Landscape resource mapping for wildlife research using very high resolution satellite imagery

Monitoring Urban Green Infrastructure Changes and Impact on Habitat Connectivity Using High-Resolution Satellite Data

In recent decades, the City of Stockholm, Sweden, has grown substantially and is now the largest city in Scandinavia. Recent urban growth is placing pressure on green areas within and around the city. In order to protect biodiversity and ecosystem services, green infrastructure is part of Stockholm municipal planning. This research quantifies land-cover change in the City of Stockholm between 2003 and 2018 and examines what impact urban growth has had on its green infrastructure. Two 2018 WorldView-2 images and three 2003 QuickBird-2 images were used to produce classifications of 11 land-cover types using object-based image analysis and a support vector machine algorithm with spectral, geometric and texture features. The classification accuracies reached over 90% and the results were used in calculations and comparisons to determine the impact of urban growth in Stockholm between 2003 and 2018, including the generation of land-cover change statistics in relation to administrative boundaries and green infrastructure. For components of the green infrastructure, i.e., habitat networks for selected sensitive species, habitat network analysis for the European crested tit (Lophophanes cristatus) and common toad (Bufo bufo) was performed. Between 2003 and 2018, urban areas increased by approximately 4% while green areas decreased by 2% in comparison with their 2003 areal amounts. The most significant urban growth occurred through expansion of the transport network, paved surfaces and construction areas which increased by 12%, mainly at the expense of grassland and coniferous forest. Examination of urban growth within the green infrastructure indicated that most land area was lost in dispersal zones (28 ha) while the highest percent change was within habitat for species of conservation concern (14%). The habitat network analysis revealed that overall connectivity decreased slightly through patch fragmentation and areal loss mainly caused by road expansion on the outskirts of the city. The habitat network analysis also revealed which habitat areas are well-connected and which are most vulnerable. These results can assist policymakers and planners in their efforts to ensure sustainable urban development including sustaining biodiversity in the City of Stockholm.

Ensemble modeling to predict habitat suitability for a large-scale disturbance specialist

To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black-backed Woodpeckers (Picoides arcticus; a burned-forest specialist) at 20 recently (≤6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D² models) and various combinations of four environmental variables describing burn severity, the north–south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled “ensemble predictions,” consisting of the number of models (0–8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., “no-analogue”) environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black-backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide managers attempting to balance salvage logging with habitat conservation in burned-forest landscapes where black-backed woodpecker nest location data are not immediately available. Ensemble modeling represents a promising tool for guiding conservation of large-scale disturbance specialists.