The population of critically endangered white-thighed colobus monkeys (Colobus vellerosus) at Boa... more The population of critically endangered white-thighed colobus monkeys (Colobus vellerosus) at Boabeng-Fiema Monkey Sanctuary (BFMS) is possibly the only growing population of this species in West Africa. We assessed the current population status of C. vellerosus in BFMS and the surrounding fragments in Ghana. We undertook a complete count of the population in 2020, and this data was combined with previously conducted complete counts from 1990 to 2014. Results show that the total population growth rate of colobus monkeys at BFMS and the surrounding forest fragments was 353.9% between the 1990 and 2020 censuses (at a rate of 11.8% annually). In the BFMS alone, the total population growth rate was 252.3% between 1990 and 2020 (i.e., at a rate of 8.4% annually). The total population growth rate in the surrounding forest fragments was 97.0% between the first census year of 1997 and the 2020 census (i.e., at a rate of 4.2% annually). The mean group size in the BFMS was 16.7 individuals (S...
Anthropogenic activities as predictors of species assemblages in the Owabi Ramsar Wetland were in... more Anthropogenic activities as predictors of species assemblages in the Owabi Ramsar Wetland were investigated between May and September 2019. Data were collected in 154 plots at five different sites. The prevalence index method was used to categorise the species into wetland and non-wetland indicators. Log series and Hill number models were applied to quantify community assemblages, whereas the CCA technique was used to examine the relationship between anthropogenic activities and species presence or absence. In all, 2 185 individuals, belonging to 32 families and 68 species were recorded. Paspalum orbiculare and Persicaria lanigera were the most abundant, indicating their wide distribution. Mean number of individuals were highest at Atafua and lowest at Owabi. An abundance of terrestrial species (41.2%; i.e. plant species not listed as obligate wetland plants) and facultative species (30.9%), compared with obligate wetland species (27.9%), suggests a dominance of species from dryland habitats into the wetland. Farming activities, increased levels of NH 4 + , PO 4 3+ and NO 3-N, were the predictors that explained 72.01% of the overall variability in community assemblages. The results revealed the impact of the anthropogenic activities on the ecological integrity of the Owabi Ramsar Wetland and the need to institute conservation measures outlined in this study.
ABSTRACT Support vector machines (SVMs) have been frequently shown to result in more accurate cla... more ABSTRACT Support vector machines (SVMs) have been frequently shown to result in more accurate classification than other image classification methods. However, few studies have successfully quantified their performance for mapping oil palm plantations. Various sustainability criteria developed by the Round Table on Sustainable Palm Oil (RSPO) have a spatial component but they provide little guidance on mapping oil-palm-related cover changes. SVM and maximum likelihood classifier (MLC) classification approaches in classifying oil palm plantations with Landsat ETM+ were compared. The best combination of three bands from the satellite image was selected based on Bhattacharyya distance. SVM and MLC performance was evaluated using overall accuracy assessment and kappa statistics. Bands 4, 5, and 3 provided the best spectral separability indices based on Bhattacharyya distance. SVM classification resulted in an overall accuracy of 78.3% (kappa statistic 0.73) compared with MLC, with an overall accuracy of 71.9% (kappa statistic 0.65). The performance of the SVM method is mainly affected by the accurate setting of parameters involved in the algorithm. The radial basis function parameter setting in SVM was an important variable in the classification process, and SVM improved the classification of oil palm mapping. Although the classification accuracy is still insufficient for large-scale implementation of the technique, further refinements may provide a way forward towards producing baseline information useful for RSPO certification.
Anthropogenic activities as predictors of species assemblages in the Owabi Ramsar Wetland were in... more Anthropogenic activities as predictors of species assemblages in the Owabi Ramsar Wetland were investigated between May and September 2019. Data were collected in 154 plots at five different sites. The prevalence index method was used to categorise the species into wetland and non-wetland indicators. Log series and Hill number models were applied to quantify community assemblages, whereas the CCA technique was used to examine the relationship between anthropogenic activities and species presence or absence. In all, 2 185 individuals, belonging to 32 families and 68 species were recorded. Paspalum orbiculare and Persicaria lanigera were the most abundant, indicating their wide distribution. Mean number of individuals were highest at Atafua and lowest at Owabi. An abundance of terrestrial species (41.2%; i.e. plant species not listed as obligate wetland plants) and facultative species (30.9%), compared with obligate wetland species (27.9%), suggests a dominance of species from dryland habitats into the wetland. Farming activities, increased levels of NH 4 + , PO 4 3+ and NO 3-N, were the predictors that explained 72.01% of the overall variability in community assemblages. The results revealed the impact of the anthropogenic activities on the ecological integrity of the Owabi Ramsar Wetland and the need to institute conservation measures outlined in this study.
International Journal of Remote Sensing, Jul 8, 2014
Support vector machines (SVMs) have been frequently shown to result in more accurate classificati... more Support vector machines (SVMs) have been frequently shown to result in more accurate classification than other image classification methods. However, few studies have successfully quantified their performance for mapping oil palm plantations. Various sustainability criteria developed by the Round Table on Sustainable Palm Oil (RSPO) have a spatial component but they provide little guidance on mapping oil-palm-related cover changes. SVM and maximum likelihood classifier (MLC) classification approaches in classifying oil palm plantations with Landsat ETM+ were compared. The best combination of three bands from the satellite image was selected based on Bhattacharyya distance. SVM and MLC performance was evaluated using overall accuracy assessment and kappa statistics. Bands 4, 5, and 3 provided the best spectral separability indices based on Bhattacharyya distance. SVM classification resulted in an overall accuracy of 78.3% (kappa statistic 0.73) compared with MLC, with an overall accuracy of 71.9% (kappa statistic 0.65). The performance of the SVM method is mainly affected by the accurate setting of parameters involved in the algorithm. The radial basis function parameter setting in SVM was an important variable in the classification process, and SVM improved the classification of oil palm mapping. Although the classification accuracy is still insufficient for large-scale implementation of the technique, further refinements may provide a way forward towards producing baseline information useful for RSPO certification.
The population of critically endangered white-thighed colobus monkeys (Colobus vellerosus) at Boa... more The population of critically endangered white-thighed colobus monkeys (Colobus vellerosus) at Boabeng-Fiema Monkey Sanctuary (BFMS) is possibly the only growing population of this species in West Africa. We assessed the current population status of C. vellerosus in BFMS and the surrounding fragments in Ghana. We undertook a complete count of the population in 2020, and this data was combined with previously conducted complete counts from 1990 to 2014. Results show that the total population growth rate of colobus monkeys at BFMS and the surrounding forest fragments was 353.9% between the 1990 and 2020 censuses (at a rate of 11.8% annually). In the BFMS alone, the total population growth rate was 252.3% between 1990 and 2020 (i.e., at a rate of 8.4% annually). The total population growth rate in the surrounding forest fragments was 97.0% between the first census year of 1997 and the 2020 census (i.e., at a rate of 4.2% annually). The mean group size in the BFMS was 16.7 individuals (S...
Anthropogenic activities as predictors of species assemblages in the Owabi Ramsar Wetland were in... more Anthropogenic activities as predictors of species assemblages in the Owabi Ramsar Wetland were investigated between May and September 2019. Data were collected in 154 plots at five different sites. The prevalence index method was used to categorise the species into wetland and non-wetland indicators. Log series and Hill number models were applied to quantify community assemblages, whereas the CCA technique was used to examine the relationship between anthropogenic activities and species presence or absence. In all, 2 185 individuals, belonging to 32 families and 68 species were recorded. Paspalum orbiculare and Persicaria lanigera were the most abundant, indicating their wide distribution. Mean number of individuals were highest at Atafua and lowest at Owabi. An abundance of terrestrial species (41.2%; i.e. plant species not listed as obligate wetland plants) and facultative species (30.9%), compared with obligate wetland species (27.9%), suggests a dominance of species from dryland habitats into the wetland. Farming activities, increased levels of NH 4 + , PO 4 3+ and NO 3-N, were the predictors that explained 72.01% of the overall variability in community assemblages. The results revealed the impact of the anthropogenic activities on the ecological integrity of the Owabi Ramsar Wetland and the need to institute conservation measures outlined in this study.
ABSTRACT Support vector machines (SVMs) have been frequently shown to result in more accurate cla... more ABSTRACT Support vector machines (SVMs) have been frequently shown to result in more accurate classification than other image classification methods. However, few studies have successfully quantified their performance for mapping oil palm plantations. Various sustainability criteria developed by the Round Table on Sustainable Palm Oil (RSPO) have a spatial component but they provide little guidance on mapping oil-palm-related cover changes. SVM and maximum likelihood classifier (MLC) classification approaches in classifying oil palm plantations with Landsat ETM+ were compared. The best combination of three bands from the satellite image was selected based on Bhattacharyya distance. SVM and MLC performance was evaluated using overall accuracy assessment and kappa statistics. Bands 4, 5, and 3 provided the best spectral separability indices based on Bhattacharyya distance. SVM classification resulted in an overall accuracy of 78.3% (kappa statistic 0.73) compared with MLC, with an overall accuracy of 71.9% (kappa statistic 0.65). The performance of the SVM method is mainly affected by the accurate setting of parameters involved in the algorithm. The radial basis function parameter setting in SVM was an important variable in the classification process, and SVM improved the classification of oil palm mapping. Although the classification accuracy is still insufficient for large-scale implementation of the technique, further refinements may provide a way forward towards producing baseline information useful for RSPO certification.
Anthropogenic activities as predictors of species assemblages in the Owabi Ramsar Wetland were in... more Anthropogenic activities as predictors of species assemblages in the Owabi Ramsar Wetland were investigated between May and September 2019. Data were collected in 154 plots at five different sites. The prevalence index method was used to categorise the species into wetland and non-wetland indicators. Log series and Hill number models were applied to quantify community assemblages, whereas the CCA technique was used to examine the relationship between anthropogenic activities and species presence or absence. In all, 2 185 individuals, belonging to 32 families and 68 species were recorded. Paspalum orbiculare and Persicaria lanigera were the most abundant, indicating their wide distribution. Mean number of individuals were highest at Atafua and lowest at Owabi. An abundance of terrestrial species (41.2%; i.e. plant species not listed as obligate wetland plants) and facultative species (30.9%), compared with obligate wetland species (27.9%), suggests a dominance of species from dryland habitats into the wetland. Farming activities, increased levels of NH 4 + , PO 4 3+ and NO 3-N, were the predictors that explained 72.01% of the overall variability in community assemblages. The results revealed the impact of the anthropogenic activities on the ecological integrity of the Owabi Ramsar Wetland and the need to institute conservation measures outlined in this study.
International Journal of Remote Sensing, Jul 8, 2014
Support vector machines (SVMs) have been frequently shown to result in more accurate classificati... more Support vector machines (SVMs) have been frequently shown to result in more accurate classification than other image classification methods. However, few studies have successfully quantified their performance for mapping oil palm plantations. Various sustainability criteria developed by the Round Table on Sustainable Palm Oil (RSPO) have a spatial component but they provide little guidance on mapping oil-palm-related cover changes. SVM and maximum likelihood classifier (MLC) classification approaches in classifying oil palm plantations with Landsat ETM+ were compared. The best combination of three bands from the satellite image was selected based on Bhattacharyya distance. SVM and MLC performance was evaluated using overall accuracy assessment and kappa statistics. Bands 4, 5, and 3 provided the best spectral separability indices based on Bhattacharyya distance. SVM classification resulted in an overall accuracy of 78.3% (kappa statistic 0.73) compared with MLC, with an overall accuracy of 71.9% (kappa statistic 0.65). The performance of the SVM method is mainly affected by the accurate setting of parameters involved in the algorithm. The radial basis function parameter setting in SVM was an important variable in the classification process, and SVM improved the classification of oil palm mapping. Although the classification accuracy is still insufficient for large-scale implementation of the technique, further refinements may provide a way forward towards producing baseline information useful for RSPO certification.
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