未来土地利用情景下城市地表径流变化模拟预测研究

【目的】在快速城市化背景下,土地利用格局发生改变,建筑用地等不透水面比例增大,导致地表径流深度增加。因此,理解土地利用变化与地表径流的相互关系在城市防灾减灾工作中具有重要作用。【方法】以杭州市为例,利用CLUMondo模型,得到2030年土地利用情景模拟结果,并在此基础上,利用SCS模型模拟预测了未来地表径流深度信息,探讨了土地利用变化对地表径流的影响。【结果】结果显示:(1)到2030年,耕地面积减少到2070.72 km^(2),共减少283.55 km^(2);建筑用地增加到1227.74 km^(2),共增加了528.19 km^(2),林地和草地均出现了不同程度的减少。(2)在同等降水条件情景下,随着土地利用格局的变化,研究区平均地表径流深度呈逐年增加趋势,但增幅存在差异。在研究区西部的林地地区地表径流增加不明显,在研究区东北部的中心城区,地表径流的范围与深度显著增加。其中杭州主城区径流深度增加最为明显。【结论】2005年到2030年杭州的土地利用结构变化中,耕地与建筑用地变化最为明显,耕地面积不断减少,建筑用地面积不断增加;地表径流深度整体呈增加趋势,但在不同时段和区域都存在一定的差异性,其中杭州主城区径流深度增加最为明显。2005—2030年间杭州降水径流变化的原因与杭州城市化过程中的土地利用转化特别是耕地向建筑用地的转化密切相关。研究成果可为优化城市土地利用政策、缓解洪涝灾害提供理论依据。

An improved area-based approach for estimating plot-level tree DBH from airborne LiDAR data

The diameter at breast height(DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, systematically measuring the DBH of individual trees over large areas using conventional ground-based approaches is labour-intensive and costly. Here, we present an improved area-based approach to estimate plot-level tree DBH from airborne Li DAR data using the relationship between tree height and DBH, which is widely available for most forest types and many individual tree species. We first determined optimal functional forms for modelling heightDBH relationships using field-measured tree height and DBH. Then we estimated plot-level mean DBH by inverting the height-DBH relationships using the tree height predicted by Li DAR. Finally, we compared the predictive performance of our approach with a classical area-based method of DBH. The results showed that our approach significantly improved the prediction accuracy of tree DBH(R^(2)=0.85–0.90, rRMSE=9.57%–11.26%)compared to the classical area-based approach(R^(2)=0.80–0.83, rRMSE=11.98%–14.97%). Our study demonstrates the potential of using height-DBH relationships to improve the estimation of the plot-level DBH from airborne Li DAR data.

Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982 to 2019 based on remote sensing data from East Asia

Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature,rainfall,and normalized difference vegetation index(NDVI).These factors are linked to excesses drought frequency and severity on the regional scale,and their effect on vegetation remains an important topic for climate change study.East Asia is very sensitive and susceptible to climate change.In this study,we examined the effect of drought on the seasonal variations of vegetation in relation to climate variability and determined which growing seasons are most vulnerable to drought risk;and then explored the spatio-temporal evolution of the trend in drought changes in East Asia from 1982 to 2019.The data were studied using a series of several drought indexes,and the data were then classified using a heat map,box and whisker plot analysis,and principal component analysis.The various drought indexes from January to August improved rapidly,except for vegetation health index(VHI)and temperature condition index(TCI).While these indices were constant in September,they increased again in October,but in December,they showed a descending trend.The seasonal and monthly analysis of the drought indexes and the heat map confirmed that the East Asian region suffered from extreme droughts in 1984,1993,2007,and 2012among the study years.The distribution of the trend in drought changes indicated that more severe drought occurred in the northwestern region than in the southeastern area of East Asia.The drought tendency slope was used to describe the changes in drought events during 1982–2019 in the study region.The correlations among monthly precipitation anomaly percentage(NAP),NDVI,TCI,vegetation condition index(VCI),temperature vegetation drought index(TVDI),and VHI indicated considerably positive correlations,while considerably negative correlations were found among the three pairs of NDVI and VHI,TVDI and VHI,and NDVI and TCI.This ecological and climatic mechanism provides a good basis for the assessment of vegetation and drought-change variations within the East Asian region.This study is a step forward in monitoring the seasonal variation of vegetation and variations in drought dynamics within the East Asian region,which will serve and contribute to the better management of vegetation,disaster risk,and drought in the East Asian region.

Landslide inventory and susceptibility modelling using geospatial tools,in Hunza-Nagar valley,northern Pakistan

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.

New evidence for the links between the local water cycle and the underground wet sand layer of a mega-dune in the Badain Jaran Desert, China

Scientists and the local government have great concerns about the climate change and water resources in the Badain Jaran Desert of western China. A field study for the local water cycle of a lake-desert system was conducted near the Noertu Lake in the Badain Jaran Desert from 21 June to 26 August 2008. An underground wet sand layer was observed at a depth of 20–50 cm through analysis of datasets collected during the field experiment. Measurements unveiled that the near surface air humidity increased in the nighttime. The sensible and latent heat fluxes were equivalent at a site about 50 m away from the Noertu Lake during the daytime, with mean values of 134.4 and 105.9 W/m2 respectively. The sensible heat flux was dominant at a site about 500 m away from the Noertu Lake, with a mean of 187.7 W/m2, and a mean latent heat flux of only 26.7 W/m2. There were no apparent differences for the land surface energy budget at the two sites during the night time. The latent heat flux was always negative with a mean value of –12.7 W/m2, and the sensible heat flux was either positive or negative with a mean value of 5.10 W/m2. A portion of the local precipitation was evaporated into the air and the top-layer of sand dried quickly after every rainfall event, while another portion seeped deep and was trapped by the underground wet sand layer, and supplied water for surface psammophyte growth. With an increase of air humidity and the occurrence of negative latent heat flux or water vapor condensation around the Noertu Lake during the nighttime, we postulated that the vapor was transported and condensed at the lakeward sand surface, and provided supplemental underground sand pore water. There were links between the local water cycle, underground wet sand layer, psammophyte growth and landscape evolution of the mega-dunes surrounding the lakes in the Badain Jaran Desert of western China.

Comparison of machine learning models for gully erosion susceptibility mapping

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

Fully Convolutional Networks for Street Furniture Identification in Panorama Images

Panoramic images are widely used in many scenes,especially in virtual reality and street view capture.However,they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images.This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks(FCN).FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction.In this study,we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data.Then replace cross entropy loss function with focal loss function in the FCN model and train it again to produce the predictions.The results show that in all results from pre-trained model,fine-tuning,and FCN model with focal loss,the light poles and traffic signs are detected well and the transformed images have better performance than panoramic images in the prediction according to the Recall and IoU evaluation.

Habitat requirements of Boreal Owl(Aegolius funereus)and Pygmy Owl(Glaucidium passerinum)in rear edge montane populations on the Balkan Peninsula

Rear-edge populations of montane species are known to be vulnerable to environmental change,which could affect them by habitat reduction and isolation.Habitat requirements of two cold-adapted boreo-alpine owl species—Boreal Owl(Aegolius funereus)and Pygmy Owl(Glaucidium passerinum)—have been studied in refugial montane populations in the western Rhodopes,South Bulgaria.Data on owl presence and forest stand attributes recorded in situ have been used to identify significant predictors for owl occurrence.The results revealed Boreal Owl’s preference for comparatively dense forests(high canopy closure values),big trees(diameter at breast height≥50 cm)and large amount of fallen dead wood in penultimate stage of decay.For Pygmy Owl the only significant explanatory variable was the total amount of fallen dead wood.Results suggest preference of both owl species for forests with structural elements typical of old-growth forests(i.e.,veteran trees,deadwood),the Pygmy Owl being less prone to inhabit managed forests.Being at the rear edge of their Palearctic breeding range in Europe both Boreal and Pygmy Owls are of high conservation value on the Balkan Peninsula.Hence,additional efforts are needed for their conservation in the light of climate change and resulting alteration of forest structural parameters.Current findings can be used for adjusting forest management practices in order to ensure both,sustainable profit from timber and continuous species survival.

From spatio-temporal landslide susceptibility to landslide risk forecast

The literature on landslide susceptibility is rich with examples that span a wide range of topics.However,the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored.This statement holds true,particularly in the context of landslide risk,where few scientific contributions investigate risk dynamics in space and time.This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years(from 2013 to 2021).For the analyses,the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit,resulting in a total of 236,997 units.This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature(e.g.,variable interaction plots).However,the main innovative effort is in the subsequent phase of the protocol we propose,as we used climate projections of the main trigger(rainfall)to obtain future estimates of yearly susceptibility patterns.These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model,assuming vulnerability=1.Overall,this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.

Digital earth applications in the twenty-first century

In these early years of the twenty-first century,we must look at how the truly cross-cutting information technology supports other innovations,and how it will funda-mentally change the information positions of government,private sector and the scientific domain as well as the citizen.In those positions,location will be a prominent linking pin.The classical top-down system architectures of information exchange will be diluted by peer-to-peer and bottom-up channels,forcing us to rethink their designs.We should not only focus on better architectures,but need to attend to a different economy of information exchange,in which the‘client’is not only the information sink,but has become an important source as well.The laws of this rising‘infoconomy’have yet to be settled on.This special issue on‘Digital Earth Applications:Technological design and organizational strategies’brings together a number of papers that shed light on this future information ecosystem in which location-specific information will be exchanged between stakeholders.The introduction presents a framework that combines geoinformation streams and organisations brokering between government,science,private sector and citizens.This novel framework helps us improve the appreciation of those papers.

Sensors, empowerment, and accountability: a Digital Earth view from East Africa

Several innovative‘participatory sensing’initiatives are under way in East Africa.They can be seen as local manifestations of the global notion of Digital Earth.The initiatives aim to amplify the voice of ordinary citizens,improve citizens’capacity to directly influence public service delivery and hold local government accountable.The popularity of these innovations is,among other things,a local reaction to the partial failure of the millennium development goals(MDGs)to deliver accurate statistics on public services in Africa.Empowered citizens,with access to standard mobile phones,can‘sense’via text messages and report failures in the delivery of local government services.The public disclosure of these reports on the web and other mass media may pressure local authorities to take remedial action.In this paper,we outline the potential and research challenges of a‘participatory sensing’platform,which we call a‘human sensor web.’Digital Africa’s first priority could be to harness continent-wide and national data as well as local information resources,collected by citizens,in order to monitor,measure and forecast MDGs.

Land cover classification from remote sensing images based on multi-scale fully convolutional network

Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite images.Meanwhile,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing images.To verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal datasets.The proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.

Geographic variability of Twitter usage characteristics during disaster events

Twitter is a well-known microblogging platform for rapid diffusion of views,ideas,and information.During disasters,it has widely been used to communicate evacuation plans,distribute calls for help,and assist in damage assessment.The reliability of such information is very important for decision-making in a crisis situation,but also difficult to assess.There is little research so far on the transferability of quality assessment methods from one geographic region to another.The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters.We examine tweeting activity during two earthquakes in Italy and Myanmar.We compare the granularity of geographic references used,user profile characteristics that are related to credibility,and the performance of Naive Bayes models for classifying Tweets when used on data from a different region than the one used to train the model.Our results show similar geographic granularity for Myanmar and Italy earthquake events,but the Myanmar earthquake event has less information from locations nearby when compared to Italy.Additionally,there are significant and complex differences in user and usage characteristics,but a high performance for the Naive Bayes classifier even when applied to data from a different geographic region.This research provides a basis for further research in credibility assessment of users reporting about disasters.

The landscape epidemiology of echinococcoses

Echinococcoses are parasitic diseases of major public health importance globally.Human infection results in chronic disease with poor prognosis and serious medical,social and economic consequences for vulnerable populations.According to recent estimates,the geographical distribution of Echinococcus spp.infections is expanding and becoming an emerging and re-emerging problem in several regions of the world.Echinococcosis endemicity is geographically heterogeneous and over time it may be affected by global environmental change.Therefore,landscape epidemiology offers a unique opportunity to quantify and predict the ecological risk of infection at multiple spatial and temporal scales.Here,we review the most relevant environmental sources of spatial variation in human echinococcosis risk,and describe the potential applications of landscape epidemiological studies to characterise the current patterns of parasite transmission across natural and human-altered landscapes.We advocate future work promoting the use of this approach as a support tool for decision-making that facilitates the design,implementation and monitoring of spatially targeted interventions to reduce the burden of human echinococcoses in disease-endemic areas.

Enhanced data and methods for improving open and free global population grids:putting‘leaving noonebehind’into practice

Data on global population distribution are a strategic resource currently in high demand in an age of new Development Agendas that call for universal inclusiveness of people.However,quality,detail,and age of census data varies significantly by country and suffers from shortcomings that propagate to derived population grids and their applications.In this work,the improved capabilities of recent remote sensing-derived global settlement data to detect and mitigate major discrepancies with census data is explored.Open layers mapping builtup presence were used to revise census units deemed as‘unpopulated’and to harmonize population distribution along coastlines.Automated procedures to detect and mitigate these anomalies,while minimizing changes to census geometry,preserving the regional distribution of population,and the overall counts were developed,tested,and applied.The two procedures employed for the detection of deficiencies in global census data obtained high rates of true positives,after verification and validation.Results also show that the targeted anomalies were significantly mitigated and are encouraging for further uses of free and open geospatial data derived from remote sensing in complementing and improving conventional sources of fundamental population statistics.

Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest

Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies.Here,we compared a radiative transfer model(RTM)inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content(LCC),canopy chlorophyll content(CCC),and leaf area index(LAI),in a mixed temperate forest.The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park,Germany,was evaluated using in situ measurements collected contemporaneously.The RTM inversion using merit function resulted in estimations of LCC(R^(2)=0.26,RMSE=3.9µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.33 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.73 m^(2)/m^(2)),comparable to the estimations based on the machine learning method Random forest regression of LCC(R^(2)=0.34,RMSE=4.06µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.34 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.75 m^(2)/m^(2)).Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function.The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.

Opportunities and challenges of using big data for global health

The past two decades have witnessed the burgeoning of enormous digital technologies and data collected via countless channels.They are combined in numerous ways in different fields,including epidemiology,mHealth and modeling of health systems,with the intention to improve human health(e.g.,clinical decision support,electronic medical record management)[1-6].However,this is a new interdisciplinary area where no single scientific discipline knows how to take full advantage of these data and technologies to solve health problems[1].

Citizen sensors or extreme publics? Transparency and accountability interventions on the mobile geoweb

Transparency and Accountability(T&A)interventions are emergent social technologies in middle and low-income countries.They bring together citizen sensors,mobile communications,geo-browsers and social organization to raise public awareness on the extent of governance deficits,and monitor government’s(in)action.Due to their novelty,almost all we know about the effectiveness of T&A interventions comes from gray literature.Can citizen sensors radically increase the transparency of the state,or are changes brought about by T&A interventions more likely to be incremental?We review the literature on transparency policies and describe their drivers,characteristics and supply-demand dynamics.We discuss promising cases of T&A interventions in East Africa,the empirical focus of an on-going collaborative research program.We conclude that the effect of T&A interventions is more likely to be incremental and mediated by existing organizations and professional users who populate the space between the state and citizens.Two elements at the interface between supply and demand seem rather crucial for designers of T&A interventions:accountability-relevant data and extreme publics.

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining

Holistic understanding of wind behaviour over space,time and height is essential for harvesting wind energy application.This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining(MDSPM).This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded(0.125°×0.125°)wind data for the Netherlands every 6 h and at six height levels.The wind data were first transformed into two spatio-temporal sequence databases(for speed and direction,respectively).Then,the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns,which were then visualized using a 3D wind rose,a circular histogram and a geographical map.These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines.Our analysis identified four frequent wind profile patterns.One of them highly suitable to harvest wind energy at a height of 128 m and 68.97%of the geographical area covered by this pattern already contains wind turbines.This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.

Does DE need a C?A proposal for a DE curriculum

The vision of a digital earth(DE)is continuously evolving,and the next-generation infrastructures,platforms and applications are being implemented.In this article,we attempt to initiate a debate within the DE and with affine communities about‘why’a digital earth curriculum(DEC)is needed,‘how’it should be developed,and‘what’it could look like.It is impossible to do justice to the Herculean effort of DEC development without extensive consultations with the broader community.We propose a frame for the debate(what,why,and how of a DEC)and a rationale for and elements of a curriculum for educating the coming generations of digital natives and indicate possible realizations.We particularly argue that a DEC is not a déjàvu of classical research and training agendas of geographic information science,remote sensing,and similar fields by emphasizing its unique characteristics.