## Materials and Methods
### Change Vector Analysis  
Change vector analysis (CVA) measures the magnitude and direction of change between dates across spectral space. Statistically, it is a variation of the Pythagorean Theorem that calculates the Euclidean distance of spectral change, defined as the position of a given pixel in Time 1 (\(T_{1}\)) with Time 2 (\(T_{2}\)), and the change that occurs between the two [20]. CVA eliminates the need to collect training or reference data for historical markers, as unchanged locations can be used as reference data [12]. It is flexible enough to be used with diverse sensor data, and radiometric change applications, including the monitoring of land use/cover change and ecosystem dynamics. For our sites, the raw index values were normalized to values between 0 and 1 in order to eliminate bias by any one index when calculating CVA magnitude. If two indicators are moderately correlated some land use/cover classes would be providing similar information but not for others. This makes them ideal for CVA as they provide four different change directions. Band statistics for NDVI and NDMI indicate there is also a moderate positive correlation, and hence it would be useful to explore their relationship, as this has not been done in other studies. The CVA method for magnitude was applied to NDVI and NDMI data using Equation (1),  
\[\text{CM}=\sqrt{\left(\text{NDVI}_{2}-\text{NDVI}_{1}\right)^{2}+\left(\text{ NDMI}_{2}-\text{NDMI}_{1}\right)^{2}} \tag{1}\]  
where CM is change magnitude. CM values at or near zero identify areas with little or no change, while high CM values represent areas of high change. However, unchanged pixels fall within a range about the origin due to factors such as noise and imperfect normalization. This noise is removed by applying a threshold to CM; a threshold is determined by standard deviations [13; 16; 21; 22]. The type of change is determined by examining change direction, calculated using NDVI and NDMI from Equation (2),  
\[\alpha=atan2(\frac{\Delta\text{NDMI}}{\Delta\text{NDVI}}) \tag{2}\]  
where \(atan2\) converts rectangular coordinates to polar coordinates, and where \(\alpha\) is the angle from the \(x\)-axis. This operation is based on sign and gives values that represent all quadrants in a Cartesian matrix. The output values are in the range of \(-\pi\) and \(\pi\). Output from the arctangent function is in radians and converted to degrees by 180/\(\pi\). 360 is added to all negative values to ensure output is within the range 0 to 360 degrees. The resulting angle in degrees is placed in one of four classes: +NDMI and +NDVI = chlorophyll increase, +NDMI and \(-\text{NDVI}\) = increase in wetness, \(-\text{NDMI}\) and \(-\text{NDVI}\) = bare soil expansion, and \(-\text{NDMI}\) and +NDVI = decrease in wetness. Using tasseled cap bands of brightness, greenness and wetness (BGW), magnitude and direction of change is determined by differences between each band and between each date. Brightness is usually associated with bare soil or bare ground, with an increasing brightness indicating a change towards bare soil while a decreasing brightness indicates a change away. However, if for example lianas are considerably brighter spectrally than dense forest vegetation, then increasing brightness may be an indication of the increasing abundance of lianas in vegetation [17]. Moreover, as brightness is a weighted sum of all six reflective bands of Landsat, it can be interpreted as the overall brightness or albedo at the surface. Greenness is directly related to chlorophyll presence and photosynthetic activity, and hence changes in greenness are related to increase or decrease in vegetation [13; 14; 17; 23; 24; 25; 26; 27; 28]. Wetness can be an indicator of vegetation density, soil moisture, and hence decrease in wetness indicates declining vegetation health or senescence [11; 14]. However, the presence of water, as in flooded forests, also adds wetness and hence increase in wetness may be related to actual increase of water.