Analysis of Changes in Forest Structure using Point Clouds from Historical Aerial Photographs

Abstract

Dynamic changes in land use, many of which are related to land abandonment, are taking place in many regions of the world. As a result, forest vegetation appears, which in part is a consequence of planned afforestation programs and in part has the characteristics of secondary forest succession. Monitoring of forest structure allows the range and dynamics of such changes to be identified. The aim of the study was to assess the usefulness of historical aerial photographs in the determination of forest structure. On the basis of such data, a point cloud was created which represented the forest structure in 1966. Subsequently, using airborne laser scanning data for the same area, corresponding datasets describing the situation in 2012 were created. Comparison of the two tall vegetation models made it possible to perform four analyses related to forest structure changes over a period of 46 years. The analyses were carried out in four areas in southern Poland. The analysis of the results confirmed that historical aerial photographs may be a valuable source in long-term analyses of changes in the range and height structure of areas containing tall vegetation.

Structure-from-Motion-Derived Digital Surface Models from Historical Aerial Photographs: A New 3D Application for Coastal Dune Monitoring

“…Another option would be to measure the check points with a DGNSS, even though in our case, given the large area of AOI, not every measuring site would have been physically reachable. If the LiDAR data are accurate enough (here the values were 0.100 and 0.069 m for X-Y and Z, respectively) to detect long-term geomorphic processes (e.g., dune erosion over several decades), the SfM approach for historical aerial photos can also be adopted elsewhere without the use of a DGNSS and with a reliable GCP configuration, as demonstrated by [34,36]. The option of validating AP-derived DSMs against LiDAR data is a common approach, especially when the AOIs are large [34,36] or where the field sites are not easily accessible [2,38].…”

“…GCP coordinates (X,Y,Z) were retrieved from the 2014 LiDAR-derived DSM by carefully selecting points where the Z value was not influenced by nearby walls, buildings, or tall vegetation (Figure 3a-i). Highly dependent on local landscape changes (natural and anthropic) in the area during the 50 years in between, this was the most laborious but important step, since poor GCP recognition and distribution can adversely influence the final DSM output [34,35]. For the entire dataset, a total of 28 GCPs were identified, with a good spatial spread across the area represented by the six aerial photographs (Figure 4a).…”

“…Products deriving from the SfM process of historical aerial photographs have also been used for archeological purposes and have been validated against DGNSS and UAV measurements [33]. Historical aerial photographs have been recently used as valuable sources for long-term analyses of areas with tall vegetation [34], comparing the resulting point clouds with modern airborne laser scanning data.…”

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

“…Specifically, the majority of studies identified AAL using object-based image analysis with or without a fusion of machine learning algorithms [18,19] based on vegetation indexes, such as the normalized vegetation index (NDVI) [20,21]. Although ALS data were used less frequently as a primary data source [22,23], the combination of optical and/or radar data with ALS data was defined as a prospective solution for RS-based identification of AAL [8]. While many studies have examined the RS-based spatial identification of AAL, less attention has been paid to predicting AGB specifically in these areas.…”

“…Many studies have used different RS data and modelling methods to predict AGB in forest, shrub, or grassland ecosystems (e.g., [10,[32][33][34][35][38][39][40][41][42]). However, as far as we know, relatively few studies (e.g., [22,23,26]) have dealt with the spatial identification of AAL and prediction of AGB on AAL using ALS data. Moreover, an author-developed algorithm for the calculation of ALS metrics, which was used in this study, has not yet been broadly tested and reported.…”