基于面向对象的地貌自动分类

本文介绍了基于面向对象的地貌自动分类系统。首先，由数字地形模型生成高程、坡度、剖面曲率和平面曲率；其次，通过图像分割将同类对象分为多个级别。依据分类模型（建立在地表形态和对象高程之上的分类模型）将初始分割对象划分为不同的地貌类型。截至目前，坡向信息还未在分类中使用。该分类系统共有9个地貌类型：山顶和坡脚（由高程位置和显性度来定义），陡坡、平坦区和缓坡区（由坡度值来定义），肩坡和负向坡（由剖面曲率来定义），头坡、侧坡和鼻坡（由平面曲率来定义）。分类采用灵活的模糊隶属函数确定。将分类结果叠加在DTMs上进行分析，用特定的模糊分类选项对分类结果进行了精度评价。该方法在罗马尼亚和德国的Berchtesgaden国家公园两个区域作了对比研究，证明是可重复的，且容易适应不同的自然景观和数据集，可以为地貌和景观研究提供有用信息。该分类方法的主要优势在于它容易推广使用，因为该方法只使用相对值和相对位置，几乎可以用于所有探讨地形特征和其他地貌组分相关关系的领域。

Urban restaurants and online food delivery during the COVID-19 pandemic:a spatial and socio-demographic analysis

In this research,we analyzed the delivery service areas of restaurants,customer satisfaction,and restaurant sales of urban restaurants during the COVID-19 pandemic.We obtained the datasets on food ordering options and restaurant rankings based on Google Maps,Open Street Map,and widely known online food order applications in Iran.Based on this analysis we further modeled suitable areas for future extension of restaurants.We analyzed the online food order data of restaurants’sales and food delivery reports for 1050 restaurants in the city of Tabriz.We collected and analyzed data on the restaurant locations,the number of food orders for each restaurant,and the number of customers and their locations.Our results revealed that the spatial dimension of the newly emerging food delivery areas is of utmost importance for the success of restaurants.This indicates that an optimal location is not longer only dependent on factors like population density and competitors in the direct vicinity but on the services density even from more distant competitors.The results indicate that an optimized spatial distribution of the restaurants together with efficient quality in services can contribute to optimistic urban development.

Big Earth data:disruptive changes in Earth observation data management and analysis?

Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges.We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains.The disruptive element is that analysts and end-users increasingly rely on Web-based workflows.In this contribution we study selected systems and portals,put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

GIS-based ordered weighted averaging and Dempster-Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran

In this paper,GIS-based ordered weighted averaging(OWA)is applied to landslide susceptibility mapping(LSM)for the Urmia Lake Basin in northwest Iran.Nine landslide causal factors were used,whereby the respective parameters were extracted from an associated spatial database.These factors were evaluated,and then the respective factor weight and class weight were assigned to each of the associated factors using analytic hierarchy process(AHP).A landslide suscept-ibility map was produced based on OWA multicriteria decision analysis.In order to validate the result,the outcome of the OWA method was qualitatively evaluated based on an existing inventory of known landslides.Correspondingly,an uncertainty analysis was carried out using the Dempster-Shafer theory.Based on the results,very strong support was determined for the high susceptibility category of the landslide susceptibility map,while strong support was received for the areas with moderate susceptibility.In this paper,we discuss in which respect these results are useful for an improved understanding of the effectiveness of OWA in LSM,and how the landslide prediction map can be used for spatial planning tasks and for the mitigation of future hazards in the study area.

Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data

Airborne laser scanning （ALS） is a technique used to obtain Digital Surface Models （DSM） and Digital Terrain Models （DTM） efficiently, and filtering is the key procedure used to derive DTM from point clouds. Generating seed points is an initial step for most filtering algorithms, whereas existing algorithms usually define a regular window size to generate seed points. This may lead to an inadequate density of seed points, and further introduce error type I, especially in steep terrain and forested areas. In this study, we propose the use of object- based analysis to derive surface complexity information from ALS datasets, which can then be used to improve seed point generation. We assume that an area is complex if it is composed of many small objects, with no buildings within the area. Using these assumptions, we propose and implement a new segmentation algorithm based on a grid index, which we call the Edge and Slope Restricted Region Growing （ESRGG） algorithm. Surface complexity information is obtained by statistical analysis of the number of objects derived by segmentation in each area. Then, for complex areas, a smaller window size is defined to generate seed points. Experimental results show that the proposed algorithm could greatly improve the filtering results in complex areas, especially in steep terrain and forested areas.

Monitoring long-term shoreline dynamics and human activities in the Hangzhou Bay,China,combining daytime and nighttime EO data

Shorelines are vulnerable to anthropogenic activities including urbanization,land reclamation and sediment loading.Shoreline changes may be a reflection of the degradation of coastal ecosystems because of human activities.Understanding the shoreline dynamics is,therefore,a topic of global concern.Earth observation data,such as multi-temporal satellite images,are an important resource for assessing changes in coastal ecosystems.In this research,we used Google Earth Engine(GEE)to monitor and map historical shoreline dynamics in the Hangzhou Bay in China where the Qiantang River flows into the East China Sea.Specifically,we aimed to capture and quantify both the spatial and temporal shoreline changes and to assess the link between anthropogenic activities and shoreline changes on the integrity of this coastal area.We implemented a Tasselled Cap analysis(TCA)on Landsat imagery from 1985 to 2018 in GEE to calculate the wetness coefficient.We then applied Otsu method for automatic image thresholding on the wetness coefficient to detect waterbodies and shoreline changes.Further,we adopted the nighttime light data from the Defense Meteorological Satellite Program’s Operational Linescan System(DMSP-OLS)from 1992 to 2013 as a proxy of human activities.The results show that in the hotspot areas,the shoreline has moved by more than 5 km in the last decades,accounting for approximately 900 km^(2) of land accretion.Within this area,the human activity,indicated by the intensity of nighttime light,increased significantly.The results of this work reveal the influence of human activities on the shoreline dynamics and can support policies that promote the sustainable use and conservation of coastal environments.Our methodology can be transferred and applied to other coastal zones in various regions and scaled up to larger areas.