The Yangtze River Basin (YZRB) and Yellow River Basin (YRB) are important ecological regions in China. In recent decades, intense climate change and human activities have altered the natural ...landscapes of both basins and degraded their ecosystem functions. This study focused on clarifying the major factors influencing the ecosystem services (ESs) in different regions of China, which will be key to manage ecosystems sustainably. Five ESs, including water yield, carbon storage, soil conservation, water purification and habitat quality were selected for mapping. The geographical detector model was used to analyze the independent and joint effects of driving factors, and the spatial changes in driving factors were expressed using geographically weighted regression model. Results showed that (1) all five ESs decreased from southeast to northwest in the YRB but had an irregular distribution in the YZRB. (2) Precipitation and woodland proportion were dominant factors for total ecosystem services in both basins, followed by temperature, slope, and human activity intensity in the YRB, and temperature, human activity intensity, and population density in the YZRB. The combined effects of the driving factors were much higher than the individual effects in both basins (3) Natural factors (e.g., precipitation, slope, and woodland proportion). exerted positive effects in most areas of both basins. Human factors (e.g., GDP, human activity intensity, and built-up land proportion) had negative effects, with an uneven spatial distribution. Overall, natural factors promoted the formation and spatial distribution of ESs, and their direct impacts on total ecosystem services were insignificant in the short term. The impact of land-use changes played a leading role in joint impacts. In the YRB, the increase of woodland had a positive effect on ESs; the continuous growth of build-up land led to the degradation of ESs in the YZRB. Accordingly, adhering to the program of Grain for Green and changing the mode of economic development are crucial for future development. A comparative analysis of the driving factors of ESs in different regions provides a scientific basis for understanding and optimizing management policies.
Spatial distribution of total ecosystem services in the (a-c) Yellow and (d-f) Yangtze River Basins in (a,d) 2000, (b,e) 2015, and (c,f) 2000–2015. Display omitted
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•First-hand multi-hazard susceptibility mapping framework based on Convolutional Neural Network.•Evaluation of flash floods, landslides, and debris flows.•62.57% of the study area is ...prone to multi-hazards, of which 0.18% are prone to three main hazards.
Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.
Remote sensing image scene classification has been widely applied and has attracted increasing attention. Recently, convolutional neural networks (CNNs) have achieved remarkable results in scene ...classification. However, scene images have complex semantic relationships between multiscale ground objects, and the traditional stacked network structure lacks the ability to effectively extract multiscale and key features, resulting in limited feature representation capabilities. By simulating the way that humans understand and perceive images, attention mechanisms can be beneficial for quickly and accurately acquiring key features. In our study, we propose a channel-attention-based DenseNet (CAD) network for scene classification. First, the lightweight DenseNet121 is selected as the backbone for the spatial relationship between multiscale ground objects. In the spatial domain, densely connected CNN layers can extract spatial features at multiple scales and correlate with each other. Second, in the channel domain, a channel attention mechanism is introduced to strengthen the weights of the important feature channels adaptively and to suppress the secondary feature channels. Third, the cross-entropy loss function based on label smoothing is used to reduce the impact of interclass similarity upon feature representations. The proposed CAD network is evaluated on three public datasets. The experimental results demonstrate that the CAD network can achieve performance comparable to those of other state-of-the-art methods. The visualization through the Grad-CAM ++ algorithm also reflects the effectiveness of channel attention and the powerful feature representation capabilities of the CAD network.
Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional ...geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.
In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of ...precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, memory and network demands on resources. Cloud computing can increase processing efficiency with high scalability and low cost, but results in high latency and great pressure on the network bandwidth. The emerging of edge intelligence, although still in the early stages, provides a promising solution for artificial intelligence (AI) applications on intelligent edge devices at the edge of the network close to data sources. These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways). Therefore, in this paper, a comprehensive survey on the latest developments of precision agriculture with UAV RS and edge intelligence is conducted for the first time. The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays.
Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of "big data". Cloud computing resources are a natural fit for ...processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users' direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a same replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers.
Land cover mapping (LCM) in complex surface-mined and agricultural landscapes could contribute greatly to regulating mine exploitation and protecting mine geo-environments. However, there are some ...special and spectrally similar land covers in these landscapes which increase the difficulty in LCM when employing high spatial resolution images. There is currently no research on these mixed complex landscapes. The present study focused on LCM in such a mixed complex landscape located in Wuhan City, China. A procedure combining ZiYuan-3 (ZY-3) stereo satellite imagery, the feature selection (FS) method, and machine learning algorithms (MLAs) (random forest, RF; support vector machine, SVM; artificial neural network, ANN) was proposed and first examined for both LCM of surface-mined and agricultural landscapes (MSMAL) and classification of surface-mined land (CSML), respectively. The mean and standard deviation filters of spectral bands and topographic features derived from ZY-3 stereo images were newly introduced. Comparisons of three MLAs, including their sensitivities to FS and whether FS resulted in significant influences, were conducted for the first time in the present study. The following conclusions are drawn. Textures were of little use, and the novel features contributed to improve classification accuracy. Regarding the influence of FS: FS substantially reduced feature set (by 68% for MSMAL and 87% for CSML), and often improved classification accuracies (with an average value of 4.48% for MSMAL using three MLAs, and 11.39% for CSML using RF and SVM); FS showed statistically significant improvements except for ANN-based MSMAL; SVM was most sensitive to FS, followed by ANN and RF. Regarding comparisons of MLAs: for MSMAL based on feature subset, RF achieved the greatest overall accuracy of 77.57%, followed by SVM and ANN; for CSML, SVM had the highest accuracies (87.34%), followed by RF and ANN; based on the feature subsets, significant differences were observed for MSMAL and CSML using any pair of MLAs. In general, the proposed approach can contribute to LCM in complex surface-mined and agricultural landscapes.
•More intensive land use activities during 2010–2018 was observed in UAMRYR.•Ecological functions exacerbated due to the built-up land expansion.•ESV decreased from $238.86 billion in 2000 to $234.28 ...billion in 2018.•Ecological protection should be strengthen in high altitudes of natural vegetation.
Increasing human activities have notably altered the compositions of global ecosystems, and consequently the services provided by them. As one of the most important economic functional areas in China, Urban Agglomerations in the middle reaches of the Yangtze River (UAMRYR) has undergone dramatic land use changes due to the rapid urbanization over recent decades. In this study, we utilized a revised proxy-based approach to evaluate ecosystem services value (ESV), applied the ecological contribution degree to quantify the response of ESV to land use change, coupled with RDA to explore the socioeconomic forces of ecosystem services. Our findings showed that land use activities were more intensive during 2010–2018 than that in 2000–2010. The total ESV decreased by $4.58 billion and most of the ecological functions exacerbated due to the expansion of built-up land and the reduction of cultivated land during 2010–2018. Besides, based on RDA result, park green land per capita, population density, and distance from the central city were estimated as the top three frontal factors affecting ESV; yet per capita water consumption was a negative one. Therefore, different development strategies should be adopted along the altitude gradient in UAMRYR. That is to emphasize the protection of permanent cultivated land and the diversification of land composition in low altitude areas, while ecological protection should be strengthened in higher areas. Understanding how ecosystem services respond to LUCC and socioeconomic drivers can help to enhance ecosystem functions and provide a theoretical basis for the construction of ecological civilization of UAMRYR, which may also be applied at the other urban agglomerations around the globe.
Petroleum industry has started to embrace the advanced petroleum cyber-physical system (CPS) technologies. Offshore petroleum CPS is particularly hard to build, mainly due to the difficulty in ...detecting and preventing offshore oil leaking. During the oil exploration and transportation process, the remote multi-sensing technology is typically employed for emerging service. It can be utilized for leak detection by enabling the underwater modeling of an offshore petroleum CPS. However, such a technology suffers from insufficient remote sensing resources and expensive computational overhead. In this work, a cross-entropy based leak detection technique is proposed to detect the oil leak, which facilitates the understanding of the oil leak induced marine pollution. Furthermore, a hierarchical parallel approach is proposed on the super computer Tianhe-2 to improve the efficiency of the proposed leak detection technique. Experimental results on Penglai oil spill events demonstrate that the proposed method can effectively identify the sources of oil spilling with accuracy up to Formula: see text.
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