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ŠUMARSKI LIST 11-12/2018 str. 10 <-- 10 --> PDF |
2017; Talbot et al., 2017), as well as environmental aspect of harvesting technologies (Cambi et al., 2018; Salmivaara et al., 2018). However, accurate terrain modelling, either using terrestrial or remote sensing methods in the complex forest environment, is challenging as it often includes elevation errors that are hard to detect. Labour-intensive and time-consuming terrestrial surveys are difficult to obtain due to complex forest structure that often blocks satellite signals to Global Navigation Satellite Systems (GNSS) receivers or interrupts measurements with total stations. With the development of remote sensing technology, however, the collection of terrain information has become more practical and more feasible. The airborne Light Detection and Ranging (LiDAR) technology nowadays presents the most prominent and effective remote sensing method for DTM generation in complex forested areas (Gill et al., 2013; Stereńczak et al., 2016). Although many countries are capable of conducting nation-wide airborne LiDAR campaigns to produce DTMs, a comparatively large number of countries worldwide (e.g. European countries such as Croatia, Greece, Hungary, Slovakia, etc.) still rely on photogrammetrically-derived terrain data. In these countries, photogrammetrically-derived terrain data still present the national standard for DTMs (Höhle and Potuckova, 2011). However, only a limited number of studies have evaluated the accuracy of photogrammetrically derived DTM (DTMPHM) in forested areas either from aerial (Balenović et al., 2018; DeWitt et al., 2015; Gill et al., 2013) or satellite images (DeWitt et al., 2017; Hu et al., 2016). Studies confirmed a lower accuracy of DTMPHM when compared to LiDAR DTM (DTMLiD), commonly observed through a certain number of outliers (i.e., gross errors). Balenović et al. (2018) conducted a comparative accuracy assessment of DTMLiD and DTMPHM in dense lowland even-aged pedunculated oak forests in Croatia. The authors discovered that the nature of the national digital photogrammetric data (from which DTM was generated) considerably affected the DTM accuracy. After manual detection and elimination of the outliers from photogrammetric data, the accuracy of DTMPHM was notably improved. Unlike the studies related to the accuracy of DTMPHM, there are several studies related to DTM errors detection and accuracy improvements of free global DTMs (Tran et al., 2014) or DTMs derived from aerial (Schultz et al., 1999; López, 2002) and satellite data (Felicísimo et al., 2004). To the best of the authors’ knowledge, no previous studies have considered the automatization of error detection and improvements of DTMPHM in forested areas. The main aim of this study is to develop an automatic method for detection and elimination of elevation errors in photogrammetrically derived terrain data, and consequently to improve the vertical accuracy of DTMPHM for lowland pedunculated oak forests in Croatia. The idea is to develop a fast, simple and efficient method, which will be applicable for this and other similar forested areas worldwide. This paper presents the continuation of the previous research conducted by Balenović et al. (2018), which confirmed the improvements of DTMPHM accuracy after manual detection and elimination of the outliers. MATERIALS AND METHODS MATERIJAL I METODE Study area – Područje istraživanja The study area is the management unit Jastrebarski lugovi, located in the Pokupsko Basin forest complex. The area covers 2,005.74 ha of the state-owned productive lowland forests, located in Central Croatia, approximately 35 km southwest of Zagreb (Figure 1). Even-aged pedunculate oak (Quercus robur L.) forests of different age classes ranging from 0 to 160 years are the main forest type and cover approximately 77% of the study area. The oak stands are commonly mixed with other tree species such as common hornbeam (Carpinus betulus L.), black alder (Alnus glutinosa (L.) Geartn.), and narrow-leaved ash (Fraxinus angustifolia Vahl.). The rest of the study area (≈20%) is covered by even-aged narrow-leaved ash forests aged between 0 to 80 years. The ash stands are predominantly homogeneous and occasionally mixed with other tree species such as black |