Spatial Analysis of Socioeconomic Factors Contributing to Food Desert in North Carolina

Food deserts (FD) have attracted attention after the post-COVID-19 pandemic, primarily due to adverse health and other implications of living in areas designated as food deserts. Most studies have focused on various aspects of the impact of food deserts, including the nutritional and health risks of living in FDs. Spatial integration and analysis of the GIS data in food provide a powerful way to expose the issues of creating deserts and how they change over space and time. This study aims to investigate the socioeconomic factors influencing food deserts using geospatial analyses. Guilford, Bladen, and Rutherford Counties in North Carolina were selected as case studies due to their higher percentage of the population with limited healthy food access. This study used open-source data, such as the USDA's Crop Land Layer (CDL) land cover maps, census data, and the Food Access Research Atlas. This research provides a geostatistical analysis of FDs based on income/expenditure, population, vehicle, and food aid. The study results generally showed that geospatial technologies are vital for investigating FDs. The results will assist policy makers and other responsible agencies in formulating appropriate intervention policies tailored to meet the demands of these counties.

Preface

International Conference on Geospatial Science for Digital Earth Observation (GSDEO 2021)The international conference on "Geospatial Science for Digital Earth Observation” (GSDEO) 2021 was successfully held on a virtual platform of Zoom on March 26th and 27th, 2021. The conference was jointly organized by the Indian Society of Remote Sensing (ISRS), Kolkata chapter, and the Department of Geography, School of Basic and Applied Sciences, Adamas University. Due to the non-predictable behaviour of the COVID-19 second wave, which imposed restrictions on organizing offline events, the GSDEO (2021) organizing committee decided to organize the conference online, instead of postponing the event.Remotely sensed data and geographic information systems have been increasingly used together for a vast range of applications, which include land use/land cover mapping, water resource management, weather forecasting, environmental monitoring, agriculture, disaster management, etc. Currently, intensive research is being carried out using remotely sensed data on the geoinformatics platform. New developments have led to dynamic advances in recent years. The objective of the international conference on Geospatial Science for Digital Earth Observation (GSDEO 2021) was to bring the scientists, academicians, and researchers, in the field of geo-environmental sciences on a common platform to exchange ideas and their recent findings related to the latest advances and applications of geospatial science. The call for papers received an enthusiastic response from the academic community, and over 100+ participants from 50+ colleges, universities, and institutions participated in the conference. In total 50+ research papers had been presented through the virtual Zoom conference platform in GSDEO 2021.The conference witnessed the presentation of research papers from diverse applied fields of geospatial sciences, which include the application of geoinformatics in geomorphology, hydrology, urban science, land use planning, climate, and environmental studies. There were four sessions namely, TS 1: Geomorphology and Hydrology, TS 2: Urban Science, TS 3: Social Sustainability and Land Use Planning, and TS 4: Climate and Environment. Each session was further subdivided, into two parts, namely Technical Session 1-A and 1-B. Each sub-session had been designed with one keynote speech and 5 oral presentations. Oral sessions were organized in two parts and offered through live and pre-recorded components based on the preference of the presenters. The presentation session was followed by a live Q&A session. The session chairs moderated the discussions. Similarly, poster sessions were organized in three parts and offered e-poster, live, and pre-recorded components. The best presenter of each sub-session received the best paper award.Dr. Prithvish Nag, Ex-Director of NATMO & Ex Surveyor General of India delivered the inaugural speech, and Dr. P. Chakrabarti, Former Chief Scientist of the DST&B, Govt. of West Bengal delivered a special lecture after the inaugural session. Eight eminent keynote speakers, Prof. S.P. Agarwal from the Indian Institute of Remote Sensing, Prof. Ashis Kumar Paul from Vidyasagar University, Prof. Soumya Kanti Ghosh from the Indian Institute of Technology, Kharagpur, Prof. L. N. Satpati from the University of Calcutta, Prof. R.B. Singh from the University of Delhi, Dr. A.K. Raha, IFS (Retd), Prof. Gerald Mills from the University College Dublin and Prof. Sugata Hazra from Jadavpur University enriched the knowledge of participants in the field of geoinformatics by their informative lectures. The presentations and discussions widely covered the various spectrums of geoinformatics and its application in monitoring natural resources like vegetation mapping, agricultural resource monitoring, forest health assessment, water, and ocean resource management, disaster management, land resource management, water and climate studies, drought vulnerability assessment, groundwater quality monitoring, accretion mapping and the use of geospatial sci nce in studying morphological, hydrological, and other biophysical characteristics of a region etc. Application of geoinformatics in predicting urban expansion, urban climate, disaster management, healthcare accessibility, anthropogenic resource monitoring, spatial-interaction mapping, and, sustainable regional planning were well-discussed topics of the conference.List of Committees, photos are available in the pdf.

Untangling the Integral Impact of Land Use Change, Economic, Ecological and Social Factors on the Development of Burabay District (Kazakhstan) during the Period 1999–2021

Long-term spatiotemporal Land Use and Land Cover (LULC) analysis is an objective tool for assessing patterns of sustainable development (SD). The basic purpose of this research is to define the Driving Mechanisms (DM) and assess the trend of SD in the Burabay district (Kazakhstan), which includes a city, an agro-industrial complex, and a national natural park, based on the integrated use of spatiotemporal data (STD), economic, environmental, and social (EES) indicators. The research was performed on the GEE platform using Landsat and Random Forest. The DM were studied by Multiple Linear Regression and Principal Component Analysis. SD trend was assessed through sequential transformations, aggregations, and integrations of 36 original STD and EES indicators. The overall classification accuracy was 0.85–0.97. Over the past 23 years, pasture area has changed the most (−16.69%), followed by arable land (+14.72%), forest area increased slightly (+1.81%), and built-up land—only +0.16%. The DM of development of the AOI are mainly economic components. There has been a noticeable drop in the development growth of the study area in 2021, which is apparently a consequence of the COVID-19. The upshots of the research can serve as a foundation for evaluating SD and LULC policy.

Relationship between Lightning, Lightning Fire and Human Activities

Lightning is the main source of natural fire, and lightning fire and other types of forest fires together constitute the global forest fire system. It is generally believed that lightning fire, as a natural fire source, has nothing to do with human beings and is different from man-made fire sources, but in fact, human activities have inextricable links with the occurrence of lightning fire. Since 2019, due to the severe impact of COVID-19 lockdowns, non-essential activities and mobility have decreased, which has led to a significant decrease in pollutant concentrations and lightning. In this paper, we linked the lightning fire with modernization process of human beings, the expansion of habitation, the change of underlying surface, the development of prediction technology and firefighting technology, and the laws and regulations of the country, to explore the impact of human activities on the occurrences of lightning and the forest lightning fire. Lightning is the fire source of the three elements in lightning fire occurrence, the lightning that can cause lightning fire is mainly cloud-to-ground lightning. The human activities in recent decades have profoundly affected the content of aerosols in environment. Aerosols are the main factors affecting lightning, and the large amount of pollution aerosols emitted from urban areas, soot aerosols emitted from biomass combustion and urban heat island effect have all increased the probability of lightning occurrence. The average annual ground lightning density of different land cover types is obviously different, and the construction land has the highest average annual ground lightning density. Intense lightning in forest areas has a higher density and slope. Most of the forests are located in high altitude areas, which is consistent with previous studies showing high lightning frequency in high altitude areas. The lightning in forests is intenser, steeper and more destructive, so forest areas are prone to lightning strikes. Lightning has the characteristic of selective discharge, that is, it will discharge into some special areas, which are also known as lightning selection areas, such as the place groundwater is exposed to the ground, where different conductive soils are connected, and where there are underground metal mines, such as copper and iron mines, and underground lake and water reservoir areas. Lightning strikes are caused by changes in soil conductivity caused by human activities such as mining waste rock sites, reservoir construction on mountain tops, and power transmission lines in mountainous areas. At the same time, due to the abundant trees in the mountainous area, it is also important to avoid the resulting lightning fire. With the development of lightning monitoring technology, a lightning location monitoring system has been established in some areas of China. Especially in 2021, the National Forestry and Grassland Administration launched the "Enlisting and Leading" emergency science and technology project of forest lightning fire prevention and control, and the project team has constructed a lightning fire sensing system in the Daxing'anling region with three-dimensional lightning full-wave detection network as the main body, covering the forest area of the Daxing'anling forest region, which can accurately locate the location of cloud-to-ground lightning in real time, improve the monitoring and warning ability of lightning fires, and improve the efficiency of lightning fire discovery. National laws and regulations indirectly affect lightning fires by affecting forest cover and climate change. This paper is expected to provide reference for the occurrence, prevention and control of forest lightning fire in the future, and provide a basis for the formulation of corresponding policies.

Automatic detection and classification of land subsidence in deltaic metropolitan areas using distributed scatterer InSAR and Oriented R-CNN

Multi-temporal interferometric synthetic aperture radar (InSAR) is an effective tool for measuring large-scale land subsidence. However, the measurement points generated by InSAR are too many to be manually analyzed, and automatic subsidence detection and classification methods are still lacking. In this study, we developed an oriented R-CNN deep learning network to automatically detect and classify subsidence bowls using InSAR measurements and multi-source ancillary data. We used 541 Sentinel-1 images acquired during 2015–2021 to map land subsidence of the Guangdong-Hong Kong-Macao Greater Bay Area by resolving persistent and distributed scatterers. Multi-source data related to land subsidence, including geological and lithological, land cover, topographic, and climatic data, were incorporated into deep learning, allowing the local subsidence to be classified into seven categories. The results showed that the oriented R-CNN achieved an average precision (AP) of 0.847 for subsidence detection and a mean AP (mAP) of 0.798 for subsidence classification, which outperformed the other three state-of-the-art methods (Rotated RetinaNet, R3Det, and ReDet). An independent effect analysis showed that incorporating all datasets improved the AP by 11.2% for detection and the mAP by 73.9% for classification, respectively, compared with using InSAR measurements only. Combining InSAR measurements with globally available land cover and digital elevation model data improved the AP for subsidence detection to 0.822, suggesting that our methods can be potentially transferred to other regions, which was further validated this using a new dataset in Shanghai. These results improve the understanding of deltaic subsidence and facilitate geohazard assessment and management for sustainable environments. • Land subsidence of the GBA from 2015 to 2021 was measured by PS/DS detection. • The oriented R-CNN was applied to automatically identify local subsidence. • Incorporating multi-source data improved the performance of subsidence detection. • COVID-19 lockdown ceased groundwater extraction and decelerated subsidence. [ABSTRACT FROM AUTHOR] Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Natural land cover positively correlates with COVID-19 health outcomes.

BACKGROUND: The coronavirus disease 2019 (COVID-19) poses special challenges for societies, as the disease causes millions of deaths. Although the direct prevention measures affect the prevalence and mortality the most, the other indirect factors, including natural environments and economics, could not be neglected. Evaluating the effect of natural land cover on COVID-19 health outcomes is an urgent and crucial public health topic. METHODS: Here, we examine the relationships between natural land cover and the prevalence and mortality of COVID-19 in the United States. To probe the effects of long-term living with natural land cover, we extract county-level land cover data from 2001 to 2019. Based on statistically spatial tests, we employ the Spatial Simultaneous Autoregressive (SAC) Model to estimate natural land cover's impact and monetary values on COVID-19 health outcomes. To examine the short-term effects of natural environments, we build a seasonal panel data set about the greenery index and COVID-19 health outcomes. The panel SAC model is used to detect the relationship between the greenery index and seasonal COVID-19 health outcomes. RESULTS: A 1% increase in open water or deciduous forest is associated with a 0.004-death and 0.163-conformed-case, or 0.006-death and 0.099-confirmed-case decrease in every 1,000 people. Converting them into monetary value, for the mortality, a 1% increase in open water, deciduous forest, or evergreen forest in a county is equivalent to a 212-, 313-, or 219-USD increase in household income in the long term. Moreover, for the prevalence, a 1% change in open water, deciduous forest, or mixed forest is worth a 382-, 230-, or 650-USD increase in household income. Furthermore, a rational development intensity is also critical to reduce the risk of the COVID-19 pandemic. More greenery in the short term is also linked to lower prevalence and mortality. CONCLUSIONS: Our study underscores the importance of incorporating natural land cover as a means of mitigating the risks and negative consequences of future pandemics like COVID-19 and promoting overall public health.

The relationship of land tenure, land use and land cover changes in Lake Victoria basin

Water basins characterise both physical and social environmental aspects such as land tenure. As such, the basins extend beyond spatial units of physical resources and human relations analysis to policy research and reform units. The comprehensive view of water basins in research goes along with an observed increase in anthropogenic-driven changes, such as land use and land cover changes, and cases of ineffective remedial measures to the adverse change, such as through applying integrated watershed management approaches. The human-induced land cover changes affect the water basin's biodiversity, for instance, contributing to an increase in zoonotic disease outbreaks like coronaviruses. The Lake Victoria basin exhibits similar patterns of change and effects due to, among other factors, land tenure, whose contribution is less known empirically. Therefore, this paper integrates satellite imagery and catchment survey data to examine the relationship between land tenure and land uses and land cover changes in the Lake Victoria basin of Eastern Africa. Additionally, explore the contextual character and role of three land tenure systems of Customary, Native freehold and Mailo found in the Uganda country segment of the basin in explaining the outcomes. The aim is to provide information that, among other benefits, improves water basin management and governance. The results indicate a statistically significant relationship exists between the perceived extent of land use and land cover change;drivers of change;the extent of adopting sustainable land-use practices, and the prevailing land tenure. Though with different tenure systems, the three case study water catchments experienced adverse land use and cover changes. The changes mainly affected land tenure indicative land use and cover classes, prominently on the Customary, Mailo, and Native freehold land tenure systems. However, marginal differences occur among the land tenure systems, as the systems feature both de jure and de facto systems and an orientation towards customary tenure characters. The situation likely explains the observed closeness in perceptions regarding the role and relationship between land tenure and land use and cover changes, tenure systems character, perceived drivers of change and eventual outcomes, including the sustainable land use practices adoption. In addition to explaining the land use and cover change, land tenure is an essential tool for restoration and sustainable basin development and sustainability. We, thus, recommend land tenure responsiveness in water basin management approaches for sustainable societal development. © 2023 Elsevier Ltd

Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data

Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data;innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time;and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows "centripetal centralization at daytime, centrifugal dispersion at night" spatiotemporal variation characteristics, that the interaction between the purpose of residents' activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China's "surgical control and dynamic zero COVID-19" epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues.

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe.We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands).We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE.We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures ("plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well.We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at 10.18160/20Z1-AYJ2 .

Transition Modelling of Land Use Land Cover Dynamics in Bardez Taluka of Goa-India (1991-2021)

In the age of global climate change, land use and land cover mapping help us to understand the vital modifications taking place in our environment. LULC mapping assumes great significance in planning, management of resources and keeping track of various programmes at different levels. The data acquired from the land use and land cover investigations are vital for policy formulation and sustainable development of our towns, cities and villages and also to track the disorganized growth of urban areas. Tourism is a tool for economic development in many developing countries of the world. The unplanned tourism growth has led to many ecological problems. This study makes an earnest effort to examine the LULC change using the transition model in the Bardez taluka, which is a well-known global tourist destination in Goa, India. The study has been investigated by using satellite imageries and GIS technologies have been used to analyse the changes occurring in LULC patterns for the years 1991, 2001 and 2021. The result indicates that the area under the built-up class has increased substantially by 11.12 sq. km. as a result of the rise in commercialization, tourism growth and tourism-related activities. Bardez taluka is known for some of the most breath-taking beaches in the world. During 2019-20, just before Covid-19, about 25, 33,234 domestic and 2, 74,840 foreign tourists visited the enchanting beaches of Bardez taluka. Land use classes such as residential, commercial and services, industrial, transportation and utilities also witnessed the growth in their land use and land cover classes whereas classes like agricultural land, coconut plantation, cashew plantation, barren land, DM and FDM forest land, open scrub and fairly dense scrub witnessed a negative change in their class values. © 2023, World Research Association. All rights reserved.

Natural climate solutions in Indonesia: wetlands are the key to achieve Indonesia’s national climate commitment

Indonesia offers a dramatic opportunity to contribute to tackling climate change by deploying natural climate solutions (NCS), increasing carbon sequestration and storage through the protection, improved management, and restoration of drylands, peatlands, and mangrove ecosystems. Here, we estimate Indonesia’s NCS mitigation opportunity for the first time using national datasets. We calculated the maximum NCS mitigation potential extent using datasets of annual national land cover, peat soil, and critical lands. We collated a national emissions factor database for each pathway, calculated from a meta-analysis, recent publications from our team, and available literature. The maximum NCS mitigation potential in 2030 is 1.3 ± 0.04 GtCO2e yr−1, based on the historical baseline period from 2009–2019. This maximum NCS potential is double Indonesia’s nationally determined contribution (NDC) target from the forestry and other land use sector. Of this potential opportunity, 77% comes from wetland ecosystems. Peatlands have the largest NCS mitigation potential (960 ± 15.4 MtCO2e yr−1 or 71.5 MgCO2e ha−1 yr−1) among all other ecosystems. Mangroves provide a smaller total potential (41.1 ± 1.4 MtCO2e yr−1) but have a much higher mitigation density (12.2 MgCO2e ha−1 yr−1) compared to dryland ecosystems (2.9 MgCO2e ha−1 yr−1). Therefore, protecting, managing, and restoring Indonesia’s wetlands is key to achieving the country’s emissions reduction target by 2030. The results of this study can be used to inform conservation programs and national climate policy to prioritize wetlands and other land sector initiatives to fulfill Indonesia’s NDC by 2030, while simultaneously providing additional co-benefits and contributing to COVID-19 recovery and economic sustainability.

Urban Heat Island Mitigation Strategy based on Local Climate Zone Classification using Landsat 8 satellite imagery

Appropriate strategies on urban climate mitigation should be formulated by considering the physical morphology of the urban landscape. This study aimed to investigate, analyze, and promote possible strategies to mitigate Jakarta’s urban heat island (UHI) phenomena. Jakarta’s local climate zone (LCZ) was classified into 17 classes using Landsat 8 data and the random forest method. Land surface temperature (LST) characteristic in each LCZ class was analyzed from 2018, 2019 and 2020. The result revealed that most of the local climate zone in Jakarta is dominated by LCZ 6 (open low-rise) and LCZ 3 (compact low-rise), which is the typical residential area in Jakarta. However, the mean LST in 2018, 2019 and 2020 showed that LCZ 3 (compact low-rise) and LCZ 7 (lightweight low-rise) are the areas that were most likely causing high surface temperature with the highest UHI intensity. During the COVID-19 pandemic in 2020, LST in Jakarta decreased drastically in some parts of the area, especially in public facility such as airport. However, the LST value in low-rise areas (LCZ 3 and LCZ 7) remains higher than the other LCZ classes. Materials of the building and land cover play a significant role in raising the land surface temperature. Therefore, mitigation strategies for urban heat islands in Jakarta should be focused on such particular areas mentioned.

Changes in air quality over different land covers associated with COVID-19 in Turkey aided by GEE.

With the increased urbanization, the rise of the manufacturing industry, and the use of fossil fuels, poor air quality is one of the most serious and pressing problems worldwide. The COVID-19 outbreak prompted absolute lockdowns in the majority of countries throughout the world, posing new research questions. The study's goals were to analyze air and temperature parameters in Turkey across various land cover classes and to investigate the correlation between air and temperature. For that purpose, remote sensing data from MODIS and Sentinel-5P TROPOMI were used from 2019 to 2021 over Turkey. A large amount of data was processed and analyzed in Google Earth Engine (GEE). Results showed a significant decrease in NO2 in urban areas. The findings can be used in long-term strategies for lowering global air pollution. Future research should look at similar investigations in various study sites and evaluate changes in air metrics over additional classes.

Using ABM to Study the Potential of Land Use Change for Mitigation of Food Deserts

Land-use transition is one of the most profound human-induced alterations of the Earth’s system. It can support better land management and decision-making for increasing the yield of food production to fulfill the food needs in a specific area. However, modeling land-use change involves the complexity of human drivers and natural or environmental constraints. This study develops an agent-based model (ABM) for land use transitions using critical indicators that contribute to food deserts. The model’s performance was evaluated using Guilford County, North Carolina, as a case study. The modeling inputs include land covers, climate variability (rainfall and temperature), soil quality, land-use-related policies, and population growth. Studying the interrelationships between these factors can improve the development of effective land-use policies and help responsible agencies and policymakers plan accordingly to improve food security. The agent-based model illustrates how and when individuals or communities could make specific land-cover transitions to fulfill the community’s food needs. The results indicate that the agent-based model could effectively monitor land use and environmental changes to visualize potential risks over time and help the affected communities plan accordingly.

Spatial Humanities and Geo-computation for Social Sciences:Advances and Applications

Humanities and Social Sciences (HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics (including RS: Remote Sensing;GIS: Geographical Information System;GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences (SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover (LUCC), Specially Protected Natural Areas (SPNA) analysis, editing behavior analysis of Volunteered Geographic Information (VGI), electricity consumption anomaly detection, First and Last Mile Problem (FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.

Quantifying the Impacts of the 2020 Flood on Crop Production and Food Security in the Middle Reaches of the Yangtze River, China

This study uses satellite imagery and geospatial data to examine the impact of floods over the main planting areas for double-cropping rice and grain crops in the middle reaches of the Yangtze River. During summer 2020, a long-lasting 62-day heavy rainfall caused record-breaking floods over large areas of China, especially the Yangtze basin. Through close examination of Sentinel-1/2 satellite imagery and Copernicus Global Land Cover, between July and August 2020, the inundation area reached 21,941 and 23,063 km2, and the crop-affected area reached 11,649 and 11,346 km2, respectively. We estimated that approximately 4.66 million metric tons of grain crops were seriously affected in these two months. While the PRC government denied that food security existed, the number of Grains and Feeds imported from the U.S. between January to July 2021 increased by 316%. This study shows that with modern remote sensing techniques, stakeholders can obtain critical estimates of large-scale disaster events much earlier than other indicators, such as disaster field surveys or crop price statistics. Potential use could include but is not limited to monitoring floods and land use coverage changes.

A micro-level analysis of commuting and urban land using the Simpson's index and socio-demographic factors

This study explores the association between urban form, socio-demographics, and travel behavior for 1990, 2000, and 2010 in Shelby County, Tennessee, at a micro-level using U.S. Census tracts capturing active and passive transportation modes. We used bivariate correlations between land use and land cover mix (estimated separately by Simpson's index), population, race, age, education, and commuting modes. Major findings indicate that land use mix is positively related to public transportation use while the land cover mix is negatively related;the opposite is found for both diversity measures and working from home. Greater land cover diversity discourages walking and biking and encourages car commuting;Blacks are the majority who use public transportation;older travelers are more likely to use transportation alternatives;higher-educated people tend to work from home or commute by bike. This study helps city planners in designing sustainable cities and increasing active modes use. Understanding travel patterns may help policymakers to control local/regional problems like increasing traffic congestions and emissions due to a modal shift in commuting to a private car during a COVID-19 pandemic, as well as develop strategies for encouraging active modes and public transport use in the post-COVID-19 world.

Predicting Poverty Using Geospatial Data in Thailand

Poverty statistics are conventionally compiled using data from socioeconomic surveys. This study examines an alternative approach to estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. In particular, the geospatial data examined in this study include the intensity of night-time light (NTL), land cover, vegetation index, land surface temperature, built-up areas, and points of interest. The study also compares the predictive performance of various econometric and machine-learning methods such as generalized least squares, neural network, random forest, and support-vector regression. Results suggest that the intensity of NTL and other variables that approximate population density are highly associated with the proportion of an area’s population that are living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered in this study, primarily due to its capability to fit complex association structures even with small-to-medium-sized datasets. This obtained result suggests the potential applications of using publicly accessible geospatial data and machine-learning methods for timely monitoring of the poverty distribution. Moving forward, additional studies are needed to improve the predictive power and investigate the temporal stability of the relationships observed.

Land cover change in Badung Regency 2016-2020 : An approach using machine learning method: Random Forest Extreme Gradient Boost (XGB)

Badung Regency is one area that mostly suffered from Covid-19 pandemic. Their gross regional domestic product has decreased 21.5% from 2019 to 2020 because of sluggishness of the tourism sector. It also affects the physical development of Badung Regency as a fast-changing area. To map the change of its land cover, satellite imagery-based classification was conducted. Both optical and radar imagery has its own deficiencies due to cloud cover in optical imagery and difficulties in interpretation in radar imagery. Therefore, combining optical and radar imagery and classifying the land cover through machine learning (ML) algorithm is necessary. In this study, we compare two methods of ML which are Random Forest and Extreme Gradient Boost. Sentinel 1 and 2 imageries utilized as the input to derive land cover change from 2016 to 2020. The data is classified into five classes: dense vegetation, sparse vegetation, bare land, water body, and urban, using supervised classification. As for training and validation, the field survey data was conducted. With similar input and set of training data, Extreme Gradient Boost (XGB) methods yield higher average accuracy than Random Forest (RF). The XGB has around 93% of accuracy, while RF has around 76% accuracy. From the result of land cover change using XGB method, bare land and water bodies are decreasing 22.9% and 4.1% consecutively. While urban areas and sparse vegetation, slightly develop around 5.6% and 1.26%. Dense vegetation has almost not changed with increasing 0.34% of its area. © 2021 IEEE.

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown.