Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities.

The lockdown of cities against the COVID-19 epidemic directly decreases urban socioeconomic activities. Remotely sensed night-time light (NTL) provides a macro perspective to capture these variations. Here, taking 20 global megacities as examples, we adopted the NASA's Black Marble NTL data with a daily resolution to investigate their spatio-temporal changes. We collected daily NTL products for four weeks (one month) before and after the date of lockdown in each city, which were then summarized as weekly and monthly averaged NTL images after pre-processing (cloud removing, outlier detection, etc.). Results show that NTL overall decreased after the lockdown of cities, but with regional disparities and varying spatial patterns. Asian cities experienced the most obvious reduction of NTL. Particularly, the monthly averaged NTL in Mumbai, India, decreased by nearly 20% compared to one month before. However, there was no significant decline in NTL in European cities. African cities also experienced stable changes of NTL. Spatially, city centers darkened more obviously than the urban periphery. Facing emergencies, NTL data has broad applications in monitoring socioeconomic dynamics and assessing public policies in a near real-time manner.

A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series.

Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset.

Vertical Optical Scanning with Panoramic Vision for Tree Trunk Reconstruction.

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.

Possibilities of a personal laser scanning system for forest mapping and ecosystem services.

A professional-quality, personal laser scanning (PLS) system for collecting tree attributes was demonstrated in this paper. The applied system, which is wearable by human operators, consists of a multi-constellation navigation system and an ultra-high-speed phase-shift laser scanner mounted on a rigid baseplate and consisting of a single sensor block. A multipass-corridor-mapping method was developed to process PLS data and a 2,000 m2 forest plot was utilized in the test. The tree stem detection accuracy was 82.6%; the root mean square error (RMSE) of the estimates of tree diameter at breast height (DBH) was 5.06 cm; the RMSE of the estimates of tree location was 0.38 m. The relative RMSE of the DBH estimates was 14.63%. The results showed, for the first time, the potential of the PLS system in mapping large forest plots. Further research on mapping accuracy in various forest conditions, data correction methods and multi-sensoral positioning techniques is needed. The utilization of this system in different applications, such as harvester operations, should also be explored. In addition to collecting tree-level and plot-level data for forest inventory, other possible applications of PLS for forest ecosystem services include mapping of canopy gaps, measuring leaf area index of large areas, documenting and visualizing forest routes feasible for recreation, hiking and berry and mushroom picking.

Automatic stem mapping by merging several terrestrial laser scans at the feature and decision levels.

Detailed up-to-date ground reference data have become increasingly important in quantitative forest inventories. Field reference data are conventionally collected at the sample plot level by means of manual measurements, which are both labor-intensive and time-consuming. In addition, the number of attributes collected from the tree stem is limited. More recently, terrestrial laser scanning (TLS), using both single-scan and multi-scan techniques, has proven to be a promising solution for efficient stem mapping at the plot level. In the single-scan method, the laser scanner is placed at the center of the plot, creating only one scan, and all trees are mapped from the single-scan point cloud. Consequently, the occlusion of stems increases as the range of the scanner increases, depending on the forest's attributes. In the conventional multi-scan method, several scans are made simultaneously inside and outside of the plot to collect point clouds representing all trees within the plot, and these scans are accurately co-registered by using artificial reference targets manually placed throughout the plot. The additional difficulty of applying the multi-scan method is due to the point-cloud registration of several scans not being fully automated yet. This paper proposes a multi-single-scan (MSS) method to map the sample plot. The method does not require artificial reference targets placed on the plot or point-level registration. The MSS method is based on the fully automated processing of each scan independently and on the merging of the stem positions automatically detected from multiple scans to accurately map the sample plot. The proposed MSS method was tested on five dense forest plots. The results show that the MSS method significantly improves the stem-detection accuracy compared with the single-scan approach and achieves a mapping accuracy similar to that achieved with the multi-scan method, without the need for the point-level registration.