Natural and anthropogenic activities surrounding a Protected Area (PA) may cause its natural area to change in terms of Land Use-Land Cover (LULC). Thus, there is need of environmental change monitoring within and around PA because of its significant values to ecosystem at conservation scales. Effects and influences of local community within and around PA turn into the major problems for natural resource and conservations management as well as environmental impact assessment. Ascertaining the complex interface in relations to changes and its driving factors over period of time within and around PA is significant in order to predict future LULC changes, build alternative scenarios and serve as tools for decision making. The main objective of this work was to evaluate temporal change detection and prediction of LULC as well as the trends of changes from 1989 to 2016 within and around Krau Wildlife Reserve (KWR). The cloud issues were mitigated by producing cloud free image and object-based image analysis (OBIA) was adopted after a comparison with pixel-based analysis for overall accuracy and kappa statistics. The comparison of classified maps had produced a satisfactory results of overall accuracies of 91%, 86% and 90% for 1989, 2004 and 2016 respectively. The natural/dense forest between periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest were increased. The simulation model of Land Change Modeler (LCM) was utilized with digital elevation model (DEM) and past LULC maps to project future LULC pattern using Markov chain. The predicted map trend showed an increase of dense forest converted to agricultural/sparse forest in the north-western, and urban/built-up in east-southern part of KWR. The study is important for the conservation of habitat species and monitoring the current status of the KWR
A number of research have been carried out on geomorphology using a conventional approach to classify the landform; this has a tendency of producing misleading result, due to ruggedness and inaccessibility of the terrain. Geographic Information System (GIS) and remote sensing techniques are capable of generating automated landform classes using Topographic Position Index techniques (TPI). This research is set to achieve the following objectives: to categorize landform elements and to illustrate the complexity of the terrain in Negeri Sembilan state based on ASTER GDEM with 30 m resolution. TPI-based algorithm for landscape classification was applied to slope position and landform classification automation. We used 300 and 3000 neighbourhood size on the TPI grids to determine the landform categories. To quantify the spatial pattern of topographic position, Deviation from mean elevation (DEV) is adopted. Maximum Elevation Deviation was selected to measure the spatial landscape pattern at the maximum (3000) scale of the absolute DEV value within the scale (DEVmax), and finally, high-pass filter algorithm was used to identify the extreme topography (ridges/valleys). The combination of the TPI and slope position of DEV that formed the landform classification results show four prominent landform classes these include canyons, U-shape valley, local ridges/ hill valleys, and mountaintops/high ridges. The slope position classes revealed only two (valley/cliff base and ridges/canyons edge) classes based on slope position index. The canyons had the maximum of 63% and minimum was Ushaped valley with 1.04% for the landform of the area of interest. To achieve better results, there is a need to utilize a high spatial resolution remotely sensed DEM derived data and sensitivity analysis need to be incorporated. For that, laser scanning data is capable of improving the results.
Malaria is a significant public health issue in Nigeria where it accounts for more infections and deaths than any other nation in the world. Malaria is a concern for 97 percent of Nigeria’s population. The remaining 3 percent of the people reside in the malaria free highlands. There are an estimated 100 million malaria cases with over 300,000 deaths per year in Nigeria. It contrasts with 215,000 deaths a year in Nigeria from HIV / AIDS. Malaria contributes to an estimated 11 percent of maternal mortality. The study employed interpolated approach for the assessment and mapping of malaria cases from 2014 to 2018 in Hadejia metropolis and compares five year data of malaria prevalence within the political wards in the study area, using geo-spatial tools. The results showed that certain wards in the city have malaria cases danger which have a direct impact on human safety, social welfare which economy. The prevalence of the malaria parasites primarily exists in north and east of the sample country. Consequently the district is divided into eleven strata (political wards) which demonstrate that the higher incidence of Malaria for both years around Kasuwar Kofa, Kasuwar Kuda, Dubantu, Yayari and some portion of Matsaro and Gagulmari.
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