The potential of digital surface models based on aerial images for automated vegetation mapping

Granholm, Ann-Helen; Olsson, Håkan; Nilsson, Mats; Allard, Anna; Holmgren, Johan

doi:10.1080/01431161.2015.1029094

Cited by 22 publications

(12 citation statements)

References 26 publications

(34 reference statements)

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“…The main benefit of the photogrammetric canopy model is that no separate flights are required for the acquisition of 3D data and imagery, which often have different flight parameters in relation to the imaging altitude and coverage in case of ALS and aerial imagery. Furthermore, aerial images are typically acquired at frequent intervals for purposes other than forestry such as mapping and surveying, which increases their availability compared to ALS data (Granholm et al 2015;Stepper et al 2016). Therefore, aerial images have significant potential for operational use in forestry applications (Bohlin et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Tuominen¹,

Pitkänen²,

Balazs³

et al. 2017

Silva Fenn.

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Highlights• 3D aerial imaging provides a feasible method for estimating forest variables in the form of thematic maps in large area inventories.• Photogrammetric 3D data based on aerial imagery that was originally acquired for orthomosaic production was tested in estimating stand variables.• Photogrammetric 3D data highly improved the accuracy of forest estimates compared to traditional 2D remote sensing imagery. AbstractOptical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km². Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials.On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower shortrange correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.

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Section: Introductionmentioning

confidence: 99%

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Tuominen¹,

Pitkänen²,

Balazs³

et al. 2017

Silva Fenn.

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“…The majority of research on using height data in combination with spectral data for the classification of vegetation has been primarily made related to forestry (e.g., [9][10][11]). However, there are considerable differences in height and spatial extent between trees and aquatic plants.…”

Section: Introductionmentioning

confidence: 99%

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

2017

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Abstract:Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM) created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a) Growth forms including the classes of water, nymphaeid, and helophyte; and (b) dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each) with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%-21% for growth-forms and 3%-30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities for operative mapping.

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“…measurements from sample plots) following the example of recent worldwide studies (e.g. [11][12][13][19][20][21][22][23][24][25].…”

Section: Future Research Directionsmentioning

confidence: 99%

Quality assessment of high density digital surface model over different land cover classes

Balenović¹

2016

Period Biol

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Background and Purpose: Recent research on generation of digital surface models (DSMs) using image matching methods revealed a great potential of DSM application in forestry, especially in forest inventory. However, research dealing with DSM generation from digital aerial images are still lacking in Croatia. Therefore, the main objective of this study was to present the workflow for generating high density DSM from colour infrared (CIR) digital stereo aerial images using area-based image matching algorithm. Materials and Methods:The high density DSM was generated from colour infrared digital aerial stereo images using Dense DTM algorithm of PHOTOMOD software -an area-based image matching algorithm which operates on the principle of cross-correlation approach. To evaluate the quality of the generated DSM, an agreement assessment with manual stereo measurements was conducted over three different land cover classes (forests, shrubs, grasslands) INTRODUCTION Photogrammetry is a typical engineering discipline that is greatly influenced by developments in computer science and electronics (1). This has become especially evident over the past thirty years during which photogrammetry has gone through transition from analogue, over analytical to digital photogrammetry (1, 2). During this development period, analogue aerial photographs as well as analogue and analytical stereo instruments have been replaced with digital aerial images and digital photogrammetric workstations (3,4). Current digital aerial photogrammetric cameras are capable of capturing digital aerial images of very high spatial resolution with ground sampling distance (GSD) of only several centimetres (5). In addition, the radiometric resolution of digital images is superior in comparison to the resolution of analogue photographs (6). Such quality digital images in combination with digital photogrammetric workstations have led to automation, facilitation and improvements in performance of many demanding photogrammetric procedures (aerial triangulation, digital terrain models generation, orthophoto generation, etc.) (7,8).The automation in digital photogrammetry is based on stereo photogrammetric processing called image matching, also referred to as image correlation (9). The aim of image matching is to find corresponding points in two or more stereo images based on their radiometric and geometric similarity (10). The result of image matching is image 3D point cloud which is then used to generate digital terrain model (DTM) which represents the elevation of the Earth's terrain (bare ground) or digital surface model (DSM) which represents the elevation of the Earth's surface including the human-made (e.g. buildings) or natural (e.g. trees) features.In recent years, the automatic DTM and DSM generation through image matching has encountered great interest among the researchers worldwide (11). Various image matching algorithms and photogrammetric software packages have been developed and have become commercially available (11). The main image matchi...

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The potential of digital surface models based on aerial images for automated vegetation mapping

Cited by 22 publications

References 26 publications

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

Quality assessment of high density digital surface model over different land cover classes

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