A Survey of Big Data Analytics for Smart Forestry
---
paper_title: Big Data Analytics in Intelligent Transportation Systems: A Survey

paper_content:

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.
---
paper_title: Big spatial vector data management: a review

paper_content:

AbstractSpatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other data-sets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest...
---
paper_title: A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

paper_content:

Because of their rapid maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring, detecting, and fighting forest fires. Over the last decade, UAV-based forest fire fighting technology has shown increasing promise. This paper presents a systematic overview of current progress in this field. First, a brief review of the development and system architecture of UAV systems for forest fire monitoring, detection, and fighting is provided. Next, technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs. The final section outlines existing challenges and potential solutions in the application of UAVs to forest firefighting.
---
paper_title: Toward efficient and privacy-preserving computing in big data era

paper_content:

Big data, because it can mine new knowledge for economic growth and technical innovation, has recently received considerable attention, and many research efforts have been directed to big data processing due to its high volume, velocity, and variety (referred to as "3V") challenges. However, in addition to the 3V challenges, the flourishing of big data also hinges on fully understanding and managing newly arising security and privacy challenges. If data are not authentic, new mined knowledge will be unconvincing; while if privacy is not well addressed, people may be reluctant to share their data. Because security has been investigated as a new dimension, "veracity," in big data, in this article, we aim to exploit new challenges of big data in terms of privacy, and devote our attention toward efficient and privacy-preserving computing in the big data era. Specifically, we first formalize the general architecture of big data analytics, identify the corresponding privacy requirements, and introduce an efficient and privacy-preserving cosine similarity computing protocol as an example in response to data mining's efficiency and privacy requirements in the big data era.
---
paper_title: Data analytics of urban fabric metrics for smart cities

paper_content:

Abstract Comprehensive understanding of the built environment, especially the urban form, is a prerequisite for building a smart city. Data analytics of urban fabric metrics using quantitative methods is critical to understanding a city’s complexity. This paper aims to study urban fabric using comprehensive computation methods. A series of morphological indexes of urban blocks are established to measure the blocks’ overall features and subtle differences. This study uses multiple statistical methods with computation techniques and machine learning to fulfill factor analysis and clustering to classify major block types and their spatial distribution, and this study aims to precisely position the important continuous zone and fracture locations within the study area based on a geo-information system (GIS), effectively revealing the potential morphological order of different block types in the urban fabric. The study provides accurate basis and technical support for the optimization of urban construction. It has important and practical significance for promoting the scientific and reasonable implementation of a new type of urbanization.
---
paper_title: Applications of big data to smart cities

paper_content:

Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.
---
paper_title: Scientific big data and Digital Earth

paper_content:

Big data has been a focus of research in science, technology, economics, and social studies. Many countries have already incorporated big data research into their national strategies. This paper elaborates upon the origin, connotation, and development of big data from both a spatial and temporal perspective. It proposes that scientific big data will become a new solution in scientific research as the paradigm changes from being model-driven to data-driven. This paper defines the concept of “scientific big data” and proposes strategies for solving “big data problems”. Theoretical frameworks and data systems for Digital Earth are discussed with a clear conclusion that scientific big data is a prominent feature of Digital Earth. As an example, spatial cognition of the formation mechanism of China’s Heihe-Tengchong Line—a geo-demographic demarcation line dividing China into two parts—is discussed within the context of big data computation and analysis for Digital Earth.
---
paper_title: Big Data Analytics for Emergency Communication Networks: A Survey

paper_content:

Disaster management is a crucial and urgent research issue. Emergency communication networks (ECNs) provide fundamental functions for disaster management, because communication service is generally unavailable due to large-scale damage and restrictions in communication services. Considering the features of a disaster (e.g., limited resources and dynamic changing of environment), it is always a key problem to use limited resources effectively to provide the best communication services. Big data analytics in the disaster area provides possible solutions to understand the situations happening in disaster areas, so that limited resources can be optimally deployed based on the analysis results. In this paper, we survey existing ECNs and big data analytics from both the content and the spatial points of view. From the content point of view, we survey existing data mining and analysis techniques, and further survey and analyze applications and the possibilities to enhance ECNs. From the spatial point of view, we survey and discuss the most popular methods and further discuss the possibility to enhance ECNs. Finally, we highlight the remaining challenging problems after a systematic survey and studies of the possibilities.
---
paper_title: Big Data for Remote Sensing: Challenges and Opportunities

paper_content:

Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we are now living in the age of big remote sensing data. Furthermore, these data are becoming an economic asset and a new important resource in many applications. In this paper, we specifically analyze the challenges and opportunities that big data bring in the context of remote sensing applications. Our focus is to analyze what exactly does big data mean in remote sensing applications and how can big data provide added value in this context. Furthermore, this paper describes the most challenging issues in managing, processing, and efficient exploitation of big data for remote sensing problems. In order to illustrate the aforementioned aspects, two case studies discussing the use of big data in remote sensing are demonstrated. In the first test case, big data are used to automatically detect marine oil spills using a large archive of remote sensing data. In the second test case, content-based information retrieval is performed using high-performance computing (HPC) to extract information from a large database of remote sensing images, collected after the terrorist attack to the World Trade Center in New York City. Both cases are used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.
---
paper_title: Big Data in Agriculture: A Challenge for the Future

paper_content:

This article examines the challenge and opportunities of Big Data, and concludes that these technologies will lead to relevant analysis at every stage of the agricultural value chain. Big Data is defined by several characteristics beyond size, particularly, the volume, velocity, variety, and veracity of the data. We discuss a set of analytical techniques that are increasingly relevant to our profession as one addresses these issues. Ultimately, we resolve that agricultural and applied economists are uniquely positioned to contribute to the research and outreach agenda on Big Data. We believe there are relevant policy, farm management, supply chain, consumer demand, and sustainability issues where our profession can make major contributions. The authors are thankful to the anonymous reviewers and editor Craig Gundersen for helpful comments. Support was provided by the Mississippi Agricultural and Forestry Experiment Station Special Research Initiative.
---
paper_title: Big Data: A Survey

paper_content:

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.
---
paper_title: Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

paper_content:

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.
---
paper_title: Big Data Analytics for System Stability Evaluation Strategy in the Energy Internet

paper_content:

With the significant improvements in the Energy Internet, we have witnessed the explosion of multisource energy big data, whose characteristics of vast volume, fast velocity, and diverse variety not only formulate an essential infrastructure of the Energy Internet, but also bring threats to the system's stability. In this paper, we concern with the system-level stability issues in the Energy Internet and study how to maintain a stable and healthy energy network environment. To this end, we propose a system-level stability evaluation model in the Energy Internet based on a critical energy function to explore small disturbance stability region (SDSR), where SDSR can be acquired via estimating the operational data threshold of distributed generations. The threshold is estimated based on energy consumption rather than equilibrium nodes, which applies the energy function theory and reduces the computation complexity. Moreover, in our proposed model, we add the big data approximate analytics algorithm into hyperplane fitting to optimize and analyze the SDSR. Simulation results on SDSR in a single dominant oscillation mode and multiple dominant oscillation mode have demonstrated the advantages and superiority of our proposed method over the prior schemes.
---
paper_title: Undefined By Data: A Survey of Big Data Definitions

paper_content:

The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term.
---
paper_title: Application of WSN in precision forestry

paper_content:

In order to conveniently access information of forestry, a new technology, wireless sensor network is introduced in precision forestry applications. A forestry information monitoring system based on wireless sensor network technology is proposed, which can sample temperature, soil moisture, light intensity, nitrogen concentration and other environmental factors. The networks of sensor nodes are managed by a central node which collects and transmits data to remote host computer through GPRS. The system design methodology based on ZigBee chip 2430 is presented and the corresponding system hardware architecture and software flow are given. This system effectively makes it easy to collect and transmit data of environmental factors in forestry product and posses high practical values.
---
paper_title: Applications of remote sensing, GIS and GPS in glaciology: a review

paper_content:

Remote sensing has served as an efficient method of gathering data about glaciers since its emergence. The recent advent of Geographic Information Systems (GIS) and Global Positioning Systems (GPS) has created an effective means by which the acquired data are analysed for the effective monitoring and mapping of temporal dynamics of glaciers. A large number of researchers have taken advantage of remote sensing, GIS and GPS in their studies of glaciers. These applications are comprehensively reviewed in this paper. This review shows that glacial features identifiable from aerial photographs and satellite imagery include spatial extent, transient snowline, equilibrium line elevation, accumulation and ablation zones, and differenti- ation of ice/snow. Digital image processing (e.g., image enhancement, spectral ratioing and automatic classification) improves the ease and accuracy of mapping these parameters. The traditional visible light/infrared remote sensing of two-dimensional glacier distribution has been extended to three-dimensional volume estimation and dynamic monitoring using radar imagery and GPS. Longitudinal variations in glacial extent have been detected from multi- temporal images in GIS. However, the detected variations have neither been explored nor modelled from environmental and topographic variables. GPS has been utilized independent of remote sensing and GIS to determine glacier ice velocity and to obtain information about glacier surfaces. Therefore, the potential afforded by the integration of nonconventional remote sensing (e.g., SAR interferometry) with GIS and GPS still remains to be realized in glaciology. The emergence of new satellite images will make remote sensing of glaciology more predictive, more global and towards longer terms.
---
paper_title: Terrestrial laser scanning in forest inventories

paper_content:

Abstract Decision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on the goal, scale, resources and the required accuracy. Most of the forest inventories are based on field sample. Therefore, the accuracy of the forest inventories depends on the quality and quantity of the field sample. Conventionally, field sample has been measured using simple tools. When map is required, remote sensing materials are needed. Terrestrial laser scanning (TLS) provides a measurement technique that can acquire millimeter-level of detail from the surrounding area, which allows rapid, automatic and periodical estimates of many important forest inventory attributes. It is expected that TLS will be operationally used in forest inventories as soon as the appropriate software becomes available, best practices become known and general knowledge of these findings becomes more wide spread. Meanwhile, mobile laser scanning, personal laser scanning, and image-based point clouds became capable of capturing similar terrestrial point cloud data as TLS. This paper reviews the advances of applying TLS in forest inventories, discusses its properties with reference to other related techniques and discusses the future prospects of this technique.
---
paper_title: GIS: An Updated Primer on a Powerful Management Tool

paper_content:

One of the most significant technological developments for forest and natural resource organizations during the past century has been the advent of geographic information systems (GIS). For a variety of reasons, includingincreased mapping and analytical capabilities, affordability, and accessibility, GIS has become a standard planningand analysis tool for many organizations. This article presents a brief history of the development of GIS and examines some current challenges and opportunities facing GIS use in forest and natural resource management, including those related to data collection and accuracy, data structures, personnel, legal issues, and user certification. Organizations that use GIS should be aware of these challenges and opportunities, and be prepared to address them if GIS implementation is to be successful.
---
paper_title: Big spatial vector data management: a review

paper_content:

AbstractSpatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other data-sets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest...
---
paper_title: A Parallel File System with Application-Aware Data Layout Policies for Massive Remote Sensing Image Processing in Digital Earth

paper_content:

Remote sensing applications in Digital Earth are overwhelmed with vast quantities of remote sensing (RS) image data. The intolerable I/O burden introduced by the massive amounts of RS data and the irregular RS data access patterns has made the traditional cluster based parallel I/O systems no longer applicable. We propose a RS data object-based parallel file system for remote sensing applications and implement it with the OrangeFS file system. It provides application-aware data layout policies, together with RS data object based data I/O interfaces, for efficient support of various data access patterns of RS applications from the server side. With the prior knowledge of the desired RS data access patterns, HPGFS could offer relevant space-filling curves to organize the sliced 3-D data bricks and distribute them over I/O servers. In this way, data layouts consistent with expected data access patterns could be created to explore data locality and achieve performance improvement. Moreover, the multi-band RS data with complex structured geographical metadata could be accessed and managed as a single data object. Through experiments on remote sensing applications with different access patterns, we have achieved performance improvement of about 30 percent for I/O and 20 percent overall.
---
paper_title: Precision Forestry: Trees Counting in Urban Areas Using Visible Imagery based on an Unmanned Aerial Vehicle

paper_content:

Abstract: This research work describes an approach to count trees in an urban environment. Furthermore it addresses the problems involved in detection of trees in aerial imagery. This work can be used to solve the problem of forest degradation and deforestation. Right now forest man labor isn’t efficient enough to detect or prevent this problem. A multi-rotor UAV equipped with high resolution RGB camera was used to acquire aerial images and to count number of trees in surveyed area. Various issues involved in the robust implementation of proposed algorithm are discussed. The result of successful implementation of the proposed algorithm on multiple scenarios are also presented and we show that our naive approach is able to achieve ≈ 0.72 accuracy within reasonable amount of time.
---
paper_title: SpatialHadoop: towards flexible and scalable spatial processing using mapreduce

paper_content:

Recently, MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include tera-byte sorting, machine learning, and graph processing. With the huge volumes of spatial data coming from different sources, there is an increasing demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in spatial data processing. However, Hadoop falls short in supporting spatial data efficiently as the core is unaware of spatial data properties. This paper describes SpatialHadoop; a full-edged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and ex- pressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. The flexibility and open source nature of SpatialHadoop allows more spatial operations to be implemented efficiently using MapReduce. Extensive experiments on a real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.
---
paper_title: Remote sensing big data utilization for paddy growth stages detection

paper_content:

Predicting and estimating the character of big data becomes paramount since it is laborious to deal with big data with conventional models and algorithms. Remote sensing big data from various satellites consist of many large-scale images that are exceptionally complex in terms of their structural, spectral, and textual features. Also, most of them are still in annotated form. Therefore, it is a challenge to explore them for detecting objects on the ground that are beneficial to humans using their sophisticated features. In this paper, we proposed a remote sensing big data for paddy growth stages detection, through multi-temporal analysis with a heuristic algorithm. Information derived from growth stages is very useful to know the needs of water, fertilizer, and crop planting calendar to increase productivity.
---
paper_title: A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

paper_content:

Abstract. In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.
---
paper_title: Improving metadata management for small files in HDFS

paper_content:

Scientific applications are adapting HDFS/MapReduce to perform large scale data analytics. One of the major challenges is that an overabundance of small files is common in these applications, and HDFS manages all its files through a single server, the Namenode. It is anticipated that small files can significantly impact the performance of Namenode. In this work we propose a mechanism to store small files in HDFS efficiently and improve the space utilization for metadata. Our scheme is based on the assumption that each client is assigned a quota in the file system, for both the space and number of files. In our approach, we utilize the compression method ‘harballing', provided by Hadoop, to better utilize the HDFS. We provide for new job functionality to allow for in-job archival of directories and files so that running MapReduce programs may complete without being killed by the JobTracker due to quota policies. This approach leads to better functionality of metadata operations and more efficient usage of the HDFS. Our analysis results show that we can reduce the metadata footprint in main memory by a factor of 42.
---
paper_title: GridFS: Targeting Data Sharing in Grid Environments

paper_content:

GridFS is a system that enables data sharing in a cluster or grid environment. By deploying a set of servers over several nodes, it is possible to build a federated wide area file system integrating tera scale sized data. The data is stored in different hosts under a single name space controlled by GridFS. It was designed and developed considering interoperability, scalability and performance issues. Also, GridFS provides special functions for process schedulers, such as file transfer rate estimates, and supports legacy applications
---
paper_title: Data Storage Scheme of Sensor Network Based on HDFS

paper_content:

In order to solve the problem of growing massive of data in sensor network, we propose a new scheme of data storage for sensor network based on HDFS which is a cloud-based storage platform, it effectively alleviate the pressure of mass data storage on sensor network, and improved the scalability of storage system and part of the enhanced the data storage security on sensor network. And this scheme is based on cloud storage platform, storage the data which collected by sensors to each data node using a distributed architecture solution, and keep multiple copies of data in order to maintain its high reliability of data. As reducing the pressure of data storage, but also protects the security of stored data as shown by security analysis.
---
paper_title: The Hadoop Distributed File System

paper_content:

The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers both host directly attached storage and execute user application tasks. By distributing storage and computation across many servers, the resource can grow with demand while remaining economical at every size. We describe the architecture of HDFS and report on experience using HDFS to manage 25 petabytes of enterprise data at Yahoo!.
---
paper_title: Ceph: a scalable, high-performance distributed file system

paper_content:

We have developed Ceph, a distributed file system that provides excellent performance, reliability, and scalability. Ceph maximizes the separation between data and metadata management by replacing allocation tables with a pseudo-random data distribution function (CRUSH) designed for heterogeneous and dynamic clusters of unreliable object storage devices (OSDs). We leverage device intelligence by distributing data replication, failure detection and recovery to semi-autonomous OSDs running a specialized local object file system. A dynamic distributed metadata cluster provides extremely efficient metadata management and seamlessly adapts to a wide range of general purpose and scientific computing file system workloads. Performance measurements under a variety of workloads show that Ceph has excellent I/O performance and scalable metadata management, supporting more than 250,000 metadata operations per second.
---
paper_title: Querying multi-dimensional data indexed using the Hilbert space-filling curve

paper_content:

Mapping to one-dimensional values and then using a one-dimensional indexing method has been proposed as a way of indexing multi-dimensional data. Most previous related work uses the Z-Order Curve but more recently the Hilbert Curve has been considered since it has superior clustering properties. Any approach, however, can only be of practical value if there are effective methods for executing range and partial match queries. This paper describes such a method for the Hilbert Curve.
---
paper_title: A Parallel File System with Application-Aware Data Layout Policies for Massive Remote Sensing Image Processing in Digital Earth

paper_content:

Remote sensing applications in Digital Earth are overwhelmed with vast quantities of remote sensing (RS) image data. The intolerable I/O burden introduced by the massive amounts of RS data and the irregular RS data access patterns has made the traditional cluster based parallel I/O systems no longer applicable. We propose a RS data object-based parallel file system for remote sensing applications and implement it with the OrangeFS file system. It provides application-aware data layout policies, together with RS data object based data I/O interfaces, for efficient support of various data access patterns of RS applications from the server side. With the prior knowledge of the desired RS data access patterns, HPGFS could offer relevant space-filling curves to organize the sliced 3-D data bricks and distribute them over I/O servers. In this way, data layouts consistent with expected data access patterns could be created to explore data locality and achieve performance improvement. Moreover, the multi-band RS data with complex structured geographical metadata could be accessed and managed as a single data object. Through experiments on remote sensing applications with different access patterns, we have achieved performance improvement of about 30 percent for I/O and 20 percent overall.
---
paper_title: Open and scalable analytics of large Earth observation datasets: From scenes to multidimensional arrays using SciDB and GDAL

paper_content:

Abstract Earth observation (EO) datasets are commonly provided as collection of scenes, where individual scenes represent a temporal snapshot and cover a particular region on the Earth’s surface. Using these data in complex spatiotemporal modeling becomes difficult as soon as data volumes exceed a certain capacity or analyses include many scenes, which may spatially overlap and may have been recorded at different dates. In order to facilitate analytics on large EO datasets, we combine and extend the geospatial data abstraction library (GDAL) and the array-based data management and analytics system SciDB. We present an approach to automatically convert collections of scenes to multidimensional arrays and use SciDB to scale computationally intensive analytics. We evaluate the approach in three study cases on national scale land use change monitoring with Landsat imagery, global empirical orthogonal function analysis of daily precipitation, and combining historical climate model projections with satellite-based observations. Results indicate that the approach can be used to represent various EO datasets and that analyses in SciDB scale well with available computational resources. To simplify analyses of higher-dimensional datasets as from climate model output, however, a generalization of the GDAL data model might be needed. All parts of this work have been implemented as open-source software and we discuss how this may facilitate open and reproducible EO analyses.
---
paper_title: An Array Database Approach for Earth Observation Data Management and Processing

paper_content:

Over the past few years, Earth Observation (EO) has been continuously generating much spatiotemporal data that serves for societies in resource surveillance, environment protection, and disaster prediction. The proliferation of EO data poses great challenges in current approaches for data management and processing. Nowadays, the Array Database technologies show great promise in managing and processing EO Big Data. This paper suggests storing and processing EO data as multidimensional arrays based on state-of-the-art array database technologies. A multidimensional spatiotemporal array model is proposed for EO data with specific strategies for mapping spatial coordinates to dimensional coordinates in the model transformation. It allows consistent query semantics in databases and improves the in-database computing by adopting unified array models in databases for EO data. Our approach is implemented as an extension to SciDB, an open-source array database. The test shows that it gains much better performance in the computation compared with traditional databases. A forest fire simulation study case is presented to demonstrate how the approach facilitates the EO data management and in-database computation.
---
paper_title: Cloud platform wireless sensor network detection system based on data sharing

paper_content:

In order to explore the data sharing in the cloud platform, which is easy to manage and use data, but also ensures the safety of the data, on the basis of data sharing, the study of cloud platform wireless sensor network monitoring system for environmental monitoring methods is carried out. The prototype system of environmental monitoring cloud platform is built, which achieves the support and management of the sensor data and file data. The sensor data, in the system, adopts the OGCSWE standard, and provides the corresponding interface for other systems. In the platform, it is capable of querying the data needed by users, and making the paid or unpaid data sharing, which is convenient for users to use more and more useful data. The test results show that, the system runs stably and runs normally at the same time of multi users landing, and the data acquisition speed is fast. Based on the above findings, it is conclude that, under the condition of relying on distributed computing and parallel computing in cloud platform, the system can effectively monitor in a wide range and process the data quickly to achieve the hardware and software services required by users.
---
paper_title: An improved distributed storage and query for remote sensing data

paper_content:

Abstract With the rapid development of information technology, the amount of remote sensing data is increasing at an unprecedented scale. In the presence of massive remote sensing data, the traditional processing methods have the problems of low efficiency and lack of scalability, so this paper uses open source big data technology to improve it. Firstly, the storage model of remote sensing image data is designed by using the distributed storage database HBase. Then, the grid index and the Hibert curve are combined to establish the index for the image data. Finally, the method of MapReduce parallel processing is used to write and query remote sensing images. The experimental results show that the method can effectively improve the data writing and query speed, and has good scalability.
---
paper_title: HBaseSpatial: A Scalable Spatial Data Storage Based on HBase

paper_content:

Recent years, the scale of spatial data is developing more and more huge and its storage has encountered a lot of problems. Traditional DBMS can efficiently handle some big spatial data. However, popular open source relational database systems are overwhelmed by the high insertion rates, querying requirements and terabytes of data that these systems can handle. On the other hand, key-value storage can effectively support large scale operations. To resolve the problems of big vector spatial data's storage and query, we bring forward HBase Spatial, a scalable spatial dada storage based on HBase. At first, we analyze the distributed storage model of HBase. Then, we design a distributed storage and index model. Finally, the advantages of our storage model and index algorithm are proven by experiments with both big sample sets and typical benchmarks on cluster compared with MongoDB and Mysql, which shows that our model can effectively enhance the query speed of big spatial data and provide a good solution for storage.
---
paper_title: SciDB: A Database Management System for Applications with Complex Analytics

paper_content:

A description and discussion of the SciDB database management system focuses on lessons learned, application areas, performance comparisons against other solutions, and additional approaches to managing data and complex analytics.
---
paper_title: SparkGIS: Resource Aware Efficient In-Memory Spatial Query Processing

paper_content:

Much effort has been devoted to support high performance spatial queries on large volumes of spatial data in distributed spatial computing systems, especially in the MapReduce paradigm. Recent works have focused on extending spatial MapReduce frameworks to leverage high performance in-memory distributed processing capabilities of systems such as Spark. However, the performance advantage comes with the requirement of having enough memory and comprehensive configuration. Failing to fulfill this falls back to disk IO, defeating the purpose of such systems or in worst case gets out of memory and fails the job. The problem is aggravated further for spatial processing since the underlying in-memory systems are oblivious of spatial data features and characteristics. In this paper we present SparkGIS - an in-memory oriented spatial data querying system for high throughput and low latency spatial query handling by adapting Apache Spark's distributed processing capabilities. It supports basic spatial queries including containment, spatial join and k-nearest neighbor and allows extending these to complex query pipelines. SparkGIS mitigates skew in distributed processing by supporting several dynamic partitioning algorithms suitable for a rich set of contemporary application scenarios. Multilevel global and local, pre-generated and on-demand in-memory indexes, allow SparkGIS to prune input data and apply compute intensive operations on a subset of relevant spatial objects only. Finally, SparkGIS employs dynamic query rewriting to gracefully manage large spatial query workflows that exceed available distributed resources. Our comparative evaluation has shown that the performance of SparkGIS is on par with contemporary Spark based platforms for relatively smaller queries and outperforms them for larger data and memory intensive workflows by dynamic query rewriting and efficient spatial data management.
---
paper_title: A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

paper_content:

ABSTRACT Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.
---
paper_title: SpatialHadoop: towards flexible and scalable spatial processing using mapreduce

paper_content:

Recently, MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include tera-byte sorting, machine learning, and graph processing. With the huge volumes of spatial data coming from different sources, there is an increasing demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in spatial data processing. However, Hadoop falls short in supporting spatial data efficiently as the core is unaware of spatial data properties. This paper describes SpatialHadoop; a full-edged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and ex- pressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. The flexibility and open source nature of SpatialHadoop allows more spatial operations to be implemented efficiently using MapReduce. Extensive experiments on a real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.
---
paper_title: A Raster Data Framework Based on Distributed Heterogeneous Cluster

paper_content:

Advancements in satellite imaging and sensor technologies result in capturing of large amount of spatial data. Many parallel processing techniques based on data or control parallelism have been attempted during the past 2 decades to provide performance improvement in image processing applications such as urban sprawl, weather prediction and crop estimation. These techniques have used block-based distributed file processing or the more modern MapReduce-based programming for implementation which still have gaps between optimal and best processing in terms of resource scheduling, data distribution and ease of programming. In this paper, we present a layered framework for parallel data processing to improve storage, retrieval and processing performance of spatial data on an underlying distributed file system. The paper presents a data placement strategy across a distributed HDFS cluster in a way to optimize spatial data retrieval and processing. The presence of neighborhood pixels local to the processing node in a distributed environment reduces network latencies and improves the efficiency of applications such as object recognition, change detection and site selection. We evaluate the data placement strategy on a four-node HDFS cluster and show that it can deliver good performance benefits by way of reading blocks of data at almost 10–12 times the default, which contributes to the improvement in efficiency of the various applications that use region growing methods.
---
paper_title: A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data

paper_content:

This demo presents SpatialHadoop as the first full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that pushes spatial data inside the core functionality of Hadoop. SpatialHadoop runs existing Hadoop programs as is, yet, it achieves order(s) of magnitude better performance than Hadoop when dealing with spatial data. SpatialHadoop employs a simple spatial high level language, a two-level spatial index structure, basic spatial components built inside the MapReduce layer, and three basic spatial operations: range queries, k-NN queries, and spatial join. Other spatial operations can be similarly deployed in SpatialHadoop. We demonstrate a real system prototype of SpatialHadoop running on an Amazon EC2 cluster against two sets of real spatial data obtained from Tiger Files and OpenStreetMap with sizes 60GB and 300GB, respectively.
---
paper_title: Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce

paper_content:

Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.
---
paper_title: $k$ NN-DP: Handling Data Skewness in $kNN$ Joins Using MapReduce

paper_content:

In this study, we discover that the data skewness problem imposes adverse impacts on MapReduce-based parallel kNN-join operations running clusters. We propose a data partitioning approach—called kNN-DP —to alleviate load imbalance incurred by data skewness. The overarching goal of kNN-DP is to equally divide data objects into a large number of partitions, which are processed by mappers and reducers in parallel. At the heart of kNN-DP is a data partitioning module, which dynamically and judiciously partitions data to optimize kNN-join performance by suppressing data skewness on Hadoop clusters. Data partitioning decisions largely depends on data properties (e.g., distributions), the analysis of which is highly expensive for a massive amount of data. To speed up the data-property analysis, we incorporate a sampling technique to profile the data distribution of a small sample dataset representing big datasets. After building a data-partitioning cost model for parallel kNN-join s, we derive the time-complexity upper and lower bounds of parallel kNN-join algorithms. The cost model offers us a guidance to systematically investigate kNN-DP ’s performance. kNN-DP obtains global nearest neighbors using local nearest neighbors. To improve the accuracy of such an approximation solution, we augment each node’s local data by a small amount of redundant data. We develop two kNN-DP -based schemes called LSH+ and z-value+ , which seamlessly integrate kNN-DP with the existing LSH and z-value algorithms for kNN-join computing. We implement and evaluate LSH+ and z-value+ on a 24-node Hadoop cluster driven by both synthetic and real-world high-dimensional datasets. The experimental results show that kNN-DP significantly improves the performance of LSH and z-value while offering high extensibility and scalability on Hadoop clusters.
---
paper_title: A Novel Query Method for Spatial Data in Mobile Cloud Computing Environment

paper_content:

With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly.
---
paper_title: SpatialHadoop: towards flexible and scalable spatial processing using mapreduce

paper_content:

Recently, MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include tera-byte sorting, machine learning, and graph processing. With the huge volumes of spatial data coming from different sources, there is an increasing demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in spatial data processing. However, Hadoop falls short in supporting spatial data efficiently as the core is unaware of spatial data properties. This paper describes SpatialHadoop; a full-edged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and ex- pressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. The flexibility and open source nature of SpatialHadoop allows more spatial operations to be implemented efficiently using MapReduce. Extensive experiments on a real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.
---
paper_title: Distributed retrieval for massive remote sensing image metadata on spark

paper_content:

The massive data is constantly and rapidly growing in remote sensing field. How to achieve efficient storage and rapid retrieval of massive remote sensing image metadata is a difficult problem. Big Data technologies provide convenient and fast tools for the storage, retrieval and analysis of remote sensing image metadata. According to the feature of remote sensing image metadata, we first give new concepts of fat grid and thin grid and propose a grid indexing method called Spark-Fat-Thin-Grid-Index (SFTGridIndex). In SFTGridIndex, the index file and partitions files are stored in HDFS. We then design an optimized retrieval method based on SFTGrid-Index. The method can avoid the intersection computing of a large number of polygons. This research can ensure efficient storage and fast query of massive remote sensing image metadata and has good scalability.
---
paper_title: A framework for processing large scale geospatial and remote sensing data in MapReduce environment

paper_content:

In recent years distributed data processing has reached many areas of computer science including geographic and remote sensing information systems. With the continuing increase of data, existing algorithms and data management need to be moved to new architecture, which may require a great deal of effort.This paper describes a geospatial data processing framework designed to enable the management and processing of spatial and remote sensing data in distributed environment. The framework is based on the mainstream MapReduce paradigm, and its open-source implementation, the Apache Hadoop library. The primary design goals of this framework are extensibility and adaptability, to enable previously implemented algorithms and existing toolkits to be easily adapted to distributed execution without major effort.The possibilities of the framework are demonstrated on remote sensing image processing operations in comparison with existing solutions in the Hadoop environment. Results show that this toolkit provides a much broader possibility to advance geospatial data processing than the currently available solutions. Graphical abstractDisplay Omitted HighlightsThe advancement of the AEGIS geospatial toolkit to enable spatial data processing in MapReduce environment.Presentation of high level data and operation environment model, and their application in the Apache Hadoop environment.Moving one phase single machine operations into multi-phase processes in a distributed system.Feature based comparison of geospatial and image processing tools in the Apache Hadoop environment.
---
paper_title: The land transformation model-cluster framework: applying k-means and the Spark computing environment for large scale land change analytics

paper_content:

Abstract This study introduces a novel framework for land change simulation that combines the traditional Land Transformation Model (LTM) with data clustering tools for the purposes of conducting land change simulations of large areas (e.g., continental scale) and over multiple time steps. This framework, called “LTM-cluster”, subsets massive land use datasets which are presented to the artificial neural network-based LTM. LTM-cluster uses the k-means clustering algorithm implemented within the Spark high-performance compute environment. To illustrate the framework, we use three case studies in the United States which vary in simulation extents, cell size, time intervals, number of inputs, and quantity of urban change. Findings indicate consistent and substantial improvements in accuracy performance for all three case studies compared to the traditional LTM model implemented without input clustering. Specifically, the percent correct match, the area under the operating characteristics curve, and the error rate improved on average of 9%, 11%, and 4%. These results confirm that LTM-cluster has high reliability when handling large datasets. Future studies should expand on the framework by exploring other clustering methods and algorithms.
---
paper_title: Deep Fusion of Remote Sensing Data for Accurate Classification

paper_content:

The multisensory fusion of remote sensing data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyperspectral and light detection and ranging data. Then, a fully connected DNN is designed to fuse the heterogeneous features obtained by the previous CNNs. Through the aforementioned deep networks, one can extract the discriminant and invariant features of remote sensing data, which are useful for further processing. At last, logistic regression is used to produce the final classification results. Dropout and batch normalization strategies are adopted in the deep fusion framework to further improve classification accuracy. The obtained results reveal that the proposed deep fusion model provides competitive results in terms of classification accuracy. Furthermore, the proposed deep learning idea opens a new window for future remote sensing data fusion.
---
paper_title: Deep-STEP: A Deep Learning Approach for Spatiotemporal Prediction of Remote Sensing Data

paper_content:

With the advent of advanced remote sensing technologies in past few decades, acquiring higher resolution satellite images has become easier and cheaper in recent days. However, on the other hand, it has offered a big challenge to the remote sensing community in smart image interpretation from such huge volume of data. Deep learning, which offers efficient algorithms for extracting multiple levels of feature abstractions, may be suitable to serve the purpose. This letter presents a deep learning approach (Deep-STEP) for spatiotemporal prediction of satellite remote sensing data. The proposed learning architecture is derived from a deep stacking network, consisting of a stack of multilayer perceptron, each of which models the spatial feature of the associated region at a particular time instant. The proposed method has been demonstrated on normalized difference vegetation index (NDVI) data sets, derived from satellite remote sensing imagery, containing several thousands to millions of pixels/records. The experimental results (related to NDVI prediction) reveal that the proposed architecture exhibits fairly satisfactory performance with promising learning capabilities.
---
paper_title: XHAMI – extended HDFS and MapReduce interface for Big Data image processing applications in cloud computing environments

paper_content:

Summary ::: Hadoop distributed file system (HDFS) and MapReduce model have become popular technologies for large-scale data organization and analysis. Existing model of data organization and processing in Hadoop using HDFS and MapReduce are ideally tailored for search and data parallel applications, for which there is no need of data dependency with its neighboring/adjacent data. However, many scientific applications such as image mining, data mining, knowledge data mining, and satellite image processing are dependent on adjacent data for processing and analysis. In this paper, we identify the requirements of the overlapped data organization and propose a two-phase extension to HDFS and MapReduce programming model, called XHAMI, to address them. The extended interfaces are presented as APIs and implemented in the context of image processing application domain. We demonstrated effectiveness of XHAMI through case studies of image processing functions along with the results. Although XHAMI has little overhead in data storage and input/output operations, it greatly enhances the system performance and simplifies the application development process. Our proposed system, XHAMI, works without any changes for the existing MapReduce models and can be utilized by many applications where there is a requirement of overlapped data. Copyright © 2016 John Wiley & Sons, Ltd.
---
paper_title: Matrix Computations and Optimization in Apache Spark

paper_content:

We describe matrix computations available in the cluster programming framework, Apache Spark. Out of the box, Spark provides abstractions and implementations for distributed matrices and optimization routines using these matrices. When translating single-node algorithms to run on a distributed cluster, we observe that often a simple idea is enough: separating matrix operations from vector operations and shipping the matrix operations to be ran on the cluster, while keeping vector operations local to the driver. In the case of the Singular Value Decomposition, by taking this idea to an extreme, we are able to exploit the computational power of a cluster, while running code written decades ago for a single core. Another example is our Spark port of the popular TFOCS optimization package, originally built for MATLAB, which allows for solving Linear programs as well as a variety of other convex programs. We conclude with a comprehensive set of benchmarks for hardware accelerated matrix computations from the JVM, which is interesting in its own right, as many cluster programming frameworks use the JVM. The contributions described in this paper are already merged into Apache Spark and available on Spark installations by default, and commercially supported by a slew of companies which provide further services.
---
paper_title: A Model of Parallel Mosaicking for Massive Remote Sensing Images Based on Spark

paper_content:

Image mosaicking is an important part of remote sensing image processing and plays a vital role in the analysis of trans-regional remote sensing images. In order to solve the problems of low utilization of nodes and frequent data I/O in traditional parallel mosaicking algorithms, we propose a parallel mosaicking algorithm based on Apache Spark. First, multi-nodes parallel computation for image overlapping region estimation are implemented in the algorithm. Then, we self-define the Resilient Distributed Data sets (RDD) for remote sensing image processing, and use the three key steps of the image mosaicking, including overlapping region estimation, image registration, and image fusion, which are as the transformation-type operators of the self-defined RDD (the self-defined RDD is what we get by extending the functionality of RDD in Spark). Finally, the parallel processing of image mosaicking is realized by calling the operators of self-defined RDD with the method of implicit conversion. Experimental results show that the parallel mosaicking algorithm of massive remote sensing image based on Spark can effectively improve the mass data image mosaicking efficiency on the basis of guaranteeing the image mosaicking effect.
---
paper_title: The GPlates Portal: Cloud-Based Interactive 3D Visualization of Global Geophysical and Geological Data in a Web Browser

paper_content:

The pace of scientific discovery is being transformed by the availability of ‘big data’ and open access, open source software tools. These innovations open up new avenues for how scientists communicate and share data and ideas with each other and with the general public. Here, we describe our efforts to bring to life our studies of the Earth system, both at present day and through deep geological time. The GPlates Portal (portal.gplates.org) is a gateway to a series of virtual globes based on the Cesium Javascript library. The portal allows fast interactive visualization of global geophysical and geological data sets, draped over digital terrain models. The globes use WebGL for hardware-accelerated graphics and are cross-platform and cross-browser compatible with complete camera control. The globes include a visualization of a high-resolution global digital elevation model and the vertical gradient of the global gravity field, highlighting small-scale seafloor fabric such as abyssal hills, fracture zones and seamounts in unprecedented detail. The portal also features globes portraying seafloor geology and a global data set of marine magnetic anomaly identifications. The portal is specifically designed to visualize models of the Earth through geological time. These space-time globes include tectonic reconstructions of the Earth’s gravity and magnetic fields, and several models of long-wavelength surface dynamic topography through time, including the interactive plotting of vertical motion histories at selected locations. The globes put the on-the-fly visualization of massive data sets at the fingertips of end-users to stimulate teaching and learning and novel avenues of inquiry.
---
paper_title: A cloud-based remote sensing data production system

paper_content:

The data processing capability of existing remote sensing system has not kept pace with the amount of data typically received and need to be processed. Existing product services are not capable of providing users with a variety of remote sensing data sources for selection, either. Therefore, in this paper, we present a product generation programme using multisource remote sensing data, across distributed data centers in a cloud environment, so as to compensate for the low productive efficiency, less types and simple services of the existing system. The programme adopts “master–slave” architecture. Specifically, the master center is mainly responsible for the production order receiving and parsing, as well as task and data scheduling, results feedback, and so on; the slave centers are the distributed remote sensing data centers, which storage one or more types of remote sensing data, and mainly responsible for production task execution. In general, each production task only runs on one data center, and the data scheduling among centers adopts a “minimum data transferring” strategy. The logical workflow of each production task is organized based on knowledge base, and then turned into the actual executed workflow by Kepler. In addition, the scheduling strategy of each production task mainly depends on the Ganglia monitoring results, thus the computing resources can be allocated or expanded adaptively. Finally, we evaluated the proposed programme using test experiments performed at global, regional and local areas, and the results showed that our proposed cloud-based remote sensing production system could deal with massive remote sensing data and different products generating, as well as on-demand remote sensing computing and information service.
---
paper_title: Hadoop based real-time Big Data Architecture for remote sensing Earth Observatory System

paper_content:

Recently, Big Data analytics emerged as a hot topic because of the incredible growth of the information and communication technology. One of the exceedingly anticipated key contributors of the Big Data is real-time Earth Observatory System (EOS). Although the data generated by the individual satellite in EOS may not be significant, the overall data generated across numerous satellites may yield to the significant amount of the Big Data. Thus, extracting the useful information in an efficient manner leads a system towards major computational challenges in EOS, such as, to analyze, to aggregate, and to store, where data is remotely collected. Therefore, the paper proposes a set of requirements for achieving pervasive, integrated information system of EOS and associated services (real-time and offline data processing). The Big Data Architecture is also proposed to address all the aspect of the Big Data ecosystem and includes the following components: Data Acquisition Unit, Data Processing Unit, Data Storage Unit, and Data Analysis and Decision Unit. The proposed architecture is termed as Holistic as it considers the flow of data from satellites to services, which is designed for efficiently process and analyze the Big Data. Finally, a detailed analysis of remotely sensed earth observatory Big Data for Land and Sea area are provided using UBUNTU 14.04 LTS core™i5 machine with 3.2 GHz processor and 4 GB memory. The results show that the proposed network architecture efficiently process EOS data at a real-time as well as offline.
---
paper_title: Deep learning-based tree classification using mobile LiDAR data

paper_content:

Our work addresses the problem of extracting and classifying tree species from mobile LiDAR data. The work includes tree preprocessing and tree classification. In tree preprocessing, voxel-based upward-growing filtering is proposed to remove ground points from the mobile LiDAR data, followed by a tree segmentation that extracts individual trees via Euclidean distance clustering and voxel-based normalized cut segmentation. In tree classification, first, a waveform representation is developed to model geometric structures of trees. Then, deep learning techniques are used to generate high-level feature abstractions of the trees’ waveform representations. Quantitative analysis shows that our algorithm achieves an overall accuracy of 86.1% and a kappa coefficient of 0.8 in classifying urban tree species using mobile LiDAR data. Comparative experiments demonstrate that the uses of waveform representation and deep Boltzmann machines contribute to the improvement of classification accuracies of tree species.
---
paper_title: Parallel ISODATA Clustering of Remote Sensing Images Based on MapReduce

paper_content:

The ISODATA clustering algorithm is regarded as a common method in the field of analyzing remote sensing images. It is very effective to generate a preliminary overview of images. These kinds of clustering methods are currently done in personal computers. However, with the development of remote sensing technology, the spatial resolutions are increasing rapidly and the sizes of the data are becoming larger. Clustering large amounts of images is considerably time-consuming in personal computers because of the limitation of both hardware and software resources. Researchers have developed many kinds of variants of the ISODATA algorithm executing in parallel, and most of them are implemented by using MPI. Generally, writing programs in MPI requires sophisticated skills of the user. Different with the former studies, we propose in this paper to parallel ISODATA clustering algorithm on Map Reduce, another parallel programming model that is very easy to use. The algorithm is mainly divided into two steps defined by the framework of Map Reduce, and they are detailed by pseudo-codes. To improve the accuracies of the color values, the color space CIELAB is used instead of RGB. The experiment results demonstrates that our proposed algorithm possess a robust scalability and the computational time substantially reduced through increasing the number of nodes and it may inspire new solutions of other similar problems.
---
paper_title: Review of studies on tree species classification from remotely sensed data

paper_content:

Abstract Spatially explicit information on tree species composition of managed and natural forests, plantations and urban vegetation provides valuable information for nature conservationists as well as for forest and urban managers and is frequently required over large spatial extents. Over the last four decades, advances in remote sensing technology have enabled the classification of tree species from several sensor types. While studies using remote sensing data to classify and map tree species reach back several decades, a recent review on the status, potentials, challenges and outlooks in this realm is missing. Here, we search for major trends in remote sensing techniques for tree species classification and discuss the effectiveness of different sensors and algorithms based on a literature review. This review demonstrates that the number of studies focusing on tree species classification has increased constantly over the last four decades and promising local scale approaches have been presented for several sensor types. However, there are few examples for tree species classifications over large geographic extents, and bridging the gap between current approaches and tree species inventories over large geographic extents is still one of the biggest challenges of this research field. Furthermore, we found only few studies which systematically described and examined the traits that drive the observed variance in the remote sensing signal and thereby enable or hamper species classifications. Most studies followed data-driven approaches and pursued an optimization of classification accuracy, while a concrete hypothesis or a targeted application was missing in all but a few exceptional studies. We recommend that future research efforts focus stronger on the causal understanding of why tree species classification approaches work under certain conditions or – maybe even more important - why they do not work in other cases. This might require more complex field acquisitions than those typically used in the reviewed studies. At the same time, we recommend reducing the number of purely data-driven studies and algorithm-benchmarking studies as these studies are of limited value, especially if the experimental design is limited, e.g. the tree population is not representative and only a few sensors or acquisition settings are simultaneously investigated.
---
paper_title: In-Memory Parallel Processing of Massive Remotely Sensed Data Using an Apache Spark on Hadoop YARN Model

paper_content:

MapReduce has been widely used in Hadoop for parallel processing larger-scale data for the last decade. However, remote-sensing (RS) algorithms based on the programming model are trapped in dense disk I/O operations and unconstrained network communication, and thus inappropriate for timely processing and analyzing massive, heterogeneous RS data. In this paper, a novel in-memory computing framework called Apache Spark (Spark) is introduced. Through its merits of transferring transformation to in-memory datasets of Spark, the shortages are eliminated. To facilitate implementation and assure high performance of Spark-based algorithms in a complex cloud computing environment, a strip-oriented parallel programming model is proposed. By incorporating strips of RS data with resilient distributed datasets (RDDs) of Spark, all-level parallel RS algorithms can be easily expressed with coarse-grained transformation primitives and BitTorrent-enabled broadcast variables. Additionally, a generic image partition method for Spark-based RS algorithms to efficiently generate differentiable key/value strips from a Hadoop distributed file system (HDFS) is implemented for concealing the heterogeneousness of RS data. Data-intensive multitasking algorithms and iteration-intensive algorithms were evaluated on a Hadoop yet another resource negotiator (YARN) platform. Experiments indicated that our Spark-based parallel algorithms are of great efficiency, a multitasking algorithm took less than 4 h to process more than half a terabyte of RS data on a small YARN cluster, and 9*9 convolution operations against a 909-MB image took less than 260 s. Further, the efficiency of iteration-intensive algorithms is insensitive to image size.
---
paper_title: Forest canopy height from the Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar

paper_content:

In this study retrievals of forest canopy height were obtained through adjustment of a simple geometricoptical (GO) model against red band surface bidirectional reflectance estimates from NASA's Multiangle Imaging SpectroRadiometer (MISR), mapped to a 250 m grid. The soil-understory background contribution was partly isolated prior to inversion using regression relationships with the isotropic, geometric, and volume scattering kernel weights of a Li-Ross kernel-driven bidirectional reflectance distribution function (BRDF) model. The height retrievals were assessed using discrete return lidar data acquired over sites in Colorado as part of the Cold Land Processes Experiment (CLPX) and used with fractional crown cover retrievals to obtain aboveground woody biomass estimates. For all model runs with reasonable backgrounds and initial b/r (vertical to horizontal crown radii) values b2.0, root mean square error (RMSE) distributions were centered between 2.5 and 3.7 m while R2 distributions were centered between 0.4 and 0.7. The MISR/ GO aboveground biomass estimates predicted via regression on fractional cover and mean canopy height for the CLPX sites showed good agreement with U.S. Forest Service Interior West map data (adjusted R2=0.84). The implication is that multiangle sensors such as MISR can provide spatially contiguous retrievals of forest canopy height, cover, and aboveground woody biomass that are potentially useful in mapping distributions of aboveground carbon stocks, tracking disturbance, and in initializing, constraining, and validating ecosystem models. This is important because the MISR record is spatially comprehensive and extends back to the year 2000 and the launch of the NASA Earth Observing System (EOS) Terra satellite; it might thus provide a ~10-year baseline record that would enhance exploitation of data from the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission, as well as furthering realization of synergies with active instruments.
---
paper_title: Apache Hadoop YARN: yet another resource negotiator

paper_content:

The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agora---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler. In this paper, we summarize the design, development, and current state of deployment of the next generation of Hadoop's compute platform: YARN. The new architecture we introduced decouples the programming model from the resource management infrastructure, and delegates many scheduling functions (e.g., task fault-tolerance) to per-application components. We provide experimental evidence demonstrating the improvements we made, confirm improved efficiency by reporting the experience of running YARN on production environments (including 100% of Yahoo! grids), and confirm the flexibility claims by discussing the porting of several programming frameworks onto YARN viz. Dryad, Giraph, Hoya, Hadoop MapReduce, REEF, Spark, Storm, Tez.
---
paper_title: Towards a Framework for Offering Remote Sensing Data in an Analysis-Ready Format

paper_content:

Diverse storage formats, archive dispersal, and inconsistent naming make it difficult for researchers and the general public to find and access remote sensing data. To facilitate the use of remote sensing data, this paper provides an integrated framework for direct reading remote sensing data in a widely compatible and analysis-ready format, NumPy ndarray. The framework is composed of two main components. One is the raster data processing and storage model. All the operational gridded remote sensing data are split into tiles, and reorganized in n-dimensional array. Then the N-Dimensional data array is serialized into netCDF and stored into distributed file system. The other is the spatiotemporal filter to achieve parallel query, and it has been encapsulated into Internet-accessible application programming interfaces (APIs). The scenario of calculating NDVI of a specified spatiotemporal range given at last illustrate the efficiency and convenience of our platform provided for remote sensing data analysis.
---
paper_title: Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo reconstruction methods

paper_content:

Abstract This study provides insight into the assessment of canopy biophysical parameter retrieval using passive sensors and specifically into the quantification of tree height in a discontinuous canopy using a low-cost camera on board an unmanned aerial vehicle (UAV). The UAV was a 2-m wingspan fixed-wing platform with 5.8 kg take-off weight and 63 km/h ground speed. It carried a consumer-grade RGB camera modified for color-infrared detection (CIR) and synchronized with a GPS unit. In this study, the configuration of the electric UAV carrying the camera payload enabled the acquisition of 158 ha in one single flight. The camera system made it possible to acquire very high resolution (VHR) imagery (5 cm pixel−1) to generate ortho-mosaics and digital surface models (DSMs) through automatic 3D reconstruction methods. The UAV followed pre-designed flight plans over each study site to ensure the acquisition of the imagery with large across- and along-track overlaps (i.e. >80%) using a grid of parallel and perpendicular flight lines. The validation method consisted of taking field measurements of the height of a total of 152 trees in two different study areas using a GPS in real-time kinematic (RTK) mode. The results of the validation assessment conducted to estimate tree height from the VHR DSMs yielded R2 = 0.83, an overall root mean square error (RMSE) of 35 cm, and a relative root mean square error (R-RMSE) of 11.5% for trees with heights ranging between 1.16 and 4.38 m. An assessment conducted on the effects of the spatial resolution of the input images acquired by the UAV on the photo-reconstruction method and DSM generation demonstrated stable relationships for pixel resolutions between 5 and 30 cm that rapidly degraded for input images with pixel resolutions lower than 35 cm. RMSE and R-RMSE values obtained as a function of input pixel resolution showed errors in tree quantification below 15% when 30 cm pixel−1 resolution imagery was used to generate the DSMs. The study conducted in two orchards with this UAV system and the photo-reconstruction method highlighted that an inexpensive approach based on consumer-grade cameras on board a hand-launched unmanned aerial platform can provide accuracies comparable to those of the expensive and computationally more complex light detection and ranging (LIDAR) systems currently operated for agricultural and environmental applications.
---
paper_title: Predicting southeastern forest canopy heights and fire fuel models using GLAS data

paper_content:

The Geoscience Laser Altimeter System (GLAS) is a waveform lidar system carried on board the Ice, Cloud, and Elevation Satellite (ICESat). This study tested the use of GLAS data, from the L3e and L3g campaigns, to estimate total canopy height. GLAS footprint locations were sampled for reference data. The GLAS-derived and field-derived canopy heights portrayed good correlation (R 2 = 0.8354). This study also examined two representative fire fuel models within forests in East-Central Mississippi. GLAS waveforms were compared with field data for fire fuel models 9 and 10 of the fire fuel models described by Anderson (1982). GLAS data intensities were extracted and averaged to create predictive variables. Two variables were applied in Logistic regression to predict the probability of belonging to either fuel model (overall accuracy = 0.6875).
---
paper_title: A New Method for Segmenting Individual Trees from the Lidar Point Cloud

paper_content:

Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3Dpoint data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diameter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Mountains in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86 percent of the trees (“recall”), 94 percent of the segmented trees were correct (“precision”), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data.
---
paper_title: Scaling-based forest structural change detection using an inverted geometric-optical model in the Three Gorges region of China

paper_content:

We use the Li-Strahler geometric-optical model combined with a scaling-based approach to detect forest structural changes in the Three Gorges region of China. The physical-based Li-Strahler model can be inverted to retrieve forest structural properties. One of the main input variables for the inverted model is the fractional component of sunlit background, which is calculated by using pure reflectance spectra (endmembers) of surface components. In this study, we extract these endmembers from moderate spatial resolution MODIS data using two scaling-based methods (namely, a regional based linear unmixing and a purest-pixel approach) relying on corresponding high spatial resolution Landsat TM images. Then, the forest structural property crown closure (CC) is estimated by inverting the Li-Strahler model based on the extracted endmembers. Changes in CC are mapped using MODIS mosaics dated 2002 and 2004 for the whole Three Gorges region. Validation of the estimated CC using 25 sample sites indicates that the regional scaling-based endmembers extracted using linear unmixing are more suitable to be used in combination with the inverted Li-Strahler model for monitoring the forest CC than the purest-pixel approach, and results in significantly better estimates in both years (R-2002(2)=0.614, RMSE2002=6%, R-2004(2)=0.631 and RMSE2004=5.2%). A change detection map of the model derived CC in 2002 and 2004 shows a decrease in CC in the eastern counties of the Three Gorges region located close to the Three Gorges Dam. An increase in CC has been observed in other counties of the Three Gorges region, implying a preliminary positive feedback on certain policy measures taken safeguarding forest structure. (C) 2008 Elsevier Inc. All rights reserved.
---
paper_title: Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping

paper_content:

A comparison of the performance of three feature extraction methods was made for mapping forest crown closure (CC) and leaf area index (LAI) with EO-1 Hyperion data. The methods are band selection (SB), principal component analysis (PCA) and wavelet transform (WT). Hyperion data were acquired on October 9, 2001. A total of 38 field measurements of CC and LAI were collected on August 10–11, 2001, at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) conducting atmospheric correction with High Accuracy Atmospheric Correction for Hyperspectral Data (HATCH) to retrieve surface reflectance, (2) extracting features with the three methods: SB, PCA and WT, (3) establishing multivariate regression prediction models, (4) predicting and mapping pixel-based CC and LAI values, and (5) validating the CC and LAI mapped results with photo-interpreted CC and LAI values. The experimental results indicate that the energy features extracted by the WT method are the most effective for mapping forest CC and LAI (mapped accuracy (MA) for CC=84.90%, LAI MA=75.39%), followed by the PCA method (CC MA=77.42%, LAI MA=52.36%). The SB method performed the worst (CC MA=57.77%, LAI MA=50.87%).
---
paper_title: Using MapReduce for Data Processing in the Cloud for Forest Pest Control

paper_content:

Forest pests, fires and deforestation, are known as the three forest disasters. And the severity of forest pest infection has increased in recent decades. Therefore, it’s becoming more important to have strategic approach toward forest pest control. After decades of research, we have accumulated and summed up a wealth of forest pest data. We need to process these data efficiently, and make sure that forest pest data are better service to pest control work. Here, we try to set up a platform for forest pest control which is based on cloud computing. And as a programming method in the cloud, MapReduce destines to be an ideal data processing framework in handling the huge amount of pest data. In this paper, we build up a data processing framework as one part of the cloud computing platform for forest pest control. This framework mainly includes 3 modules: HDFS stores Geo-data, HBase stores Attribute-Data, and MapReduce processes data.
---
paper_title: Cloud platform wireless sensor network detection system based on data sharing

paper_content:

In order to explore the data sharing in the cloud platform, which is easy to manage and use data, but also ensures the safety of the data, on the basis of data sharing, the study of cloud platform wireless sensor network monitoring system for environmental monitoring methods is carried out. The prototype system of environmental monitoring cloud platform is built, which achieves the support and management of the sensor data and file data. The sensor data, in the system, adopts the OGCSWE standard, and provides the corresponding interface for other systems. In the platform, it is capable of querying the data needed by users, and making the paid or unpaid data sharing, which is convenient for users to use more and more useful data. The test results show that, the system runs stably and runs normally at the same time of multi users landing, and the data acquisition speed is fast. Based on the above findings, it is conclude that, under the condition of relying on distributed computing and parallel computing in cloud platform, the system can effectively monitor in a wide range and process the data quickly to achieve the hardware and software services required by users.
---
paper_title: Internet of Things and Big Data Analytics for Smart and Connected Communities

paper_content:

This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy.
---
paper_title: Virtual Reality Smart City Based on WebVRGIS

paper_content:

A network virtual reality (VR) engine for interactive analysis and smart city immersive visualization which integrates several latest information technologies, i.e., Web virtual reality (WebVR), Internet of Things (IoT), and three-dimensional (3-D) geographical information system (3-DGIS) with peer-to-peer (P2P) network, has been proposed in this study. It can be used in big spatial data (e.g., remote sensing data) organization and presentation while achieving the online sharing by hash-based P2P networks. In the P2P network, a map, which contains the real geographic space users and the virtual scene user avatar with prep the virtual network nodes, has been made. Moreover, it can also support the following integrated functions of VRGIS: 3-D spatial analysis function, 3-D visualization for spatial information process, and Web engine for 3-D globe and smart big city. To evaluate WebVRGIS system, a user study had been conducted by comparing it with virtual community system. The performance evaluation results had confirmed the higher usability and user satisfaction of the proposed WebVRGIS system.
---
paper_title: Dynamic Spectrum Access Algorithm Based on Game Theory in Cognitive Radio Networks

paper_content:

As an enabling technology for dynamic spectrum access (DSA), cognitive radio (CR) is widely regarded as one of the most promising technologies for future the fifth generation (5G) wireless communications. Although there have been significant prior researches to combat interference on primary users (PUs), the problem of mitigating mutual interference between secondary users (SUs), -which is tightly coupled with SU's spectrum leasing- is still not understood well. This paper proposes a DSA algorithm based on game theory, which jointly performs spectrum leasing and interference mitigation among SUs. The problem is modeled as an oligopolistic competition using Stackelberg model. We have carefully studied the SU's spectrum utilization behavior with respect to various criteria of the proposed game theoretic model. Simulation results shows that, Compared with Cournot game model, the proposed scheme enables SUs to efficiently utilize the licensed spectrum shared with PUs in a dynamic environment while maximizing the spectrum utilization.
---
paper_title: ERDT: Energy-Efficient Reliable Decision Transmission for Intelligent Cooperative Spectrum Sensing in Industrial IoT

paper_content:

Due to harsh environment, large number of sensors, limited energy, and spectrum scarcity, intelligent sensing becomes a key issue to enable many practical applications in industrial Internet of Things (IoT). In such an industrial environment with noise and interference, an efficient cooperative spectrum sensing (CSS) scheme can achieve spectrum sharing between primary users (PUs) and secondary users (SUs), and effectively solve the spectrum scarcity and reduce energy consumption to make the IoT smarter. As a vital part of CSS, decision transmission (DT) between SUs and fusion center (FC) plays a crucial role. In traditional DT, each SU will transmit its local decision to FC with orthogonal channel in each sensing, which does not consider the packet error and packet loss due to noise during transmission, and aggravates spectrum scarcity and energy consumption. An energy-efficient reliable DT (ERDT) scheme is proposed to enhance CSS in industrial IoT, which considers both packet error and packet loss. First, the CSS mathematical model based on DT is formulated. Second, with rigorous mathematical deduction, the correct decision probability and the energy consumption are analyzed for both ERDT and DT based on logic OR-rule and AND-rule under three cases, respectively: 1) bit error only; 2) packet loss only; and 3) both bit error and packet loss. Detailed simulation results show that, compared with DT, the proposed ERDT can increase correct decision probability and reduce energy consumption for CSS under three different cases. When the existence probability of PU is 50%, the energy consumption of ERDT is only half of that of DT in CSS. Furthermore, when there are 30 SUs in CSS, the existence probability of PU is 50%, both pocket loss rate and bit error rate are 0.05, and the correct decision probability of ERDT is approaching to 1 for CSS in industrial IoT.
---

Title: A Survey of Big Data Analytics for Smart Forestry

Section 1: INTRODUCTION
Description 1: Introduce the concept of big data, its characteristics, and the importance of big data technology in smart forestry.

Section 2: FORESTRY BIG DATA'S PAST LIFE AND PRESENT LIFE
Description 2: Trace the development of forestry big data, including historical background, key methods, and the types of forestry data.

Section 3: OPPORTUNITIES BROUGHT BY FORESTRY BIG DATA
Description 3: Outline the opportunities that forestry big data brings to the field, including theoretical structures and practical applications.

Section 4: RESEARCH AND APPLICATION OF FORESTRY BIG DATA
Description 4: Discuss the research and application aspects of forestry big data, detailing technologies for data storage, query, analysis, and visualization.

Section 5: CHALLENGES FACED BY FORESTRY BIG DATA
Description 5: Summarize the challenges and issues currently faced by forestry big data technology, particularly those based on open source frameworks.

Section 6: FUTURE TREND OF FORESTRY BIG DATA
Description 6: Predict future trends and developments in forestry big data, including advancements in data acquisition, analysis, and the opening of data.

Section 7: CONCLUSION
Description 7: Summarize the key points discussed in the survey and reiterate the potential and future directions of forestry big data.