Dongchuan Yan’s research while affiliated with Chengdu Institute of Geology and Mineral Resources and other places

Land surface reflectance (LSR) data form the basis of quantitatively remotely sensed applications. For accurate LSR retrieval, atmospheric correction has been investigated by many researchers and implemented in typical processing systems, including common atmospheric correction software for various types of datasets and automatic operating systems for application to certain individual data sources. In recent years, China has launched multiple medium–high-resolution satellites but has not provided standard LSR products partly because of the lack of an appropriate operational system. In this paper, a multi-source remote sensing LSR product system for medium- and high-resolution data is proposed, called the “Operational Atmospheric Correction Framework for multi-source Medium-high-resolution Remote Sensing data of China” (ACFrC). The AC algorithm, processing flow, and design of the multi-source LSR system were described in detail. A practical atmospheric correction algorithm was proposed specially for data in only the visible and near-infrared (VNIR) bands. The entire processing chain was divided into modules for multi-source data ingestion, apparent reflectance calculation, cloud and water identification, atmospheric correction, and standard LSR product generation. To date, most types of multi-source data have been tested using the ACFrC system, with reasonable results being obtained. From the preliminary results, the 313 scenes of LSR products from the GaoFen-2 (GF-2) satellite over China for the period from 2015 to 2018 were cross-compared with Landsat-8 LSR acquired on the same day, showing an overall uncertainty less than 0.112 × LSR + 0.0112. Further, the ACFrC data processing efficiency was found to be suitable for automatic operation. System improvement is ongoing and future refinements will include online cloud parallel computing functionality and services, more robust algorithms, and other radiometric processing functions.

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.

Baiyangdian Lake is the largest wetland ecosystem in North China, which has a special strategic position, so it is of great significance to study its temporal and spatial changes. Based on the 112 Landsat satellite remote sensing data from 1984 to 2020, this paper extracted the thematic information of Baiyangdian Lake open water body, carried out the study on the temporal and spatial evolution of the Baiyangdian Wetland ecosystem, and analyzed its area change trend as “increase-decrease-increase-decrease again-increase again ”. Among them, the Landsat7 ETM SLC-off satellite digital product with band noise during 2003-2012 was repaired, so that it can better reflect the mutation characteristics and overall laws of Baiyangdian Lake water body in the past 30 years. On this basis, the Baiyangdian Lake water quality parameters were inverted, and the conclusions drawn have certain reference significance for maintaining the ecological environment of Baiyangdian Lake.

The aerosol optical depth (AOD) measured by sun photometer is obtained after subtracting the Rayleigh optical depth, ozone optical depth and other absorption gas optical depth from total optical depth. Wherein, it was according to Beer's attenuation law. However, the band filter function of sun photometers should be considered when retrieving AODs. In this paper, it was systemically analyzed the influence of sun photometer filter function on retrieving aerosol optical depth. Numerous study showed that the band filter function has a significant impact on retrieving AODs: 1)The uncertainty of AODs may become large when the filter function is not well desgined; 2) Under large zenith observation condition, it may introduce non-neglected errors if Rayleigh scattering optical depth is calculated directly at central wavelength without filter function considered; 3) The band weighted absorption coefficients of O₃ and NO₂ almost hold constant when gas amounts increase, except for questionable designed band filter; 4) These weak absorption optical depths can not be ignored such as H₂O absorption optical depths in 1020nm &1640nm band and CH₄, CO₂ absorption optical depths in 1640nm band.