The Application of Remote Sensing and Geographic Information System in Assessing Probable Tsetse Flies Habitats in Ikom LGA, Cross River State, Nigeria

Abstract

This paper examined the possible breading space of tsetse flies in Ikom Local Governement Area and it's environs in Cross River State, Nigeria, using remote sensing and geographic information system techniques. The study used supervised classification techniques with maximum likelihood algorithms to classify the satellite imagery in to four categories as vegetation-agriculture, water bodies, built-up lands, and other land uses and digitized all the rivers on the imagery as polylines. A buffer operation of 1km away from the digitized area was done; an overlaid analysis was carried out on the buffered operation and the classified image, so as to analyze the probability of breeding of the tsetse flies on each parcel of land. The study grouped the possible breeding sites of the tsetse flies into three; most probable, moderately probable and less probable breeding space. Result of the findings reveals that 48.496 Hectares (24.64 %) is most probable for tsetse flies to breed on, 107.54 hectares (54.24 %) is moderately probable, and 42.231 Hectares (21.3 %) is less probable. Based on the findings of the study it's apparent that Ikom Local Governement Area and it's environs provide a good breeding space and habitat for tsetse flies, it becomes imperative for all stakeholders in the control and eradication of tsetse flies, to work towards its total eradication in the area.

Teaching of Remote Sensing Technology for Landscape Architecture in the Context of Spatial Information Technology

“…The emerging digital technology provides a rational and objective method to mine and quantify the endogenous laws of landscape architecture, making the landscape architecture research much more scientific and efficient. Spatial information technology has quickly permeated every aspect of the teaching, scientific research, and engineering of landscape architecture [1][2][3][4][5][6][7][8][9][10]. Representative spatial information techniques, including remote sensing (RS), spatial big data, earth observation monitoring, and spatial positioning and simulation, directly drive the synergistic development between the teaching of RS in landscape architecture and the teaching of landscape architecture design, and stimulate the continued breakthroughs and innovations of relevant teaching platforms, methods, and experimental means [11].…”