Allometric models and aboveground biomass stocks of a West African Sudan Savannah watershed in Benin
© The Author(s) 2016
Received: 6 April 2016
Accepted: 3 August 2016
Published: 17 August 2016
The estimation of forest biomass changes due to land-use change is of significant importance for estimates of the global carbon budget. The accuracy of biomass density maps depends on the availability of reliable allometric models used in combination with data derived from satellites images and forest inventory data. To reduce the uncertainty in estimates of carbon emissions resulting from deforestation and forest degradation, better information on allometric equations and the spatial distribution of aboveground biomass stocks in each land use/land cover (LULC) class is needed for the different ecological zones. Such information has been sparse for the West African Sudan Savannah zone. This paper provides new data and results for this important zone. The analysis combines satellite images and locally derived allometric models based on non-destructive measurements to estimate aboveground biomass stocks at the watershed level in the Sudan Savannah zone in Benin.
We compared three types of empirically fitted allometric models of varying model complexity with respect to the number of input parameters that are easy to measure at the ground: model type I based only on the diameter at breast height (DBH), type II which used DBH and tree height and model type III which used DBH, tree height and wood density as predictors. While for most LULC classes model III outperformed the other models even the simple model I showed a good performance. The estimated mean dry biomass density values and attached standard error for the different LULC class were 3.28 ± 0.31 (for cropland and fallow), 3.62 ± 0.36 (for Savanna grassland), 4.86 ± 1.03 (for Settlements), 14.05 ± 0.72 (for Shrub savanna), 45.29 ± 2.51 (for Savanna Woodland), 46.06 ± 14.40 (for Agroforestry), 94.58 ± 4.98 (for riparian forest and woodland), 162 ± 64.88 (for Tectona grandis plantations), 179.62 ± 57.61 (for Azadirachta indica plantations), 25.17 ± 7.46 (for Gmelina arborea plantations), to 204.92 ± 57.69 (for Eucalyptus grandis plantations) Mg ha−1. The higher uncertainty of agroforestry system and plantations is due to the variance in age which affects biomass stocks.
The results from this study help to close the existing knowledge gap with respect to biomass allometric models at the watershed level and the estimation of aboveground biomass stocks in each LULC in the Sudan Savannah in West Africa. The use of model type I, which relies only on the easy to measure DBH, seems justified since it performed almost as good as the more complex model types II and III. The work provided useful data on wood density of the main species of the Sudan Savannah zone, the related local derived biomass expansion factor and the biomass density in each LULC class that would be an indispensable information tool for carbon accounting programme related to the implementation of the Kyoto Protocol and REDD+ (reducing emissions from deforestation and forest degradation, and forests conservation, sustainable management of forests, and enhancement of forest carbon stocks) initiatives.
KeywordsAllometric models Aboveground biomass stocks West African Sudan Savannah watershed Non-destructive method Biomass density Benin
The sources and sinks of carbon from land use and land cover change (LULCC) are significant elements in the global carbon budget . Current challenges of forest management are related to verifiable, reliable, accurate and cost-effective methods to adequately document forest resources dynamics . The accuracy of biomass density maps depends on the availability of reliable allometric models to infer aboveground biomass (AGB) of trees from tree census data . Large uncertainties in emission estimates arise from inadequate data on the biomass density of forests and the regional rates of deforestation [1, 4]. These uncertainties compromise the estimation of terrestrial carbon emissions [5–8] and required knowledge on biomass stocks.
A number of comprehensive allometric models for biomass estimation have been developed for the major tree species in Europe, America and Asia [3, 9–22]. In sub-Saharan Africa and especially West-African countries, most of the estimation of the total carbon stocks has also used allometric models together with forest inventory data [22–35]. The majority of studies so far have focused on forest ecosystems, specific tree species or plantations for the estimation of AGB and carbon stocks [3, 23, 25, 26, 28, 31, 32, 36–46]. Very few studies have dealt with the estimation of AGB in the agricultural landscapes .
Attempts to estimate AGB at the watershed level requires typically satellite images derived LULC information as well as allometric models from each LULC class. The data for allometric models for estimating biomass in woody vegetation comes either from destructive or from non-destructive methods. Destructive methods are based on the harvesting of the living trees together with measurements of diameter at breast height (DBH) or stem girth and total height as well as the dry mass of stem, foliage and branches. The collected variables are then used as input for estimating tree volume and biomass for selected trees species [22, 30, 37, 45, 47]. According to Djomo et al. , the application of destructive methods is labour intensive and time consuming. This method is therefore restricted to small trees at small scales [38, 48]. Additionally, harvesting trees requires in general special authorization which is often not easy to acquire especially when the study region involves protected areas.
Recent assessments have switched to the use of non-destructive methods [42, 49–55]. The tools and approaches used thereby vary considerably between regions. A biomass expansion factor (BEF) which expresses the relationship between stem biomass and the total biomass of a single tree species as well as information on wood density of the involved tree species are the keys variables used by allometric models to assess total biomass of living trees. If shape characteristics are included in the estimation of the BEF the approach is similar to the volume based approach in which information on height and diameter of a tree are used together with species specific shape and wood density factors . The importance of wood density for estimating forest biomass and greenhouse-gas emissions from LULCC has been stressed by Nogueira et al. . A variety of different approaches has been applied in case studies worldwide: Montes et al.  e.g. estimated the biomass of thuriferous juniper woodland in Morocco based on component volumes estimated from two orthogonal-view photographs and the density of each component. This approach is not well suited to estimate biomass in natural environments, especially when the environment is degraded by human use and wood supply for the local populations is at stake. Lehtonen et al.  developed expansion factors conditional on stand age and dominant tree species to estimate total biomass of pine trees in Norway. Flombaum and Sala  presented an approach for the calibration of a fast non-destructive method to estimate aboveground plant biomass by double-sampling vegetation cover and AGB in the Patagonian steppe. The author fitted linear regression models to describe the relationship between vegetation cover and biomass for the dominant species and life forms. Tackenberg  presented a non-destructive method based on scaled digital images analysis of the plants silhouettes, addressing not only aboveground fresh biomass and oven-dried biomass, but also vertical biomass distribution as well as dry matter content and growth rates. The method used by Tackenberg  is time and cost effective compared with destructive measurements, especially if development or growth rates are to be measured repeatedly. Another branch of approaches aims at identifying wood volumes by remote sensing approaches [83–86]—however for relating volume estimates with biomass information on wood density for the relevant species is necessary which is missing for many natural and semi-natural tree species in the tropics and sub-tropics.
Two problems hinder the transfer of the currently used non-destructive methods in the West-African context. First, BEFs are not available for most relevant local tree species and most devices used in other regions of the world are not suitable. In the southern part of the Republic of Benin, Guendehou et al.  assessed stem biomass based on stem volume and wood density for selected tropical tree species using an increment borer as the device of stem wood sample extraction. Unfortunately, the obtained BEF could not be applied in the context of the present study since the study was undertaken under the tropical conditions in West Africa which are different from the conditions in the case study region. The work by Guendehou et al.  therefore needs to be expanded to reflect conditions and tree species in different land use systems to allow a more precise estimation of the relevance of African trees for biomass and carbon stocks.
The goal of this paper was to accurately estimate AGB stocks at the watershed level in the Sudan Savannah zone using satellite images derived LULC data and adjusted allometric models based on data from non-destructive method.
satellite images analysis and LULC classification,
forest inventory in each identified LULC class of the watershed,
trees communities analysis and identification of the main species of the watershed based on importance value index (IVI),
estimation of basic wood density and BEF model of selected species,
assessment of non-destructive method to the destructive one based on available data,
development of allometric models using DBH, tree height and basic wood density of main trees species,
calculation of biomass data at the tree and the plot level using the best allometric equations of each LUCa and extrapolation at the watershed level,
mapping the biomass density using ArcGIS 10.2.1 software.
Long-term (1952–2010) minimal daily temperature at Natitingou station located 50 km from the site ranged from 15.25 to 25.08 °C with an average of 20.53 °C. Daily maximum temperature ranged from 26.63 to 39.27 °C with a mean temperature of 32.59 °C. Long-term (1971–2013) mean monthly precipitation for Tanguieta station (15–20 km from the study area) was 87.5 mm.
We used the standardized precipitation index (SPI) programme developed by Brown  and the result showed two periods (1978–1979; 1985–1986) of extreme drought with some years of moderate to severe drought during these 42 years of observation.
Data sources for images classification
The estimation of aboveground tree biomass at the watershed scale was complicated by the heterogeneous tree species distribution across the different LUCa. Two Landsat 8 scenes (http://glovis.usgs.gov) were used for LULC classification. The acquisition dates were 13 October 2013 and 29 October 2013 both with path-row 193-53. The acquisition dates were chosen since they fit well with the high photosynthetic activity of natural vegetation, crops and offset cloud cover and fire pattern disturbance. The scenes selected had zero percent cloud cover. Landsat 8 images were provided atmospherically and geometrically corrected (Landsat 8, level 2A product).
To separate agroforestry and plantation which are easily confused with natural vegetation at the 30 m Landsat resolution we used Worldview-2 imagery1 (0.46/1.84 m resolution panchromatic/multispectral) from with additional ground truthing data (field surveys of plots data from agroforestry systems and plantations).
Establishment of gridded vegetation index map using MODIS data
To select sample points that cover the different land use classes adequately, we first derived clusters of land use based on time series of the moderate-resolution imaging spectro-radiometer (MODIS) normalized difference vegetation index (NDVI) product. These clusters were then used as strata in a stratified sampling procedure to select sampling points.
The NDVI  is one of the most widely used vegetation indexes and is correlated with several biophysical properties of the vegetation canopy, such as leaf area index, fractional vegetation cover, vegetation condition, and biomass. The NDVI is defined base on the relationship between near infrared (NIR) and red light (RED) and relates the difference of both wave lengths to their sum (Eq. 1). It is built on the observation that chlorophylls a and b in green leaves strongly absorb light in the Red while the cell walls strongly scatter light in the NIR region . NDVI normalizes values between −1 and +1; dense vegetation has a high NDVI, while soil values are low but positive, and water is negative due to its strong absorption of NIR.
Both sample points for calibration as well as for validation were sampled using the same procedure. Validation sample points were not used for the training of the classifier.
We used seven LULC classes that reflect the dominant land use classes for biomass stocks assessment in our case study region: riparian forest and woodland, Savanna Woodland, shrub savanna, cropland and fallow, settlements, agroforestry and plantation. At some locations in the text we refer to forest land that incorporates the land use category (LUCa) riparian forest and woodland, Savanna Woodland and shrub savanna. We further separated agroforestry and plantation from cropland since an increase of agroforestry and plantation could be a mitigation strategy to climate change.
Based on the ground truthing data derived for the sample points, a random forest classifier was trained and used to classify the Landsat 8 data. For the classification bands 2, 3 and 4 were used. The random forest approach is a machine-learning approach that builds on classification and regression trees but overcomes their sensitivity towards noise in the data. Instead of relying on a single decision tree, using the majority vote of a forest of decision trees fit to bootstrap samples from the original data. While individual decision trees suffer from a high variance of estimates the averaging across the bootstrap sample leads to a significant variance reduction [90, 91]. In addition to bagging approaches, random forests decorrelate the trees by using only a random sample of the variables (i.e. spectral bands) for each split. The analysis was done in R  using the package randomForest . Random forest classifiers have been applied successfully in a number of remote sensing studies [92–94] which showed that the approach is superior to the widely used maximum likelihood classifier.
Since it was not possible to separate agroforestry and plantations from forest land at the scale of the Landsat 8 data these classes were separated based on several high resolution Worldview-2 images, known plantation and agroforestry sites and their geometric properties (regular spacing between trees) in ArcGIS 10.2.1. Areas that were identified as either agroforestry or plantation were validated in the field and assigned to the proper class based on the field validation. For the final LULC map identified agroforestry and plantations were superimposed on the existing classification (Fig. 1).
Accuracy assessment of the classification
The accuracy of the random forest classification (without the superimposed classification of agroforestry and plantations) was based on independent validation points that were sampled similar to the training sample points. By comparing classification results and observed land use classes at the location of the validation points a confusion matrix was derived. Based on the confusion matrix overall accuracy and the kappa index  were derived to assess the accuracy of the classification.
Land use/land cover (LULC) classes and number of installed plots
IPCC  land use categories (LUCa)
Others land use
Percentage in the watershed
Area sampled (ha)
Number of establishing plots
Importance value index (IVI) analysis
The objective of IVI calculation was the selection of the main species of the watershed for the development of tree biomass allometric equations. The IVI analysis has been used for the first time by Curtis  to determine the overall importance of tree species for a tree community structure. The IVI of a species is the sum of the relative frequency, relative density and relative dominance of the species in a region.
Among the 84 inventoried species within the entire watershed, only three were not taken into the account for IVI calculation. The first species was Adansonia digitata which has DBH range 9.2–185 cm, with a relative abundance of 9.87 % and the density of 0.54 (<1 plant ha−1). Phoenix reclinata and Borassus flabellifer were removed since we could rely on published allometric equations for coconut biomass estimation, by Schoroth et al.  for these two species. For the remaining 81 species the IVI was calculated using Eqs. 4–7 to obtain 15 most important tree species. These 15 tree species were used for the further analysis.
Field campaign and the estimation of wood density of the main species of the watershed
The materials used for this study were an increment borer, scale weight of 25 kg, metric tape scaling, metre increment and an oven for drying the wood samples. During the second field campaign (from October 2014 to December 2014) wood samples from 270 trees within the 15 main species (Terminalia macroptera, Acacia seyal, Combretum glutinosum, Pterocarpus erinaceus, Anogeisus leiocarpus, Mitragyna inermis, Lannea microcrapa, Lannea acida, Ficus sp., Crosopteryx febrifuga, Entada africana, Parkia biglobosa, Vitelaria paradoxa, Azadirachta indica, Anacardium occidentale) were extracted with the increment borer at 1.3 m above the ground. A. occidentale was surveyed in the agroforestry system (cashew). The basic wood density of the samples was estimated after oven-drying them at 75 °C—over 2–3 days depending on the water content of the wood samples.
Tree measurements (destructive and non-destructive methods)
It was possible to analyse trees selected for logging in a rural electrification project along the road from Dassari-Tigniga (Fig. 2) in the Dassari basin. Seven species (T. macroptera, Ficus sp., A. seyal, Entanda Africana, C. glutinosum, C. febrifuga and A. leiocarpus) and 13 individual trees were selected. Only tree species which were going to be logged by rural electrification project officers and that belonged to the previously mentioned 15 main species (Table 3) of the watershed were analysed. These samples allowed the estimation of the parameters of the BEF function as well as an assessment of the uncertainties attached.
Measurement of stem girth at 1.3 m, 2.3 m and crown base, and stem height (Fig. 3);
Extraction of stem wood sample of the tree at 1.3 m above ground using the increment borer;
Oven-drying the wood sample obtained with the increment borer and estimation of the wood density of the surveyed tree;
Logging of the tree species by rural electrification project officers,
Weighting of fresh mass of stem, branches and foliage using scale weighting of 25 kg,
Oven-drying of fresh wood samples selected from stem, branches and foliage at 75 °C for 2–3 days to constant weight;
Estimation of dry mass of stem, branches and foliage of the tree using Eq. 12,
Calculation of BEF based on dry mass of stem, branches and foliage using Eq. 13,
Modelling BEF as a function of stem dry mass using Eq. 14,
Comparison of the non-destructive method to the destructive method based on predictive total biomass by BEF function.
Figure 3 shows the various properties collected from sample trees in this watershed.
Collection of wood samples in the field
The inner diameter of the bit of the increment borer device was 0.5 cm leading to a diameter of the sample of 0.5 cm. The length L of the sample was measured after its extraction. The application of the tool is shown in Fig. 4.
The main activities preformed related to the destructive approach were the estimation of dry mass of wood samples of stem, branches and foliage followed by the estimation of BEF.
Estimation of basic wood density
Estimation of stem volume and stem biomass of surveyed trees
Modelling BEF as a function of stem dry mass
Fitting aboveground biomass (AGB) equations for the surveyed individual tree species
Total samples consisted of 270 individual trees that have been non-destructively surveyed (Table 3). For each tree of that sample, the BEF was applied to calculate the AGB. This AGB was then modelled by generalized linear models (GLM)  using predictors easily measured in the field. We selected DBH, total height (H) and wood density (ρ) as predictors. Since the effort to measure the predictors increases from DBH to H and to ρ we fitted models for three sets of predictors: (1) just on DBH, (2) DBH and H, (3) all three predictors together. Based on the properties of the residuals we decided on a Gamma GLM with a log link. For each level of complexity we started with a model that contained the interactions between all involved predictors as well as the main effects (conditional on the interactions). We tried to simplify the model structure based on the small sample size of corrected Aikaike information criteria (AICc) [77, 78]. Quadratic effects were not considered since their inclusion led to unrealistic model behaviour for higher response values which we interpreted as a result of overfitting the model.
The aim was the fitting of model at land use categories (LUCa) level—i.e. data were subsetted by LUCa before fitting—in addition to a generic category which included all LUCas. Effects of species on the model fit as well as on the structure of the residuals were tested but effects were small. We used the following LUCa to fit the models: forest land (the combination of riparian forest, Savanna Woodland and shrub savanna), savanna grassland (grassland), settlement, cropland (cropland and fallow). The sample size differed by LUCa: agroforestry: 25, forest: 181, cropland: 178, settlements: 63, grassland: 90. We did not fit models for the LUCa plantation but applied published equations. AGB from plots plantations of Tectona grandis and Eucalyptus grandis were obtained using respectively published allometric equations from Guendehou et al.  and Montagu et al.  whereas the generic equation (cf. Fig. 7; Additional file 2) was applied to estimate AGB of A. indica and Gmelina arborea.
Validity domain of equations for DBH
The models were run under certain ranges of DBH obtained from each LUCa. The DBH ranges were 5.6–44.7, 7.6–40.7, 6.9–62.4, 7–52.7 and 9.2–57.9 cm respectively in forest land, savanna grassland, cropland and fallow, settlement and agroforestry systems (cashew plantation).
Estimating aboveground biomass at the watershed level
We generated the biomass density map using the best specific equation for each LUCa especially equation type III which involved the three predictors. For agroforestry model II was used since wood density did not have a significant effect on AGB estimates in this LUCa. We estimated biomass content of each plot in two steps when we found P. reclinata and B. flabellifer in the plot data. We first retrieved these species from each plot data and we estimated their biomass using equation from Schoroth et al.  developed for the estimation of AGB of coconut. In the second step we applied specific equations for the concerned plots and we summed up together the two results to obtain the total biomass of the plot. The total biomass stocks of each LULC class is equal to the mean AGB density expressed in Mg ha−1 times the area in ha of the defined LULC class. The biomass stocks map was edited using ArcGIS 10.2.1 software.
Results and discussion
Land use classification
Main species in the study area
Importance value index of main species in each land use/cover category (LUCa)
Importance value index (IVI) (%)
Basic wood density of the main species of the study area
The basic wood density (g cm−3) of the main species of the watershed
The present study
ρ (g cm−3)
Basic wood density
Coefficients for the BEF–stem dry biomass relationship and for the BEF–DBH relationship fitted
BEF–stem dry biomass relationship
3.66 × 10−8
ln(stem dry biomass)
4.61 × 10−9
Total biomass obtained by the destructive method (observed values from 13 trees) and the total biomasses estimated by the non-destructive method (predicted values from these trees using the estimated BEF–stem dry mass relationship) were highly similar (Fig. 6 centre panel, Pearson correlation coefficient of 0.99).
Our results differ significantly from relationships identified in other land systems stressing the importance of deriving BEF relationships adjusted to the conditions in the ecozone. Segura  used a similar approach based on an estimated BEF function for the per-humid premontane transitional forest zone in Costa Rica. In contrast to our findings BEF decreased with stem biomass in Costa Rica. While the Costa Rican study underestimated total biomass of trees on average by 17.31 %, the application of the BEF to our data overestimated total biomass slightly by 1.82 %. Levy et al.  estimated the biomass expansion of coniferous species in Great Britain as a function of tree height of stand tree. An application of Levy’s BEF to our data overestimated the total biomass of our sampled tree species to on average by 4.46 %. Magalhães and Seifert  used BEF as a function of DBH when estimating AGB of Androstachysjohnsonii Prain in Mozambique. The application of the BEF of Magalhães and Seifert  to our data underestimated the total biomass of our sampled tree species on average by 62.54 %.
Given the small sample size (13 individual trees within seven species) and the limited range of DBH (<25 cm) care should be taken not to extrapolate results. However, the sampled trees represent the common size distribution of trees in the human influenced ecosystems of the study region. Therefore, our results can be assumed to provide a good estimate for BEF assessments in the region.
Alternatively, BEF could be estimated based on DBH of the 13 trees assessed by the destructive approach (Fig. 6, right panel).The model based on DBH was slightly superior to the model based on stem dry biomass if compared by means of the small sample size corrected AIC (AICc) or a likelihood ratio test and it explained 75 % of the variance in the BEF (Table 4).
If this model was used to predict total biomass, the values derived by the destructive approach were overestimated on average by 2.27 %—a bit higher compared to the model based on stem dry mass. We therefore stuck to the estimation based on stem dry biomass.
Aboveground biomass (AGB) models at the watershed level
For model type I regression coefficients for DBH differ by around 40 % with lowest estimates for agroforestry and highest values for forests and grasslands. For model II agroforestry and settlements have clearly distinct coefficient estimates from the other land use classes. Model coefficients for model type III are hard to compare since the interaction between wood density and height was only significant for forest and grasslands. Effects plots (cf. Additional file 1) indicated, that the differences between regression coefficients across the LUCa led to important changes in prediction.
Comparing the equations to previously published equations
The average deviation of various models compared to the models type of the present study in each LUCa
Chave et al. 
Chave et al. 
Average deviation δ (%)
Aboveground biomass density and stocks at the watershed level
Aboveground biomass density (Mg ha−1) and total biomass stocks (Mg) with the sample plots data and attached uncertainty
Range of biomass density (Mg ha−1)
Mean biomass density (SE)
Percentage error (% error)
Total biomass stocks (Mg) and its SE
340,534.70 ± 36,445.4
Riparian forest and woodland
32,271.87 ± 334.74
24,8050.22 ± 27,019.98
60,212.61 ± 6090.67
349.66 ± 68.81
349.66 ± 68.81
26,409.82 ± 5024.04
Cropland and fallow
26,409.82 ± 5024.04
2375.84 ± 988.13
2375.84 ± 988.13
1132.73 ± 584.46
1132.73 ± 584.46
3138.20 ± 1777.35
2819.78 ± 1556.44
145.80 ± 114.46
129.33 ± 81.30
43.29 ± 25.14
The biomass density map (Fig. 5) was generated based on the best model for each LUCa. The map in Fig. 5 shows the LULC types and the biomass density at the watershed level specifically for each LULC class. Information on the uncertainties of the biomass density estimation is provided in Table 6.
The results from this study help to close the existing knowledge gap with respect to biomass estimation in the Sudan Savannah environment. The derived empiric equations fitted to local data should be useful for further work in the Sudan Savannah environment which is characterized by the main species of the present study. The estimation of biomass density and AGB in each LUCa are of great importance for carbon balance calculations in the Sudan Savannah in West Africa. Results include data on wood density of the main species of the Sudan Savannah zone, the related BEF and the biomass density in each LUCa. Our results highlight the importance of model parameters adjusted to the regional conditions in the eco-zone. Plantations and agroforestry system could be a useful mitigation option to battle climate change—however, the differences between the species and the effect of age which could not be satisfyingly handled in our study call for additional research activities. Still, the results provide important information for the carbon accounting programme related to the implementation of the Kyoto Protocol and REDD+ initiatives.
biomass expansion factor
diameter at breast height
land use/land cover
land use category
AC carried out data collection, designed the methodology and drafted the manuscript. SL performed statistical analysis, provided literature indications and revised the manuscript. VO supervised data collection in the field, provided methodology and revised the manuscript. NKB revised the manuscript and supervised the research. All authors read and approved the final manuscript.
The study is part of Ph.D. thesis in Climate Change and Land Use (CCLU) hosted by the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. The programme CCLU is under West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL). The authors are most grateful to the German Federal Ministry of Education and Research (BMBF) for sponsoring. We are also grateful to local staff of Dassari basin for their assistance during the field work. Finally, we are grateful to Michael Thiel, Prof. S. N. Odai (Director, CCLU-KNUST, Kumasi), Dr. W. A. Agyare (Coordinator, CCLU-KNUST, Kumasi), Dr. M. I. Ouattara (Director, GSP, WASCAL, Accra) and Dr. L. Sédogo (Executive Director, WASCAL, Accra). We are grateful to the editor and one anonymous reviewer for constructive comments that helped to improve the manuscript.
AC is the Ph.D. student in Climate Change and Land Use (CCLU) hosted by the Kwame Nkrumah University of Science and Technology. SL is Assistant Professor for Land use Modelling and Ecosystem Services in the University of Bonn at the Institute of Geodesy and Geoinformation. VO is Associate Professor at the University of Abomey-Calavi in Benin. NKB is Professor at Kwame Nkrumah University of Science and Technology in Ghana.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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