ABSTRACT
Wildlife management is dynamic — it has been evolving with time. Modern wildlife management is data-hungry and data-centric — requiring lots of data during planning, implementation, and monitoring stages. A large portion of this data is now being collected using the techniques of remote sensing — gathering information from a distance using equipment like cameras, satellites, and drones. This is especially useful to identify locations requiring management interventions such as removal of weeds, construction of fire lines, and provisioning of drinking water for animals.
The setting up of in-situ conservation areas such as wildlife reserves also requires data to justify their creation and / or continuation. This is especially so because increase in human population and affluence has skyrocketed the cost of land. Land can be put to so many multiple uses — like agriculture, setting up of industries, constructing houses and schools, mining, etc. — that it often becomes difficult for policymakers to “spare” and assign land for “wildlife purposes.” This is so even when we know fundamentally the benefits of conserving wildlife — and the benefits of ecological security. Thus we cannot just rely on “appealing to the hearts of people” — they already know that conservation of wildlife is necessary. We need to demonstrate that the benefits vastly exceed the costs — in “monetary terms.” The ecosystem benefits have to be measured in terms of their dollar (or currency) value to permit cost-benefit analyses — a comparison of the costs of setting up wildlife reserves by “sparing” land for such uses and managing the habitats — versus the benefits that such a decision will bring to the people. Only when the benefits — in monetary terms — exceed the costs, will the policy makers agree to create reserves.
In this chapter, we discuss these emerging aspects — using remote sensing and GIS to gather data about wildlife management, and using economic valuation of ecosystem benefits to facilitate cost-benefit analyses.
