ALBERT

Description: To facilitate the advances in Sentinel-2A products for land cover from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery, Sentinel-2A MultiSpectral Instrument Level-1C (MSIL1C) images are investigated for large-scale vegetation mapping in an arid land environment that is located in the Ili River delta, Kazakhstan. For accurate classification purposes, multi-resolution segmentation (MRS) based extended object-guided morphological profiles (EOMPs) are proposed and then compared with conventional morphological profiles (MPs), MPs with partial reconstruction (MPPR), object-guided MPs (OMPs), OMPs with mean values (OMPsM), and object-oriented (OO)-based image classification techniques. Popular classifiers, such as C4.5, an extremely randomized decision tree (ERDT), random forest (RaF), rotation forest (RoF), classification via random forest regression (CVRFR), ExtraTrees, and radial basis function (RBF) kernel-based support vector machines (SVMs) are adopted to answer the question of whether nested dichotomies (ND) and ensembles of ND (END) are truly superior to direct and error-correcting output code (ECOC) multiclass classification frameworks. Finally, based on the results, the following conclusions are drawn: 1) the superior performance of OO-based techniques over MPs, MPPR, OMPs, and OMPsM is clear for Sentinel-2A MSIL1C image classification, while the best results are achieved by the proposed EOMPs; 2) the superior performance of ND, ND with class balancing (NDCB), ND with data balancing (NDDB), ND with random-pair selection (NDRPS), and ND with further centroid (NDFC) over direct and ECOC frameworks is not confirmed, especially in the cases of using weak classifiers for low-dimensional datasets; 3) from computationally efficient, high accuracy, redundant to data dimensionality and easy of implementations points of view, END, ENDCB, ENDDB, and ENDRPS are alternative choices to direct and ECOC frameworks; 4) surprisingly, because in the ensemble learning (EL) theorem, “weaker” classifiers (ERDT here) always have a better chance of reaching the trade-off between diversity and accuracy than “stronger” classifies (RaF, ExtraTrees, and SVM here), END with ERDT (END-ERDT) achieves the best performance with less than a 0.5% difference in the overall accuracy (OA) values, but is 100 to 10000 times faster than END with RaF and ExtraTrees, and ECOC with SVM while using different datasets with various dimensions; and, 5) Sentinel-2A MSIL1C is better choice than the land cover products from MODIS and Landsat imagery for vegetation species mapping in an arid land environment, where the vegetation species are critically important, but sparsely distributed.

Description: Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely manner. However, conventional approaches always have the limitations of large amounts of sample collection and exploitation of relational knowledge between multi-modality remote sensing datasets. With some global land-cover products being available, it is important to produce new land-cover maps based on the existing land-cover products and time series images. To this end, a novel transfer learning based automatic approach was proposed for updating land cover maps of rapidly urbanizing regions. In detail, the proposed method is composed of the following three steps. The first is to design a strategy to extract reliable land-cover information from the historical land-cover map for one of the images (source domain). Then, a novel relational knowledge transfer technique is applied to transfer label information. Finally, classifiers are trained on the transferred samples with spatio-spectral features. The experimental results show that aforementioned steps can select sufficient effective samples for target images, and for the main land-cover classes in a rapidly urbanizing region; the results of an updated map show good performance in both precision and vision. Therefore, the proposed approach provides an automatic solution for urban land-cover mapping with a high degree of accuracy.

Description: The contextual-based multi-source time-series remote sensing and proposed Comprehensive Heritage Area Threats Index (CHATI) index are used to analyze the spatiotemporal land use/land cover (LULC) and threats to the Mount Wutai World Heritage Area. The results show disturbances, such as forest coverage, vegetation conditions, mining area, and built-up area, in the research area changed dramatically. According to the CHATI, although different disturbances have positive or negative influences on environment, as an integrated system it kept stable from 1987 to 2018. Finally, this research uses linear regression and the F-test to mark the remarkable spatial-temporal variation. In consequence, the threats on Mount Wutai be addressed from the macro level and the micro level. Although there still have some drawbacks, the effectiveness of threat identification has been tested using field validation and the results are a reliable tool to raise the public awareness of WHA protection and governance.

Description: Automatic 3D forest mapping and individual tree characteristics estimation are essential for forest management and ecosystem maintenance. The low-cost unmanned aerial vehicle (UAV) laser scanning (ULS) is a newly developed tool for cost-effectively collecting 3D information and attempts to use it for 3D forest mapping have been made, due to its capability to provide 3D information with a lower cost and higher flexibility than the standard ULS and airborne laser scanning (ALS). As the direct georeferenced point clouds may suffer from distortion caused by the poor performance of a low-cost inertial measurement unit (IMU), and 3D forest mapping using low-cost ULS poses a great challenge. Therefore, this paper utilized global navigation satellite system (GNSS) and IMU aided Structure-from-Motion (SfM) for trajectory estimation, and, hence, overcomes the poor performance of low-cost IMUs. The accuracy of the low-cost ULS point clouds was compared with the ground truth data collected by a commercial ULS system. Furthermore, the effectiveness of individual trees segmentation and tree characteristics estimation derived from the low-cost ULS point clouds were accessed. Experiments were undertaken in Dongtai forest farm, Yancheng City, Jiangsu Province, China. The results showed that the low-cost ULS achieved good point clouds quality from visual inspection and comparable individual tree segmentation results (P = 0.87, r = 0.84, F = 0.85) with the commercial system. Individual tree height estimation performed well (coefficient of determination (R2) = 0.998, root-mean-square error (RMSE) = 0.323 m) using the low-cost ULS. As for individual tree crown diameter estimation, low-cost ULS achieved good results (R2 = 0.806, RMSE = 0.195 m) after eliminating outliers. In general, such results illustrated the high potential of the low-cost ULS in 3D forest mapping, even though 3D forest mapping using the low-cost ULS requires further research.