Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions

Abstract

Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes.

Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

“…Many previous studies reported their effects on soil and water processes [5][6][7]. Nowadays, many artificial terraces are threatened by land degradation and soil erosion because of land abandonment and a lack of maintenance [8]. The Loess Plateau in China, which is a major agricultural production region in China, suffers from severe soil erosion and is which is a major agricultural production region in China, suffers from severe soil erosion and is a fragmented terrain [9].…”

“…However, the time expenditure, financial costs, and inconvenience make long-term monitoring over large areas difficult. Automatic methods are possible with the technological development of high spatial resolution remote sensing and terrain modeling by unmanned aerial vehicle (UAV) photogrammetry [16][17][18] or light detection and ranging (lidar) technology [8]. Highly accurate terrain modeling by UAV photogrammetry is consistently difficult because fully and automatically interpolating the surface model into the terrain model, i.e., the digital elevation model (DEM), is problematic in hilly areas covered with dense vegetation [19].…”

Effects of Topography on Planted Trees in a Headwater Catchment on the Chinese Loess Plateau

“…It's suggested that affordable and portable UAV-borne LiDAR system can generate very high resolution three-dimensional (3D) habitat information, including terrain and vegetation structure [27]. For example, Grijseels et al [28] utilized LiDAR imaging and found that the riparian vegetation structure follows the patterns of topography related to energy and water subsidies; Cucchiaro et al [29] combined the 3D photogrammetry and terrestrial LiDAR scanning (TLS) techniques and concluded that this data fusion can be utilized to create highly accurate digital terrain models (DTMs) in the context of complex and unreachable topography and vegetation cover. The high-resolution UAV and LiDAR techniques facilitate the observation practice of geomorphic change characterization [30].…”

A Rapid Method of the Rock Mass Surface Reconstruction for Surface Deformation Detection at Close Range

“…Moreover, MVS generates a broad range of point clouds based on the predicted object position and reference point, which is used for dense reconstruction. This technology has been extended to land surface changes [ 16 , 17 ], river erosion [ 18 ], and rock failure [ 19 ], among others. Some works even achieve a highly accurate reconstruction model that indicates SfM-MVS could provide a survey precision comparable to the current measurement methods [ 20 , 21 , 22 , 23 , 24 ].…”