bluuebunny/crossref_metadata_2025 huggingface dataset:
DMS oxidation and sulfur aerosol formation in the marine troposphere: a focus on reactive halogen and multiphase chemistry
Authors: Chen Qianjie , Sherwen Tomás , Evans Mathew , Alexander Becky
Published: May 2018

Abstract. The oxidation of dimethyl sulfide (DMS) in the troposphere and subsequent chemical conversion into sulfur dioxide (SO2) and methane sulfonic acid (MSA) are key processes for the formation and growth of sulfur-containing aerosol and cloud condensation nuclei (CCN), but is highly simplified in large-scale models of the atmosphere. In this study, we implement a series of gas-phase and multiphase sulfur oxidation mechanisms into the GEOS-Chem global chemical transport model, including two important intermediates dimethyl sulfoxide (DMSO) and methane sulphinic acid (MSIA), to investigate the sulfur cycle in the global marine troposphere. We found that DMS is mainly oxidized in the gas phase by OH (66 %), NO3 (16 %) and BrO (12 %) globally. DMS+BrO is important for the model's ability to reproduce the observed seasonality of surface DMS mixing ratio in the Southern Hemisphere. MSA is mainly produced from multiphase oxidation of MSIA by O3(aq) (69 %) and OH(aq) (25 %) in cloud droplets and aerosols. Aqueous-phase reaction with OH accounts for 38 % of MSA removal globally and is important for the model's ability to reproduce observations of MSA/nssSO42– ratio in the Southern Hemisphere. The modeled conversion yield of DMS into SO2 and MSA is 78 % and 13 %, respectively, compared to 91 % and 9 % in the standard model run that includes only gas-phase oxidation of DMS by OH and NO3. The largest uncertainties for modeling sulfur chemistry in the marine boundary layer (MBL) are unknown concentrations of reactive halogens (BrO and Cl) and OH(aq) concentrations in cloud droplets and aerosols. To reduce uncertainties in MBL sulfur chemistry, we should prioritize observations of reactive halogens and OH(aq).
Ecosystem carbon transit versus turnover times in response to climate
warming and rising atmospheric CO2 concentration
Authors: Lu Xingjie , Wang Ying-Ping , Luo Yiqi , Jiang Lifen
Published: May 2018

Abstract. Ecosystem carbon (C) transit time is a critical diagnostic parameter to characterize land C sequestration. This parameter has different variants in literatures, including a commonly used turnover time. However, neither of them has been carefully examined under transient C dynamics in response to climate change. In this study, we estimated both C turnover time as defined by the conventional stock-over-flux (i.e., Olson method) and mean C transit time as defined by the mean age of C mass leaving the system (i.e., Rasmussen method). We incorporated them into Community Atmosphere-Biosphere-Land Exchange model (CABLE) to estimate C turnover time and transit time, respectively, in response to climate warming and rising atmospheric [CO2]. Modeling analysis showed that both C turnover time and transit time increased with climate warming but decreased with rising atmospheric [CO2]. The increase of C turnover time with warming was estimated to be 2.4 years with Olson method whereas the transit time increased by 11.8 years with Rasmussen method. The decrease with rising atmospheric [CO2] was estimated to be 3.8 years with Olson method and 5.5 years with Rasmussen method. Our analysis based on Rasmussen method showed that 65 % of the increase in global mean C transit time with climate warming results from the depletion of fast-turnover C pool. The remaining 35 % increase results from accompanied changes in compartment C age structures. Similarly, the decrease in mean C transit time with rising atmospheric [CO2] results approximately equally from replenishment of C into fast-turnover C pool and subsequent decrease in compartment C age structure. Greatly different from the Rasmussen method, the Olsen method, which does not account for changes in either C age structure or composition of respired C, underestimated impacts of either warming or rising atmospheric [CO2] on C diagnostic time and potentially lead to biases in estimating land C sequestration.
Storage dynamics, hydrological connectivity and flux ages in a karst catchment: conceptual modelling using stable isotopes
Authors: Zhang Zhicai , Chen Xi , Soulsby Chris , Cheng Qinbo
Published: May 2018

Abstract. We integrated unique high temporal resolution hydrometric and isotope data to calibrate a catchment-scale conceptual flow-tracer model representing the two main landscape units of hillslopes, and depressions (with fast and slow flow systems) for cock-pit karst terrain. The model could track hourly water and isotope fluxes through each landscape unit, and we could estimate the associated storage and water age dynamics. This inferred that the fast flow reservoir in the depression had the smallest storage, the hillslope unit was intermediate, and the slow flow reservoir had the largest. The estimated mean ages of the hillslope unit, fast and slow flow reservoirs were 137, 326 and 493 days, respectively. Marked seasonal variability in hydroclimate conditions and associated water storage dynamics were the main drivers of non-stationary hydrological connectivity between the hillslope and depression. Meanwhile, the hydrological connectivity between the slow and fast slow reservoirs had reversible directionality, which was determined by the hydraulic head within each medium. Sinkholes can make an important hydrological connectivity between surface water and underground stream flow in the fast reservoir after heavy rain. New water recharges the underground stream via sinkholes, introducing younger water in the underground stream flow.
Toward continental hydrologic–hydrodynamic modeling in South
America
Authors: Siqueira Vinícius A. , Paiva Rodrigo C. D. , Fleischmann Ayan S. , Fan Fernando M. , Ruhoff Anderson L. , Pontes Paulo R. M. , Paris Adrien , Calmant Stéphane , Collischonn Walter
Published: May 2018

Abstract. Providing reliable estimates of streamflow and hydrological fluxes is a major challenge for water resources management over national and transnational basins in South America. Global hydrological models and land surface models are a possible solution to simulate the terrestrial water cycle at the continental scale, but issues on parameterization and limitations in representing lowland river systems put into question their utility for basin-scale analysis and to deliver daily discharges to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic–hydrodynamic model (MGB) to the continental domain of South America and assessed its performance using daily river discharges, water levels from independent sources (in situ, satellite altimetry), estimates of terrestrial water storage (TWS) and evapotranspiration (ET) from remote sensing and other available global datasets. In addition, river discharges were compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental × global models) that makes use of spatially consistent daily discharge data. A satisfactory representation of discharges and water levels was obtained (NSE > 0.6 in 55 % of the cases) and MGB was able to capture patterns of seasonality and magnitude of TWS and ET especially over the largest basins of South America. Continental-scale modeling significantly improved discharge estimates when compared with global models, which resulted in a large number of gauges with negative (or close to 0) NSE values. Models were largely affected by positive bias mainly over East/Northeast Brazil and Argentina as well as over regions of Sao Francisco and Parnaiba basins, while major issues on flow timing were observed in regions affected by floodplain processes such as the Amazon, La Plata, Tocantins–Araguaia, Orinoco and Magdalena basins. We state that efforts in calibrating rainfall-runoff parameters within large basins are necessary to simulate daily river discharges appropriately in this continent, but implementing a hydrodynamic routing component is also important. We hope that our study provides further insights about hydrological simulation in South America, helping to reduce the gap between global and regional hydrological modeling communities.
Demonstrating the “unit hydrograph” and flow routing processes involving active student participation – a university lecture experiment
Authors: Schulz Karsten , Burgholzer Reinhard , Klotz Daniel , Wesemann Johannes , Herrnegger Mathew
Published: May 2018

Abstract. The unit hydrograph (UH) has been one of the most widely employed
hydrological modelling techniques to predict rainfall–runoff behaviour of
hydrological catchments, and is still used to this day. Its concept is based
on the idea that a unit of effective precipitation per time unit
(e.g. mm h−1) will always lead to a specific catchment response in
runoff. Given its relevance, the UH is an important topic that is addressed
in most (engineering) hydrology courses at all academic levels. While the
principles of the UH seem to be simple and easy to understand, teaching
experiences in the past suggest strong difficulties in students' perception
of the UH theory and application. In order to facilitate a deeper understanding of the theory and
application of the UH for students, we developed a simple and cheap lecture theatre
experiment which involved active student participation. The seating of the
students in the lecture theatre represented the “hydrological catchment”
in its size and form. A set of plastic balls, prepared with a piece of
magnetic strip to be tacked to any white/black board, each represented a
unit amount of effective precipitation. The balls are evenly distributed
over the lecture theatre and routed by some given rules down the catchment
to the “catchment outlet”, where the resulting hydrograph is monitored and
illustrated at the black/white board. The experiment allowed an illustration of the underlying principles of the
UH, including stationarity, linearity, and superposition of the generated
runoff and subsequent routing. In addition, some variations of the
experimental setup extended the UH concept to demonstrate the impact of
elevation, different runoff regimes, and non-uniform precipitation events on
the resulting hydrograph. In summary, our own experience in the classroom, a first set of student
exams, as well as student feedback and formal evaluation suggest that the
integration of such an experiment deepened the learning experience by active
participation. The experiment also initialized a more experienced based
discussion of the theory and assumptions behind the UH. Finally, the
experiment was a welcome break within a 3 h lecture setting, and great fun to
prepare and run.
DISASTER MONITORING AND EMERGENCY RESPONSE SERVICES IN CHINA
Authors: Wu J. , Han X. , Zhou Y. , Yue P. , Wang X. , Lu J. , Jiang W. , Li J. , Tang H. , Wang F. , Li X. , Fan J.
Published: May 2018

Abstract. The Disaster Monitoring and Emergency Response Service(DIMERS) project was kicked off in 2017 in China, with the purpose to improve timely responsive service of the institutions involved in the management of natural disasters and man-made emergency situations with the timely and high-quality products derived from Space-based, Air-based and the in-situ Earth observation. The project team brought together a group of top universities and research institutions in the field of Earth observations as well as the operational institute in typical disaster services at national level. The project will bridge the scientific research and the response services of massive catastrophe in order to improve the emergency response capability of China and provide scientific and technological support for the implementation of the national emergency response strategy. In response to the call for proposal of “Earth Observation and Navigation” of 2017 National Key R&D Program of China, Professor Wu Jianjun, the deputy chairman of Faculty of Geographical Science of Beijing Normal University, submitted the Disaster Monitoring and Emergency Response Service (DIMERS) project, jointly with the experts and scholars from Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Wuhan University, China Institute of Earthquake Forecasting of China Earthquake Administration and China Institute of Water Resources and Hydropower Science. After two round evaluations, the proposal was funded by Ministry of Science and Technology of China.
3D SEMANTIC LABELING OF ALS DATA BASED ON DOMAIN ADAPTION BY TRANSFERRING AND FUSING RANDOM FOREST MODELS
Authors: Wu J. , Yao W. , Zhang J. , Li Y.
Published: May 2018

Abstract. Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain) to new data scenes (target domain), which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling); another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city). Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.
AUTOMATIC MATCHING OF LARGE SCALE IMAGES AND TERRESTRIAL LIDAR BASED ON APP SYNERGY OF MOBILE PHONE
Authors: Xia G. , Hu C.
Published: May 2018

Abstract. The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.
INTELLIGENT DETECTION OF STRUCTURE FROM REMOTE SENSING IMAGES BASED ON DEEP LEARNING METHOD
Authors: Xin L.
Published: May 2018

Abstract. Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.
CONSTRUCTION OF GREEN TIDE MONITORING SYSTEM AND RESEARCH ON ITS KEY TECHNIQUES
Authors: Xing B. , Li J. , Zhu H. , Wei P. , Zhao Y.
Published: May 2018

Abstract. As a kind of marine natural disaster, Green Tide has been appearing every year along the Qingdao Coast, bringing great loss to this region, since the large-scale bloom in 2008. Therefore, it is of great value to obtain the real time dynamic information about green tide distribution. In this study, methods of optical remote sensing and microwave remote sensing are employed in Green Tide Monitoring Research. A specific remote sensing data processing flow and a green tide information extraction algorithm are designed, according to the optical and microwave data of different characteristics. In the aspect of green tide spatial distribution information extraction, an automatic extraction algorithm of green tide distribution boundaries is designed based on the principle of mathematical morphology dilation/erosion. And key issues in information extraction, including the division of green tide regions, the obtaining of basic distributions, the limitation of distribution boundary, and the elimination of islands, have been solved. The automatic generation of green tide distribution boundaries from the results of remote sensing information extraction is realized. Finally, a green tide monitoring system is built based on IDL/GIS secondary development in the integrated environment of RS and GIS, achieving the integration of RS monitoring and information extraction.
LANDSLIDES TRIGGERED BY THE 2015 GORKHA, NEPAL EARTHQUAKE
Authors: Xu C.
Published: May 2018

Abstract. The 25 April 2015 Gorkha Mw 7.8 earthquake in central Nepal caused a large number of casualties and serious property losses, and also induced numerous landslides. Based on visual interpretation of high-resolution optical satellite images pre- and post-earthquake and field reconnaissance, we delineated 47,200 coseismic landslides with a total distribution extent more than 35,000 km2, which occupy a total area about 110 km2. On the basis of a scale relationship between landslide area (A) and volume (V), V = 1.3147 × A1.2085, the total volume of the coseismic landslides is estimated to be about 9.64 × 108 m3. Calculation yields that the landslide number density, area density, and volume density are 1.32 km−2, 0.31 %, and 0.027 m, respectively. The spatial distribution of these landslides is consistent with that of the mainshock and aftershocks and the inferred causative fault, indicating the effect of the earthquake energy release on the pattern on coseismic landslides. This study provides a new, more detailed and objective inventory of the landslides triggered by the Gorkha earthquake, which would be significant for further study of genesis of coseismic landslides, hazard assessment and the long-term impact of the slope failure on the geological environment in the earthquake-scarred region.
STABILITY ANALYSIS OF RAILWAY SUBGRADE IN MINING AREA BASED ON DINSAR
Authors: Xu J. , Hu J. , Ding J.
Published: May 2018

Abstract. DInSAR technology have been applied to monitor the mining subsidence and the stability of the railway subgrade. A total of 10 Sentinel-1A images acquired from 2015/9/26 to 2016/2/23 were used in DInSAR analysis. The study mining area is about 13.4 km2. Mining have induced serious land subsidence involve a large area that causing different levels of damages to infrastructures on the land. There is an important railway near the mining area, the DInSAR technology is applied to analyse the subsidence near the railway, which can warn early the possible deformation that may occur during underground mining. The DInSAR results was verified by the field measurement. The results show that the mining did not cause subsidence of railway subgrade and did not affect the stability of railway subgrade.
GENERATION OF GROUND TRUTH DATASETS FOR THE ANALYSIS OF 3D POINT CLOUDS IN URBAN SCENES ACQUIRED VIA DIFFERENT SENSORS
Authors: Xu Y. , Sun Z. , Boerner R. , Koch T. , Hoegner L. , Stilla U.
Published: May 2018

Abstract. In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.
A ROBUST METHOD FOR FUNDAMENTAL MATRIX ESTIMATION WITH RADIAL DISTORTION
Authors: Xue J.-S. , Chen X.-N. , Yi H.
Published: May 2018

Abstract. Fundamental Matrix Estimation is of vital importance in many vision applications and is a core part of 3D reconstruction pipeline. Radial distortion makes the problem to be numerically challenging. We propose a novel robust method for radial fundamental matrix estimation. Firstly, two-sided radial fundamental matrix is deduced to describe epipolar geometry relationship between two distorted images. Secondly, we use singular value decomposition to solve the final nonlinear minimization solutions and to get the outliers removed by multiplying a weighted matrix to the coefficient matrix. In every iterative step, the criterion which is the distance between feature point and corresponding epipolar line is used to determine the inliers and the weighted matrix is update according to it. The iterative process has a fast convergence rate, and the estimation result of radial fundamental matrix remains stable even at the condition of many outliers. Experimental results prove that the proposed method is of high accuracy and robust for estimating the radial fundamental matrix. The estimation result of radial fundamental matrix could be served as the initialization for structure from motion.
RESEARCH ON DISASTER EARLY WARNING AND DISASTER RELIEF INTEGRATED SERVICE SYSTEM BASED ON BLOCK DATA THEORY
Authors: Yang J. , Zhang H. , Wang C. , Tang D.
Published: May 2018

Abstract. With the continuous development of social economy, the interaction between mankind and nature has become increasingly evident. Disastrous global catastrophes have occurred from time to time, causing huge losses to people's lives and property. All governments recognize the importance of the establishment of disaster early warning and release mechanisms, and it is also an urgent issue to improve the comprehensive service level of emergency response and disaster relief. However, disaster early warning and emergency relief information is usually generated by different departments, and the diverse data sources, difficult integration, and limited release speed have always been difficult issues to be solved. Block data is the aggregation of various distributed (point data) and segmentation (data) big data on a specific platform and make them happen continuous polymerization effect, block data theory is a good solution to cross-sectoral, cross-platform Disaster information data sharing and integration problems. This paper attempts to discuss the integrated service mechanism of disaster information aggregation and disaster relief based on block data theory and introduces a location-based integrated service system for disaster early warning and disaster relief.
A GLOBAL REGISTRATION ALGORITHM OF THE SINGLE-CLOSED RING MULTI-STATIONS POINT CLOUD
Authors: Yang R. , Pan L. , Xiang Z. , Zeng H.
Published: May 2018

Abstract. Aimed at the global registration problem of the single-closed ring multi-stations point cloud, a formula in order to calculate the error of rotation matrix was constructed according to the definition of error. The global registration algorithm of multi-station point cloud was derived to minimize the error of rotation matrix. And fast-computing formulas of transformation matrix with whose implementation steps and simulation experiment scheme was given. Compared three different processing schemes of multi-station point cloud, the experimental results showed that the effectiveness of the new global registration method was verified, and it could effectively complete the global registration of point cloud.
INFORMATION EXTRACTION IN TOMB PIT USING HYPERSPECTRAL DATA
Authors: Yang X. , Hou M. , Lyu S. , Ma S. , Gao Z. , Bai S. , Gu M. , Liu Y.
Published: May 2018

Abstract. Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.
STUDY ON KARST INFORMATION IDENTIFICATION OF QIANDONGNAN PREFECTURE BASED ON RS AND GIS TECHNOLOGY
Authors: Yao M. , Zhou G. , Wang W. , Wu Z. , Huang Y. , Huang X.
Published: May 2018

Abstract. Karst area is a pure natural resource base, at the same time, due to the special geological environment; there are droughts and floods alternating with frequent karst collapse, rocky desertification and other resource and environment problems, which seriously restrict the sustainable economic and social development in karst areas. Therefore, this paper identifies and studies the karst, and clarifies the distribution of karst. Provide basic data for the rational development of resources in the karst region and the governance of desertification. Due to the uniqueness of the karst landscape, it can’t be directly recognized and extracted by computer in remote sensing images. Therefore, this paper uses the idea of “RS + DEM” to solve the above problems. this article is based on Landsat-5 TM imagery in 2010 and DEM data, proposes the methods to identify karst information research what is use of slope vector diagram, vegetation distribution map, distribution map of karst rocky desertification and other auxiliary data in combination with the signs for human-computer interaction interpretation, identification and extraction of peak forest, peaks cluster and isolated peaks, and further extraction of karst depression. Experiments show that this method achieves the “RS + DEM” mode through the reasonable combination of remote sensing images and DEM data. It not only effectively extracts karst areas covered with vegetation, but also quickly and accurately locks down the karst area and greatly improves the efficiency and precision of visual interpretation. The accurate interpretation rate of karst information in study area in this paper is 86.73 %.
UNDERSTANDING REGIONAL EFFECTS ON CLIMATE CHANGE AND MULTIDISCIPLINARY APPROARCH FOR COPING STRATEGIES - CASE STUDY AT RURAL VILLAGE IN SUB-SAHARAN AFRICA -
Authors: Yoshimura M. , Yamashita M.
Published: May 2018

Abstract. This paper describes on understanding the regional effects on global climate change and subsistence farmers’ coping strategies through our field investigation and multidimensional data analysis from the resilience point of view. The major research question of this study is to understand what actions villagers took as the coping strategy against the heavy rainfall shock caused by climate change. Our research interest is how geospatial information technique can contribute to this research question. The study area is located in Sinazongwe district, Southern province of Zambia. As for the field investigation, we set the study sites A, B and C where are located in the lower terrace, middle escarpment and upper terrace, respectively. In the rainy season of 2007/2008, our study site had a heavy rainfall and many crop fields were damaged. In this crop year, the annual rainfalls in site A and C were 1442 mm/year and 1332 mm/year respectively. This is about two times different with the long term average of rainfall 694.9 mm/year in Sinazongwe district. It is confirmed that approximately 20 % of crop fields were damaged by heavy rainfall through our field investigation. It was so severe negative consequences for all of villages because about 80 % of whole damaged crop fields were maize fields. Maize is staple food in this region. Here, we have analysed and discussed how villagers cope from the serious damage of crops. The maize harvest varies every year depending on rainfall and topographic position. If a farmer possesses maize fields at various topographic positions, they can avoid severe negative consequences of climate variability such as drought or heavy rainfall. However, not all farmers have access to fields at various topographic positions. It is important to know where each household’s characteristics when we consider their strategy of livelihood for climate variabilities. Through this study, we confirmed the way to strengthen resilience of subsistence farmers as follows: it is necessary to prepare variable crops and emergency food stock with not only diverse but also composite social institution. The resilient society seems to be the society with insurance such as adaptation ability against environmental shock caused by climate change. From the agricultural production point of view, both diversity and yield ability are also important.
A MODIFIED TWO-SCALE MICROWAVE SCATTERING MODEL FOR A GAUSSIAN-DISTRIBUTED CONDUCTING ROUGH SURFACE
Authors: Yu F. , Wang H. , Chen Z. Y.
Published: May 2018

Abstract. A modified two-scale microwave scattering model (MTSM) was presented to describe the scattering coefficient of natural rough surface in this paper. In the model, the surface roughness was assumed to be Gaussian so that the surface height z(x, y) can be split into large-scale and small-scale components relative to the electromagnetic wavelength by the wavelet packet transform. Then, the Kirchhoff Model (KM) and Small Perturbation Method (SPM) were used to estimate the backscattering coefficient of the large-scale and small-scale roughness respectively. Moreover, the ‘tilting effect’ caused by the slope of large-scale roughness should be corrected when we calculated the backscattering contribution of the small-scale roughness. Backscattering coefficient of the MTSM was the sum of backscattering contribution of both scale roughness surface. The MTSM was tested and validated by the advanced integral equation model (AIEM) for dielectric randomly rough surface, the results indicated that, the MTSM accuracy were in good agreement with AIEM when incident angle was less than 30° (θi <30°) and the surface roughness was small (ks = 0.354).
THE TYPHOON DISASTER ANALYSIS BASED ON WEIBO TOPIC HEAT
Authors: Yuan J. , Gong A. , Wang J. , Li J.
Published: May 2018

Abstract. Could social media data be utilized in hazard evaluation? Typhoon disaster as one of the costly disaster has become devastating threats for human. Moreover, social media change the communication way of human and citizens can turn to this platform to express disasterrelated information at real time. Therefore, social media improves situational awareness and widens the method of hazard information acquiring. With more and more studies investigating in relationship between social media response and degree of damage, the strong correlation has been proved. Weibo as one of the most popular social media in China can provide data with posted text, location, user identification and other additional information. Combining with 10 tropical cyclones and Weibo data in 2013, We perform a quantitative analysis between the grade of hazard situation and Weibo related topic heat in province scale. We provide a new model of Weibo topic heat to evaluate the Weibo activity in study area. Also we demonstrate the hazard assessing formula is H = 1.8845 ln(α) + 15.636 in tropical cyclone disaster. High level goodness of curve fitting also suggest that this equation can be used for rapid assessment of hazard caused by tropical cyclones.
MONITORING FARMLAND LOSS CAUSED BY URBANIZATION IN BEIJING FROM MODIS TIME SERIES USING HIERARCHICAL HIDDEN MARKOV MODEL
Authors: Yuan Y. , Meng Y. , Chen Y. X. , Jiang C. , Yue A. Z.
Published: May 2018

Abstract. In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.
VEGETATION COVER ANALYSIS IN SHAANXI PROVINCE OF CHINA BASED ON GRID PIXEL TERND ANALYSIS AND STABILITY EVALUATION
Authors: Yue H. , Liu Y.
Published: May 2018

Abstract. As a key factor affecting the biogeochemical cycle of human existence, terrestrial vegetation is vulnerable to natural environment and human activities, with obvious temporal and spatial characteristics. The change of vegetation cover will affect the ecological balance and environmental quality to a great extent. Therefore, the research on the causes and influencing factors of vegetation cover has become the focus of attention of scholars at home and abroad. In the evolution of human activities and natural environment, the vegetation coverage in Shaanxi has changed accordingly. Using MODIS/NDVI 2000–2014 time series data, using the method of raster pixel trend analysis, stability evaluation, rescaled range analysis and correlation analysis, the climatic factors in Shaanxi province were studied in the near 15 years vegetation spatial and temporal variation and influence of vegetation NDVI changes. The results show that NDVI in Shaanxi province in the near 15 years increased by 0.081, the increase of NDVI in Northern Shaanxi was obvious, and negative growth was found in some areas of Guanzhong, southern Shaanxi NDVI overall still maintained at a high level; the trend of vegetation change in Shaanxi province has obvious spatial differences, most of the province is a slight tendency to improve vegetation, there are many obvious improvement areas in Northern Shaanxi Province. Guanzhong area vegetation area decreased, the small range of variation of vegetation in Shaanxi province; the most stable areas are mainly concentrated in the southern, southern Yanan, Yulin, Xi'an area of Weinan changed greatly; Shaanxi Province in recent 15 a, the temperature and precipitation have shown an increasing trend, and the vegetation NDVI is more closely related to the average annual rainfall, with increase of 0.48 °C/10 years and 69.5 mm per year.
A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS
Authors: Zang Z. , Pan X. , You W. , Liang Y.
Published: May 2018

Abstract. A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.
DESIGN AND APPLICATIONS OF RAPID IMAGE TILE PRODUCING SOFTWARE BASED ON MOSAIC DATASET
Authors: Zha Z. , Huang W. , Wang C. , Tang D. , Zhu L.
Published: May 2018

Abstract. Map tile technology is widely used in web geographic information services. How to efficiently produce map tiles is key technology for rapid service of images on web. In this paper, a rapid producing software for image tile data based on mosaic dataset is designed, meanwhile, the flow of tile producing is given. Key technologies such as cluster processing, map representation, tile checking, tile conversion and compression in memory are discussed. Accomplished by software development and tested by actual image data, the results show that this software has a high degree of automation, would be able to effectively reducing the number of IO and improve the tile producing efficiency. Moreover, the manual operations would be reduced significantly.
MAPPING THE EXTENT AND MAGNITUDE OF SEVER FLOODING INDUCED BY HURRICANE IRMA WITH MULTI-TEMPORAL SENTINEL-1 SAR AND INSAR OBSERVATIONS
Authors: Zhang B. , Wdowinski S. , Oliver-Cabrera T. , Koirala R. , Jo M. J. , Osmanoglu B.
Published: May 2018

Abstract. During Hurricane Irma’s passage over Florida in September 2017, many sections of the state experienced heavy rain and sequent flooding. In order to drain water out of potential flooding zones and assess property damage, it is important to map the extent and magnitude of the flooded areas at various stages of the storm. We use Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) observations, acquired by Sentinel-1 before, during and after the hurricane passage, which enable us to evaluate surface condition during different stages of the hurricane. This study uses multi-temporal images acquired under dry condition before the hurricane to constrain the background backscattering signature. Flooded areas are detected when the backscattering during the hurricane is statistically significantly different from the average dry conditions. The detected changes can be either an increase or decrease of the backscattering, which depends on the scattering characteristics of the surface. In addition, water level change information in Palmdale, South Florida is extracted from an interferogram with the aid of a local water gauge as the reference. The results of our flooding analysis revealed that the majority of the study area in South Florida was flooded during Hurricane Irma.
THE METHOD OF MULTIPLE SPATIAL PLANNING BASIC MAP
Authors: Zhang C. , Fang C.
Published: May 2018

Abstract. The “Provincial Space Plan Pilot Program” issued in December 2016 pointed out that the existing space management and control information management platforms of various departments were integrated, and a spatial planning information management platform was established to integrate basic data, target indicators, space coordinates, and technical specifications. The planning and preparation will provide supportive decision support, digital monitoring and evaluation of the implementation of the plan, implementation of various types of investment projects and space management and control departments involved in military construction projects in parallel to approve and approve, and improve the efficiency of administrative approval. The space planning system should be set up to delimit the control limits for the development of production, life and ecological space, and the control of use is implemented. On the one hand, it is necessary to clarify the functional orientation between various kinds of planning space. On the other hand, it is necessary to achieve “multi-compliance” of various space planning. Multiple spatial planning intergration need unified and standard basic map(geographic database and technical specificaton) to division of urban, agricultural, ecological three types of space and provide technical support for the refinement of the space control zoning for the relevant planning. The article analysis the main space datum, the land use classification standards, base map planning, planning basic platform main technical problems. Based on the geographic conditions, the results of the census preparation of spatial planning map, and Heilongjiang, Hainan many rules combined with a pilot application.
STUDY ON THE CLASSIFICATION OF GAOFEN-3 POLARIMETRIC SAR IMAGES USING DEEP NEURAL NETWORK
Authors: Zhang J. , Zhang J. , Zhao Z.
Published: May 2018

Abstract. Polarimetric Synthetic Aperture Radar（POLSAR） imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, the paper studied the types of the surface cover by using the method of Deep Learning. We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test the classification of data validation.First of all, referring to the optical image, we mark the surface coverage type of GF-3 POLSAR image with 8m resolution, and then collect the samples according to different categories. To meet the GoogLeNet model requirements of 256 × 256 pixel image input and taking into account the lack of full-resolution SAR resolution, the original image should be pre-processed in the process of resampling. In this paper, POLSAR image slice samples of different scales with sampling intervals of 2 m and 1 m to be trained separately and validated by the verification dataset. Among them, the training accuracy of GoogLeNet model trained with resampled 2-m polarimetric SAR image is 94.89 %, and that of the trained SAR image with resampled 1 m is 92.65 %.
ESTIMATING PM2.5 IN XI’AN , CHINA USING AEROSOL OPTICAL DEPTH OF NPP VIIRS DATA AND METEOROLOGICAL MEASUREMENTS
Authors: Zhang K. , Yang Z. , Zheng J. , Jiao J. , Gao W.
Published: May 2018

Abstract. In recent years, the air pollution is becoming more and more serious, which not only causes the decrease of the visibility, but also affects the human health. As the most important pollutant particulate matter, remote sensing satellite measurements have been widely used to estimate PM2.5 concentration on the ground. Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the instruments which is taken in the National Polar-orbiting Partnership (NPP) satellite. In this study, VIIRS was used to retrieve aerosol optical depth (AOD) with the way of dark pixels, and several other major meteorological variables (wind speed, relative humidity, NO2 concentration, ground surface relative humidity and planetary boundary layer height) were combined with AOD to construct a nonlinear multiple regression mode for establishing the relationship between AOD and PM2.5 concentration. The North Basin of Shaanxi province of China, which includes Xi’an, is located in the north of Qinling Mountains, south of the Loess Plateau, and in the central of Weihe basin, with special structure and other adverse weather conditions (static wind, less rain) to cause the frequent haze weather in Xi'an. Xi’an city was selected as the area of the experiment due to its particularity. This research obtained the AOD results from August 1, 2013 to October 30, 2013. The inversion results were compared with ground-based PM2.5 concentration date from air quality monitoring station of Xi’an. The result showed that there is a significant correlation between the two, and the correlation coefficient is 0.783. The inversion result verified that the model of VIIRS data agreed well AOD, which could be used to estimate the surface PM2.5 concentration and monitor the regional air quality.
MONITORING GROUND DEFORMATION OF SUBWAY AREA DURING THE CONSTRUCTION BASED ON THE METHOD OF MULTI-TEMPORAL COHERENT TARGETS ANALYSIS
Authors: Zhang L. , Wu J. , Zhao J. , Yuan M.
Published: May 2018

Abstract. Multi-temporal coherent targets analysis is a high-precision and high-spatial-resolution monitoring method for urban surface deformation based on Differential Synthetic Aperture Radar (DInSAR), and has been successfully applied to measure land subsidence, landslide and strain accumulation caused by fault movement and so on. In this paper, the multi-temporal coherent targets analysis is used to study the settlement of subway area during the period of subway construction. The eastern extension of Shanghai Metro Line. 2 is taking as an example to study the subway settlement during the construction period. The eastern extension of Shanghai Metro Line. 2 starts from Longyang Road and ends at Pudong airport. Its length is 29.9 kilometers from east to west and it is a key transportation line to the Pudong Airport. 17 PalSAR images during 2007 and 2010 are applied to analyze and invert the settlement of the buildings nearby the subway based on the multi-temporal coherent targets analysis. But there are three significant deformation areas nearby the Line 2 between 2007 and 2010, with maximum subsidence rate up to 30 mm/y in LOS. The settlement near the Longyang Road station and Chuansha Town are both caused by newly construction and city expansion. The deformation of the coastal dikes suffer from heavy settlement and the rate is up to −30 mm/y. In general, the area close to the subway line is relatively stable during the construction period.