Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 14 de 14
Filter
1.
Environmental Research Letters ; 17(11):114045, 2022.
Article in English | ProQuest Central | ID: covidwho-2118785

ABSTRACT

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.

2.
IOP Conference Series. Earth and Environmental Science ; 1039(1):012013, 2022.
Article in English | ProQuest Central | ID: covidwho-2037319

ABSTRACT

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.

3.
Environ Monit Assess ; 194(10): 762, 2022 Sep 10.
Article in English | MEDLINE | ID: covidwho-2014248

ABSTRACT

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.


Subject(s)
Air Pollution , COVID-19 , COVID-19/epidemiology , Communicable Disease Control , Environmental Monitoring , Humans , Turkey/epidemiology
4.
Sustainability ; 14(15):9715, 2022.
Article in English | ProQuest Central | ID: covidwho-1994199

ABSTRACT

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.

5.
Journal of Geodesy and Geoinformation Science ; 5(2):1-6, 2022.
Article in English | ProQuest Central | ID: covidwho-1964616

ABSTRACT

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.

6.
Remote Sensing ; 14(13):3140, 2022.
Article in English | ProQuest Central | ID: covidwho-1934191

ABSTRACT

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.

7.
Applied Geography ; 145:102755, 2022.
Article in English | ScienceDirect | ID: covidwho-1914155

ABSTRACT

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.

8.
ISPRS International Journal of Geo-Information ; 11(5):293, 2022.
Article in English | ProQuest Central | ID: covidwho-1871586

ABSTRACT

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.

9.
4th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2021 ; : 38-45, 2021.
Article in English | Scopus | ID: covidwho-1672560

ABSTRACT

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.

10.
ISPRS International Journal of Geo-Information ; 11(1):23, 2022.
Article in English | ProQuest Central | ID: covidwho-1629518

ABSTRACT

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.

11.
Remote Sensing ; 13(24):4986, 2021.
Article in English | ProQuest Central | ID: covidwho-1593543

ABSTRACT

In the past two decades, Earth observation (EO) data have been utilized for studying the spatial patterns of urban deprivation. Given the scope of many existing studies, it is still unclear how very-high-resolution EO data can help to improve our understanding of the multidimensionality of deprivation within settlements on a city-wide scale. In this work, we assumed that multiple facets of deprivation are reflected by varying morphological structures within deprived urban areas and can be captured by EO information. We set out by staying on the scale of an entire city, while zooming into each of the deprived areas to investigate deprivation through land cover (LC) variations. To test the generalizability of our workflow, we assembled multiple WorldView-3 datasets (multispectral and shortwave infrared) with varying numbers of bands and image features, allowing us to explore computational efficiency, complexity, and scalability while keeping the model architecture consistent. Our workflow was implemented in the city of Nairobi, Kenya, where more than sixty percent of the city population lives in deprived areas. Our results indicate that detailed LC information that characterizes deprivation can be mapped with an accuracy of over seventy percent by only using RGB-based image features. Including the near-infrared (NIR) band appears to bring significant improvements in the accuracy of all classes. Equally important, we were able to categorize deprived areas into varying profiles manifested through LC variability using a gridded mapping approach. The types of deprivation profiles varied significantly both within and between deprived areas. The results could be informative for practical interventions such as land-use planning policies for urban upgrading programs.

12.
IOP Conference Series. Earth and Environmental Science ; 916(1), 2021.
Article in English | ProQuest Central | ID: covidwho-1556741

ABSTRACT

Throughout 2016-2021, there were 31 landslides that have caused physical, economic, and social damages. Bumiaji Sub-District has several tourist destinations that are potentially exposed to landslides. This study aims to create a landslide risk map in Bumiaji Sub-District. This research was conducted during the COVID-19 pandemic situation. Therefore, the data collected was secondary data obtained from Google satellite images, Google Street View, the digital elevation model from the National Geospatial Institution, and other literature reviews. The data was then analysed using a landslide risk assessment based on Perka BNPB Number 2/2012. The results of this risk analysis show that Bumiaji Sub-District is dominated by low-level risk (48%), followed by high-level risk (30%), and medium-level risk (15%). High-risk level is affected by high hazards and vulnerabilities, especially in Giripurno Village. High hazard level is affected by high intensity of rainfall, slope degree, the sensitivity of soil to erosion, and the type of land cover. High vulnerabilities are affected by physical, social, and economic aspects susceptible to losses.

13.
Toxics ; 9(12)2021 Dec 03.
Article in English | MEDLINE | ID: covidwho-1554780

ABSTRACT

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil's hyperspectral data and arsenic concentration using NASA's Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R2=0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

14.
Int J Environ Res Public Health ; 18(7)2021 04 04.
Article in English | MEDLINE | ID: covidwho-1167588

ABSTRACT

The heterogenous distribution of both COVID-19 incidence and mortality in Catalonia (Spain) during the firsts moths of the pandemic suggests that differences in baseline risk factors across regions might play a relevant role in modulating the outcome of the pandemic. This paper investigates the associations between both COVID-19 incidence and mortality and air pollutant concentration levels, and screens the potential effect of the type of agri-food industry and the overall land use and cover (LULC) at area level. We used a main model with demographic, socioeconomic and comorbidity covariates highlighted in previous research as important predictors. This allowed us to take a glimpse of the independent effect of the explanatory variables when controlled for the main model covariates. Our findings are aligned with previous research showing that the baseline features of the regions in terms of general health status, pollutant concentration levels (here NO2 and PM10), type of agri-food industry, and type of land use and land cover have modulated the impact of COVID-19 at a regional scale. This study is among the first to explore the associations between COVID-19 and the type of agri-food industry and LULC data using a population-based approach. The results of this paper might serve as the basis to develop new research hypotheses using a more comprehensive approach, highlighting the inequalities of regions in terms of risk factors and their response to COVID-19, as well as fostering public policies towards more resilient and safer environments.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Humans , Incidence , Particulate Matter/analysis , Risk Factors , SARS-CoV-2 , Spain/epidemiology , Time Factors
SELECTION OF CITATIONS
SEARCH DETAIL