Mapping standing dead trees in temperate montane forests using a pixel- and object-based image fusion method and stereo WorldView-3 imagery. (December 2021)

• Record Type: Journal Article
• Authors: Liu, Xiang; Frey, Julian; Denter, Martin; Zielewska-Büttner, Katarzyna; Still, Nicole; Koch, Barbara
• Is Part Of: Ecological indicators. Volume 133(2021)
• Publication Date: 2021-12
• Subjects: Standing dead trees -- Stereo WorldView-3 -- Object-based classification -- Machine learning -- Fusion method; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2021.108438
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Stereo WV-3 imagery is used to map standing dead trees in the Black Forest. The POBIF approach significantly outperforms pixel- and object- based methods. Adding CHM greatly reduces commission and omission errors caused by bare ground. Vegetation indices cannot significantly improve the accuracy of SDT mapping. Abstract: Information about the distribution of standing dead trees (SDT) is essential for forest biodiversity estimation, forest disturbances monitoring, and forest management strategy planning. Although remote sensing techniques offer unique capabilities to map SDT over large areas, three major hurdles exist: (1) the sporadic distribution of SDT in the study area; (2) often poor spectral separability between SDT and bare ground in forests; (3) the prominent spectral variability within SDT due to variations in background effect and canopy illumination. To address these problems, we proposed a pixel- and object-based image fusion (POBIF) approach using very high-resolution (VHR) stereo WorldView-3 (WV-3) data. The stereo WV-3 derived spectral bands, canopy height model (CHM), vegetation indices (VIs), and texture features were used as inputs in six classification scenarios with different variable combinations. A deep learning algorithm, deep neural network (DNN), and two machine learning algorithms, support vector machine (SVM) and random forest (RF), were utilized to process the pixel-based (PB) and object-based (OB) information. All PB and OB classifiers were then combined using a stacked generalization strategy to develop the POBIF model. Comparing the six scenarios we assessed the importance of the CHM, VIs, and textures for accurate SDT mapping. As a result, we found (1) the POBIF outperformed both PB and OB methods for SDT mapping, generating notably higher F1-score (p < 0.05); (2) Adding the CHM reduced the commission and omission errors caused by bare ground and artificial surfaces, and the highest classification accuracy was achieved when combing all the WV-3 derived variables; (3) SDT covered 0.89% of the forest areas in the study area and was particularly distributed on higher and steeper north-facing (northeast and northwest) slopes. Large tracts of SDT were found in the strictly protected forests. The study highlights the potential of VHR stereo WV-3 imagery and the POBIF for SDT mapping in a temperate montane forest area with high accuracy. The map created in this study could be used for guiding field investigations and for planning management measures.