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
]. The woodland forms the transition zone between the tropical rainforest and the African Savannah. Being a transition zone, it is sensitive to climate change where dry-out could possibly trigger ecosystem shifts. The word Miombo is used to describe Brachystegia, one of the many species found across the ecosystem. The Miombo ecoregion plays a crucial role in the food, water, and energy nexus in sub-Sahara Africa and maintain carbon stocks, thus regulating climate [1
]. Furthermore, studies [6
] 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. However, 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
]. Thus, the relationships of canopy cover with variables such as canopy water and temperature must be well understood and consequently taken into account in climate and hydrological modelling [9
] in the Miombo Woodland.
However, the species composition, richness, and heterogeneity (even at local forest level) [2
] and the vastness of the Miombo ecosystem in sub-Sahara Africa makes it impossible to employ field observations at a scale capable of capturing an acceptable representation of, for instance, the various plant-water and plant-temperature dynamics of the ecosystem. For instance, field data-based studies like Vinya et al. [13
] and Chidumayo [14
] only considered a few Miombo species out of the many that exist in the Miombo ecosystem. Furthermore, in addition to financial limitations, field observations normally are spatially and temporally constrained. This has possibly contributed to the paucity of data on the various aspects of forest-climate interactions in the Miombo ecosystem. The application of satellite based remote sensing techniques in assessing forest-climate interaction provides a significant and expanded platform for data collection and monitoring of the dynamic state of forests, especially for vast transboundary woodlands like the Miombo Woodland where ground-based methods are virtually not feasible. This is because satellite remote sensing comes with the advantage of area coverage (spatial resolution), regular revisit time (temporal resolution), acquisition of data in a wide range of the electromagnetic spectrum (spectral resolution), and has a high degree of homogeneity. Furthermore, a large number of satellite sensors, with global coverage, are currently generating enormous amounts of data at various resolutions that form the base for both historical and continued monitoring of forest-climate interactions [15
]. This is even more important for Africa with limited capacity to undertake field measurements.
Some studies, including satellite data-based ones, related to plant-water and plant-temperature interaction, have been done in the Savannah woodland, specifically in the Miombo Woodland [13
]. However, the type of satellite data used and the climate variables considered leave room for further investigations using other freely available satellite data products such as the moderate resolution imaging spectroradiometer (MODIS). For instance, most previous studies used air temperature and rainfall to understand the plant water and plant-temperature relationships in the Miombo Woodland. These studies suggested that the Miombo Woodland was water controlled and that temperature could be a critical determinant of plant distribution [13
]. The satellite data-based studies typically used air temperature and normalized difference vegetation index (NDVI) [23
] (i.e., Chidumayo [14
]), the leaf area index (LAI), transpiration and vegetation optical depth (VOD) (i.e., Tian et al. [19
]) to understand the relationships. Chidumayo [14
] analyzed the relationship between rainfall, minimum, and maximum air temperature with the NDVI. Chidumayo [14
] observed that the combined minimum and maximum temperature accounted for the largest variation (R2
0.96) in NDVI values followed by rainfall (R2
= 0.35). Further, Chidumayo [14
] observed a negative relationship between NDVI and maximum temperature. Tian et al. [19
] studied seasonal variations in ecosystem-scale plant water storage and their relationship with leaf phenology. They found that in the Miombo Woodland transpiration co-varied with LAI seasonal variations. They further observed that plant water storage played a critical role in “buffering seasonal dynamics of water supply and demand, as well as sustaining fresh leaves formed before the rain”.
However, climate variables such as land surface temperature (LST), which is the radiative skin temperature of a surface derived from remotely sensed thermal radiation, is increasingly becoming more useful in hydrological modelling, vegetation monitoring, global circulation models (GCM), and many more applications [24
]. The surface temperature is regarded as a direct representation of the canopy thermal status due to its interaction with radiation (long wave and short wave) and other biotic and abiotic processes [30
]. The land surface temperature (LST) is considered as a significant variable in controlling several environmental factors including the hydrology, carbon cycle, and land surface energy budget [31
]. In the case of the Miombo Woodland, Mildrexler et al. [32
] showed that during the dry season, with reduced canopy cover, the LST is higher than air temperature in African Savannah woodland. Furthermore, during the period of high moisture (i.e., rainy season) and high canopy cover maximum LST is almost 1:1 ratio with the maximum air temperature. The surface in the case of the Miombo Woodland would largely be the forest canopy. The discrepancy between LST and air temperature in the dry season could possibly render different results from those observed with the use of air temperature. This makes it necessary to understand the LST (as proxy for canopy temperature) interaction with canopy cover proxies such as the LAI and NDVI. Based on increased application of LST in vegetation monitoring and climate and hydrological modelling the use of LST to understand the canopy cover and canopy temperature relationship in the Miombo woodland could be a significant addition to the body of knowledge on the Miombo ecosystem.
In the context of plant water assessment using satellite data, Mobasheri and Fatemi [33
] showed that there exists strong correlation between equivalent water thickness (ETW) (defined as the weight of water per unit area of leaf) and the near infrared (NIR) and shortwave infrared (SWIR) wavelengths in the electromagnetic spectrum. Additionally, they showed that band combinations such as ratio and normalized difference had higher regressions with leaf water content. Some satellite data-based indices such as the normalized difference infrared index (NDII) [34
], a normalized band ratio, have shown capacity as proxy for both the vegetation water content and root-zone storage and is, therefore, being used in hydrological modelling [35
]. When plant water balance is considered, rainfall can generally be assumed as the gross water received in a forest [7
]. The vegetation water content and root-zone storage (i.e., as proxied by NDII) can be considered more representative of the net water available for use by plants. What is also important is that the NDII can be derived from freely available optical satellite data from sensors such as the MODIS. Therefore, instead of the use of the VOD as proxy for vegetation water [19
] or rainfall to indicate water availability [14
] this study used the NDII as proxy for canopy water content.
When it comes to observing canopy display and vegetation density the LAI (i.e., the one-sided green leaf area per unit ground surface area in broadleaf canopies [41
]) and the NDVI [23
] (i.e., measure of density of vegetation) have been used to assess canopy cover in dry forests of Africa and the Miombo Woodland [14
]. For instance, Fuller et al. [42
] used the radiative transfer model to demonstrate the NDVI as a good predictor of canopy cover in the Miombo Woodland in Zambia. In this study MODIS based LAI and the NDVI were utilized as proxies for canopy cover.
The premise for the use of the MODIS data is that it is readily available for free, has a near-global spatial coverage (250 m–1 km) suitable for assessing vegetation dynamics in a vast forest like the Miombo Woodland, has a comparatively better temporal scale (1–2 times/day), and can easily be processed with most available imagery computing applications. Other than the availability of raw data MODIS also has several ready to use highly processed products including but not limited to vegetation indices, land surface temperature and evaporation [25
]. These attributes make MODIS data suitable for application at local, catchment and regional scales in a cheaper and timely manner especially in the African context with limited capacity to deploy field-based assessments.
To better understand the interaction of canopy cover with canopy water content and canopy temperature this study used several statistical approaches. Key among the statistical analyses was the correlated component regression linear model (CCR.LM) [50
]. The most significant attributes for selecting the CCR.LM were the capacity to account for multicollinearity in predictor variables and to eliminate less important predictor variables, using the step-down variable reduction approach, based on importance in affecting the response variable. By using the CCR.LM we sought to evaluate which of the two variables, i.e., canopy water content (proxied by NDII) and canopy temperature (proxied by LST), accounted for the most variations in the canopy cover at different time scales and seasons. This was important for comparison with the previous studies that used rainfall and air temperature to analyze the interactions with canopy cover.
This paper, therefore, describes the results of the assessment of the variations in canopy cover (proxied by the LAI and the NDVI) and its relationship with canopy water content (proxied by the NDII) and temperature (proxied by the LST) in the Miombo Woodland using MODIS satellite based-data. The objectives of this study were to: (i) analyze the seasonal and inter-annual patterns in canopy cover, canopy water content, and canopy temperature; (ii) ascertain the correlation of canopy cover with canopy water content and canopy temperature; (iii) determine the most determinant factor of variations in the canopy cover between canopy water content and canopy temperature in the Miombo Woodland; and (iv) observe if any significant change points and historical trends in the means of the canopy cover, canopy water content, and canopy temperature occurred from 2009–2018. The analysis of change points and historical trends was meant to further understand the relationships between canopy cover, canopy water content, and canopy temperature in the Miombo Woodland. The results of the study could be useful in monitoring the impacts of climate change on the Miombo Woodland phenology.
The study sought to analyze the relationships between the variations in canopy cover and the changes in canopy water content and canopy temperature at a local Miombo woodland using satellite data and statistical methods. The study mainly focused on the dry season. The following conclusions can be drawn from the results.
Canopy cover and water content were significantly higher during the rainy season, while canopy temperature was significantly higher during the dry season. Over the years, there appeared to be no significant difference in dry season canopy temperature. Across seasons, variations in canopy cover were more strongly associated with canopy water content than canopy temperature.
Plant water status (i.e., canopy water content), as a single variable, appeared to be a major determinant factor of the temporal variation in canopy cover across seasons, though combination with canopy temperature appeared to significantly improve accounting for variations in the canopy cover during the dry season. Based on the satellite data analyzed in this study, seasonal variations in canopy cover could be a water conservation strategy by Miombo plant species as a result of the changes in plant water availability and canopy temperature.
Type of variables used in assessing the most determinant factor of variations in canopy cover in the Miombo Woodland is important. This is because our results—based on the use of the satellite data LST and NDII as proxies for canopy temperature and canopy water content—suggest different outcomes in terms of which variable accounts for the most variations in canopy cover proxies (i.e., LAI and NDVI) compared to the studies that used air temperature and rainfall. This study found canopy water content and not temperature as the major determining factor in accounting for variations in canopy cover in the Miombo Woodland.
Satellite-based LST and NDII seem to have captured the seasonal variations in the Miombo Woodland and, therefore, they may be good indicators of plant water status in this ecosystem. However, field measurements are needed to determine the degree of correlation with actual physical conditions.