Assessing ecological connectivity in the Serra do Cando and Serra do Candán area of Galicia: A multitemporal classification and least-cost path modelling approach

Abstract

Ecological connectivity is essential for mitigating the anthropogenic impact caused by urbanization, infrastructure, and the production of goods on natural habitats and their fragmentation. This study assesses the state of ecological connectivity between hardwoods habitats in different years for a Natura 2000 area in Galicia, in northwestern Spain, the Serra do Cando and Candán. A supervised land cover classification was performed using two different machine learning algorithms, an Artificial Neural Network (ANN) and Random Forest (RF), and Sentinel-2 images from 2015 and 2022. A possible future land use scenario for the year 2029 was generated with Modules for Land Use Change Evaluation (MOLUSCE) plugin for QGIS from a Multilayer Perceptron ANN. Land use information was used to construct resistance surfaces on which ecological corridors were modelled as least-cost paths between habitat patches. The equivalent connected area (ECA) was calculated to quantify the level of connectivity and compare different time periods. Classifications achieved an accuracy of 91 % in RF and 88 % in ANN for the year 2015, and 92 % and 91 % respectively in 2022. The results for the year 2029 show a decrease in areas under crops and grassland according to RF and conifers in the case of ANN. The highest ECA values were reached in 2022 with 864 ha according to the RF-based methodology and 757 ha according to ANN. The area of hardwoods patches was the fundamental parameter that affects ECA. Combining remote sensing techniques with the least-cost paths method, including a simulation of future land use changes, has made it possible to compare the degree of ecological connectivity in different scenarios. This methodology shows the effects of land-cover changes and provides a tool to support decision making in land use planning.