Utilization of earth observation technology for mapping spatio-temporal changes of urban water bodies (ponds) and its environmental impacts in Hadejia, Nigeria

Ponds locally called (Kududfi in Hausa) are either naturally or artificially created ditches which usually contained water and constitute significant elements of the settlement in Northern Nigeria which can be expanded beyond their natural depth. Many ponds in the urban centers of developing nations have inlets and outlets for transporting water from small ponds to large ones especially ponds that serve as reservoir for the domestic and rain water storage. Earth observation technology allowed researchers to accurately study the past, current and even predict the future of spatial temporal changes of urban environment including the water bodies (ponds). In developing nations like Nigeria many ancients’ cities became over crowded, this is likely because of their history, opportunities or economic advantages. Nevertheless, many of the ancient’s cities in Africa experienced regular annual urban flood that make the city centers as water logging throughout the wet season due to community culture toward destructions and claiming ownership of water bodies (ponds) either by government officials or individuals which normally serve as a domestic and rain water reservoir. Therefore, this research aimed on the utilization of geospatial technologies for mapping spatial temporal changes of urban water bodies (ponds) and its environment impacts in the study. Research also designed to map the geospatial distribution of ponds (urban water bodies) and how does human activities affect its functions. The satellite image data acquired for years 1999 and 2019 respectively. Nevertheless, the imageries were geometric and radiometric corrected using the quick atmospheric correction (QUAC). The findings indicated that most of the Ponds changed in their size, shape and mainly filled with solid waste. From the analysis of the research shown that annual urban flood is attributed from the destructions of ponds. Changes from other land use types also affect the water bodies such as schools, residential, commercial, etc. the findings also showed the impacts of ponds destructions such as making areas water logged, occurrence of urban flood, change in local climate and alteration of hydro-geomorphic nature of the area

Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms

Human-dominated ecosystems speed up the loss of habitats, populations, and species. Thus, monitoring and managing the Earth’s heritage of biodiversity is a challenge in natural resource management. Mapping protected areas (PAs) is essential in understanding the disturbance that can affect biodiversity and conservation management. Land use-land cover (LULC) maps can be used as a decision making tool by policy makers to ensure sustainable development and understanding of the effect of human activities within and around PAs. However, in Malaysia, the limited updated maps of PAs make the effective management of PAs problematic. Therefore, this study aimed to produce an updated Land LULC map for the PA Krau Wildlife Reserve (KWR) and its surroundings using remote sensing and related geospatial technologies. Three supervised classification algorithms were used and compared. Multidated images from Landsat 8 were utilized, and spectral angle mapper (SAM), support vector machine (SVM), and artificial neural network (ANN) classifiers were applied and evaluated. The approaches of pan-sharpening and cloud patching were used to enhance the accuracy of LULC classification. The images were classified into five classes: dense forest, less dense forest or agriculture, built-up area, bare soil, and water. The overall accuracies of SAM, ANN, and SVM for the 15 m spatial resolution images were 81.96%, 98.22% and 97.40%, respectively. The ANN map produced the highest overall accuracy and was consequently utilized to extract additional information related to disturbance and encroachment within and around the PA. Findings indicated that socioeconomic activities played a major role in altering the environment of KWR

Fish Diversity and Abundance Using Statistical Modelling in Hadejia-Nguru Wetlands (HNWs), Nigeria: An Adaptive Environmental Assessment

Fisheries and aquaculture plays a significant role in the Nigerian economy by providing employment, diversifying livelihoods, providing animal nutrition, and earning returns on foreign exchange. Fish is an important economic factor for many nations, as serves as a staple diet in many countries. As evident, in many developed nations, fishing is a crucial source of livelihood, particularly for low-income families in rural areas, where it offer local jobs in many communities and is a key source of food for millions. Over the past few decades, fish populations have deteriorated dramatically, and species at risk have experienced growing environmental challenges. Dams, overfishing, pollution, erosion, soil loss and other human activities are main threats to fisheries ecology. The presents study aimed to analyze the decline in diversity of the fish, adaptive management of artisanal fishermen in the wetlands of Hadejia-Nguru, Guri local government area of Jigawa State and to explore the correlation of environmental factors for the decline in fish diversity. In this study the data were collected through questionnaire interview (QI), focus group conversation (FGD), and field data collection (FDC), and the test objectives were accomplished via the analysis workflow. Geostatistical software was used to analyze the information obtained from QI, FGD and other sources while other auxiliary data and field data were collected using GPS receiver. The research findings can be considered as a tools for decision-making, policy-making, management plan development, fish conservation strategies plan and ultimately help to achieve the UN's Sustainable Development Goals (SDGs) 1, 2, 8, 14 and 15 of the 2030 agenda

Unveiling groundwater potential zones as catalyst for multidimensional poverty reduction using analytical hierarchical process and geospatial decision support systems (S-DSS) approach in the semiarid region, Jigawa, Nigeria

Integrating agricultural production with the identification and use of groundwater resources has been shown to reduce multidimensional poverty in semi-arid regions. Poverty reduction and socioeconomic growth depend on sustainable water supply, especially in developing countries with limited rainy seasons. Poverty eradication is a top priority among the 17 Sustainable Development Goals (SDGs), and its reduction in the 21st century has led to significant advances in research. This study used remote sensing, geographic information system (GIS), and geospatial decision support system (S-DSS) approaches to uncover potential groundwater zones. The Analytic Hierarchy Process (AHP) integrates geospatial data to create a groundwater potential zone map and suggests the best land requirements for groundwater abstraction for poverty alleviation programs. The groundwater potential zone maps indicate that the majority of the region was in the high-potential zone, covering 59.75 of the total area, followed by a moderate-potential zone at 23.21, an extremely high-potential zone at 14.6, a low-potential zone at 2.44, and an extremely low-potential zone at 0. In addition, the study emphasizes the need for alternative water sources and infrastructure development in dry seasons in areas with fewer drainage systems and proposes measures such as rainwater harvesting structures and small reservoirs. Diversifying income sources by promoting alternative livelihoods can help reduce poverty and vulnerability to fluctuations in agricultural productivity. The integration of socioeconomic data into the S-DSS framework will provide a comprehensive understanding of the complex relationship between groundwater resources, poverty, and socioeconomic development, enabling informed decision-making in water resource management for poverty reduction initiatives and the achievement of the 2030 Agenda for Sustainable Development Goals. © 2023 Elsevier B.V