A framework for extracting urban functional regions based on multi prototype word embeddings using points-of-interest data
چارچوبی برای استخراج مناطق عملکردی شهری بر اساس تعبیه چند کلمه نمونه اولیه با استفاده از داده های مورد علاقه-2020
Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geodata. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a centercontext pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed.
Keywords: Urban functional regions | Word embeddings | Points-of-interest | Spatial clusters
Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500m spatial resolution
سنجش از دور و سنجش اجتماعی برای سیستمهای اقتصادی اقتصادی: مطالعه مقایسه ای بین چراغ های شب و رسانه های اجتماعی مبتنی بر مکان در وضوح مکانی 500 متر-2020
With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States
Keywords: Nighttime lights imagery | Geo-tagged tweets | Socioeconomic factors | Social sensing
Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China
تأثیرات مکانی متغیر از عوامل محیطی ساخته شده بر رکود حمل و نقل ریلی در سطح ایستگاه: یک مطالعه موردی در گوانگژو ، چین-2020
Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multisource spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use.
Keywords: Transit ridership | Built environment | Geographically weighted regression | K-means | Guangzhou
The law of the sea and current practices of marine scientific research in the Arctic
قانون دریا و شیوه های فعلی تحقیقات علمی دریایی در قطب شمال-2020
The rapid changes in both climate and human activity occurring in the Arctic Ocean demands improved knowledge about this region. Combined with eased accessibility due to reduced sea ice cover and new technologies, this has led to increased research activity in the region. These circumstances put pressure on the applicable legal framework, i.e. the United Nations Convention on the Law of the Sea. Therefore, a conversation is needed between legal and marine scientists to promote the alignment between the legal framework and current practices of marine scientific research in the Arctic. This article showcases three current practices of marine scientific research in the Arctic, which are subsequently analysed in light of the existing legal framework, highlighting the legal questions arising from the use of these three technologies. The three technologies analysed here are seabed structures off Svalbard, floating ice-tethered observatories deployed across the marine Arctic, and remote sensing activities paired with in situ measurements.
Keywords: Marine scientific research | Arctic | Law of the sea | Technology | Ocean observatories | Remote sensing
Evolution of evapotranspiration models using thermal and shortwave remote sensing data
تکامل مدلهای تبخیر و تعرق با استفاده از داده های سنجش از راه دور حرارتی و موج کوتاه-2020
Evapotranspiration (ET) from the land surface is an important component of the terrestrial hydrological cycle. Since the advent of Earth observation by satellites, various models have been developed to use thermal and shortwave remote sensing data for ET estimation. In this review, we provide a brief account of the key milestones in the history of remote sensing ET model development in two categories: temperature-based and conductance-based models. Temperature-based ET models utilize land surface temperature (LST) observed through thermal remote sensing to calculate the sensible heat flux from which ET is estimated as a residual of the surface energy balance or to estimate the evaporative fraction from which ET is derived from the available energy. Models of various complexities have been developed to estimate ET from surfaces of different vegetation fractions. One-source models combine soil and vegetation into a composite surface for ET estimation, while two-source models estimate ET of soil and vegetation components separately. Image contexture-based triangular and trapezoid models are simple and effective temperature-based ET models based on spatial and/or temporal variation patterns of LST. Several effective temporal scaling schemes are available for extending instantaneous temperature-based ET estimation to daily or longer time periods. Conductance-based ET models usually use the Penman-Monteith (P-M) equation to estimate ET with shortwave remote sensing data. A key put to these models is canopy conductance to water vapor, which depends on canopy structure and leaf stomatal conductance. Shortwave remote sensing data are used to determine canopy structural parameters, and stomatal conductance can be estimated in different ways. Based on the principle of the coupling between carbon and water cycles, stomatal conductance can be reliably derived from the plant photosynthesis rate. Three types of photosynthesis models are available for deriving stomatal or canopy conductance: (1) big-leaf models for the total canopy conductance, (2) two-big-leaf models for canopy conductances for sunlit and shaded leaf groups, and (3) two-leaf models for stomatal conductances for the average sunlit and shaded leaves separately. Correspondingly, there are also big-leaf, two-big-leaf and two-leaf ET models based on these conductances. The main difference among them is the level of aggregation of conductances before the P-M equation is used for ET estimation, with big-leaf models having the highest aggregation. Since the relationship between ET and conductance is nonlinear, this aggregation causes negative bias errors, with the big-leaf models having the largest bias. It is apparent from the existing literature that two-leaf conductance-based ET models have the least bias in comparison with flux measurements. Based on this review, we make the following recommendations for future work: (1) improving key remote sensing products needed for ET mapping purposes, including soil moisture, foliage clumping index, and leaf carboxylation rate, (2) combining temperature-based and conductance-based models for regional ET estimation, (3) refining methodologies for tight coupling between carbon and water cycles, (4) fully utilizing vegetation structural and biochemical parameters that can now be reliably retrieved from shortwave remote sensing, and (5) to improve regional and global ET monitoring capacity.
The forgotten land use class: Mapping of fallow fields across the Sahel using Sentinel-2
کلاس استفاده از اراضی فراموش شده: نقشه برداری از مزارع مزرعه در ساحل با استفاده از سنتینل-2-2020
Remote sensing-derived cropland products have depicted the location and extent of agricultural lands with an ever increasing accuracy. However, limited attention has been devoted to distinguishing between actively cropped fields and fallowed fields within agricultural lands, and in particular so in grass fallow systems of semiarid areas. In the Sahel, one of the largest dryland regions worldwide, crop-fallow rotation practices are widely used for soil fertility regeneration. Yet, little is known about the extent of fallow fields since fallow is not explicitly differentiated within the cropland class in any existing remote sensing-based land use/cover maps, regardless of the spatial scale. With a 10 m spatial resolution and a 5-day revisit frequency, Sentinel-2 satellite imagery made it possible to disentangle agricultural land into cropped and fallow fields, facilitated by Google Earth Engine (GEE) for big data handling. Here we produce the first Sahelian fallow field map at a 10 m resolution for the baseline year 2017, accomplished by designing a remote sensing driven protocol for generating reference data for mapping over large areas. Based on the 2015 Copernicus Dynamic Land Cover map at 100 m resolution, the extent of fallow fields in the cropland class is estimated to be 63% (403,617 km2) for the Sahel in 2017. Similar results are obtained for five contemporary cropland products, with fallow fields occupying 57–62% of the cropland area. Yet, it is noted that the total estimated area coverage depends on the quality of the different cropland products. The share of cropped fields within the Copernicus cropland area is found to be higher in the arid regions (200–300 mm rainfall) as compared to the semi-arid regions (300–600 mm rainfall). The woody cover fraction within cropped and fallow fields is found to have a reversed pattern between arid (higher woody cover in cropped fields) and semi-arid (higher woody cover in fallow fields) regions. The method developed, using cloud-based Earth Observation (EO) data and computation on the GEE platform, is expected to be reproducible for mapping the extent of fallow fields across global croplands. Future applications based on multi-year time series is expected to improve our understanding of crop-fallow rotation dynamics in grass fallow systems being key in teasing apart how cropland intensification and expansion affect environmental variables, such as soil fertility, crop yields and local livelihoods in low-income regions such as the Sahel. The mapping result can be visualized via a web viewer (https://buwuyou.users.earthengine.app/view/fallowinsahel).
Keywords: Fallow fields | Cropland | Satellite image time series | Land use/cover mapping | Sentinel-2 | Drylands | Sahel
Long-term remote tracking the dynamics of surface water turbidity using a density peaks-based classification: A case study in the Three Gorges Reservoir, China
Long-term remote tracking the dynamics of surface water turbidity using a density peaks-based classification: A case study in the Three Gorges Reservoir, China-2020
Surface water turbidity (SWT), as a low-cost proxy of surface suspended sediment, is important for characterizing the hydro-ecological process and light availability in the lake or reservoir ecosystem. In this study, we proposed the combined use of HJ-1 charge-coupled device imaging and field observation to track the long-term SWT dynamics with environmental changes in Lakes Gaoyang, Hanfeng, and Changshou of the Three Gorges Reservoir, China. In situ remote sensing reflectance spectra were utilized to develop the characteristic spectral indexes for the SWT estimation in different water optical classes separated by a density peaks-based classification. Significant correlations were found between the red-, four-band, band ratio spectral indexes and SWT (determination coefficient>0.71 and root-mean-square error<8.32 nephelometric turbidity unit), suggesting a crucial role of the class-specific retrieval models for the SWT estimation in optically complex waters. The proposed method was further used to monitor the spatio-temporal SWT dynamics over the three lakes from 2008 to 2019, demonstrating that the significant SWT decline in Lakes Gaoyang and Hanfeng and the relatively stable trend in Lake Changshou during the 11-year period. Specifically, the SWT decreasing trends may be attributed to the water level linkage mechanism of Three Gorges and Wuyang Dams. In addition, analyses with simultaneous environmental factors showed that the seasonal and inter-annual variations of SWT appear to be closely correlated with water level and rainfall. Long-term remote tracking of the SWT dynamics presented in this study could provide new insight and reference for reservoir management in the post-Three Gorges Project Era.
Keywords: Surface water turbidity | Remote sensing | Density peaks | Water optical classification | Long-term trend | Three Gorges Reservoir
Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou
اندازه گیری فقر شهری با استفاده از داده های چند منبع و الگوریتم جنگل تصادفی: یک مطالعه موردی در گوانگژو-2020
Conventional measurements of urban poverty mainly rely on census data or aggregated statistics. However, these data are produced with a relatively long cycle, and they hardly reflect the built environment characteristics that affect the livelihoods of the inhabitants. Open-access social media data can be used as an alternative data source for the study of poverty. They typically provide fine-grained information with a short updating cycle. Therefore, in this study, we developed a new approach to measure urban poverty using multi-source big data. We used social media data and remote sensing images to represent the social conditions and the characteristics of built environments, respectively. These data were used to produce the indicators of material, economic, and living conditions, which are closely related to poverty. They were integrated into a composite index, namely the Multi-source Data Poverty Index (MDPI), based on the random forest (RF) algorithm. A dataset of the General Deprivation Index (GDI) derived from the census data was used as a reference to facilitate the training of RF. A case study was carried out in Guangzhou, China, to evaluate the performance of the proposed MDPI for measuring the community-level urban poverty. The results showed a high consistency between the MDPI and GDI. By analyzing the MDPI results, we found a significantly positive spatial autocorrelation in the community-level poverty condition in Guangzhou. Compared with the GDI approach, the proposed MDPI could be updated more conveniently using big data to provide more timely information of urban poverty.
Keywords : Urban poverty | Multi-source Data Poverty Index | General Deprivation Index | Random forest
Analyzing the Influencing Factors of Urban Thermal Field Intensity Using Big-Data-Based GIS
تجزیه و تحلیل عوامل مؤثر از شدت میدان حرارتی شهری با استفاده از GIS مبتنی بر داده های بزرگ-2020
The effects of human activities and land cover changes on urban thermal field patterns are closely related to the land surface temperature (LST) and air temperature. At present, the number of studies on the quantitative relationship between these two indexes and the effect of the observational scale on their influence is insufficient. In this study, spatial analysis methods such as geographic modeling were combined with remote sensing images, meteorological data, and points of insert and used to investigate the composition and scale of the factors influencing the temperature field in Beijing. The results showed that there are differences in the positive and negative correlations between LST and air temperature and various influencing factors. At a spatial resolution of 90 m, LST had a strong linear relationship with the average air temperature. Indicators reflecting elements of human activity, such as buildings, roads, and entertainment, were easily measured by meteorological stations at a small scale, and the natural green space ratio could also be easily captured by satellite thermal sensors at small scales. These results have substantial implications for environmental impact assessments in areas experiencing an increasing urban heat island effect due to rapid urbanization.
Keywords: land-surface temperature | thermal field pattern | POI data | GIS | air temperature
Steady infiltration rate spatial modeling from remote sensing data and terrain attributes in southeast Brazil
سرعت نفوذ ثابت مدل سازی فضایی از داده های سنجش از دور و زمین ورزشی در جنوب شرقی برزیل-2020
This paper aims to describe the development of steady infiltration rate (SIR) spatial prediction models using accessible input data. The models were created from SIR data collected through simulated rainfall at 71 points in part of the Cachimbal stream watershed (a Paraíba do Sul River tributary watershed) in Rio de Janeiro state – Brazil, using as covariates: terrain attributes derived from digital elevation model (DEM), remote sensing data and soil class, physical and chemical attributes maps. Itwas discussed how different land uses and soil degradation levels affect SIR and how NDVI can be used to represent themon SIR modeling. Among the soil physical properties, bulk density (BD) and total sand (TS)were selected as covariates. SIR was higherwhen lower the bulk density and higher the sand content. Soil types play a big role in SIR, highlighting the Gleissolos Háplicos (Gleysols) as the soil class that presented the lower average SIR values and the Latossolos Vermelho Amarelos and Nitossolos Háplicos (Ferralsols and Nitisols) that presented the highest. Topographic position Index (TPI), curvature, and TopographicWetness Index (TWI)were the terrain covariates used in the models. Their usage indicate lower SIR in concave, lower and wetter parts of the landscape. The results demonstrated that is possible to achieve satisfactory results for SIR spatialmodeling using easily accessible data (remote sensing and terrain attributes), but soil information is also necessary to develop better prediction models.
Keywords: Rainfall simulator | Vegetation indexes | Acrisols | Cambisols