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
Background: Modelling aboveground biomass(AGB) in forest and woodland ecosystems is critical for accurate estimation of carbon stocks. However, scarcity of allometric models for predicting AGB remains an issue that ha...Background: Modelling aboveground biomass(AGB) in forest and woodland ecosystems is critical for accurate estimation of carbon stocks. However, scarcity of allometric models for predicting AGB remains an issue that has not been adequately addressed in Africa. In particular, locally developed models for estimating AGB in the tropical woodlands of Ghana have received little attention. In the absence of locally developed allometric models, Ghana will continue to use Tier 1 biomass data through the application of pantropic models. Without local allometric models it is not certain how Ghana would achieve Tier 2 and 3 levels under the United Nations programme for reducing emissions from deforestation and forest degradation. The objective of this study is to develop a mixedspecies allometric model for use in estimating AGB for the tropical woodlands in Ghana. Destructive sampling was carried out on 745 trees(as part of charcoal production) for the development of allometric equations. Diameter at breast height(dbh, i.e. 1.3 m above ground level), total tree height(H) and wood density(ρ) were used as predictors for the models. Seven models were compared and the best model selected based on model efficiency,bias(%) and corrected Akaike Information Criterion. The best model was validated by comparing its results with those of the pantropic model developed by Chave et al.(Glob Chang Biol 20:3177–3190, 2014) using equivalence test and conventional paired t-test.Results: The results revealed that the best model for estimating AGB in the tropical woodlands is AGB =0.0580ρ((dbh)2 H)0.999. The equivalence test showed that this model and the pantropic model developed by Chave et al.(Glob Chang Biol 20:3177–3190, 2014) were equivalent within ±10% of their mean predictions(p-values <0.0001 for one-tailed t-tests for both lower and upper bounds at 5% significant level), while the paired t-test revealed that the mean(181.44 ± 18.25 kg) of the model predictions of the best model of this study was significantly(n = 745, mean diff. = 16.50 ± 2.45 kg;S.E. = 1.25 kg;p < 0.001) greater than that(164.94 ± 15.82 kg) of the pantropic model of Chave et al.(Glob Chang Biol 20:3177–3190, 2014).Conclusion: The model developed in this study fills a critical gap in estimating AGB in tropical woodlands in Ghana and other West African countries with similar ecological conditions. Despite the equivalence with the pantropic model it remains superior to the model of Chave et al.(Glob Chang Biol 20:3177–3190, 2014) for the estimation of AGB in local tropical woodlands. It is a relevant tool for the attainment of Tier 2 and 3 levels for REDD+. The model is recommended for use in the tropical woodlands in Ghana and other West African countries in place of the use of pantropic models.展开更多
West African tropical forests spread from Senegal to Togo. They are considered as one of the world biodiversity hotspots. But these forests are disappearing very quickly because of human activities. In Togo, tropical ...West African tropical forests spread from Senegal to Togo. They are considered as one of the world biodiversity hotspots. But these forests are disappearing very quickly because of human activities. In Togo, tropical forests degradation has been increased since 1980 because of improved coffee cultivation;about 67% of forests in the sub-humid mountains zone disappeared between 1978 and 2000. Facing this fast deterioration of the forests, populations begin to plant teak (an exotic species) that is the only choice proposed by local forest service. This survey consists in valuing the potentialities to restore the Togolese tropical forest using the local commercial species, recognized on the wood market. The first species retained is Nauclea diderrichii (De Wild. & Th. Dur.) Merrill, that spreads in the African tropical forest from the Sierra Leonne to Uganda and through the Congo basin. In Togo, this plant species colonises the plain of the Litimé. The objective of the present study is to analyze the distribution of the natural populations in the plain of Litimé forest but also natural and artificial regeneration of the species. The perspective is to repeat this experiment with other local species in other regions of Togo in order to diversify the plantations and to protect the local forest biodiversity.展开更多
Nauclea diderrichii is a tropical African hardwood species and a suitable candidate for plantation development. However, attack by the Orygmophora mediofoveata, Hamps shoot borer threatens establishment of the species...Nauclea diderrichii is a tropical African hardwood species and a suitable candidate for plantation development. However, attack by the Orygmophora mediofoveata, Hamps shoot borer threatens establishment of the species in plantations. A genotype * environment assessment of 15 N. diderrichii progenies from Ghana and Togo was conducted in the Wet Evergreen, Moist Semi-deciduous and Dry Semi-deciduous forest zones. Progeny performance (Attack intensity, survival and growth) varied significantly between sites, and marginally within sites after 2.7 years. Overall, incidence of shoot borer attack was lower at the wet zone than at the moist or dry zones. Percent survival was higher at the wet (79.5%) than at the moist (50.8%) or dry (55.0%) forest zones. Mean height across the 15 progenies was 5.40 m, 4.30 m, and 2.73 m at the wet, dry and moist forests, respectively. Similarly, mean diameter was 5.31 cm, 4.58 cm, and 2.83 cm at the wet, dry and moist zones, respectively. The relatively low growth rate recorded at the moist zone was attributed to the paucity of soil conditions at the experimental site. Three wet forest zone progenies (BS9, BS3 and BS2) and two moist forest zone progenies (BE2 and GA1) performed better than average and have been recommended for planting.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.
基金Federal Ministry of Education and Research (BMBF) of Germany,funded the PhD programme of the lead author through the West African Science Service Centre for Climate Change and Adapted Land use (WASCAL)。
文摘Background: Modelling aboveground biomass(AGB) in forest and woodland ecosystems is critical for accurate estimation of carbon stocks. However, scarcity of allometric models for predicting AGB remains an issue that has not been adequately addressed in Africa. In particular, locally developed models for estimating AGB in the tropical woodlands of Ghana have received little attention. In the absence of locally developed allometric models, Ghana will continue to use Tier 1 biomass data through the application of pantropic models. Without local allometric models it is not certain how Ghana would achieve Tier 2 and 3 levels under the United Nations programme for reducing emissions from deforestation and forest degradation. The objective of this study is to develop a mixedspecies allometric model for use in estimating AGB for the tropical woodlands in Ghana. Destructive sampling was carried out on 745 trees(as part of charcoal production) for the development of allometric equations. Diameter at breast height(dbh, i.e. 1.3 m above ground level), total tree height(H) and wood density(ρ) were used as predictors for the models. Seven models were compared and the best model selected based on model efficiency,bias(%) and corrected Akaike Information Criterion. The best model was validated by comparing its results with those of the pantropic model developed by Chave et al.(Glob Chang Biol 20:3177–3190, 2014) using equivalence test and conventional paired t-test.Results: The results revealed that the best model for estimating AGB in the tropical woodlands is AGB =0.0580ρ((dbh)2 H)0.999. The equivalence test showed that this model and the pantropic model developed by Chave et al.(Glob Chang Biol 20:3177–3190, 2014) were equivalent within ±10% of their mean predictions(p-values <0.0001 for one-tailed t-tests for both lower and upper bounds at 5% significant level), while the paired t-test revealed that the mean(181.44 ± 18.25 kg) of the model predictions of the best model of this study was significantly(n = 745, mean diff. = 16.50 ± 2.45 kg;S.E. = 1.25 kg;p < 0.001) greater than that(164.94 ± 15.82 kg) of the pantropic model of Chave et al.(Glob Chang Biol 20:3177–3190, 2014).Conclusion: The model developed in this study fills a critical gap in estimating AGB in tropical woodlands in Ghana and other West African countries with similar ecological conditions. Despite the equivalence with the pantropic model it remains superior to the model of Chave et al.(Glob Chang Biol 20:3177–3190, 2014) for the estimation of AGB in local tropical woodlands. It is a relevant tool for the attainment of Tier 2 and 3 levels for REDD+. The model is recommended for use in the tropical woodlands in Ghana and other West African countries in place of the use of pantropic models.
文摘West African tropical forests spread from Senegal to Togo. They are considered as one of the world biodiversity hotspots. But these forests are disappearing very quickly because of human activities. In Togo, tropical forests degradation has been increased since 1980 because of improved coffee cultivation;about 67% of forests in the sub-humid mountains zone disappeared between 1978 and 2000. Facing this fast deterioration of the forests, populations begin to plant teak (an exotic species) that is the only choice proposed by local forest service. This survey consists in valuing the potentialities to restore the Togolese tropical forest using the local commercial species, recognized on the wood market. The first species retained is Nauclea diderrichii (De Wild. & Th. Dur.) Merrill, that spreads in the African tropical forest from the Sierra Leonne to Uganda and through the Congo basin. In Togo, this plant species colonises the plain of the Litimé. The objective of the present study is to analyze the distribution of the natural populations in the plain of Litimé forest but also natural and artificial regeneration of the species. The perspective is to repeat this experiment with other local species in other regions of Togo in order to diversify the plantations and to protect the local forest biodiversity.
文摘Nauclea diderrichii is a tropical African hardwood species and a suitable candidate for plantation development. However, attack by the Orygmophora mediofoveata, Hamps shoot borer threatens establishment of the species in plantations. A genotype * environment assessment of 15 N. diderrichii progenies from Ghana and Togo was conducted in the Wet Evergreen, Moist Semi-deciduous and Dry Semi-deciduous forest zones. Progeny performance (Attack intensity, survival and growth) varied significantly between sites, and marginally within sites after 2.7 years. Overall, incidence of shoot borer attack was lower at the wet zone than at the moist or dry zones. Percent survival was higher at the wet (79.5%) than at the moist (50.8%) or dry (55.0%) forest zones. Mean height across the 15 progenies was 5.40 m, 4.30 m, and 2.73 m at the wet, dry and moist forests, respectively. Similarly, mean diameter was 5.31 cm, 4.58 cm, and 2.83 cm at the wet, dry and moist zones, respectively. The relatively low growth rate recorded at the moist zone was attributed to the paucity of soil conditions at the experimental site. Three wet forest zone progenies (BS9, BS3 and BS2) and two moist forest zone progenies (BE2 and GA1) performed better than average and have been recommended for planting.
