Differences in land cover interpretation in landscapes rich in cover gradients: reflections based on the montado of South Portugal - Doorn, Anne M; Pinto Correia, Teresa (Agroforestry Systems, 2007)
This paper contributes to the discussion on current issues in methodologies of mapping land cover in the agro-silvo-pastoral landscapes of the Mediterranean. These landscapes, characterized by intermixed land use and indeÞnite boundaries, require particular attention in applying the patch-corridor-matrix model when classifying patches and their delineation. In a case study area in southeast Portugal, mainly characterized by agro-silvo pastoral systems, the land cover for 1990 has been mapped. The paper discusses the consequences of the complexity of some Mediterranean land use systems for land cover mapping dealing with detailed landscape dynamics. Within this scope a land cover mapping project in a small case study area is compared with the mapping undertaken within a national land cover database. Both studies were carried out on the same scale and through visual interpretation of aerial photographs. Differences in land cover classiÞcation and allocation are explored using matrix with levels of agreement. Recommendations for future land cover mapping projects are: the application of fuzzy approaches to land cover mapping in agro-silvo-pastoral landscapes should be explored and land cover classiÞcations should be standardized in order to enhance consistency between databases. On the other hand, the fuzziness of the boundaries in this kind of landscapes is inherent to the system and should be accepted as such. The accompanying uncertainties should be taken into account when undertaking landscape analysis on the basis of land cover data.
Keywords: land cover classification; montado/dehesa; thematic cartography; visual aerial photo interpretation

Differences in land cover interpretation in landscapes rich in cover gradients: reflections based on the montado of South Portugal - Doorn, Anne M.; Pinto Correia, Teresa (Agroforestry Systems, 2007)
This paper contributes to the discussion on current issues in methodologies of mapping land cover in the agro-silvo-pastoral landscapes of the Mediterranean. These landscapes, characterized by intermixed land use and indeÞnite boundaries, require particular attention in applying the patch-corridor-matrix model when classifying patches and their delineation. In a case study area in southeast Portugal, mainly characterized by agro-silvo pastoral systems, the land cover for 1990 has been mapped. The paper discusses the consequences of the complexity of some Mediterranean land use systems for land cover mapping dealing with detailed landscape dynamics. Within this scope a land cover mapping project in a small case study area is compared with the mapping undertaken within a national land cover database. Both studies were carried out on the same scale and through visual interpretation of aerial photographs. Differences in land cover classiÞcation and allocation are explored using matrix with levels of agreement. Recommendations for future land cover mapping projects are: the application of fuzzy approaches to land cover mapping in agro-silvo-pastoral landscapes should be explored and land cover classiÞcations should be standardized in order to enhance consistency between databases. On the other hand, the fuzziness of the boundaries in this kind of landscapes is inherent to the system and should be accepted as such. The accompanying uncertainties should be taken into account when undertaking landscape analysis on the basis of land cover data.
Keywords: land cover classification; montado/dehesa; thematic cartography; visual aerial photo interpretation

Improving satellite images classification using remote and ground data integration by means of stochastic simulation - Carvalho, Julia; Soares, Amilcar; Bio, Ana (International Journal of Remote Sensing, 2006)
A methodology is proposed, to assess land surface cover classification using a geostatistical methodology of stochastic simulation, direct sequential cosimulation, to combine field observations with remotely sensed data classified with the classical algorithm of maximum likelihood classification. This procedure has two main advantages: (1) incorporation of a spatial continuity statistics; and (2) integration of different scales of information, contained in polygons (training areas) and point information (field observations), which also involves different qualities of information that is less reliable and more reliable, respectively. Moreover, this methodology allows production not only of a classified map, but also of maps of occupation proportions and of uncertainty for each thematic class. Local co-regionalization models are applied to account for local differences in both field data availability and distribution, and the correlation between these hard data and the classified satellite images as soft data. The methodology is based on two criteria: the influence of the hard data dependent on their availability and proportional to their proximity; and the influence of the soft data dependent on their local correlation to the hard data. The method is applied to a study of four economically important forest tree species on the Setu´bal Peninsula (south of Lisbon, Portugal). The results show more contiguous forest covers, i.e. more spatial contiguity, than the classical classification. In comparison to a contemporary field inventory, the proposed method improved forest cover estimations, showing a difference of only 3%.
Keywords: CODSS (voyant); forest cover; land cover classification; Maximum likelihood classification; stochastic simulation

Improving satellite images classification using remote and ground data integration by means of stochastic simulation - Carvalho, Julia; Soares, Amilcar; Bio, Ana (International Journal of Remote Sensing, 2006)
A methodology is proposed, to assess land surface cover classification using a geostatistical methodology of stochastic simulation, direct sequential cosimulation, to combine field observations with remotely sensed data classified with the classical algorithm of maximum likelihood classification. This procedure has two main advantages: (1) incorporation of a spatial continuity statistics; and (2) integration of different scales of information, contained in polygons (training areas) and point information (field observations), which also involves different qualities of information that is less reliable and more reliable, respectively. Moreover, this methodology allows production not only of a classified map, but also of maps of occupation proportions and of uncertainty for each thematic class. Local co-regionalization models are applied to account for local differences in both field data availability and distribution, and the correlation between these hard data and the classified satellite images as soft data. The methodology is based on two criteria: the influence of the hard data dependent on their availability and proportional to their proximity; and the influence of the soft data dependent on their local correlation to the hard data. The method is applied to a study of four economically important forest tree species on the Setu´bal Peninsula (south of Lisbon, Portugal). The results show more contiguous forest covers, i.e. more spatial contiguity, than the classical classification. In comparison to a contemporary field inventory, the proposed method improved forest cover estimations, showing a difference of only 3%.
Keywords: CODSS (voyant); forest cover; land cover classification; Maximum likelihood classification; stochastic simulation