Evaluating the spatial and temporal variations of aquatic weeds (Biomass) on Lower Volta River using multi-sensor Landsat Images and machine learning

Abstract

Aquatic invasive weeds affect hydrological, ecological, and socio-economic activities on freshwater ecosystems. On the Lower Volta River (LVR) of Ghana, invasive aquatic weeds have been known to be nuisance to fishing, navigation, aquaculture, hydropower production and other agricultural practices in the area. While information on the spatial and temporal distribution of aquatic weeds would be beneficial in improving weed management and control measures on the river, such information is very scanty. Also, these aquatic weeds are also biomass resources, that can be transformed to bioenergy. Thus, this study evaluated the spatial and temporal variations of aquatic weeds on the Lower Volta River, and assessed their potential biomass for bioenergy production.Random Forest (RF) algorithm and Landsat images were used to map the distribution of the weeds in 1975, 2003, and 2020, respectively. Accuracy assessment results showed mean Overall Accuracy (OA) of 83.44% and mean User Accuracy (UA) of 79.24%. The results indicated that as of 1975, aquatic weeds covered only 1495 ha and appeared in some specific locations such as Kpong and Ada. However, by 2003, the weeds had spread to most parts of the river covering 5600 ha, which was an increase of approximately 4-fold within a period of 28 years. The area covered by the weeds, however declined by 1505 ha between 2003 and 2020. Thus, in 2020, water hyacinth covered about 36% of the aquatic weeds relative to 28% in 2003. The results showed that, the quantity of the water hyacinth biomass per unit area was 21.5 kg/m 2 . This result can also be used as the basis for resource assessment as well as determination of its viability for bioenergy production and strategies for its modern utilisation. The conversion of water hyacinth into bioenergy remains one of the best aquatic weed management strategies that must be adopted in LVR.

Using Landsat satellite imagery to monitor the spatial and temporal dynamics of aquatic weed extent in Lakes Chivero and Manyame, located in an urban catchment of Zimbabwe

“…Although the spatial extent of aquatic weeds fluctuated over time, there is a general increase in the area occupied by aquatic weeds in both lakes. These results are in agreement with general observations across the country and across Africa, where several water bodies, especially those in urban catchments, are experiencing an increase in aquatic weeds (Nyamekye et al, 2021) , 1997;Chawira et al, 2013;Gufe et al, 2022).…”

“…In fact, the loss of aquatic plant species and associated faunal species composition results in the decline of goods and services that are derived from inland water bodies (Villamagna et al, 2010, Nhapi et al, 2004aHavel et al, 2015). Moreover, the surface layer of aquatic plants prevents sunlight penetration in water bodies and causes hypoxia (Mallin et al, 2006), which negatively influences water quality and abundance of aquatic life (Ruiz Téllez et al, 2008;Gallardo et al, 2015;Utete et al, 2018;Nyamekye et al, 2021).…”

“…According to Kamusoko et al (2013), urban sprawl, population growth and an economic meltdown have contributed to pressures that have ultimately led to a failing sewer system within the Harare Metropolitan Province and this has largely contributed to increased sewage contamination and overflows into the Lakes. Mbiba et al (1993) attributes accelerated nutrient loads into the tributaries that feed into Lake Chivero to the increased use of artificial fertilisers in urban agriculture practices (Nyamekye et al, 2021). These artificial fertilisers and sewage are rich in nitrogen and phosphorus.…”

Assessment of Past and Future Land Use/Land Cover Dynamics of the Old Kumasi Metropolitan Assembly and Atwima Nwabiagya Municipal Area, Ghana

“…The RFC utilized for the classification of satellite images and the efficiency of this algorithm as used in the works of (Frimpong & Molkenthin, 2021;Nyamekye et al, 2021) was superior, when contrasted with the traditional classifiers used in the classification satellite images. The kappa, producer and user accuracies yielded satisfactory results and agreed with works by Abass et al (2018); Buo et al (2021).…”

Automatic Detection of Aquatic Weeds: A Case Study in the Guadiana River, Spain

“…In recent decades, we have seen a tremendous growth of RS studies in which image analysis and different sensor specifications have been exploited to map and detect aquatic invasive plants [17], [18], [19], [20], [21]. However, in the literature there are few studies aimed at monitoring and controlling of aquatic invasive plants using machine learning (ML) techniques [22], [23], [24].…”