It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component decomposition and multi-scale thresholding segmentation. Firstly, the polarimetric characteristics of the port water are analyzed using modified three-component decomposition. Secondly, the volume scattering power and the power ratio of the double-bounce scattering power to the volume scattering power (PRDV) are used to extract the port water. Water and land are first separated by a global thresholding segmentation of the volume scattering power, in which the sampling region used for the threshold calculation is automatically selected by a proposed homogeneity measure. The interference water regions in the ports are then separated from the water by segmenting the PRDV using the multi-scale thresholding segmentation method. The regions of interest (ROIs) of the ports are then extracted by determining the connected interference water regions with a large area. Finally, ports are recognized by examining the area ratio of strong scattering pixels to the land in the extracted ROIs. Seven single quad-polarization SAR images acquired by RADARSAT-2 covering the coasts of Dalian, Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America are used to test the proposed method. The experimental results show that all ports are correctly and quickly detected. The false alarm rates are zero, the intersection of union section (IoU) indexes between the detected port and the ground truth can reach 75%, and the average processing time can be less than 100 s.

Existing efforts to continuously monitor land cover change using satellite image time series have mostly focused on forested ecosystems in the tropics and the Northern Hemisphere. The notable difference in spectral reflectance that occurs following deforestation allows land cover change to be detected with relative accuracy. Less progress has been made in detecting change in low productivity or disturbance-prone vegetation such as grasslands and shrublands where natural dynamics can be difficult to distinguish from habitat loss. Renosterveld is a hyperdiverse, critically endangered shrubland ecosystem in South Africa with less than 5–10% of its original extent remaining in small, highly fragmented patches. I demonstrate that classification of satellite image time series using neural networks can accurately detect the transformation of Renosterveld within a few days of its occurrence and that trained models are suitable for operational continuous monitoring. A dataset of precisely dated vegetation change events between 2016 and 2021 was obtained from daily, high resolution Planet Labs satellite data. This dataset was then used to train 1D convolutional neural networks and Transformers to continuously detect land cover change events in time series of vegetation activity from Sentinel 2 satellite data. The best model correctly identified 89% of land cover change events at the pixel-level, achieving a f-score of 0.93, a 79% improvement over the f-score of 0.52 achieved using a method designed for forested ecosystems based on trend analysis. Models have been deployed to operational use and are producing updated detections of habitat loss every 10 days. There is great potential for continuous monitoring of habitat loss in non-forest ecosystems with complex natural dynamics. A key limiting step is the development of accurately dated datasets of land cover change events with which to train machine-learning classifiers.

In the Southern Ocean, the sub-Antarctic Prince Edward Islands (PEIs) play a significant ecological role by hosting large populations of seasonally breeding marine mammals and seabirds, which are particularly sensitive to changes in the surrounding ocean environment. In order to better understand climate variability at the PEIs, this study used satellite and reanalysis data to examine the interannual variability and longer-term trends of Sea Surface Temperature (SST), wind forcing, and surface circulation. Long-term trends were mostly weak and statistically insignificant, possibly due to the restricted length of the data products. While seasonal fluctuations accounted for a substantial portion (50–70%) of SST variability, the strongest variance in wind speed, wind stress curl (WSC), and currents occurred at intra-annual time scales. At a period of about 1 year, SST and geostrophic current variability suggested some influence of the Southern Annular Mode, but correlations were weak and insignificant. Similarly, correlations with El Niño Southern Oscillation variability were also weak and mostly insignificant, probably due to strong local and regional modification of SST, wind, and current anomalies. Significant interannual and decadal-scale variability in SST, WSC, and geostrophic currents, strongest at periods of 3–4 and 7–8 years, corresponded with the variability of the Antarctic Circumpolar Wave. At decadal time scales, there was a strong inverse relationship between SST and geostrophic currents and between SST and wind speed. Warmer-than-usual SST between 1990–2001 and 2009–2020 was related to weaker currents and wind, while cooler-than-usual periods during 1982–1990 and 2001–2009 were associated with relatively stronger winds and currents. Positioned directly in the path of passing atmospheric low-pressure systems and the Antarctic Circumpolar Current, the PEIs experience substantial local and regional atmospheric and oceanic variability at shorter temporal scales, which likely mutes longer-term variations that have been observed elsewhere in the Southern Ocean.

High-precision coordinate transformation is vital for high-quality data fusion involving different coordinate systems. The transformation precision is mainly evaluated by the transformation parameters’ estimation precision, the root mean square error (RMSE) of the conversion of common points, or the RMSE of the conversion of check points. However, there are a number of issues associated with the rotation parameters’ precision estimated by the existing transformation methods. First, the estimated precision is related to the rotation matrix, so it is not suitable for scenarios where different rotation matrices are used. Second, the RMSE of the conversion of check points may not be consistent with the RMSE of the conversion of common points, so that the RMSE of the conversion of common points should not be used as a transformation precision index. In addition, some engineering applications do not have check points, and many applications need to know which range of points can meet our requirements. To deal with these limitations, this paper proposes a new way to calculate the translation parameters and evaluate the transformation precision. A lot of experimental data was used to verify the effectiveness and applicability of the proposed transformation model.

Hyperspectral (HS) images are adjacent band images that are generally used in remote-sensing applications. They have numerous spatial and spectral information bands that are extremely useful for material detection in various fields. However, their high dimensionality is a big challenge that affects their overall performance. A new data normalization method was developed to enhance the variations and data distribution using the output of principal component analysis (PCA) and quantile transformation, called QPCA. This paper also proposes a novel HS images classification framework using the meta-learner technique to train multi-class and multi-size datasets by concatenating and training the hybrid and multi-size kernel of convolutional neural networks (CNN). The high-level model works to combine the output of the lower-level models and train them with the new input data, called meta-learner hybrid models (MLHM). The proposed MLHM framework with our external normalization (QPCA) improves the accuracy and outperforms other approaches using three well-known benchmark datasets. Moreover, the evaluation outcomes showed that the QPCA enhanced the framework accuracy by 13% for most models and datasets and others by more than 25%, and MLHM provided the best performance.

The perceived quality of street lighting influences pedestrians’ perceptions of safety and visual comfort, as well as outdoors activities at night. This study explores the association between street lighting attributes, such as illuminance and wavelength, and pedestrians’ feeling of safety (FoS) and perceived lighting quality (PLQ) in eight residential districts in Dalian, China. To achieve this goal, we combine remote sensing technology with ground investigation. The ground research includes physical measurements of lighting attributes, such as intensity, color temperature, and glare, as well as survey evaluations of pedestrians’ perceptions of safety and visual comfort. We also analyze the influence of several environmental factors, such as traffic volumes and vegetation, while accounting for personal characteristics of the observers, such as gender and age. Findings from the remote sensing reveal that Dalian’s residential districts differ substantially by their nighttime light emissions, with high concentration of strong red band (i.e., long wavelength) emissions occurring in Zhongshan and Jinzhou, and strong blue band (i.e., short wavelength) emissions found in central Zhongshan. Results from the ground surveys further indicate that a satisfactory level of FoS reaches at the illumination levels of 5–17 lx, and that people feel safer if nighttime light is warm and uniform. From a multiple regression analysis, it is also found that illuminance and uniformity are the main factors affecting PLQ under conditions of low or high illuminance, while glare and color temperature play a more significant role under high illuminance. In addition, a satisfactory level of PLQ is found at illuminance levels of 25–35 lx and light color temperature of 4000 K–5500 K.