A thorough understanding of forest resources and development trends is based on quick and accurate forest inventories. Because of its flexibility and localized independence, mobile laser scanning (MLS) based on simultaneous localization and mapping (SLAM) is the best option for forest inventories. The gap in the review studies in this field is filled by this study, which offers the first comprehensive review of SLAM-based MLS in forest inventory. This synthesis includes methods, research progress, challenges, and future perspectives of SLAM-based MLS in forest inventory. The precision and efficiency of SLAM-based MLS in forest inventories have benefited from improvements in data collection techniques and the ongoing development of algorithms, especially the application of deep learning. Based on evaluating the research progress of SLAM-based MLS in forest inventory, this paper provides new insights into the development of automation in this field. The main challenges of the current research are complex forest environments, localized bias, and limitations of the algorithms. To achieve accurate, real-time, and applicable forest inventories, researchers should develop SLAM technology dedicated to forest environments in the future so as to perform path planning, localization, autonomous navigation, obstacle avoidance, and point cloud recognition. In addition, researchers should develop algorithms specialized for different forest environments and improve the information processing capability of the algorithms to generate forest maps capable of extracting tree attributes automatically and in real time.

The rational design of interfacial contacts plays a decisive role in improving interfacial carrier transfer and separation in heterojunction photocatalysts. In Z-scheme photocatalysts, the recombination of photogenerated electron–hole pairs is prevented so that the redox capacity is maintained. Here, one-dimensional graphitic carbon nitride (g-C₃N₄)/CoFe₂O₄ fibres were synthesised as a new type of magnetic Z-scheme visible-light photocatalyst. Compared with pure g-C₃N₄ and CoFe₂O₄, the prepared composite photocatalysts showed considerably improved performance for the photooxidative degradation of tetracycline and methylene blue. In particular, the photodegradation efficiency of the g-C₃N₄/CoFe₂O₄ fibres for methylene blue was approximately two and seven times those of g-C₃N₄ and CoFe₂O₄, respectively. The formation mechanism of the Z-scheme heterojunctions in the g-C₃N₄/CoFe₂O₄ fibres was investigated using photocurrent spectroscopy and electrochemical impedance spectroscopy. We proposed that one of the reasons for the improved photodegradation performance is that the charge transport path in one-dimensional materials enables efficient photoelectron and hole transfer. Furthermore, the internal electric field of the prepared Z-scheme photocatalyst enhanced visible-light absorption, which provided a barrier for photoelectron–hole pair recombination.

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.

A new method called Bézier curve fitting (BCF) for approximating CryoSat-2 (CS-2) SAR-mode waveform is developed to optimize the retrieval of surface elevation of both sea ice and leads for the period of late winter/early spring. We found that the best results are achieved when the retracking points are fixed on positions at which the rise of the fitted Bézier curve reaches 70% of its peak in case of leads, and 50% in case of sea ice. In order to evaluate the proposed retracking algorithm, we compare it to other empirically-based methods currently reported in the literature, namely the threshold first-maximum retracker algorithm (TFMRA) and the European Space Agency (ESA) CS-2 in-depth Level-2 algorithm (L2I). The results of the retracking procedure for the different algorithms are validated using data of the Operation Ice Bridge (OIB) airborne mission. For two OIB campaign periods in March 2015 and April 2016, the mean absolute differences between freeboard values retrieved from CS-2 and OIB data were 9.22 and 7.79 cm when using the BCF method, 10.41 cm and 8.16 cm for TFMRA, and 10.01 cm and 8.42 cm for L2I. This suggests that the sea ice freeboard data can be obtained with a higher accuracy when using the proposed BCF method instead of the TFMRA or the CS-2 L2I algorithm.

Sea-ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, primarily due to meltwater complicating the distinguishability of sea-ice types. In response to this issue, this study introduces a novel method for classifying sea ice during the Bohai Sea’s melting period. The method categorizes sea ice into five types: open water (OW), gray ice (Gi), melting gray ice (GiW), gray–white Ice (Gw), and melting gray–white Ice (GwW). To achieve this classification, 51 polarimetric features are extracted from L-, S-, and C-band PolSAR data using various polarization decomposition methods. This study assesses the separability of these features among different combinations of sea-ice type by calculating the Euclidean distance (ED). The Support Vector Machine (SVM) classifier, when employed with single-frequency polarimetric feature sets, achieves the highest accuracy for OW and Gi in the C-band, GiW in the S-band, and Gw and GwW in the L-band. Remarkably, the C-band features exhibit the overall highest accuracy when compared to the L-band and S-band. Furthermore, employing a multi-dimensional polarimetric feature set significantly improves classification accuracy to 94.55%, representing a substantial enhancement of 9% to 22% compared to single-frequency classification. Benefiting from the performance advantages of Random Forest (RF) classifiers in handling large datasets, RF classifiers achieve the highest classification accuracy of 95.84%. The optimal multi-dimensional feature composition includes the following: L-band: SE, SE_I, $\overline{\mathsf{\alpha }}$, Span; S-band: SE_I, SE, Span, P_V-Freeman, λ₁, λ₂; C-band: SE, SE_I, Span, λ₃, P_V-Freeman. The results of this study provide a reliable new method for future sea-ice monitoring during the melting season.

The Surface Wave Investigation and Monitoring (SWIM) instrument is the first to adopt a rotation detection mode at multiple small incidence angles (0°–10°), which is mainly used for wave detection. SWIM is able to cover the north and south latitudes to 83°, and detect sea ice. However, the SWIM spatial resolution is relatively low (18 km), which limits its application in sea ice detection. Therefore, this study proposes, for the first time, a sea ice image reconstruction method at small incidence angles, which can improve the spatial resolution and extend the incidence angle range of image reconstruction methods. First, several image reconstruction methods are investigated and compared using SWIM data. Second, the optimal method (Scatterometer Image Reconstruction, SIR) with more sea ice detail and stronger noise suppression is selected. Third, the parameter settings of SIR are investigated and its reconstruction quality improves with increasing iteration number. Finally, based on the 6°–10° SWIM data in the Severnaya Zemlya and northeastern Kara Sea, the image reconstruction is carried out using the SIR method. The results are evaluated using the normalized standard deviation that can reach 0.55, and discussed based on sea ice charts and Sentinel-1 SAR images with good agreement.