Spatial - temporal mapping of agro - ecosystems and the need to build thematic legends

During the past 10 years several inter-related topics were discussed at different forums. They were: (i) land cover and land use classification systems, (ii) geomatics, being the scientific management of spatial information, and (iii) monitoring of climate, land cover and land use changes. Simultaneously the spatial and temporal quality of remote sensing platforms and of GPS-technology improved substantially and provided new possibilities to study our environment in detail. Progress made by the forums and the quality increases of RS-products are only worthwhile if gathered spatial GIS-information can be integrated and processed, i.e. if the used survey, generalization and classification methods are scientifically based. In other words: the quality of gathered spatial information, at various scales, must be sufficiently high to support modeling, monitoring, and preparation of decision-support systems for sustainable land management. In spite of this, available standards for the collection, storage, generalization, classification and presentation of geo-information on climate, soil and terrain conditions, water resources, land use, land cover and bio-diversity, and on social and economic conditions, are often poorly implemented, or re-defined on a project-by-project basis. The root causes are: (i) non-familiarity with underlying concepts, (ii) use of terminology without considering their scientific definitions, (iii) complications faced when standards and rules must be followed, (iv) ignoring future uses of prepared products, and (v) institutional conservatism. To integrate available standards, a “root” concept of a comprehensive information system is presented. Each theme presented at the root can be linked to a theme-based relational database. Joint use of each thematic layer creates a full description of an agroecosystem or of an agro-ecosystem class (full descriptions help to study the system functioning). Map legends must like-wise contain entries on a theme-by-theme basis, possibly nested, or must remain theme specific (theme specific maps are required for monitoring). Following this, legends covering several themes should not be based on a simple list of suggestive names that cross theme boundaries and represent an incomplete mix of class entries of the themes covered. To further substantiate the above, the theme ‘land use’ is elaborated upon in some detail. Concepts to describe agricultural land use and the subsequent method to classify collected ‘land use’ data are presented. Shown are the key concepts: ‘crop calendar’ and ‘cropping system’ (classification system appended). They form important links between RS-images, land cover, land use, and the subsequent land use legend (= classification application). The links are demonstrated by discussing spectral classification results of multi-temporal RS-images. Derived map units link to monothematic legend classes: units having natural vegetation differ in plant phenology while units having agro-ecosystems differ in crop calendar. The used method, following only two key concepts, provides support to further improve mapping of agro-ecosystems (e.g. by describing the operation sequences followed), to improve cropmonitoring methods, and to build spatial information systems that adhere to sound survey, generalization and classification methods.