Impact of the Acquisition Geometry of Very High-Resolution Pléiades Imagery on the Accuracy of Canopy Height Models over Forested Alpine Regions

Abstract

This work focuses on the accuracy estimation of canopy height models (CHMs) derived from image matching of Pléiades stereo imagery over forested mountain areas. To determine the height above ground and hence canopy height in forest areas, we use normalised digital surface models (nDSMs), computed as the differences between external high-resolution digital terrain models (DTMs) and digital surface models (DSMs) from Pléiades image matching. With the overall goal of testing the operational feasibility of Pléiades images for forest monitoring over mountain areas, two questions guide this work whose answers can help in identifying the optimal acquisition planning to derive CHMs. Specifically, we want to assess (1) the benefit of using tri-stereo images instead of stereo pairs, and (2) the impact of different viewing angles and topography. To answer the first question, we acquired new Pléiades data over a study site in Canton Ticino (Switzerland), and we compare the accuracies of CHMs from Pléiades tri-stereo and from each stereo pair combination. We perform the investigation on different viewing angles over a study area near Ljubljana (Slovenia), where three stereo pairs were acquired at one-day offsets. We focus the analyses on open stable and on tree covered areas. To evaluate the accuracy of Pléiades CHMs, we use CHMs from aerial image matching and airborne laser scanning as reference for the Ticino and Ljubljana study areas, respectively. For the two study areas, the statistics of the nDSMs in stable areas show median values close to the expected value of zero. The smallest standard deviation based on the median of absolute differences (σMAD) was 0.80 m for the forward-backward image pair in Ticino and 0.29 m in Ljubljana for the stereo images with the smallest absolute across-track angle (−5.3°). The differences between the highest accuracy Pléiades CHMs and their reference CHMs show a median of 0.02 m in Ticino with a σMAD of 1.90 m and in Ljubljana a median of 0.32 m with a σMAD of 3.79 m. The discrepancies between these results are most likely attributed to differences in forest structure, particularly tree height, density, and forest gaps. Furthermore, it should be taken into account that temporal vegetational changes between the Pléiades and reference data acquisitions introduce additional, spurious CHM differences. Overall, for narrow forward–backward angle of convergence (12°) and based on the used software and workflow to generate the nDSMs from Pléiades images, the results show that the differences between tri-stereo and stereo matching are rather small in terms of accuracy and completeness of the CHM/nDSMs. Therefore, a small angle of convergence does not constitute a major limiting factor. More relevant is the impact of a large across-track angle (19°), which considerably reduces the quality of Pléiades CHMs/nDSMs.

Mapping with Pléiades—End-to-End Workflow

“…This data set consists of three Pléiades stereo acquisitions from adjacent orbits over the region north to Ljubljana, Slovenia, first presented in Reference [5]. The stereo sets were acquired within three days in July 2013, the first and third set with the Pléiades-1A platform and the second with Pléiades-1B.…”

“…Thus, analysis can be performed on large regions as the structural changes are smaller than for the other data sets. For any other detail on the dataset we refer to Reference [5].…”

“…Thus, it may be sufficient to extract a DSM before adjustment and later shift this DSM to some reference system (cf. Reference [5]). Therefore, a test is conducted to determine if the initial sensor models are accurate enough to get epipolar images.…”

“…In cases where no GCPs are available the sensor model adjustment should be performed with TPs (cf. Reference [5]). Each TP between two images yield four additional equations in the non-linear equation system (cf.…”

“…Mapping, in general, can be described as the process of generating 3D maps of a region of interest on the Earth's surface consisting of 3D coordinates linked with the spectral information that was observed by the underlying sensor. Exemplary applications in the domain of remote sensing are, for instance, city modeling [1][2][3], forest assessment and biomass estimation [4][5][6], change detection [7,8], land cover and land use classification [9,10], carbon reporting [11], farm land monitoring [12], glacier observation [13,14], disaster damage mapping [15,16] and mapping in general [17,18]. To allow semantic analysis those applications need mapping products in form of digital surface models (DSM), digital terrain models (DTM), their difference, that is, normalized digital surface models (nDSM) and the according multi-spectral ortho-rectified images.…”