Spatially location and working status of pollution sources are very important pieces of information for environment protection. Waste gas produced by fossil fuel consumption in the industry is mainly discharged to the atmosphere through a chimney. Therefore, detecting the distribution of chimneys and their working status is of great significance to urban environment monitoring and environmental governance. In this paper, we use an open access dataset BUAA-FFPP60 and the faster regions with convolutional neural network (Faster R-CNN) algorithm to train the preliminarily detection model. Then, the trained model is used to detect the chimneys in three high-resolution remote sensing images of Google Maps, which is located in Tangshan city. The results show that a large number of false positive targets are detected. For working chimney detection, the recall rate is 77.27%, but the precision is only 40.47%. Therefore, two spatial analysis methods, the digital terrain model (DTM) filtering, and main direction test are introduced to remove the false chimneys. The DTM is generated by ZiYuan-3 satellite images and then registered to the high-resolution image. We set an elevation threshold to filter the false positive targets. After DTM filtering, we use principle component analysis (PCA) to calculate the main direction of each target image slice, and then use the main direction to remove false positive targets further. The results show that by using the combination of DTM filtering and main direction test, more than 95% false chimneys can be removed and, therefore, the detection precision is significantly increased.

The Moon provides us with a long-term, stable, and unique place for Earth observation. Space agencies of various countries, including the United States, China, and Italy, have made the realization of Moon-based Earth observation an objective of their lunar missions. To date, although some conceptual studies have been presented, an accurate geometric model for Moon-based Earth observation has not yet been described. This paper introduces such a geometric model, which connects the attitude of a Moon-based sensor with a corresponding field of view on the Earth’s surface. The aberration and light time correction are involved. Due to the lack of high-quality experimental data, one qualitative comparison with Chang’E lander images and another quantitative comparison with software output are made. The comparison results show good similarity. The overall model accuracy is evaluated to be better than 4″ at current stage and will be better than 1.5″ if the seleno-graphic position is accurately determined. In direct geolocation process, the aberration and light time will cause a total 0.7 km deviation on the ground near the sublunar point. In SAR range history simulation, the light time effect will lead to a linear error, as large as tens of meters, throughout the integration time.

The drift angle caused by the Earth’s self-rotation may introduce rotational displacement artifact on the geolocation results of imagery acquired by an Earth observing sensor onboard the International Space Station (ISS). If uncorrected, it would cause a gradual degradation of positional accuracy from the center towards the edges of an image. One correction method to account for the drift angle effect was developed. The drift angle was calculated from the ISS state vectors and positional information of the ground nadir point of the imagery. Tests with images acquired by the International Space Station Agriculture Camera (ISSAC) using Google Earth^TM as a reference indicated that applying the drift angle correction can reduce the residual geolocation error for the corner points of the ISSAC images from over 1000 to less than 500 m. The improved geolocation accuracy is well within the inherent geolocation uncertainty of up to 800 m, mainly due to imprecise knowledge of the ISS attitude and state parameters required to perform the geolocation algorithm.

Understory vegetation plays an important ecological role in maintaining the diversity of the ecosystem, the stability of ecosystem services, and the accumulation of nutrient elements, as an important part of a forest ecosystem. In this study, a new method of recognizing areas without understory vegetation is proposed. The method makes full use of the advantages of spectral characteristics, spatial structure information and temporal resolution of UAV images, and can quickly and simply distinguish understory, without vegetation cover. Combined with fractional vegetation coverage (FVC) and vegetation dispersion, understory, with no vegetation area, can be successfully recognized, and the Pr, Re and F1 are all above 85%. The proportion of bare soil under forest in our study area is 20.40%, 19.98% and even 41.69%. The study area is located in Changting County, Fujian Province, which is a typical red soil area in China where serious soil erosion is taking place in the forest. The method provides a promising, quick and economic way of estimating understory vegetation coverage with high spatial accuracy.

In producing orthophoto mosaic in a large area from spaceborne synthetic aperture radar (SAR) images, SAR image matching from adjacent orbits is a technical difficulty due to the speckle noise and different imaging mechanism between azimuth and range direction. In this paper, an area-based method, SAR-Moravec, is proposed for SAR orthophoto matching from adjacent orbits in a large area. Compared with the classical area-based Moravec, the template of SAR-Moravec is characterized by more directions for speckle noise restraint and a main direction consistent with the azimuth. In order to get evenly distributed matching points with high accuracy, the grid control mechanism and Gaussian pyramid from coarse to fine are introduced in matching. The whole process contains three steps. First, the pyramid images are constructed by the down-sampling process. Second, under grid control, the feature points are evenly extracted by the modified template. Third, the transformation model is iteratively calculated from the first to the last layer of the pyramid. After the matching process layer-by-layer, the final matching points and transformation model can be obtained. In the experiments, we compare the SAR-Moravec with three widely used methods, including the Moravec, the SAR-scale invariant feature transform (SAR-SIFT), and the SAR-features from an accelerated segment test (SAR-FAST). The results indicate that the proposed method has the best global matching accuracy among these methods and the matching efficiency is better than SAR-SIFT and SAR-FAST methods in large area.

In recent years, ozone pollution has been increasing in some parts of the world. In this study, we used the Beijing–Tianjin–Tangshan (BJ-TJ-TS) urban agglomeration region as a case study and used satellite remotely sensed inversion data and hourly ground monitoring observations of surface ozone concentrations, meteorological data, and other factors from 2016 to 2019 to explore the spatiotemporal dynamic characteristics of surface ozone concentration and its pollution levels. We also investigated their coupling relationships with meteorological factors, including temperature, pressure, relative humidity, wind velocity, and sunshine duration, in order to support the development of effective control measures for regional ozone pollution. The results revealed that the surface ozone concentration throughout the BJ-TJ-TS region from 2016 to 2019 exhibited an overall pattern of high values in the northwest and low values in the southeast, as well as an obvious difference between built-up and non-built-up areas (especially in Beijing). Meanwhile, a notable increasing trend of ozone levels was discovered in the BJ and TJ areas from 2016 to 2019, whereas this upward trend was not evident in the TS area. In all three areas, the highest monthly average ozone values occurred in the summer month of June, while the lowest monthly average levels occurred in the winter month of December. Their diurnal variation values reached a maximum value at approximately 3:00–4:00 p.m. and a minimum value at approximately 7:00 a.m. It is clear that high temperature, long sunshine duration, low atmospheric pressure, and weak wind velocity conditions, as well as certain relative humidity levels, readily led to high-concentration ozone pollution. Meanwhile, the daily average values of the five meteorological factors on days with Grade I and Grade II ozone pollution displayed different characteristics.