Secondary succession is considered a threat to non-forest Natura 2000 habitats. Currently available data and techniques such as airborne laser scanning (ALS) data processing can be used to study this process. Thanks to these techniques, information about the spatial extent and the height of research objects—trees and shrubs—can be obtained. However, only archival aerial photographs can be used to conduct analyses of the stage of succession process that took place in the 1960s or 1970s. On their basis, the extent of trees and shrubs can be determined using photointerpretation, but height information requires stereoscopic measurements. State-of-the-art dense image matching (DIM) algorithms provide the ability to automate this process and create digital surface models (DSMs) that are much more detailed than ones obtained using image matching techniques developed a dozen years ago. This research was part of the HabitARS project on the Ostoja Olsztyńsko-Mirowska Natura 2000 protected site (PLH240015). The source data included archival aerial photographs (analogue and digital) acquired from various phenological periods from 1971–2015, ALS data from 2016, and data from botanical campaigns. First, using the DIM algorithms, point clouds were generated and converted to DSMs. Heights interpolated from the DSMs were compared with stereoscopic measurements (1971–2012) and ALS data (2016). Then, the effectiveness of tree and shrub detection was analysed, considering the relationship between the date and the parameters of aerial images acquisition and DIM effects. The results showed that DIM can be used successfully in tree and shrub detection and monitoring, but the source images must meet certain conditions related to their quality. Based on the extensive material analysed, the detection of small trees and shrubs in aerial photographs must have a scale greater than 1:13,000 or a 25 cm GSD (Ground Sample Distance) at most, an image acquisition date from June–September (the period of full foliage in Poland), and good radiometric quality.
One of the threats that has a significant impact on the conservation status and on the preservation of non-forest Natura 2000 habitats, is secondary succession, which is currently analyzed using airborne laser scanning (ALS) data. However, learning about the dynamics of this phenomenon in the past is only possible by using archival aerial photographs, which are often the only source of information about the past state of land cover. Algorithms of dense image matching developed in the last decade have provided a new quality of digital surface modeling. The aim of this study was to determine the extent of trees and shrubs, using dense image matching of aerial images. As part of a comprehensive research study, the testing of two software programs with different settings of image matching was carried out. An important step in this investigation was the quality assessment of digital surface models (DSM), derived from point clouds based on reference data for individual trees growing singly and in groups with high canopy closure. It was found that the detection of single trees provided worse results. The final part of the experiment was testing the impact of the height threshold value in elevation models on the accuracy of determining the extent of the trees and shrubs. It was concluded that the best results were achieved for the threshold value of 1.25–1.75 m (depending on the analyzed archival photos) with 10 to 30% error rate in determining the trees and shrubs cover.
ABSTRACT:Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.
ABSTRACT:Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.
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