Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis

Abstract

Abstract:The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT) in image segmentation, and to apply the object-based image analysis (OBIA) approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial) imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping.

Forest cover changes in Gorce NP (Poland) using photointerpretation of analogue photographs and GEOBIA of orthophotos and nDSM based on image-matching based approach

“…Moskal and Jakubauskas (2013), studying fire-disturbed forests using aerial photos and GEOBIA methods, achieved classification accuracies between 68% and 78% depending on the level of image analysis (three classes in each level; total nine classes). Similar results were obtained by Moskal et al (2011) for urban tree cover assessment using digital aerial imagery (GSD 1.0 m) and object-oriented classification, with tree cover classified with a user's accuracy of 80% and a producer's accuracy of 93%.…”

“…Hernando, Tiede, Albrecht and Lang (2012) used digital Colour Infrared (CIR) orthophotos for object-based forest stand delineation. Moskal and Jakubauskas (2013) applied GEOBIA to aerial CIR images to examine how post-fire disturbance affects forest density and to map forest regeneration. GEOBIA was also employed by Meneguzzo, Liknes and Nelson (2013), who used high-resolution aerial imagery to map trees outside forests.…”

Monitoring the Secondary Forest Succession and Land Cover/Use Changes of the Błędów Desert (Poland) Using Geospatial Analyses

“…The geodata and modern technologies provide accurate information on the spatial and temporal distribution of LULC classes and deliver indicators that show the dynamic process of the landscape changes (especially process of the secondary forest succession) including the spatial range and structure of vegetation (Bergen, Dronova 2007, Falkowski et al 2009, Mancino et al 2014, Suzanchi, Kaur 2011. The automation of the processing using GIS analysis and GEOBIA tools, allows for the obtainment of very accurate borders of the land cover classes compared to the traditionally applied photo-interpretation and on-screen vectorization methods, but in a faster, more cost-effective, objective and efficient way (Moskal, Jakubauskas 2013, Szostak et al 2014, Wężyk, de Kok 2005.…”

Investigating Forest Disturbance Using Landsat Data in the Nagagamisis Central Plateau, Ontario, Canada

“…Many studies make use of multiple image dates in order to detect changes in land cover through time [2,17,19]. There are many different analytical methods presented in the literature yet, there is no consensus on the selection of methods that can be applied to all land cover applications with equal success and accuracy [15,32,33]. In the comprehensive review of Coppin and Bauer [34], many change detection techniques are tested and the authors conclude that image differencing and linear transformations perform better than other change detection methods [35].…”