## 3 Results
### Vegetation Dynamics and Relationships with Drought
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Vegetation Type** & **Trend (Winter)** & **Trend (Spring)** & **Trend (Summer)** & **Trend (Fall)** \\ \hline Chaparral & \(-\)0.0078 & \(-\)0.0082 & \(-\)0.0080 & \(-\)0.0078 \\ Coastal Sage Scrub (CSS) & \(-\)0.0051 & \(-\)0.0083 & \(-\)0.0051 & \(-\)0.0051 \\ Annual and Perennial Grassland & \(-\)0.0040 & \(-\)0.0078 & \(-\)0.0030 & \(-\)0.0040 \\ Forest and Woodland & \(-\)0.0065 & \(-\)0.0055 & \(-\)0.0061 & \(-\)0.0065 \\ Developped Land & \(-\)0.0027 & \(-\)0.0033 & \(-\)0.0029 & \(-\)0.0027 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Median NDVI Theil–Sen’s slopes for the major vegetation types. Figure 5: Correlation coefficients (\(p<0.05\)) between NDVI anomalies and PDSI during 2000–2017. Dark gray lines refer to burned areas. Insignificant pixels have been excluded. White areas indicate data gaps due to snow and cloud. Correlation coefficients are the largest in summer and smallest in winter. Although desert vegetation is adapted to low moisture availability, the vegetation greenness in the Mojave Desert (northeast study area) showed surprisingly strong positive correlations with PDSI, indicating vegetation sensitivity to moisture stress there (Figure 5). However, the regression slopes were smaller compared to the coast, perhaps due to lower relative sensitivity and the already scarce vegetation and lower NDVI value in the desert (Figure 6). Weak correlations and high regression coefficients were found in the burned regions, which can be attributed to abrupt fire disturbances (Figures 5 and 6). For example, wildfires in drought years may largely reduce the NDVI of the burned areas, and when precipitation rebounds after drought, post-fire regeneration can bring a rapid increase in NDVI. Besides the desert and burned areas, the vegetation in the coastal mountain ranges demonstrated striking relations with the PDSI (e.g., in the San Rafael Mountains, Santa Ynez Mountains, Santa Monica Mountains, Santa Ana Mountains, and Riverside-Perris Plain). We found that most of the browning regions showed close relationships between NDVI anomalies and PDSI (Figures 4 and 5). This is consistent with the general browning in Southern California due to \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Vegetation Type** & **r (Winter)** & **r (Spring)** & **r (Summer)** & **r (Fall)** \\ \hline Chaparral & 0.45 & 0.58 & 0.69 & 0.60 \\ Coastal Sage Scrub (CSS) & 0.52 & 0.62 & 0.74 & 0.61 \\ Annual and Perennial Grassland & 0.42 & 0.53 & 0.61 & 0.47 \\ Forest and Woodland & 0.39 & 0.48 & 0.61 & 0.52 \\ Developed land & 0.34 & 0.36 & 0.37 & 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Median correlation coefficients (r) between NDVI anomalies and PDSI for the major vegetation types. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Vegetation Type** & **\(\beta\) (Winter)** & \(\beta\) (Spring) & \(\beta\) (Summer)** & \(\beta\) (Fall) \\ \hline Chaparral & 0.0143 & 0.0146 & 0.0148 & 0.0169 \\ Coastal Sage Scrub (CSS) & 0.0212 & 0.0204 & 0.0116 & 0.0149 \\ Annual and Perennial Grassland & 0.0215 & 0.0192 & 0.0070 & 0.0118 \\ Forest and Woodland & 0.0125 & 0.0109 & 0.0104 & 0.0128 \\ Developed land & 0.0069 & 0.0057 & 0.0044 & 0.0062 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Median regression slopes (\(\beta\)) between NDVI anomalies and PDSI for the major vegetation types. Figure 6: Regression coefficients (\(p<0.05\)) between NDVI anomalies and PDSI during 2000–2017. Dark gray lines refer to burned areas. Insignificant pixels have been excluded. White areas indicate data gaps due to snow and cloud. Regression coefficients are the largest in winter and smallest in summer. decreasing PDSI during recent years (Figure 3). As a measurement of dryness based on precipitation and temperature, reduction of precipitation and elevated temperature can both decrease PDSI. A large portion of Southern California has shown an appreciable warming trend in all seasons over the past 37 years (Figures 7 and 8).