Forests are in a permanent state of change due to natural and anthropogenic processes. Long-term time series analysis makes it possible to reconstruct the forest history and perform a multitemporal analysis on the cause and effect of changes. This paper describes an approach for successional stage classification in a tropical forest based on vertical structure variations. Stereophotogrammetry and novel image matching methods are used to produce dense digital surface models (DSMs) from optical images (historical and contemporary). An approach was developed to classify the successional stages of trees using local height variations provided by a DSM and image intensity values. Experiments were performed in a semi-deciduous tropical forest fragment located in the West of São Paulo State, Brazil. Six test sample plots and a line transect were established and field surveys were conducted to collect forest variables. These variables were used to characterize and validate five successional classes based on secondary tree species that stratify the forest canopy. The current status of the entire forest fragment was characterized using recent photogrammetric imagery, and a map of historical successional stages was established by analyzing the historical photogrammetric imagery. The investigation demonstrated that the proposed technique can be used to reconstruct the geometric structure of a forest canopy from aerial images. The successional stages can be identified and compared over time using multitemporal photogrammetric imagery and DSMs, which enables an analysis of forest cover changes. The results indicated that the successional stage has changed dramatically during the 50 years period of time.
Image orientation requires ground control as a source of information for both indirect estimation and quality assessment to guarantee the accuracy of the photogrammetric processes. However, the orientation still depends on interactive measurements to locate the control entities over the images. This paper presents an automatic technique used to generate 3D control points from vertical panoramic terrestrial images. The technique uses a special target attached to a GPS receiver and panoramic images acquired in nadir view from different heights. The reference target is used as ground control to determine the exterior orientation parameters (EOPs) of the vertical images. These acquired multi-scale images overlap in the central region and can be used to compute ground coordinates using photogrammetric intersection. Experiments were conducted in a terrestrial calibration field to assess the geometry provided by the reference target and the quality of the reconstructed object coordinates. The analysis was based on the checkpoints, and the resulting discrepancies in the object space were less than 2 cm in the studied cases. As a result, small models and ortho-images can be produced as well as georeferenced image chips that can be used as high-quality control information.
Forest variables are typically surveyed using sample plots, from which parameters for large areas are estimated. The diameter at breast height (DBH) is one of the main variables collected in the field and can be used with other forest measures. This study presents an automatic technique for the mapping and measurement of individual tree stems using vertical terrestrial images collected with a fisheye camera. Distinguishable points from the stem surface are automatically extracted in the images, and their 3D ground coordinates are determined by bundle adjustment. The XY coordinates of each stem define an arc shape, and these points are used as observations in a circle fitting by least squares. The circle centre determines the tree position in a local reference system, and the estimated radius is used to calculate the DBH. Experiments were performed in a sample plot to assess the approach and compare it with a technique based on terrestrial laser scanning. In the validation with measurements collected on the stems using a measuring tape, the discrepancies had an average error of 1.46 cm with a standard deviation of 1.09 cm. These results were comparable with the manual measurements and with the values generated from laser point clouds.
The objective of the proposed approach is to locate and measure ground control points in aerial images when large image search spaces are defined due to the use of inaccurate initial exterior orientation parameters, such as those provided by consumer-grade navigation systems. Vertical terrestrial image patches covering control point areas are generated and compared with aerial patches using feature-and area-based matching algorithms to automatically determine their corresponding positions in aerial images with sub-pixel precision. The approach is based on techniques for both image search space reduction and adaptive least squares matching. Experiments with real data were performed with bundle block triangulation and the results were analysed using control and check points in both object and image spaces. The proposed technique enabled a significant reduction in the search space within which it was feasible to successfully locate control points. Compared with manual measurements, the results obtained by the automatic technique were more accurate, achieving one-fifth of the ground sample distance in planimetric check point discrepancies.
This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras.
<p><strong>Abstract.</strong> New low-cost hyperspectral frame sensors have created a new perspective for remote sensing applications. In this work, we investigate some issues related to the geometric calibration of a hyperspectral frame camera based of FPI (Fabry-Pérot Interferometer), the Rikola camera. The approach proposed in paper is to study the changes in internal optical path caused by the FPI and by the splitting prism. The aim is to model the changes in the IOPs with an analytical function and also to estimate the misalignments between sensors. Several experiments were performed. The changes in position of a specific point were analasyzed to confirm that the bundle of rays is deviated. A self-calibrating bundle adjustment was performed and the Interior Orientation Parameters (IOP) of each band were estimated. The IOPs were analysed and it was concluded that a single set of symmetrical radial distortion parameters can be used for all band. Also, the estimated parameters for each image band were analysed as a function of the air gap of the FPI interferometer. It was noticed some correlation between the focal length and the air gap, and an air-gap dependent model was estimated. Thus, instead of considering an IOP set for each band or for each sensor, a single set of distortion parameters and another set of parameters that is “air-gap dependent”, was assessed. Another important issue was the determination of the misalignment angles between the two sensors, which can explain some differences in the recovered camera trajectory when performing the bundle adjustment.</p>
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