The role of cocoa systems for climate change mitigation and adaptation has increased substantially because of their capability to trap carbon dioxide from the atmosphere and deposited in the cocoa trees as carbon. Dev...The role of cocoa systems for climate change mitigation and adaptation has increased substantially because of their capability to trap carbon dioxide from the atmosphere and deposited in the cocoa trees as carbon. Development of aboveground biomass (AGB) models for cocoa plantations is crucial for accurate estimation of carbon stocks in the cocoa systems, however, allometric models for estimating AGB for cocoa plantations remain a challenge for cocoa producing countries in Sub-Saharan Africa especially Ghana. The aim of this study is to develop allometric model that can be used for the estimation of AGB for cocoa plantations in Ghana, as well as West Africa. Destructive sampling was carried out on 110 cocoa trees obtained from the cocoa rehabilitation exercise for the development of the allometric models. Diameter at breast height (D), total tree height (H) and wood density (ρ) were used as predictors to develop seven models. The best model was selected based on coefficient of determination (R<sup>2</sup>), index of agreement (I<sub>A</sub>), root mean squared error (RMSE), bias (E%), mean absolute error (MAE) and corrected akaike information criterion (AIC<sub>C</sub>) and percentage relative standard error (PRSE) of the estimated parameters. The selected model, which was the one with the predictors D and ρ, was given as;AGB = 0.7217ρ(D<sup>2</sup>)<sup>0.921</sup>. It was compared with the Yuliasmara et al. (2009) cocoa model using equivalence test and paired sample t-test. The two models were found to be equivalent within ±10% of their mean predictions (p < 0.0001) for one-tailed tests for both lower and upper limits, while the paired sample t-test rejected the null hypothesis with mean difference of 14.16 kg between the two models. This study is significant because it has provided a model to estimate AGB for the cocoa plantations in Ghana which is very important for the Ghana Cocoa-Forest REDD+ Programme and also can be used by other West African cocoa producing countries.展开更多
Satellite image classification has been used for long time in the field of remote sensing since classification results are used in environmental research, agriculture, climate change and natural resource management. T...Satellite image classification has been used for long time in the field of remote sensing since classification results are used in environmental research, agriculture, climate change and natural resource management. The cocoa landscape of Ghana is complex and diverse in nature, composing of mixture of closed forest, open forest, settlements, croplands and cocoa farms which make mapping the landscape difficult. The purpose of this research is to assess and compare the classification performances of three machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and a statistical classification algorithm: Maximum Likelihood (ML) to know which classifier is best suited for mapping the cocoa landscape of Ghana using Juaboso and Bia West districts of Ghana as study area. A representative sampling approach was adopted to collect 1246 sample points for the various Land Use/Land Cover (LULC) types. These sample points were divided at random into 869 which form 70% for classification and 377 which constitute 30% of the total sample points for validation. The Stacked sentinel-2 image, classification data and validation data storing the identities of the LULC classes were imported in R to run supervised classification for each classifier. The classification results show that the highest overall accuracy and kappa statistics were produced by the support vector machine (86.47%, 0.7902);next is the artificial neural network (85.15%, 0.7700), followed by the random forest (84.08%, 0.7559) and finally the maximum likelihood (78.51%, 0.6668). The final LULC map produced under this study can be used to monitor cocoa driven deforestation especially in the gazetted forest and game reserves. This map will also be very useful in the national forest monitoring framework for the REDD + cocoa landscape project.展开更多
文摘The role of cocoa systems for climate change mitigation and adaptation has increased substantially because of their capability to trap carbon dioxide from the atmosphere and deposited in the cocoa trees as carbon. Development of aboveground biomass (AGB) models for cocoa plantations is crucial for accurate estimation of carbon stocks in the cocoa systems, however, allometric models for estimating AGB for cocoa plantations remain a challenge for cocoa producing countries in Sub-Saharan Africa especially Ghana. The aim of this study is to develop allometric model that can be used for the estimation of AGB for cocoa plantations in Ghana, as well as West Africa. Destructive sampling was carried out on 110 cocoa trees obtained from the cocoa rehabilitation exercise for the development of the allometric models. Diameter at breast height (D), total tree height (H) and wood density (ρ) were used as predictors to develop seven models. The best model was selected based on coefficient of determination (R<sup>2</sup>), index of agreement (I<sub>A</sub>), root mean squared error (RMSE), bias (E%), mean absolute error (MAE) and corrected akaike information criterion (AIC<sub>C</sub>) and percentage relative standard error (PRSE) of the estimated parameters. The selected model, which was the one with the predictors D and ρ, was given as;AGB = 0.7217ρ(D<sup>2</sup>)<sup>0.921</sup>. It was compared with the Yuliasmara et al. (2009) cocoa model using equivalence test and paired sample t-test. The two models were found to be equivalent within ±10% of their mean predictions (p < 0.0001) for one-tailed tests for both lower and upper limits, while the paired sample t-test rejected the null hypothesis with mean difference of 14.16 kg between the two models. This study is significant because it has provided a model to estimate AGB for the cocoa plantations in Ghana which is very important for the Ghana Cocoa-Forest REDD+ Programme and also can be used by other West African cocoa producing countries.
文摘Satellite image classification has been used for long time in the field of remote sensing since classification results are used in environmental research, agriculture, climate change and natural resource management. The cocoa landscape of Ghana is complex and diverse in nature, composing of mixture of closed forest, open forest, settlements, croplands and cocoa farms which make mapping the landscape difficult. The purpose of this research is to assess and compare the classification performances of three machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and a statistical classification algorithm: Maximum Likelihood (ML) to know which classifier is best suited for mapping the cocoa landscape of Ghana using Juaboso and Bia West districts of Ghana as study area. A representative sampling approach was adopted to collect 1246 sample points for the various Land Use/Land Cover (LULC) types. These sample points were divided at random into 869 which form 70% for classification and 377 which constitute 30% of the total sample points for validation. The Stacked sentinel-2 image, classification data and validation data storing the identities of the LULC classes were imported in R to run supervised classification for each classifier. The classification results show that the highest overall accuracy and kappa statistics were produced by the support vector machine (86.47%, 0.7902);next is the artificial neural network (85.15%, 0.7700), followed by the random forest (84.08%, 0.7559) and finally the maximum likelihood (78.51%, 0.6668). The final LULC map produced under this study can be used to monitor cocoa driven deforestation especially in the gazetted forest and game reserves. This map will also be very useful in the national forest monitoring framework for the REDD + cocoa landscape project.
