1 Department of Civil Engineering, Federal University of Technology, Owerri, Imo State, Nigeria.
2 Department of Physics, Olusegun Agagu University of Science and Technology, Okitipupa, Ondo State, Nigeria.
3 Department of Civil Engineering, Federal University of Oye Ekiti, Ekiti State, Nigeria.
4 Department of Building Technology, Federal Polytechnic Ado Ekiti, Ekiti State, Nigeria.
5 Department of Civil Engineering, Federal University of Technology Akure, Ondo State, Nigeria.
6 Department of Electronics and Telecommunication Engineering, Ahmadu Bello University Zaria, Nigeria.
7 Department of Building Technology, Federal Polytechnic Ede, Osun State, Nigeria.
8 Department of Geology, University of Nigeria, Nsukka, Nigeria.
9 Department of Civil Engineering and Construction, Superiure d’Ëlectricite, de Batiment et des Travaux Publics (ESEBAT), Senegal.
10 Department of Civil Engineering Technology, Auchi Polytechnic, Edo State, Nigeria.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 839-851
Article DOI: 10.30574/wjarr.2024.23.1.2057
Received on 30 May 2024; revised on 08 July 2024; accepted on 10 July 2024
The integration of cutting-edge remote sensing technologies, biophysical principles, and advanced spatial statistics enables innovative landscape analysis across various spatial and temporal scales. Traditional approaches relied on classification methods and indices derived from multi-spectral imagery to assess landscape degradation. However, modern techniques can extract biophysical indices like leaf area index and canopy chemistry from satellite imagery. Long-term remote sensing archives (e.g., Landsat, AVHRR) facilitate retrospective studies of landscape changes and trajectories. Recent advancements in sensors and analysis techniques, such as sub-pixel classifications and continuous fields, have improved the accuracy of variable retrieval (e.g., Albedo, chlorophyll concentration). These developments enable powerful monitoring tools for land use/cover change detection, leading to a better understanding of landscape dynamics and the mapping of previously unexplored features. However, a trade-off exists between high spatial and high temporal resolution depending on the platform used.
Remote Sensing; Spatial Statistics; Landscape Analysis; Biophysical Principle; Temporal Resolution
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Chibuike Godswill Nzeanorue, Raphael Aduramimo Olusola, Peter Dayo Fakoyede, Merinubi Sunday Daramola, Ewemade Cornelius Enabulele, Agada Olowu Innocent, Adeleke Olaniyi Benjamin, Eze Kelechi Nnaji, Mame Diarra Bousso Diouf and Grace Agbons Aruya. Advanced remote sensing technologies for tracking landscape changes and environmental conditions. World Journal of Advanced Research and Reviews, 2024, 23(1), 839-851. Article DOI: https://doi.org/10.30574/wjarr.2024.23.1.2057