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This paper presents a recent rainfall-induced landslide in China that occurred on August 21, 2020 and resulted in nine deaths. The sliding material traveled a distance of 800 m, with an altitude difference of about 180 m. A field investigation, remote sensing based on an unmanned aerial vehicle (UAV), in situ monitoring, and a rainfall data analysis were carried out to reveal the deposit characteristics, causative factors, post-landslide behavior, and the mechanism of the landslide. A saltatory micro-relief of the original slope determined the multiple-stage failure type of the slide, and also promoted the entrainment effect during the landslide movement. After the first-initiation sliding stage, the motion of this landslide involved typical progressive movement, and over time, the style of the runout generally turned into a flow-like form. Furthermore, the antecedent cumulative rainfall of 149.5 mm directly contributed to the occurrence of the landslide. Using the GB-SAR early warning system, the post-landslide residual failure was successfully predicted 10 min in advance. The combination of the UAV and GB-SAR technique can surely be beneficial for other inaccessible landslide investigations as well and improves the emergency rescue security. [ABSTRACT FROM AUTHOR]

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost. [ABSTRACT FROM AUTHOR]

Since the 1980s, with rapid economic development and increased attention given to ecological protection, China has launched a series of ecological-restoration programs to restore the local environment through afforestation and natural forest protection. The evaluation of vegetation restoration is an important part of evaluating the effectiveness of ecological restoration. The Loess Plateau is an area where ecological problems are concentrated, and it is a key area of ecological construction in China. This paper takes the Loess Plateau as the research area, using remote sensing and geographic information technology combined with ecosystem structural changes and an improved residual model to study vegetation restoration. The following main conclusions were drawn: (1) From 1990 to 2000, the farmland area increased by 3084.81 km2, resulting in the encroachment of a large area of grassland and shrubland. (2) With the implementation of ecological engineering, the area of returning farmland to forest and grassland reached 18,001.88 km2; in this period, the NDVI of vegetation increased rapidly, and the area that increased comprised 91.90% of the total area, of which the area of significant increase reached 65.78%. The quality of vegetation was restored to a great extent, and ecological engineering played a major role in this stage. (3) Under the background of large-scale implementation of ecological restoration, the urban area of the Loess Plateau continues to expand. [ABSTRACT FROM AUTHOR]

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing. [ABSTRACT FROM AUTHOR]

The atmospheric dust is an important factor in the evolution of the Martian climate and has a major impact on the scientific exploration of the Martian lander or rover and its payload. This paper used remote sensing images to calculate atmospheric optical depth that characterizes the spatial distribution of the atmospheric dust of Mars. The optical depth calculated by the images of the High Resolution Imaging Science Experiment (HiRISE) in the inspection area of the Spirit rover had a similar temporal variation to the optical depth directly measured by the Spirit rover from the sunlight decay. We also used the HiRISE images to acquire the seasonal variation of optical depths in the candidate landing area of China's Mars Mission (Tianwen-1). The results have shown that the seasonal pattern of the optical depth in the candidate landing area is consistent with the dust storm sequences in this area. After Tianwen-1 enters the orbit around Mars, the images collected by the Moderate Resolution Imaging Camera (MoRIC), and the High Resolution Imaging Camera (HiRIC) can be used to study the atmospheric optical depth in the candidate landing area, providing reference for the safe landing and operation of the lander and rover. [ABSTRACT FROM AUTHOR]

Mangrove forests are important ecosystems in the coastal intertidal zone, but China's mangroves have experienced a large reduction in area from the 1950s, and the remaining mangrove forests are exhibiting increased fragmentation. A detailed mangrove dataset of China is crucial for mangrove ecosystem management and protection, but the fragmented mangrove patches are hardly mapped by medium resolution satellite imagery. To overcome these difficulties, we presented a fine-scale mangrove map for 2018 using the 2-meter resolution Gaofen-1 and Ziyuan-3 satellite imagery together with field data. We employed a hybrid method of object-based image analysis (OBIA), interpreter editing, and field surveying for mangrove mapping. The field survey route reached 9500 km, and 2650 patches were verified during the field work. Accuracy assessment by confusion matrix showed that the kappa coefficient reached 0.98, indicating a highly thematic accuracy of the mangrove dataset. Results showed the total area of mangrove forest in China for 2018 was 25,683.88 hectares, and approximately 91% of mangroves were found in the three provinces of Guangdong, Guangxi, and Hainan. About 64% of mangroves were distributed in or near the nature reserves established by national or local governments, which indicated that China's mangroves were well protected in recent years. The new fine-scale mangrove dataset was freely shared together with this paper, and it can be used by local authorities and research groups for mangrove management and ecological planning. [ABSTRACT FROM AUTHOR]

Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids' labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement. [ABSTRACT FROM AUTHOR]

Considering the complexity of the physical model of wildfire occurrence, this paper develops a method to evaluate the wildfire risk of transmission-line corridors based on Naïve Bayes Network (NBN). First, the data of 14 wildfire-related factors including anthropogenic, physiographic, and meteorologic factors, were collected and analyzed. Then, the relief algorithm is used to rank the importance of factors according to their impacts on wildfire occurrence. After eliminating the least important factors in turn, an optimal wildfire risk assessment model for transmission-line corridors was constructed based on the NBN. Finally, this model was carried out and visualized in Guangxi province in southern China. Then a cost function was proposed to further verify the applicability of the wildfire risk distribution map. The fire events monitored by satellites during the first season in 2020 shows that 81.8% of fires fall in high- and very-high-risk regions. [ABSTRACT FROM AUTHOR]

Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability. [ABSTRACT FROM AUTHOR]

Stereo photogrammetric survey used to be used to extract the height of buildings, then to convert the height to number of stories through certain rules to estimate the number of stories of buildings by means of satellite remote sensing. In contrast, we propose a new method using deep learning to estimate the number of stories of buildings from monocular optical satellite image end to end in this paper. To the best of our knowledge, this is the first attempt to directly estimate the number of stories of buildings from monocular satellite images. Specifically, in the proposed method, we extend a classic object detection network, i.e., Mask R-CNN, by adding a new head to predict the number of stories of detected buildings from satellite images. GF-2 images from nine cities in China are used to validate the effectiveness of the proposed methods. The result of experiment show that the mean absolute error of prediction on buildings whose stories between 1–7, 8–20, and above 20 are 1.329, 3.546, and 8.317, respectively, which indicate that our method has possible application potentials in low-rise buildings, but the accuracy in middle-rise and high-rise buildings needs to be further improved. [ABSTRACT FROM AUTHOR]

Remote sensing image segmentation with samples imbalance is always one of the most important issues. Typically, a high-resolution remote sensing image has the characteristics of high spatial resolution and low spectral resolution, complex large-scale land covers, small class differences for some land covers, vague foreground, and imbalanced distribution of samples. However, traditional machine learning algorithms have limitations in deep image feature extraction and dealing with sample imbalance issue. In the paper, we proposed an improved full-convolution neural network, called DeepLab V3+, with loss function based solution of samples imbalance. In addition, we select Sentinel-2 remote sensing images covering the Yuli County, Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China as data sources, then a typical region image dataset is built by data augmentation. The experimental results show that the improved DeepLab V3+ model can not only utilize the spectral information of high-resolution remote sensing images, but also consider its rich spatial information. The classification accuracy of the proposed method on the test dataset reaches 97.97%. The mean Intersection-over-Union reaches 87.74%, and the Kappa coefficient 0.9587. The work provides methodological guidance to sample imbalance correction, and the established data resource can be a reference to further study in the future. [ABSTRACT FROM AUTHOR]

The accurate and timely access to the spatial distribution information of crops is of great importance for agricultural production management. Although widely used, supervised classification mapping requires a large number of field samples, and is consequently costly in terms of time and money. In order to reduce the need for sample size, this paper proposes an unsupervised classification method based on principal components isometric binning (PCIB). In particular, principal component analysis (PCA) dimensionality reduction is applied to the classification features, followed by the division of the top k principal components into equidistant bins. Bins of the same category are subsequently merged as a class label. Multitemporal Gaofen 1 satellite (GF-1) remote sensing images were collected over the southwest of Hulin City and Luobei County of Hegang City, Heilongjiang Province, China in order to map crop types in 2016 and 2017. Our proposed method was compared with commonly used classifiers (random forest, K-means and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA)). Results demonstrate PCIB and random forest to have the highest classification accuracies, reaching 82% in 2016 in the southwest of Hulin City. In Luobei County in 2016, the accuracies of PCIB and random forest were determined as 81% and 82%, respectively. It can be concluded that the overall accuracy of our proposed method meets the basic requirements of classification accuracy. Despite exhibiting a lower accuracy than that of random forest, PCIB does not require a large field sample size, thus making it more suitable for large-scale crop mapping. [ABSTRACT FROM AUTHOR]

Shoreline changes are of great importance for evaluating the interaction between humans and ecosystems in coastal areas. They serve as a useful metric for assessing the ecological costs of socioeconomic developmental activities along the coast. In this paper, we present an assessment of shoreline changes along the eastern coast of mainland China from ~1990 to 2019 by applying a novel method recently developed by us. This method which we call the Nearest Distance Method (NDM) is used to make a detailed assessment of shorelines delineated from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images. The results indicate a dramatic decline in natural shorelines that correspond to the rapid increase in the construction of artificial shorelines, driven by China's economic growth. Of the entire coast of mainland China, the biggest change occurred along the Bohai Sea, where artificial shorelines expanded from 42.4% in ~1990 to 81.5% in 2019. Over this period, this study indicates that China lost > 60% of its biological shorelines, a trend that is especially worrisome because these include areas that were once biologically diverse and extremely rich. As anticipated, shoreline losses were greatest where regions of low economic value had been transformed to areas of higher economic value. Overall, this influence of human activities on shorelines in China is unprecedented. The repercussions of these changes on ecosystems, and the susceptibility of new shoreline developments to population growth and sea-level rise, need to be assessed urgently before additional changes are effected. [ABSTRACT FROM AUTHOR]

Multi-stage intrusive complex mapping plays an important role in regional mineralization research. The similarity of lithology characteristics between different stages of intrusions necessitates the use of richer spectral bands, while higher spatial resolution is also essential in small-scale research. In this paper, a multi-source remote sensing data application method was proposed. This method includes a spectral synergy process based on statistical regression and a fusion process using Gram–Schmidt (GS) spectral sharpening. We applied the method with Gaofen-2 (GF2), Sentinel-2, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to the mapping of the Mountain Sanfeng intrusive complex in northwest China in which Carboniferous intrusions have been proven to be directly related to the formation of Au deposits in the area. The band ratio (BR) and relative absorption band depth (RBD) were employed to enhance the spectral differences between two stage intrusions, and the Red-Green-Blue (RGB) false colour of the BR and RBD enhancement images performed well in the west and centre. Excellent enhancement results were obtained by making full use of all bands of the synergistic image and using the Band Ratio Matrix (BRM)-Principal Component Analysis (PCA) method in the northeast part of the study area. A crucial improvement in enhancement performance by the GS fusion process and spectral synergy process was thus shown. An accurate mapping result was obtained at the Mountain Sanfeng intrusive complex. This method could support small-scale regional geological survey and mineralization research in this region. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the environmental threats affecting the sustainable development of arid oases in the northwest of China. Thus, it is necessary to assess the risk of soil salinity and analyze spatial and temporal changes. The objective of this paper is to develop a temporal and spatial soil salinity risk assessment method based on an integrated scoring method by combining the advantages of remote sensing and GIS technology. Based on correlation coefficient analysis to determine the weights of risk evaluation factors, a comprehensive scoring system for the risk of salinity in the dry and wet seasons was constructed for the Ebinur Lake Wetland National Nature Reserve (ELWNNR), and the risk of spatial variation of soil salinity in the study area was analyzed in the dry and wet seasons. The results show the following: (1) The risk of soil salinity during the wet season is mainly influenced by the plant senescence reflectance index (PSRI), deep soil water content (D_wat), and the effect of shallow soil salinity (SH_sal). The risk of soil salinity during the dry season is mainly influenced by shallow soil salinity (SH_sal), land use and land cover change (LUCC), and deep soil moisture content (D_wat). (2) The wet season was found to have a high risk of salinization, which is mainly characterized by moderate, high, and very high risks. However, in the dry season, the risk of salinity is mainly characterized by low and moderate risk of salinity. (3) In the ELWNNR, as the wet season changes to dry season (from May to August), moderate-risk area in the wet season easily shifts to low risk and risk-free, and the area of high risk in the wet season easily shifts to moderate risk. In general, the overall change in salinity risk of the ELWNNR showed a significant relationship with changes in lake water volume, indicating that changes in water volume play an important role in the risk of soil salinity occurrence. Ideally, the quantitative analysis of salinity risk proposed in this study, which takes into account temporal and spatial variations, can help decision makers to propose more targeted soil management options. [ABSTRACT FROM AUTHOR]

Urban flooding is one of the most costly and destructive natural hazards worldwide. Remote-sensing images with high temporal resolutions have been extensively applied to timely inundation monitoring, assessing and mapping, but are limited by their low spatial resolution. Sub-pixel mapping has drawn great attention among researchers worldwide and has demonstrated a promising potential of high-accuracy mapping of inundation. Aimed to boost sub-pixel urban inundation mapping (SUIM) from remote-sensing imagery, a new algorithm based on spatial attraction models and Elman neural networks (SAMENN) was developed and examined in this paper. The Elman neural networks (ENN)-based SUIM module was developed firstly. Then a normalized edge intensity index of mixed pixels was generated. Finally the algorithm of SAMENN-SUIM was constructed and implemented. Landsat 8 images of two cities of China, which experienced heavy floods, were used in the experiments. Compared to three traditional SUIM methods, SAMENN-SUIM attained higher mapping accuracy according not only to visual evaluations but also quantitative assessments. The effects of normalized edge intensity index threshold and neuron number of the hidden layer on accuracy of the SAMENN-SUIM algorithm were analyzed and discussed. The newly developed algorithm in this study made a positive contribution to advancing urban inundation mapping from remote-sensing images with medium-low spatial resolutions, and hence can favor urban flood monitoring and risk assessment. [ABSTRACT FROM AUTHOR]

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications. [ABSTRACT FROM AUTHOR]

Aquaculture plays an important role in China's total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases. [ABSTRACT FROM AUTHOR]

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications. [ABSTRACT FROM AUTHOR]

As the largest developing country, China has experienced dramatic urban expansion since the "reform and opening-up" policy started at the end of the 1970s. In this paper, we monitor three decades of urban expansion in China's 36 major cities, based on the spectral mixture analysis of remotely sensed satellite images. The results demonstrated that these major cities have expanded by 5.85 times from 1986 to 2015, with 15.51 km2 average expansion area per city per year. We found the urban expansion trajectories showed three different modes, i.e., exponential, linear and s-shaped, which were closely related to the city development level. In the old city zones, however, there was an interesting common tendency of the impervious surface area (ISA) first increasing and then decreasing, which could be largely attributed to the phenomenon of urban village reconstruction in China's cities. Based on the Granger Causality Test (GCT), the interaction between urban ISA and Gross Domestic Product (GDP) per capita (GDPPC) suggested that the former was the driver of the latter. Meanwhile, taking the Yangtze River as the division between north and south China, there exists a north–south territorial differentiation for the interaction between ISA and total population at the year-end (TP). [ABSTRACT FROM AUTHOR]

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy. [ABSTRACT FROM AUTHOR]

Fine classification of vegetation types has always been the focus and difficulty in the application field of remote sensing. Unmanned Aerial Vehicle (UAV) sensors and platforms have become important data sources in various application fields due to their high spatial resolution and flexibility. Especially, UAV hyperspectral images can play a significant role in the fine classification of vegetation types. However, it is not clear how the ultrahigh resolution UAV hyperspectral images react in the fine classification of vegetation types in highly fragmented planting areas, and how the spatial resolution variation of UAV images will affect the classification accuracy. Based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (S185) onboard a UAV platform, this paper examines the impact of spatial resolution on the classification of vegetation types in highly fragmented planting areas in southern China by aggregating 0.025 m hyperspectral image to relatively coarse spatial resolutions (0.05, 0.1, 0.25, 0.5, 1, 2.5 m). The object-based image analysis (OBIA) method was used and the effects of several segmentation scale parameters and different number of features were discussed. Finally, the classification accuracies from 84.3% to 91.3% were obtained successfully for multi-scale images. The results show that with the decrease of spatial resolution, the classification accuracies show a stable and slight fluctuation and then gradually decrease since the 0.5 m spatial resolution. The best classification accuracy does not occur in the original image, but at an intermediate level of resolution. The study also proves that the appropriate feature parameters vary at different scales. With the decrease of spatial resolution, the importance of vegetation index features has increased, and that of textural features shows an opposite trend; the appropriate segmentation scale has gradually decreased, and the appropriate number of features is 30 to 40. Therefore, it is of vital importance to select appropriate feature parameters for images in different scales so as to ensure the accuracy of classification. [ABSTRACT FROM AUTHOR]

There are a large number of lakes with beaded distribution in the semi-arid areas of the Inner Mongolian Plateau, and some of them have degraded or even disappeared during the past three decades. We studied the reasons of the disappearance of these lakes by determining the way of replenishment of these lakes and the impact of the natural-social environment of the basin, with the aim of saving these gradually disappearing lakes. Based on remote sensing image and hydrological analysis, this paper studied the recharge of Daihai Lake and Huangqihai Lake. The deep learning method was used to establish the time-series of lake evolution. The same method was combined with the innovative woodland and farmland extraction method to set up the time-series of ground classification composition in the basins. Using relevant survey data, combined with soil water infiltration test, water chemical, and isotopic signature analysis of various water bodies, we found that the Daihai Lake area is the largest in dry season and the smallest in rainy season and the other lake is not satisfied with this phenomenon. In addition, we calculated the specific recharge and consumption of the study basin. These experiments indicated that the exogenous groundwater is recharged directly through the faults at the bottom of Daihai Lake, while the exogenous groundwater is recharged in Huangqihai Lake through rivers indirectly. Large-scale exploitation of groundwater for agricultural irrigation and industrial production is the main cause of lake degradation. Reducing the extraction of groundwater for agricultural irrigation is an important measure to restore lake ecology. [ABSTRACT FROM AUTHOR]

The Han Dynasty Great Wall (GH), one of the largest and most significant ancient defense projects in the whole of northern China, has been studied increasingly not only because it provides important information about the diplomatic and military strategies of the Han Empire (206 B.C.–220 A.D.), but also because it is considered to be a cultural and national symbol of modern China as well as a valuable archaeological monument. Thus, it is crucial to obtain the spatial pattern and preservation situation of the GH for next-step archaeological analysis and conservation management. Nowadays, remote sensing specialists and archaeologists have given priority to manual visualization and a (semi-) automatic extraction approach is lacking. Based on the very high-resolution (VHR) satellite remote sensing imagery, this paper aims to identify automatically the archaeological features of the GH located in ancient Dunhuang, northwest China. Gaofen-1 (GF-1) data were first processed and enhanced after image correction and mathematical morphology, and the M-statistic was then used to analyze the spectral characteristics of GF-1 multispectral (MS) data. In addition, based on GF-1 panchromatic (PAN) data, an auto-identification method that integrates an improved Otsu segmentation algorithm with a Linear Hough Transform (LHT) is proposed. Finally, by making a comparison with visual extraction results, the proposed method was assessed qualitatively and semi-quantitatively to have an accuracy of 80% for the homogenous background in Dunhuang. These automatic identification results could be used to map and evaluate the preservation state of the GH in Dunhuang. Also, the proposed automatic approach was applied to identify similar linear traces of other generations of the Great Wall of China (Western Xia Dynasty (581 A.D.–618 A.D.) and Ming Dynasty (1368 A.D.–1644 A.D.)) in various geographic regions. Moreover, the results indicate that the computer-based automatic identification has great potential in archaeological research, and the proposed method can be generalized and applied to monitor and evaluate the state of preservation of the Great Wall of China in the future. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products are used worldwide for their reliable accuracy. However, the aerosol optical depth (AOD) usually retrieved by the operational dark target (DT) algorithm of MODIS has been missing for most of the urban regions in Central China. This was due to a high surface reflectance and heavy aerosol loading, especially in winter, when a high cloud cover fraction and the frequent occurrence of haze events reduce the number of effective satellite observations. The retrieval of the AOD from limited satellite data is much needed and important for further aerosol investigations. In this paper, we propose an improved AOD retrieval method for 500 m MODIS data, which is based on an extended surface reflectance estimation scheme and dynamic aerosol models derived from ground-based sun-photometric observations. This improved method was applied to retrieve AOD during heavy aerosol loading and effectively complements the scarcity of AOD in correspondence with urban surface of a higher spatial resolution. The validation results showed that the retrieved AOD was consistent with MODIS DT AOD (R = ~0.87; RMSE = ~0.11) and ground measurements (R = ~0.89; RMSE = ~0.15) from both the Terra and the Aqua satellite. The method can be easily applied to different urban environments affected by air pollution and contributes to the research on aerosol. [ABSTRACT FROM AUTHOR]

Remote sensing has become a primary technology for monitoring raft aquaculture products. However, due to the complexity of the marine aquaculture environment, the boundaries of the raft aquaculture areas in remote sensing images are often blurred, which will result in 'adhesion' phenomenon in the raft aquaculture areas extraction. The fully convolutional network (FCN) based methods have made great progress in the field of remote sensing in recent years. In this paper, we proposed an FCN-based end-to-end raft aquaculture areas extraction model (which is called UPS-Net) to overcome the 'adhesion' phenomenon. The UPS-Net contains an improved U-Net and a PSE structure. The improved U-Net can simultaneously capture boundary and contextual information of raft aquaculture areas from remote sensing images. The PSE structure can adaptively fuse the boundary and contextual information to reduce the 'adhesion' phenomenon. We selected laver raft aquaculture areas in eastern Lianyungang in China as the research region to verify the effectiveness of our model. The experimental results show that compared with several state-of-the-art models, the proposed UPS-Net model performs better at extracting raft aquaculture areas and can significantly reduce the 'adhesion' phenomenon. [ABSTRACT FROM AUTHOR]

With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images. [ABSTRACT FROM AUTHOR]

Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Based on the results of remote sensing data interpretation, this paper aims to simulate and predict the mountain permafrost distribution changes affected by the mean decadal air temperature (MDAT), from the 1990s to the 2040s, in the Qilian Mountains. A bench-mark map is visually interpreted to acquire a mountain permafrost distribution from the 1990s, based on remote sensing images. Through comparison and estimation, a logistical regression model (LRM) is constructed using the bench-mark map, topographic and land coverage factors and MDAT data from the 1990s. MDAT data from the 2010s to the 2040s are predicted according to survey data from meteorological stations. Using the LRM, MDAT data and the factors, the probabilities (p) of decadal mountain permafrost distribution from the 1990s to the 2040s are simulated and predicted. According to the p value, the permafrost distribution statuses are classified as 'permafrost probable' (p > 0.7), 'permafrost possible' (0.7 ≥ p ≥ 0.3) and 'permafrost improbable' (p < 0.3). From the 1990s to the 2040s, the 'permafrost probable' type mainly degrades to that of 'permafrost possible', with the total area degenerating from 73.5 × 103 km2 to 66.5 × 103 km2. The 'permafrost possible' type mainly degrades to that of 'permafrost impossible', with a degradation area of 6.5 × 103 km2, which accounts for 21.3% of the total area. Meanwhile, the accuracy of the simulation results can reach about 90%, which was determined by the validation of the simulation results for the 1990s, 2000s and 2010s based on remote sensing data interpretation results. This research provides a way of understanding the mountain permafrost distribution changes affected by the rising air temperature rising over a long time, and can be used in studies of other mountains with similar topographic and climatic conditions. [ABSTRACT FROM AUTHOR]

Yunnan pine shoot beetles (PSB), Tomicus yunnanensis and Tomicus minor have spread through southwestern China in the last five years, leading to millions of hectares of forest being damaged. Thus, there is an urgent need to develop an effective approach for accurate early warning and damage assessment of PSB outbreaks. Remote sensing is one of the most efficient methods for this purpose. Despite many studies existing on the mountain pine beetle (MPB), very little work has been undertaken on assessing PSB stress using remote sensing. The objective of this paper was to develop a spectral linear mixing model aided by radiative transfer (RT) and a new Yellow Index (YI) to simulate the reflectance of heterogeneous canopies containing damaged needles and quantitatively inverse their PSB stress. The YI, the fraction of dead needles, is a physically-explicit stress indicator that represents the plot shoots damage ratio (plot SDR). The major steps of this methods include: (1) LIBERTY2 was developed to simulate the reflectance of damaged needles using YI to linearly mix the green needle spectra with the dead needle spectra; (2) LIBERTY2 was coupled with the INFORM model to scale the needle spectra to the canopy scale; and (3) a look-up table (LUT) was created against Sentinel 2 (S2) imagery and inversed leaf chlorophyll content (LCC), green leaf area index (LAI) and plot SDR. The results show that (1) LIBERTY2 effectively simulated the reflectance spectral values on infested needles (mean relative error (MRE) = 1.4–18%), and the YI can indicate the degrees of needles damage; (2) the coupled LIBERTY2-INFORM model is suitable to estimate LAI (R2 = 0.73, RMSE = 0.17 m m−2, NRMSE = 11.41% and the index of agreement (IOA) = 0.92) and LCC (R2 = 0.49, RMSE = 56.24 mg m−2, NRMSE = 25.22% and IOA = 0.72), and is better than the original LIBERTY model (LAI: R2 = 0.38, RMSE = 0.43 m m−2, NRMSE = 28.85% and IOA = 0.68; LCC: R2 = 0.34, RMSE = 76.44 mg m−2, NRMSE = 34.23% and IOA = 0.57); and (3) the inversed YI is positively correlated with the measured plot SDR (R2 = 0.40, RMSE = 0.15). We conclude that the LIBERTY2 model improved the reflectance simulation accuracy of both the needles and canopies, making it suitable for assessing PSB stress. The YI has the potential to assess PSB damage. [ABSTRACT FROM AUTHOR]

Airborne LiDAR (Light Detection And Ranging) remote sensing for individual tree-level forest inventory necessitates proper extraction of individual trees and accurate measurement of tree structural parameters. Due to the inadequate tree finding capability offered by LiDAR technology and the complex patterns of forest canopies, significant omission and commission errors occur frequently in the segmentation results. Aimed at error reduction and accuracy refinement, this paper presents a novel adaptive mean shift-based clustering scheme aided by a tree trunk detection technique to segment individual trees and estimate tree structural parameters based solely on the airborne LiDAR data. Tree trunks are detected by analyzing points' vertical histogram to detach all potential crown points and then clustering the separated trunk points according to their horizontal mutual distances. The detected trunk information is used to adaptively calibrate the kernel bandwidth of the mean shift procedure in the fine segmentation stage by applying an original 2D (two-dimensional) estimation of individual crown diameters. Trunk detection results and LiDAR point clusters generated by the adaptive mean shift procedures serve as mutual references for final detection of individual trees. Experimental results show that a combination of adaptive mean shift clustering and detected tree trunk can provide a significant performance improvement in individual tree-level forest measurement. Compared with conventional clustering techniques, the trunk detection-aided mean shift clustering approach can detect 91.1% of the trees ("recall") with a higher tree positioning accuracy (the mean positioning error is reduced by 33%) in a multi-layered coniferous and broad-leaved mixed forest in South China, and 93.5% of the identified trees are correct ("precision"). The tree detection brings the estimation of structural parameters for individual trees up to an accuracy level: −2.2% mean relative error and 5.8% relative RMSE (Root Mean Square Error) for tree height and 0.6% mean relative error and 21.9% relative RMSE for crown diameter, respectively. [ABSTRACT FROM AUTHOR]

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area. [ABSTRACT FROM AUTHOR]

In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability and harmonization were among the main constraints. Contemporary technological assets, such as remote sensing and machine learning, allow for analyzing global changes in the settlement process with unprecedented detail. The Global Human Settlement Layer (GHSL) project set out to produce detailed datasets to analyze and monitor the spatial footprint of human settlements and their population, which are key indicators for the global policy commitments of the 2030 Development Agenda. In the GHSL, Earth Observation plays a key role in the detection of built-up areas from the Landsat imagery upon which population distribution is modelled. The combination of remote sensing imagery and population modelling allows for generating globally consistent and detailed information about the spatial distribution of built-up areas and population. The GHSL data facilitate a multi-temporal analysis of human settlements with global coverage. The results presented in this article focus on the patterns of development of built-up areas, population and settlements. The article reports about the present status of global urbanization (2015) and its evolution since 1990 by applying to the GHSL the Degree of Urbanisation definition of the European Commission Directorate General for Regional and Urban Policy (DG-Regio) and the Statistical Office of the European Communities (EUROSTAT). The analysis portrays urbanization dynamics at a regional level and per country income classes to show disparities and inequalities. This study analyzes how the 6.1 billion urban dwellers are distributed worldwide. Results show the degree of global urbanization (which reached 85% in 2015), the more than 100 countries in which urbanization has increased between 1990 and 2015, and the tens of countries in which urbanization is today above the global average and where urbanization grows the fastest. The paper sheds light on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting. [ABSTRACT FROM AUTHOR]

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors. [ABSTRACT FROM AUTHOR]