classification of different vegetation types. From the above discussion and also reported by Lunetta et al., (2006), we see that minimum and maximum NDVI values mainly aid in separation of LC classes. Therefore mean NDVI values of different LC classes (Aguilar et al., 2012; Mao et al., 2012; Raynolds et al., 2008) are explored further in analyzing the behaviour of different LC classes.

      

Figure 15: Monthly minimum, mean and maximum NDVI values for dense vegetation for the northern, central and southern Western Ghats (top, middle and bottom graphs in the figure).

Figure 16 shows temporal mean NDVI profiles for the major end members (dense vegetation, agriculture/grassland, soil/settlement and water bodies) corresponding to the 3 (northern, central and southern) climatic regions of Western Ghats. 

Figure 16: Temporal mean NDVI profiles for the major end members corresponding to the 3 (northern, central and southern) climatic regions of Western Ghats (Note: X-axis: Mean NDVI values, Y-axis: Month-Year).

Figure 16 indicates that mean NDVI of dense vegetation and agriculture follow similar patterns in northern and central Western Ghats with peak in monsoon (September–October–November) and valleys in summer (April–May) respectively. Bare soil/settlement have almost constant mean NDVI and have low NDVI values close to 0. Southern Western Ghats shows similar trend like northern and central Western Ghats, however the peaks and valleys are not uniform for dense forest/vegetation and tend to rise during October each year (see figure 16). NDVI values for water tend to increase whenever there is a peak in vegetation across the months and year (in monsoon season) in all the three regions of Western Ghats.

Figure 17: Boxplot of monthly NDVI changes of dense forest for monsoon season (June, August and September) in Western Ghats from 2003 to 2012.

Figure 17 is the boxplot of NDVI changes for forest class in June, August and September (monsoon season) for northern, central and southern Western Ghats from 2003 to 2012. It is shown that the median of the boxplot follow a somewhat sinusoidal pattern in each month in northern and central Western Ghats from 2003 to 2012. For southern Western Ghats, the pattern in the median has less deviation across the years. The noticeable points here are:

1. In northern Western Ghats, 2005, 2006, and 2010 shows higher median values (ranging approximately from 0.5 to 0.7) compared to 2003 and 2011 that have the lesser NDVI inter-quartile range for June (median is ~0.5).
2. For central Western Ghats, 2003–4 and 2011–12 show lesser median in the month of June than in the middle years.
3. In central Western Ghats, 2008 has least median (~0.5–0.55) for August for both northern and central Western Ghats indicating lesser vegetation; the reason for this may be attributed to the climatic and physical factors. The median in September is generally higher in the year 2006, 2009 and 2012 than other two months (June and August) in both the regions showing more greenery than other years.
4. Particularly, southern Western Ghats shows higher NDVI values for all three months for all the years (2003 to 2012) compared to northern and central Western Ghats indicating more greenery and this trend is static. For all the other months (except monsoon season), the records show static patterns.

Figure 18 shows summary trend maps of NDVI changes in northern, central and southern Western Ghats from 2003 to 2012 calculated from monthly NDVI time-series. It is to be noted that the NDVI differencing values can range from +2 to -2 for the difference image. For example, Map1 and Map 2 can have NDVI value range from +1 to -1. Extreme changes in NDVI can result from pixels subtracting -1 from +1 (i.e. +1 - (-1)) which is +2 where the actual change is negative and decreasing value from +1 to -1, so should be shown as -2. The difference pixel can also result from subtracting +1 from -1 (i.e. -1 - (1)) which is -2 where the actual change is positive and increasing from -1 to +1, so should be shown as +2. Therefore, the difference values are multiplied by -1 to show the actual magnitude and direction of the change in differences.
Figure 18 indicates different pattern for northern, central and southern regions. Major changes from 2003 to 2012 have taken place in the 0.0 to 0.1 (grey) and 0 to -0.3 (pink) followed by 0.1 to 0.2 (green) in northern Western Ghats. In central and southern regions, major changes have taken between 0 to -0.3 (pink) followed by 0 to 0.1 (grey) with a marginal change between 0.1 to 0.2 (green). Table 5 shows the LC change statistics in Western Ghats based on NDVI differencing. NDVI difference ranges with large change are highlighted in shaded rows corresponding to graphs in figure 18. 

Table 5: LC change statistics of Western Ghats from 2003 to 2012 based on NDVI differencing

Figure 18: Summary trend maps of NDVI changes in Western Ghats from 2003 to 2012 calculated from monthly (January and December) NDVI time-series.

5.3 Decadal climatic parameters influencing NDVI: temperature variation and rainfall pattern analysis in Western Ghats from 2003 to 2012

Many research results show that changes in vegetation are seriously influenced by temperature and precipitation. For example, Li et al. (2002) observed that a significant correlation exist between NDVI and eco-climatic parameters, and that NDVI growing degree days correlation was stronger than NDVI–rainfall correlation. These studies presented reasonable and reliable conclusions regarding NDVI changes and the relationship between vegetation NDVI and climatic parameters.
Figure 19–21 shows sample temperature maps (in °C) computed for northern, central and southern Western Ghats from January to December 2003 and 2012. Figure 22 shows minimum, mean and maximum temperature of the three regions.

Figure 19: Sample monthly temperature maps (in °C) for 2003 and 2012 for northern Western Ghats. Images of June and August had lot of pixels with cloud cover, so these images have many no data values (white in colour). (Note: July temperature map was not computed due to presence of cloud in the data.)

Figure 20: Monthly temperature maps (in °C) for 2003 and 2012 for central Western Ghats. Images of June and August had lot of pixels with cloud cover, so these images have many no data values (white in colour). (Note: July temperature map was not computed due to presence of cloud in the data.)

January, 2003	February, 2003	March, 2003	April, 2003
May, 2003	June, 2003	August, 2003	September, 2003
October, 2003	November, 2003	December, 2003
January, 2012	February, 2012	March, 2012	April, 2012
May, 2012	June, 2012	August, 2012	September, 2012
October, 2012	November, 2012	December, 2012

Figure 21: Monthly temperature maps (in °C) for 2003 and 2012 for southern Western Ghats. Images of June and August had lot of pixels with cloud cover, so these images have many no data values (white in colour). (Note: July temperature map was not computed due to presence of cloud in the data.)

 

Figure 22: Monthly minimum, mean and maximum temperature (in °C) of the three regions (top - northern, middle - central and bottom - southern Western Ghats). [X-axis: Month-Year, Y-axis: temperature (in °C)].

Figure 22 shows that minimum, mean and maximum temperature, which almost follow a wave like pattern with peak summer temperature (14–42 °C) in April–May–June every year and lowest winter temperature (7–38 °C) in December–January (shown in blue dotted line) in northern and central Western Ghats. Southern Western Ghats shows a slightly different pattern with maximum variance in temperature in December–January months and highest temperature in summer (March–April–May) each year. There is not much variability between summer (11–39 °C) and winter temperatures ranges (8–35 °C). Figure 23 shows mean monthly temperature ± standard deviation graph further highlighting the temperature variation trend.

Figure 23: Mean monthly temperature ± standard deviation (in °C) for northern, central and southern Western Ghats (top - northern, middle - central and bottom - southern Western Ghats) X-axis: Month-Year, Y-axis: temperature (in °C)

Figure 24–26 shows sample rainfall maps (in mm) for northern, central and southern Western Ghats from January to December 2003 and 2012.

Figure 24: Monthly rainfall maps (in mm) for 2003 and 2012 for northern Western Ghats.

January, 2003	February, 2003	March, 2003	April, 2003
May, 2003	June, 2003	July, 2003	August, 2003
September, 2003	October, 2003	November, 2003	December, 2003
January, 2012	February, 2012	March, 2012	April, 2012
May, 2012	June, 2012	July, 2012	August, 2012
September, 2012	October, 2012	November, 2012	December, 2012

Figure 25: Monthly rainfall maps (in mm) for 2003 and 2012 for central Western Ghats.

January, 2003	February, 2003	March, 2003	April, 2003
May, 2003	June, 2003	July, 2003	August, 2003
September, 2003	October, 2003	November, 2003	December, 2003
January, 2012	February, 2012	March, 2012	April, 2012
May, 2012	June, 2012	July, 2012	August, 2012
September, 2012	October, 2012	November, 2012	December, 2012

Figure26: Monthly rainfall maps (in mm) for 2003 and 2012 for southern Western Ghats.

Figure 27–29 shows total annual rainfall for the three regions. It is evident from the figures that the coastal areas receive highest rainfall and its intensity decreases towards the eastern side in the central and southern Western Ghats. The areas that receive lesser rainfall (called the rain shadow area) are often plains where agriculture is practiced. The rain fed areas have undulating terrain with mix of evergreen to semi-evergreen forest that regulates the regional temperatures between 22 to 32 °C. In southern Western Ghats, plantation is practiced in the hilly areas.

Figure 27: Total annual rainfall (in mm) for northern Western Ghats from 2003 to 2012 with rain gauge stations overlaid.

Figure 28: Total annual rainfall (in mm) for central Western Ghats from 2003 to 2012 with rain gauge stations overlaid.

2003	2004	2005
2006		2007
2008	2009
2010	2011	2012

 Figure29: Total annual rainfall (in mm) for southern Western Ghats from 2003 to 2012 with rain gauge stations overlaid.

Figure 30–32 shows mean monthly temperature (Y-axis in °C) and monthly rainfall (secondary Y-axis in mm) for northern, central and southern Western Ghats respectively. Table 6–8 details monthly mean (µ) ± standard deviation (σ) of NDVI, LST and rainfall for forest and agriculture/grassland (2003 and 2012) for northern, central and southern Western Ghats with missing data indicated with "--". Appendix 1 details mean NDVI, LST and rainfall for all the months and year for both the classes. Northern and central Western Ghats typically experience high rainfall (~ 450 to 500 mm) during June to September each year with 2009 receiving lowest rainfall among all the years as seen in figure 30 and 31. The mean temperature almost follows a cyclic pattern (with higher standard deviation), and extreme summer temperatures in April–May with minimum and maximum temperature between 20 to 35 °C in northern Western Ghats and 18 to 30 °C in central Western Ghats. Southern Western Ghats has early onset of the monsoon with highest rainfall in June every year and hottest days during April to May with lower temperature variations across the year (23° to 30° C) as shown in figure 32.

Figure 30: Mean monthly temperature (Y-axis in °C) and monthly rainfall (secondary Y-axis in mm) for northern Western Ghats.

Figure 31: Mean monthly temperature (Y-axis in °C) and monthly rainfall (secondary Y-axis in mm) for central Western Ghats.
 
Figure 32: Mean monthly temperature (Y-axis in °C) and monthly rainfall (secondary Y-axis in mm) for southern Western Ghats.

Table 6: Mean (µ) ± standard deviation (σ) of NDVI, LST and rainfall for forest and agriculture/grassland class for northern Western Ghats

Table 7: Mean (µ) ± standard deviation (σ) of NDVI, LST and rainfall for forest and agriculture/grassland class for central Western Ghats

Table 8: Mean (µ) ± standard deviation (σ) of NDVI, LST and rainfall for forest and agriculture/grassland class for southern Western Ghats

In general, lower NDVI values are observed during March to June and higher NDVI values are seen during August to January/February for all the years for both dense vegetation and agriculture. The variability between summer and winter mean temperatures is not high. June to September/October have maximum rainfall in both forest and agricultural areas in northern Western Ghats. 2012 witnessed higher rainfall even in October–November. In central Western Ghats, the observations are similar to that of northern region, however NDVI values for green vegetation is higher, the minimum-maximum temperature range is lower i.e. the regions is cooler and the area witnesses higher rainfall compared to northern Western Ghats. Southern Western Ghats exhibit higher NDVI values throughout the year for dense vegetation (> 0.55) with maximum NDVI reaching 0.79. Overall, forest and agricultural class exhibit high NDVI other than during the month of February to June. The mean temperature variation is very small (~23 to ~35 °C) throughout the year and rainfall is more during April to October/November.
The scale of the monthly NDVI changes over time is a key sign of the contribution of vegetation presence activity in different months to total yearly vegetation growth. Figure 33 shows change in monthly NDVI and climatic variables from 2003 to 2012. The mean monthly NDVI reached maximum values during August to November. From March to June, the mean monthly NDVI was low as also observed in figure 15 and 16. The highest mean monthly NDVI value was 0.68 during August in northern Western Ghats, 0.71 in central region and 0.74 in November in southern region as shown in figure 33.
The NDVI patterns were coupled with the climatic variables. Monthly LST showed a contrasting pattern to NDVI (figure 33). When NDVI is low, during March to June, the LST is high, 31 °C in May in northern region and 28 °C in central. However, mean monthly LST in southern Western Ghats ranged between 25 °C to 30 °C throughout the year with highest LST recorded in March and November (28 °C) as in figure 33. Rainfall follows a similar pattern as that of NDVI in northern region with a high of 375.5 mm in August, a high of 390 mm in central region in June, and 420.5 mm in southern Western Ghats in June as well (see figure 33).

Figure 33: Changes in monthly NDVI and climatic variables from 2003 to 2012. 10-years mean monthly NDVI, 10-years mean monthly temperature (in °C), and 10-years mean monthly rainfall (in mm) for northern, central and southern Western Ghats.

Principal component analysis (PCA) was carried out on the 12-months NDVI, LST and rainfall images for all the years. PCA transform multidimensional image data into a new, uncorrelated co-ordinate system or vector space. The purpose of this process is to compress all the information contained in an original n – band data set into fewer than n “new bands” or components. The new components are then used in lieu of the original data and are effective tool for change detection (Fung and LeDrew, 1987; Macleod and Congalton, 1998; Michener and Houhoulis, 1997). Major components of respective images corresponding to two time periods can determine changes.
Figure 34 shows PC1 and PC2 of 2003 and 2012 of NDVI and LST highlighting major changes in the LC pattern. The red oval shapes in southern region and circles in northern region show the status of dense vegetation during 2003 to 2012 as also highlighted in the corresponding LC maps. The major dense forest area (inside the oval shape) is highlighted in PC2 of NDVI of 2003 and 2012. PC1 of LST highlighted the temperature variations and it was found that the low and high LST pixels were clearly separable. The corresponding temperature in dense vegetation was low (~30 °C) in summer (March to May) and was as high as 42 °C in settlement/open dry soil areas in 2003. Similarly, PC1 of LST of 2012 highlighted lower LST (30 °C) in forest areas in summer while settlement areas exhibited high LST (40 °C). PCA of rainfall did not show any distinct pattern. Similar analysis was carried out for the central and southern Western Ghats, which revealed likewise observations for which the results are not displayed here.         

Figure 34: Spatial distribution of pattern in dense vegetation and LST through PCA in northern Western Ghats. Red polygonal shapes highlight past and current status of dense vegetation and the corresponding LST through PC's in those areas.

5.4 Relationship of NDVI of dense vegetation with climatic variables in Western Ghats

To analyse the effects of regional climatic changes on monthly NDVI of dense vegetation, Pearson product-moment correlation between NDVI–LST, NDVI–rainfall and LST–rainfall were explored. Monthly mean NDVI, LST and rainfall data were subject to regression analysis to understand their behaviour as shown in figure 35.

Figure 35: Regression between NDVI and climatic variables (LST and rainfall) in northern (top row), central (middle row) and southern Western Ghats (bottom row).

The trend line between data points of NDVI and LST had a negative slope indicating a negative correlation with R2 = 0.35 for northern and 0.54 for central Western Ghats. For southern Western Ghats, the trend line had almost no slope indicating a weak negative or positive correlation. Precipitation is the most important source of soil moisture and NDVI  values are also closely linked with precipitation. NDVI and rainfall had positive slope for northern and central region (R2 = 0.038 and 0.015) and a slightly negative slope for southern Western Ghats with lesser R2 value (0.003). LST and rainfall had negative slope for northern and central Western Ghats with low R2 values (0.004 and 0.022) indicating a negative relationship between these variables whereas the southern Western Ghats data showed a positive correlation with low R2 value (0.008) for the best fit line.
Figure 36 shows correlation between monthly NDVI and climatic variables. In the northern region, a negative correlation is observed between NDVI–LST and NDVI–rainfall during December to May and a positive correlation is seen between June to November (5% level). In the central Western Ghats, a positive correlation between NDVI–LST is seen in January, March, May and from September to December while other months have negative correlation. Rainfall has positive correlation with NDVI in all months except from April to September. Southern Western Ghats shows a crisscross pattern between NDVI–LST–rainfall and is complicated. From February to June, when rainfall increases, LST decreases. LST shows positive correlation from November to January.  

    

Figure 36: Correlations between 10-years (2003 to 2012) mean monthly NDVI, LST and rainfall for northern, central and southern Western Ghats.

These trends gave an overall idea of the expected relationship between vegetation and climatic variables and motivated us to explore these covariates on a pixel by pixel basis for each year to enhance visualisation of the spatial patterns and on an image to image basis for better numerical description with the time-series monthly data as discussed below. Figure 37–45 shows pixel to pixel correlation maps between NDVI, LST and rainfall for the northern, central and southern Western Ghats from 2003 to 2012.

Figure 37: Pixel to pixel correlation maps between NDVI and LST for northern Western Ghats.

2003	2004	2005
2006		2007
2008	2009
2010	2011	2012

Figure 38: Pixel to pixel correlation maps between NDVI and rainfall for northern Western Ghats.

Figure 39: Pixel to pixel correlation maps between LST and rainfall for northern Western Ghats.

Figure 40: Pixel to pixel correlation maps between NDVI and LST for central Western Ghats.

2003	2004	2005
2006		2007
2008	2009
2010	2011	2012

Figure 41: Pixel to pixel correlation maps between NDVI and rainfall for central Western Ghats.

2003	2004	2005
2006		2007
2008	2009
2010	2011	2012

Figure 42: Pixel to pixel correlation maps between LST and rainfall for central Western Ghats.

2003	2004	2005
2006		2007
2008	2009
2010	2011	2012

Figure 43: Pixel to pixel correlation maps between NDVI and LST for southern Western Ghats.

Figure 44: Pixel to pixel correlation maps between NDVI and rainfall for southern Western Ghats.

2003	2004	2005
2006		2007
2008	2009
2010	2011	2012

Figure 45: Pixel to pixel correlation maps between LST and rainfall for southern Western Ghats.

NDVI and LST were negatively correlated (at 99% confidence interval) in most areas during 2003 to 2012 in northern and central Western Ghats (figure 37 and 40) reestablishing that vegetation and dense forest decrease surface temperature. Northern Western Ghats showed minimum positive correlation value of 0.96 in 2009 and 2011 and a minimum negative correlation of -0.99 in 2011–2012 and central Western Ghats had minimum positive correlation value of 0.95 in 2012 and a minimum negative correlation of -0.99 in 2007, 2008, and 2010–12. However, a similar observation was not noticed in southern Western Ghats (figure 43) where weak negative to strong positive correlation  was found during 2004–2008 and 2010 suggesting the possible effects of temperature on NDVI in most parts of the region (with a maximum positive correlation value of 0.99 in 2003-4 and a minimum negative correlation of -0.95 in 2004). The positive correlation may have been caused due to high temperature even with dense vegetation as this region has equatorial climatic influence which enhances photosynthesis and respiration for plant growth (Mao et al., 2012). In contrast, 2003, 2009, 2011–12 showed negative correlation.

Rainfall  is  the  most  important  source of  soil  moisture;  NDVI  was  closely  linked  with  rainfall. Most areas showed weak negative to strong positive correlation (figure 38) between NDVI and rainfall during 2003–2012; the year 2009 exhibited very high correlation in the entire region. This reaffirmed that forest tend to bring rainfall in tropical region. The minimum positive and negative correlation was 0.93 in 2004 and -0.96 in 2009. A strip of area stretched from the center to the southernmost part of the image representing water body on the ground (with negative NDVI values) showed negative correlation whereas LST and rainfall showed positive correlation in this area. For the central Western Ghats, from 2003 to 2008, weak negative to positive correlation was found in most of the region except southern most part, where correlation was strongly positive. From 2009 to 2012, weak negative to strong positive correlation was observed stretching from the center to eastern most part of the region from north to south (figure 41) with a minimum positive correlation of 0.95 in 2007 and 2011 and a minimum negative correlation of -0.93 in 2006. In southern Western Ghats, 2003 to 2008 exhibited stronger negative to weak positive correlation whereas, the trend changed to weak negative to stronger positive correlation from 2009–12 (figure 44) with a minimum positive correlation of 0.88 in 2008 and minimum negative correlation of -0.97 during 2005–6 and 2009–12.

Increasing temperature enhances the intensity of transpiration, and decreasing precipitation reduces the available moisture for plants (Yang et al., 2009). The relationship between LST and rainfall in the northern Western Ghats had many variations for each year. Only data of 2003 showed positive relationship whereas 2004 and 2007 depicted positive correlation in northern part and negative correlation in southern half of the region (2004 data seems to have image anomaly). All other years showed negative to weak positive correlation with a minimum positive correlation value of 0.46 in 2006 and a minimum negative correlation value of -0.31 in 2003 in the region with weak positive correlation mainly corresponding to water bodies (figure 39). Data of central Western Ghats showed both weak negative to strong positive correlation for the year 2003–5, 2007, 2009, 20011–12. The remaining years show stronger positive correlation. The minimum positive correlation was 0.74 in 2004 to a minimum of -0.66 in 2007 (figure 42). Southern Western Ghats region showed weak negative to positive correlation in the year 2003–4 and strong negative correlation in 2009 and 2012. Stronger positive correlation exist for the year 2005–8, 2010–11 with a minimum positive correlation of 0.87 in 2003 and a minimum negative correlation of -0.17 in 2005 (figure 45).

To further understand the relationship between NDVI and climatic variables, image to image correlation coefficient (CC or r) values between monthly NDVI of forest with LST and rainfall, and monthly NDVI of agriculture/grassland class with LST and rainfall for northern, central and southern Western Ghats were computed. Table 9–11 shows monthly correlation values of 2003 and 2012 for forest and agriculture class. For other years, correlation tables are provided in Appendix 2. Correlation between monthly LST and rainfall were not computed as they did not reveal any information in previous analysis and their pattern were not clear. Missing data have been shown with "--". The analysis reconfirmed the negative correlation between NDVI and LST in northern and central Western Ghats and negative to weak positive correlation in southern Western Ghats. Most months showed weak negative to strong positive correlation between NDVI and rainfall during 2003–2012 in northern and central Western Ghats. Southern Western Ghats exhibited stronger negative to weak positive correlation between 2003 to 2008 and weak negative to stronger positive correlation from 2009–12 between NDVI and rainfall further corroborating the pixel to pixel analysis. In northern and central region, at the beginning of the summer months, most of the trees (apart from the evergreen forest) have limited vegetation growth, hence strong negative correlation implies increased temperature in those areas and less precipitation. The situation prevails until monsoon arrives at the end of summer season. 

Table 9: Image to image Pearson product-moment correlation coefficient (CC or r) between monthly NDVI of forest and agriculture/grassland class with LST and rainfall for northern Western Ghats

Table 10: Image to image Pearson product-moment correlation coefficient (CC or r) between monthly NDVI of forest and agriculture/grassland class with LST and rainfall for central Western Ghats

Table 11: Image to image Pearson product-moment correlation coefficient (CC or r) between monthly NDVI of forest and agriculture/grassland class with LST and rainfall for southern Western Ghats

Through long-term data analysis, trends in ecological indicators can also be assessed (Orr et al., 2004). The ultimate goal of the time-series analysis of the historical biophysical data is to improve our forecasting capability and make inferences about climate and drought conditions while improving critical vegetation mapping capabilities associated with critical needs such as production estimates, habitat assessments, etc.