Abstract
Understanding the canopy cover relationship with canopy water content and canopy temperature in the Miombo ecosystem is important for studying the consequences of climate change. To better understand these relationships, we studied the satellite data-based land surface temperature (LST) as proxy for canopy temperature, leaf area index (LAI), and the normalized difference vegetation index (NDVI) as proxies for canopy cover. Meanwhile, the normalized difference infrared index (NDII) was used as a proxy for canopy water content. We used several statistical approaches including the correlated component regression linear model (CCR.LM) to understand the relationships. Our results showed that the most determinant factor of variations in the canopy cover was the interaction between canopy water content (i.e., NDII) and canopy temperature (i.e., LST) with coefficients of determination (R2) ranging between 0.67 and 0.96. However, the coefficients of estimates showed the canopy water content (i.e., NDII) to have had the largest percentage of the interactive effect on the variations in canopy cover regardless of the proxy used i.e., LAI or NDVI. From 2009–2018, the NDII (proxy for canopy water content) showed no significant (at alpha level 0.05) trend. However, there was a significant upward trend in LST (proxy for canopy temperature) with a magnitude of 0.17 °C/year. Yet, the upward trend in LST did not result in significant (at alpha level 0.05) downward changes in canopy cover (i.e., proxied by LAI and NDVI). This result augments the observed least determinant factor characterization of temperature (i.e., LST) on the variations in canopy cover as compared to the vegetation water content (i.e., NDII).
Highlights
The Miombo Woodland is Africa’s largest tropical seasonal woodland and dry forest formation spread in 11 countries and covering an area between 2.7 and 3.6 million km2 [1,2]
Since actual evaporation (Ea ) and rainfall are closely linked to the leaf area index (LAI), land surface temperature (LST), normalized difference infrared index (NDII), and normalized difference vegetation index (NDVI) [30,31,32,36,65,66], they were used to augment the observations in the general patterns of the LAI, LST, NDII, and NDVI, but were not included in the statistical analyses as predictor or response variable
Contrary to observations with air temperature by Chidumayo [14], this study found that LST, at annual scale, accounted for less variation (i.e., 13% and 17% in the LAI and NDVI, respectively) compared to the canopy water content (61% and 93% in the LAI and NDVI, respectively)
Summary
The Miombo Woodland is Africa’s largest tropical seasonal woodland and dry forest formation spread in 11 countries and covering an area between 2.7 and 3.6 million km2 [1,2]. The woodland forms the transition zone between the tropical rainforest and the African Savannah. It is sensitive to climate change where dry-out could possibly trigger ecosystem shifts. Studies [6,7] have shown that forests, such as the Miombo Woodland, plays a critical role in the water balance by affecting the land surface water interaction such as precipitation canopy interception and extraction of soil water via the transpiration process. Rising temperatures, changing precipitation regimes, and changes in the amount of carbon dioxide are expected to affect phenology, composition, structure, distribution, and succession processes of forests [1,8]. The relationships of canopy cover with variables such as canopy water and temperature must be well understood and taken into account in climate and hydrological modelling [9] in the Miombo Woodland
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