Remote sensing is growing rapidly as a valuable and, at times, critical
technology in monitoring the environment. This allows large-scale data acquisitions for
ecological applications. The chapter describes the impact of integrating remote sensing
into wildlife monitoring and conservation, particularly Hyperspectral Remote Sensing
(HRS), on sustainable agriculture. Monitoring of wildlife changes with the ongoing
advancements like satellite imagery, drone sensor-based techniques, and AI
technologies. This helps to map habitats, estimate populations, and monitor
biodiversity. On the other hand, HRS has a crucial role to play in agriculture through
more detailed spectral data, enhancing productivity through crop health assessment and
soil and water-resource management.
The chapter discusses some of the recent methodologies used in wildlife conservation,
such as species tracking with GPS collars, vegetation mapping with multispectral
imagery, and machine learning ecosystem classification. For sustainable agriculture in
hyperspectral data, precision farming, disease detection, and yield have potential
applications. This chapter compares how these two areas relate through comparative
analysis to show that ecological and agricultural monitoring could evolve together
using remote sensing technologies. A case study demonstrates the application of
integrated remote sensing in an agro-ecological landscape in terms of how habitat
conservation can be made to coincide with agricultural sustainability. Additionally, a
thorough literature review synthesizes key research works, for presentation in tabular
comparison, demonstrating methodologies and applications. Also, there is a flow chart
that represents the different components and processes involved in wildlife monitoring
and conservation through remote sensing technologies.
Keywords: Artificial Intelligence, Agriculture, Ecology, Hyperspectral remote sensing, Machine learning, Remote sensing, Sustainability, Wildlife monitoring.
