“Kill Two Birds with One Stone”: Urban Tree Species Classification Using Bi-Temporal Pléiades Images to Study Nesting Preferences of an Invasive Bird

Abstract

This study presents the results of object-based classifications assessing the potential of bi-temporal Pléiades images for mapping broadleaf and coniferous tree species potentially used by the ring-necked parakeet Psittacula krameri for nesting in the urban area of Marseille, France. The first classification was performed based solely on a summer Pléiades image (acquired on 28 July 2015) and the second classification based on bi-temporal Pléiades images (a spring image acquired on 24 March 2016 and the summer image). An ensemble of spectral and textural features was extracted from both images and two machine-learning classifiers were used, Random Forest (RF) and Support Vector Machine (SVM). Regardless of the classifiers, model results suggest that classification based on bi-temporal Pléiades images produces more satisfying results, with an overall accuracy 11.5-13.9% higher than classification using the single-date image. Textural and spectral features extracted from the blue and the NIR bands were consistently ranked among the most important features. Regardless of the classification scheme, RF slightly outperforms SVM. RF classification using bi-temporal Pléiades images allows identifying 98.5% of the tree species used by the ring-necked parakeet for nesting, highlighting the promising value of remote sensing techniques to assess the ecological requirements of fauna in urban areas.

Dynamic species distribution models reveal spatiotemporal habitat shifts in native range‐expanding versus non‐native invasive birds in an urban area

“…Indeed, Platanus sp. is widely used by the RNP for breeding in its introduced range (Wegener 2007, Clergeau et al 2009, Czajka et al 2011, Hernández-Brito et al 2014, especially in the study area (Le Louarn et al 2017). Moreover, Platanus sp.…”

“…For the years 2015 and 2016, we used the GIS layer of the Platanus sp. trees coverage produced by a previous study (Le Louarn et al 2017). To obtain the density of Platanus sp.…”

Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

“…This is also an important reason as to why we chose Pléiades-1 images as data resources for our research. Compared with a single-data image, bi-temporal Pléiades images can improve the discrimination between tree species, which have been demonstrated by Louarn et al [22]. In this study, we only used a single-season Pléiades image with all overall classification accuracies below 85%.…”

“…Zhang et al [48], however, found that RF produced higher overall accuracy than SVM for mapping coastal vegetation. Louarn et al [22] shown that RF slightly outperformed SVM for urban tree species classification. These results showed that when employing different remote sensing images with different classification purposes, machine learning algorithms performed differently.…”

“…As a classic machine learning algorithm, decision tree (DT), referred to as CART (classification and regression tree) or C4.5 algorithm et al, is less used in mangrove classification. RF is a relatively new technique for mangrove species mapping, though it has been widely applied in land [20,21] and plant species [8,22] classification with different sensors in recent years. In addition, DT, RF and SVM all can be implemented in eCognition 9.0 (a popular object-based image analysis software, Trimble, Sunnyvale, CO, USA) with streamlined production models.…”