|Remote Sensing for Agriculture
The increasing world population, coupled with the growing pressure on the land resources, necessitates the application of technologies such as GIS to help maintain a sustainable water and food supply according to the environmental potential. The “sustainable rural development” concept envisages an integrated management of landscape, where the exploitation of natural resources, including climate, plays a central role. In this context, agrometeorology can help reduce inputs, while in the framework of global change; it helps quantify the contribution of ecosystems and agriculture to carbon budget (Maracchi, 1991). Agroclimatological analysis can improve the knowledge of existing problems allowing land planning and optimization of resource management. One of the most important agroclimatological applications is the climatic risk evaluation corresponding to the possibility that certain meteorological events could happen, damaging crops or infrastructure.
At the national and local level, possible GIS applications are endless. For example, agricultural planners might use geographical data to decide on the best zones for a cash crop, combining data on soils, topography, and rainfall to determine the size and location of biologically suitable areas. The final output could include overlays with land ownership, transport, infrastructure, labour availability, and distance to market centres.
The ultimate use of GIS lies in its modelling capability, using real world data to represent natural behaviour and to simulate the effect of specific processes. Modelling is a powerful tool for analyzing trends and identifying factors that affect them, or for displaying the possible consequences of human activities that affect the resource availability.
In agrometeorology, to describe a specific situation, we use all the information available on the territory: water availability, soil types, forest and grasslands, climatic data, geology, population, land-use, administrative boundaries and infrastructure (highways, railroads, electricity or communication systems). Within a GIS, each informative layer provides to the operator the possibility to consider its influence to the final result. However, more than the overlap of the different themes, the relationship of the numerous layers is reproduced with simple formulas or with complex models. The final information is extracted using graphical representation or precise descriptive indexes.
In addition to classical applications of agrometeorology, such as crop yield forecasting, uses such as those of the environmental and human security are becoming more and more important. For instance, effective forest fire prevention needs a series of very detailed information on an enormous scale.
The analysis of data, such as the vegetation coverage with different levels of inflammability, the presence of urban agglomeration, the presence of roads and many other aspects, allows the mapping of the areas where risk is greater. The use of other informative layers, such as the position of the control points and resource availability (staff, cars, helicopters, aeroplanes, fire fighting equipment, etc.), can help the decision-makers in the management of the ecosystems. Monitoring the resources and the meteorological conditions therefore allows the consideration of the dynamics of the system, with more adherences to reality.