Recent landscape change at the ecoregion scale in Southern Québec (Canada), 1993-2001.

Landsat images covering the St. Lawrence Lowlands (30,000 km(2)) and Appalachians (33,000 km(2)) ecoregions of southern Québec, Canada, have been classified for the years 1993 and 2001 to (1) quantify land use/land cover (LULC) changes and changes to agricultural landscapes and (2) relate LULC changes to changes of farm-related descriptors of economic and farming activities. Over 25 LULC classes were identified on each classification which were merged into 5 LULC classes (anthropogenic, annual crop, perennial crop, forest, water/wetlands) used to delineate a gradient of five types of agricultural landscapes. Transition matrices reveal a shift in major agricultural classes in the St. Lawrence Lowlands where perennial crops have been converted into annual crops. Furthermore, suburban sprawl was observed adjacent to major cities whereas overall forest cover was reduced. Changes in agricultural land classes were few in the agroforested landscapes of the Appalachian ecoregion. Landscapes dominated by intensive agriculture expanded onto adjacent regions previously under extensive agriculture. Most farm-related variables extracted from agriculture censuses showed an increase, reflecting an intensification of agriculture in both ecoregions though no clear association with LULC changes were revealed. Increase in annual crops may be related to intensive corn production associated with pig farming. Differences in landscape changes between the two ecoregions may be related to proximal causal factors such as soil topography and suitability for high-quality crops. Our analysis will provide baseline information to implement a monitoring program of habitat dynamics in this vast region.

Object-based classification as an alternative approach to the traditional pixel-based classification to identify potential habitat of the grasshopper sparrow.

The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. Object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Québec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.