Assessing the impacts of sprawl on net primary productivity using fusion of Landsat and MODIS data

AbstractUrbanization has profoundly altered the terrestrial ecosystem carbon cycle, especially the net primary productivity (NPP). Many attempts have

耦合卡尔曼滤波和多层次聚类的中国PM2.5时空分布分析

AbstractSerious air pollution has recently aroused wide public concerns in China. The traditional method of quantitative remote sensing model is not o

Mapping population distributions at the building level by integrating multisource geospatial data

Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p < .0001). The produced fine-scale population map can offer a more thorough understanding of inner city population distributions, which can thus help policy makers optimize the allocation of resources.

Delineating urban functional areas withsocial media data based a dynamic time warping distance

AbstractThis paper presents a novel method for delineating urban functional areas based on building-level social media data. Our method assumes that s

基于随机森林CA的东莞市多类土地利用模拟

Abstract城市土地利用及其变化对城市环境有着重要影响。很多学者已经结合元胞自动机和机器学习算法对城市扩 张进行了相关的模拟研究, 但针对复杂的多类土地利用相互变化过程的研究仍然较少。该文提出了一种基于随机 森林算法的多类元胞自动机( RFA2CA) 模型, 并将其用于模拟和预测复杂的多类土地利

Sensing spatial distribution of urban land use by integrating POI and Google Word2Vec model

AbstractUrban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past

Bag-of-Visual-Words Scene Classifier With Local and Global Features for Remote Sensing Imagery

AbstractScene classification has been studied to allow us to semantically interpret high spatial resolution (HSR) remote sensing imagery. The bag-of-v

Scene Classification Based on the Fusion Probabilistic Topic Model for Remote Sensing Imagery

AbstractScene classification has been proved to be an effective method for high spatial resolution (HSR) remote sensing image semantic interpretation.

Deriving urban dynamic evolution rules from cellular automata with remote sensing images

AbstractCellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical ph

基于模糊认知图的生态风险管理探究

Abstract生态风险管理具有不稳定性和不确定性，如何获取和调整不同利益群体对于生态风险和环境管理的看法并把它们定量表 示出来、协调其中存在的冲突是生态系统管理亟待解决的问题。以煤矿区生态系统为研究对象，探讨了模糊认知图方法在获取 利益相关方认知上的应用，并基于模糊关联矩阵，采用神经网络模型进行不