The role of soil moisture in the survival and growth of trees cannot be over-emphasized and it contributes to the net productivity of the forest. However, information on the spatial distribution of the soil moisture content regarding the tree volume in forest ecosystems especially in Nigeria is limited. Therefore, this study combined spatial and ground data to determine soil moisture distribution and tree volume in the International Institute of Tropical Agri culture (IITA) forest, Ibadan. Satellite images of 1989, 1999, 2009 and 2019 were obtained and processed using topographic and vegetation-based models to examine the soil moisture status of the forest. Satellite-based soil moisture obtained was validated with ground soil moisture data collected in 2019. Tree growth variables were obtained for tree volume computation using Newton’s formular. Forest soil moisture models employed in this study include Topo graphic Wetness Index (TWI), Temperature Dryness Vegetation Index (TDVI) and Modified Normalized Difference Wetness Index (MNDWI). Relationships between index-based and ground base Soil Moisture Content (SMC), as well as the correlation between soil moisture and tree volume, were examined. The study revealed strong relationships between tree volume and TDVI, SMC, TWI with R 2 values of 0.91, 0.85, and 0.75, respectively. The regression values of 0.89 between in-situ soil data and TWI and 0.83 with TDVI ascertain the reliability of satellite data in soil moisture mapping. The decision of which index to apply between TWI and TDVI, therefore, depends on available data since both proved to be reliable. The TWI surface is considered to be a more suitable soil moisture prediction index, while MNDWI exhibited a weak relationship (R 2 = 0.03) with ground data. The strong relationships between soil moisture and tree volume suggest tree volume can be predicted based on avail able soil moisture content. Any slight undesirable change in soil moisture could lead to severe forest conditions.
Forest Soil moisture content (SMC) is one of the important environmental factors that affect natural ecosystems (Briggs, 2016). Soil is the most treasured nonrenewable natural resource on earth and the most diverse part of the biosphere. Knowledge of soil moisture plays a crucial role in the field of hydrology (Bai et al., 2020; Gruhier et al., 2008), meteorology, climatology, ecology, land surface modeling, and studies in environmental changes (Gruhier et al., 2008; Verstraeten et al., 2006). Soil moisture content in forest ecology is influenced by the existing forest species, altitude, climatic conditions, density, age, and soil conditions (Briggs, 2016). According to Zwartendijk et al. (2017), the physico-chemical and biological soil properties such as temperature, ventilation, microbiological soil activity, nutrient uptake capacity, and accumulation of toxic substances are influenced indirectly by SMC. Xu et al. (2006), determined the contributions of soil water to forest biomass and the results of their study revealed a strong relationship between these ecological components.
Soil moisture is often expressed spatially as indices and such indices used include the Topographic Wetness Index (TWI), The Temperature-Vegetation Dryness Index (TVDI) and modified normalized soil water index (MNDWI) (Haas, 2010; Maselli & Chiesi, 2007; Seelig et al., 2008). The TWI was first developed in 1979 by Beven and Kirkby. It is based on surrounding topography and describes the proclivity of an area to become water-saturated. Remote Sensing based soil moisture retrieval has been a promising research field since the early 1970s. Sandholt et al. (2002), developed the TVDI that describes the relations between measured land surface temperature, vegetation and soil moisture, which could be expressed by the “triangle” method (Haas, 2010). These indices have shown to be reasonable estimators of surface soil moisture, although some questions of validity remained. However, there seems to be limited knowledge about soil moisture index-tree volume correlations and if one particular index can be considered to give superior results in the region of interest.
Accurate estimation of soil moisture content is crucial for planning and management of water resources, particularly in forest ecosystems where water is a major resource. However, information on the spatial distribution of the soil moisture content in the study area is very limited. Therefore, this study focused on the assessment of soil moisture distribution in the International Institute of Tropical Agriculture forest, Ibadan with a view to ascertain the impact of the soil water content on tree volume as well as determining the best index for estimating soil moisture content in the study area.
The forest reserve, as shown in
(Egbinola & Amobichukwu, 2013). It is characterized by surface elevation between 160 m and 240 m above sea level, with a rainforest vegetation type.
Since there are no permanent sample plots in the IITA forest, temporary sample plots were adopted for this study as was done by Akindele (1991), Alo & Aturamu (2014), Alo et al. (2017), Alo et al. (2011), and Onyekwelu et al. (2003). Two transects were laid in the forest at 300 m apart. In order to account for the same dimension with the 30 m resolution of Landsat images, six 30 m by 30 m temporary sample plots were laid alternatively on each transect with an interval of 150 m for tree growth variables measurement, making a total of 12 temporary sample plots. Tree growth variables obtained from each sample plot collection were limited to; dbh of all trees encountered in the sample plot with dbh ≥ 10 cm, diameters over bark at the base, middle and merchantable top, as well as total height of the trees with the use of Spiegel Relaskop.
Landsat images of 1989, 1999, 1999, and 2019 were downloaded from the official website of US Geological Survey (USGS). The study area is within Landsat path 191 and row 55.
Soil samples were collected with the use of a soil core sampler at every angle and middle of each sample plot, so to achieve a good mixture of the overall soil sample of each plot. The data sets in
Soil samples were taken from five locations of each plot at 30 cm depth by using a soil core sampler. The five soil sub-samples were bulked together to make a composite soil sample for each plot. The moist soil samples were first sieved
| Satellite Sensor | Spatial resolution | Acquisition years | Path/Row |
|---|---|---|---|
| Landsat 5, 7 and 8 | 30 m × 30 m | 1989, 1999, 2009 and 2019 | 191/55 |
| Asterdem | 30 m × 30 m | 2009 | 191/55 |
through 10 mm mesh to remove gravel, small stones and coarse roots and, then, passed through a 2 mm sieve. The initial weight of the soil sample was determined by the use of a weighing scale balance, soil samples were subjected to heat at a constant temperature of 105˚C for 24 hours. Therefore percentage soil moisture content was calculated in the following;
M C = I n i t i a l W e i g h t − F i n a l W e i g h t I n i t i a l W e i g h t × 100 (1)
MC = Moisture Content.
With readily available long-term remote sensing records and progress in digital image processing techniques as well as the tendency towards macro-scale modeling, remote sensing can provide large-scale distributed data sets where ground-based measurements are unavailable (Makinde & Agbor, 2019). Attempts have been made to combine remote sensing and hydrologic modeling (Bauer et al., 2006; Houser et al., 1998; Milly & Kabala, 1985). Studies have been carried to compare remotely sensed and simulated soil moisture over a heterogeneous watershed and found both techniques to be consistent with ground measurements (Haas, 2010). Soil moisture retrieval with remote sensing techniques can be achieved in all regions of the electromagnetic spectrum. Comprehensive comparisons between different retrieval techniques can be found in Bryant et al. (2003) and Moran et al. (2014). In the same vein, Haas (2010) reported soil moisture retrieval techniques and analysed their capabilities, advantages and disadvantages. The following methods mentioned have been successfully used to model soil moisture and are briefly described:
Studies have revealed that remotely sensed surface temperature as measured by thermal infrared (TIR) emissions and vegetation index has a strong relation with surface soil moisture (Zhang et al., 2007; Naira et al., 2007; Sandholt et al., 2002; Wang et al., 2007; Zeng et al., 2004). The relationship has proved to be important in obtaining further information. For instance, Nemani and Running (1989) and Price (1990) were the initial studies that made use of the temperature/vegetation relationship to estimate evapotranspiration. Numerous investigations regarding the validity of the relationship have been made and modifications to improve soil moisture estimates have also been tested (Carlson, 2007; Hassan et al., 2007; Kimura, 2007). The approach of a simplified land surface dryness index, often referred to as the “triangle” method was chosen in this study (Haas, 2010). It interprets the Ts/FCD space in terms of surface soil moisture status and is based on the assumption that remotely sensed surface temperatures are related to vegetation canopy cover. The TVDI was calculated with Equation (2):
TVDI = T s − T s min a + b f c d − T s min (2)
where Ts = surface temperature;
Tsmin = minimum surface temperature;
bfcd = coefficient of vegetation index;
a = the intercept.
The dry edge was developed from Equation (3)
T s = a + b f c d (3)
Ts = surface temperature
a, b define the parameters defining the dry edge of the triangle
The forest canopy density fcd was derived using Equation (4)
F C D = S V D ∗ S S I + 1 − 1 (4)
where SVD represents scaled vegetation index and SSIis the scaled shadow index calculated using a linear transformation function from normalized Advance vegetation index (AVI), Shadow index (SI) and Bare soil index (BI) (Agbor & Makinde, 2018).
The surface dryness index known as the “triangle” method as proposed by Sandholt et al. (2002) was used in this study to interpret the Ts/Fcd space in terms of forest canopy status. Sandholt et al. (2002) adopted NDVI as the vegetation index, but this study used FCD instead because NDVI is unable to highlight subtle differences in canopy density (Agbor et al., 2017).
The DEM was resampled to match the dimensions of Landsat images The DEM is the basis for TWI calculation and therefore requires the removal of spurious sinks and pits. TWI surface generation was performed using Equation (5) (Haas, 2010).
T W I = L n ( ( " F l o w A c c _ 1 " ∗ 30 ) / ( 0.00565 + T a n ( " S l o p e " / 57.29 ) ) ) (5)
where Ln = natural log;
FLOWACC = flow accumulationl
The assessment of water status of vegetation canopies from spectral remote sensing data is a major goal in ecology and agriculture. Over the past decades, various studies have assessed whether soil water status, defined by leaf water content or canopy water content, can be measured using light reflected from leaves (Equation (6)) (Rokni et al., 2014).
MNDWI = R 860 − R 1240 R 860 + R 1240 (6)
where R860 and R1240 denote reflectance at 860 nm and 1240 nm, respectively (Komeil et al., 2014).
In order to explore the distribution of soil moisture content in the area, the TWI, MNDWI and TVDI surfaces were classified into five categories of very low, low, medium, high and very high values. Natural breaks classification was used here as there is no optimum classification method and the optimum number of classes for comparing the surfaces because of their unequal value distribution. The natural breaks classification after Jenks (Haas, 2010) distributes values into classes according to their natural breaks. This however means that values classified by a natural break in one surface do not belong to a natural break in the other surface. The natural breaks classification result in different category boundaries as can be seen in Figures 2-11.
The sample points were compared to the corresponding pixel values from the generated surfaces using the regression analysis tool in SPSS. The SPSS was used to perform linear regression analysis by using the “least squares” method to fit a line through a set of observations. It explains how a single dependent variable is affected by the values of one or more independent variables. For example, it analyzes how tree volume is affected by soil moisture level. It also explains in this
study how closely measured soil moisture relates to the satellite-based soil moisture level. The linear regression equations (Figures 2-11) output the regression coefficients for each of the independent variables and the intercept. The intercept is the value for the dependent variable (e.g. tree volume) when the independent variable is zero. The coefficients indicate the effects of the independent variable on the dependent variable (soil moisture). To check if the results are reliable (statistically significant), we looked at the Significance f values if they are less than 0.05. The result is accepted when the F value is less than 0.05 and rejected if greater than 0.05.
In raw remote sensing data, each pixel has digital number value that corresponds to a raw measurement required by the sensor (Giannini et al., 2015). To obtain quantitative information from images, there is a need to convert images from their raw state to reflectance measures, using Equation (7) (Chander et al., 2009).
ρ λ = π ⋅ TOAr ⋅ d 2 E SUN λ ⋅ cos θ sz (7)
where:
ρλ = Planetary TOA reflectance (unitless);
π = mathematical constant approximately equal to 3.14159 (unitless);
Lλ = Spectral radiance at the sensors aperture [w/(m2sr µm)];
d2 = The Earth-Sun distance (Astronomical unit);
ESUN = Meanexo atmospheric solar irradiance [w/(m2sr µm)];
θSZ= the solar zenith angle (degree). The cosine of this angle is equal to the sine of the sun elevation θSE. therefore, θSZ = 90 − θSE. These are rescaling factors given in the metadata.
All the image bands are quantized as 8-bit data except Landsat 8 which is 16 bit, thus; all information is stored in DN which will then be converted to radiance with a linear Equation (8). The linear Equation (8) (Giannini et al., 2015; Makinde & Agbor, 2019) is given as:
Y = m x + b (8)
where
Y = TOAr (Top of Atmosphere) radiance—the radiance measured by the sensor;
m = Radiance multiplicative value;
x = Raw band;
b = Radiance additive value.
By applying the inverse of the Planck function, thermal bands’ radiance values will be converted to brightness temperature values using Equation (8) (Agbor & Makinde, 2018).
B t = K 2 ln ( K 1 TOAr + 1 ) − 273.15 (9)
where
Bt = Kelvin;
TOAr = Top of Atmosphere radiance;
K1 = calibration constant 1 (607.76 for TM), (666.09 for ETM+) and (774.89 for OLI band 10);
K2 = calibration constant 2 (1260.56 for TM), (1282.71 for ETM+) and (1321.08 for OLI band 10).
The volume of the trees was calculated using a common formula for volume computation, which includes; Newton’s, Huber’s, and Smalian’s formulae of volume computation. The volume obtained with formulae were compared with volume obtained by summing up all the bolt of each felled tree for significant differences using SPSS 10.0 for window
· Newton’s formula (Avery & Burkhart, 1983)
V = h 6 ( A b + 4 A m + A t ) (10)
where V = tree volume (m3);
h = total height of the tree (m);
Ab = Area at the base (m2);
Am = Area at the middle (m2);
At = Area at the top (m2).
Results of the soil moisture calculated with varying settings as previously described are shown in Figures 2-11. The presentation of soil moisture distribution in sub-pixels form is to establish the relationships between soil moisture from different models and calculated forest volume. Figures 12-17 describe the
impact of soil moisture on tree volume. They show the changes in soil moisture with respect to changes in tree volume. Soil moisture content increases with an increase in value for TWI and MNDWI while it decreases with an increase in value for TVDI. The predicted tree volume was higher in 2009 than in 2019 by about 129 m3. The only predicted tree volume was for 2009, because of the availability of the elevation (independent variable) data for that year shown in
| Plot No. | Field Tree Vol. for 2019 | 2019 | 2009 | TWI predicted Volume | 2019 TDVI predicted Volume | |||
|---|---|---|---|---|---|---|---|---|
| TVDI | TWI | TVDI | TWI | 2019 | 2009 | |||
| 1 | 50931.6 | 0.06 | 2.10 | 0.04 | 2.10 | 24041.4 | 24041.4 | 877961.0 |
| 2 | 53372.5 | 0.06 | 1.81 | 0.07 | 1.81 | 3439.8 | 3444.0 | 836119.0 |
| 3 | 86480.04 | 0.06 | 3.35 | 0.10 | 3.35 | 111502.7 | 111636.3 | 758833.0 |
| 4 | 103771.1 | 0.06 | 4.26 | 0.09 | 4.36 | 175231.5 | 182227.9 | 783289.0 |
| 5 | 118757.5 | 0.05 | 4.26 | 0.11 | 4.26 | 175231.5 | 175231.5 | 753151.0 |
| 6 | 170609.1 | 0.05 | 4.29 | 0.10 | 4.29 | 177059.0 | 177059.7 | 772777.0 |
| 7 | 198645.5 | 0.05 | 5.22 | 0.09 | 5.22 | 242641.1 | 242650.9 | 782315.0 |
| 8 | 228,425 | 0.05 | 5.29 | 0.60 | 5.29 | 247082.4 | 247058.6 | 847379.0 |
| 9 | 239,379 | 0.05 | 5.72 | 0.10 | 5.72 | 277442.6 | 277409.0 | 761487.0 |
| 10 | 239850.3 | 0.04 | 6.63 | 0.10 | 6.53 | 340811.8 | 333815.4 | 763831.0 |
| 11 | 408616.7 | 0.04 | 7.16 | 0.10 | 7.16 | 378311.1 | 378311.1 | 768295.0 |
| 12 | 600672.4 | 0.03 | 7.60 | 0.08 | 7.60 | 409064.5 | 409102.3 | 798989.0 |
| 13 | 2,499,510.7 | 2561859.4 | 2,561,988.1 | 9,504,426.0 | ||||
and TVDI, SMC, TWI, MNDWI respectively. The results also revealed strong relationships between ground soil moisture (SMC) data and satellite-based soil moisture (TWI and TVDI) data as 0.89 between SMC and TWI and 0.83 between TVDI and SMC.
From Figures 2-11, it is obvious that the soil moisture decreased from the base year to the most recent year. The soil moisture content is highest in forested and water-logged areas (dark blue to light green) and low in bare surfaces (yellow to dark red). Figures 12-15 show the effect of soil moisture level on tree volume of the study area, and the results may not be unconnected to the lower tree volume in 2019 with lower soil moisture and higher tree volume in 2009 with higher soil moisture The soil moisture by TWI model was only produced for the year 2009 and 2019 because of the availability of field elevation data for 2019 and digital elevation image for 2009. This model gives the best prediction of tree volume in 2019 and was used to determine the tree volume status of the area in 2009 (
The TWI increased along the lower surfaces, and no connection between TWI distribution and TVDI could be detected. The TVDI values change with vegetation distribution. Areas indicated as wet by the TVDI are assigned lower vegetation class, while MNDWI increased with vegetation cover. In situ measured soil moisture did not agree well with MNDWI. This weak relationship between the index and both soil moisture and tree volume raises questions of the index validity, suitability and comparability. Here it should also be mentioned that the results are largely dependent on the actual data that were used. Higher resolutions and further variations in TWI calculation might produce different results. The TWI model was a good predictor for tree volume and soil moisture content. It can be reasoned that upslope areas are correctly identified as dry and low areas classified as wet.
The Ts/Fcd values plotted against each other, as shown in
The dry edge was developed from Equation (10).
T s = a + b f c d (10)
The sample points were compared to the corresponding pixel values in the generated surfaces. Regressions values are shown in figures are 0.90 for TWI and 0.83 for the TVDI surface, respectively. Both correlation coefficients are rather high, although there seems to be a slightly higher correlation with the TWI surface. P-values of 0.02 for the TVDI correlation and 0.04 for the TWI correlation show that the sample size was large to achieve a statistically significant result. It is important to note that weather conditions (dry) at the time the samples were collected are similar to the ones of the satellite image acquisition date in 2019.
This study assessed soil moisture distribution using three different methods: Topographic Wetness Index (TWI), Temperature-Vegetation Dryness Index (TVDI) and Modified Normalized Difference Water Indexes (MNDWI) for IITA forest reserve. In-situ soil moisture was carried out and co-located to the derived moisture indices from satellite images. Index dependencies on in-situ soil moisture and tree volume were investigated and significant correlations were detected. Only MNDWI showed weak correlations with in-situ measured soil moisture and tree volume. However, since both TWI and TVDI indexes correlate strongly with in-situ measured soil moisture and that of tree volume, this suggests that both methods can be used to model soil moisture and tree volume for this area. The establishment of combined effective models for soil moisture determination over large areas requires more extensive in situ measurements and methods to fully assess the models’ capabilities, limitations and value for hydrological and tree volume predictions.
The authors declare no conflicts of interest regarding the publication of this paper.
Alo, A. A., Agbor, C. F., Jebiwott, A., & Temiloluwa, O. (2022). Spatial Distribution of Soil Moisture Content and Tree Volume Estimation in International Institute of Tropical Agriculture Forest, Ibadan, Nigeria. Journal of Geoscience and Environment Protection, 10, 364-384. https://doi.org/10.4236/gep.2022.108022