Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.Ovaj rad prikazuje dva nova spektralna indeksa tla kako bi se identificiralo golo tlo te kako bi se bolje razlikovalo od urbanih nepropusnih površina (ISA). Ti indeksi su definirani na temelju srednje prostorne rezolucije daljinskih podataka Landsat 8 OLI skupa podataka. U multispektralnim daljinskim mjerenjima (RS) prepoznavanje golog tla ili urbane ISA podloge je složenije od prepoznavanja vodenih tijela ili podloge s vegetacijom. Zbog sličnosti spektara dobivenih multispektralnim senzorima golo tlo i urbana ISA površina često se ne razlučuju. Ova studija predlaže dva normalizirana indeksa tla (NSAI1 i NSAI2) korištenjem tipičnih multispektralnih pojaseva. Eksperimenti pokazuju da ta dva indeksa imaju sveukupnu točnost od približno 90%. Indeks spektralne sličnosti (SDI) pokazuje da ta dva indeksa razlikuju golo tlo od urbane ISA podloge bolje nego dosadašnji indeksi. Rezultati pokazuju da percentilni pragovi mogu efikasno razlučiti površine s golim tlom od pozadine. Kombiniranom upotrebom oba indeksa izmjerena je površina tla veća od 71 km2. Najznačajniji rezultat je taj da predloženi indeksi tla mogu poboljšati točnost mjerenja urbanih ISA u u prostorno-vremenskim studijama

Assessing vulnerability for inhabitants of Dhaka City considering flood-hazard exposure

Globalna opasnost od poplave postupno se povećava. Iako ih je nemoguće izbjeći, gubici i šteta od opasnosti (npr. poplave, cikloni i potresi) mogu se učinkovito smanjiti smanjenjem ranjivosti kućanstava odgovarajućim mjerama. Cilj ove studije je kvantitativno mjerenje ranjivosti kućanstava obzirom na opasnosti od poplave kao alata za njihovo ublažavanje. Također je predložen jedinstveni pristup za kvantificiranje ugroženosti kućanstava obzirom na opasnosti od poplave, a kao primjer predstavljena je primjena u gradu Dhaki sklonom poplavama. Podaci su prikupljeni i sa siromašnih i bogatih područja kako bi bilo pokriveno cijelo urbano područje te kako bi se usporedila razina ugroženosti od poplava. Ukupno 300 kućanstava anketirano je strukturiranim upitnikom na temelju pet čimbenika (ekonomskih, socijalnih, okolišnih, strukturnih i institucionalnih) ugroženosti od poplava. Analitički hijerarhijski postupak (AHP) primijenjen je za mjerenje pojedinačnih rezultata ranjivosti kućanstva korištenjem relativne težine varijabli i pokazatelja uz pravilnu standardizaciju. Analitički rezultati pokazali su da je 63,06% siromašnih kućanstava i 20,02% bogatih kućanstava vrlo osjetljivo na poplave. Uz to, ovaj je rad utvrdio i procijenio čimbenike odgovorne za ranjivost kućanstava u Dhaki. Što se tiče strukturne ranjivosti, rezultati su pokazali da je 82% kućanstava u siromašnim krajevima bilo visoko ranjivo, a 95,3% kućanstava koja nisu iz siromašnih četvrti bilo je umjereno ranjivo. Društveno, 67,3% siromašnih i 78,7% kućanstava koja nisu iz siromašnih naselja bila su umjereno i slabo ranjiva. Većina kućanstava u siromašnoj i nesiromašnoj četvrti (84%, odnosno 59,3%) pokazala je visoku i umjerenu ekonomsku ranjivost. Štoviše, za 69,3% siromašnih i 65,3% nesiromašnih kućanstava institucionalna ranjivost je bila visoka. Od stanovnika siromašnih naselja, 63,3% je bilo izloženo ekološkom riziku, a 78% staništa koja nisu u siromašnim područjima bilo je u kategoriji niske ranjivosti. Uz odgovarajuću prilagodbu ovdje predložen učinkoviti alat za mjerenje ranjivosti koji je ovdje prilagođen specifičnoj lokaciji, primjenjiv je i za mjerenje ranjivosti drugih gradova u svijetu. Na temelju ove studije moglo bi se provesti buduće istraživanje s više čimbenika, varijabli i pokazatelja ljudske ranjivosti na prirodne ili umjetne opasnosti / katastrofe. Budući rad mogao bi pružiti bolju sliku stanja ranjivosti od pojedinačne / višestruke opasnosti / katastrofe.Global flood hazard is gradually increasing. Though it is impossible to avoid them, losses and damage of hazards (e.g., floods, cyclones, and earthquakes) could be efficiently reduced by reducing household vulnerability with appropriate measures. This study aims to quantitatively measure the household vulnerability of flood hazards as a mitigation tool. It also proposed a unique approach to quantify flood-hazard household vulnerability, and shows its application in the flood prone city of Dhaka as an example case. Data were collected from both slum and non-slum areas to cover the entire urban habitat, and to compare their level of flood vulnerability. A total of 300 households were surveyed by structured questionnaire on the basis of five factors (economic, social, environmental, structural, and institutional) of flood vulnerability. The analytical hierarchy process (AHP) was applied to measure individual household vulnerability scores by using the relative weightage of variables and indicators with proper standardisation. Analytical results demonstrated that 63.06% slum and 20.02% non-slum households were highly vulnerable to floods. In addition, this paper determined and assessed responsible factors for household flood vulnerability in Dhaka. For structural vulnerability, results exhibited that 82% of slum households were highly vulnerable, and 95.3% of non-slum households were moderately vulnerable. Socially, 67.3% of slum and 78.7% of non-slum households were moderately and low-vulnerable. The majority of slum and non-slum households (84% and 59.3%, respectively) showed high and moderate vulnerability with respect to economic vulnerability. Moreover, 69.3% of slum and 65.3% of nonslum household institutional vulnerability levels were high. Of slum inhabitants, 63.3% were environmentally at high risk, and 78% of non-slum habitats were in the low-vulnerability category. However, as an effective tool to measure location-specific vulnerability, it is applicable for the measuring vulnerability of other cities in the world with proper customisation. On the basis of this study, future research could be conducted with more factors, variables, and indicators of human vulnerability to natural or artificial hazards/disasters. Future work may provide a better reflection of the vulnerability status of single/multiple hazard(s)/disaster(s)

Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface

The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine learning classifiers (random forest and extreme gradient boost (XGBoost)) classifiers using an integration of optical and SAR features and simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textures and enhanced modified indices to extract features for the year 2023. The classification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three significant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seoul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an optical feature. Results showed an overall accuracy of 81% for UIS classification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperformed the RF algorithm and Dynamic World (DW) data product and comparatively showed higher classification accuracy. Still, all three results show poor separability with bare soil class compared to ground truth data. XGBoost outperformed random forest and Dynamic World in classification accuracy, highlighting its potential use in urban remote sensing applications

A novel ensemble learning approach to extract urban impervious surface based on machine learning algorithms using SAR and optical data

Accurate urban impervious surface (UIS) extraction from open-source remote sensing data remains challenging, especially for cities with heterogeneous climatic backgrounds. Contemporary, state-of-the-art techniques achieve promising results at a global scale, but accuracy is compromised at the city level. Therefore, a ensemble machine learning approach using open-source Optical-SAR remote sensing datasets was implemented to enhance the accuracy of UIS mapping. Initially, we integrated optical and radar datasets with modified urban indices to generate input features. Then, we applied four ensemble machine learning algorithms, including AdaBoost, Gradient Boost (GB), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), and fine-tuned them via a soft voting ensemble approach. The optimized UISEM approach showed a model accuracy of 98%. The UISEM method achieved a classification accuracy of 92% and consistently performed across 32 cities globally with heterogeneous climatic zones. Regarding accuracy and predictive power, the XGB ensemble classifier outperformed other ML classifiers in mapping UIS. Furthermore, a comparative analysis against three well-known datasets (ESA World Cover, ESRI Land Cover, and Dynamic World) was also performed. The proposed UISEM model outperformed renowned global datasets with a 92% classification accuracy, followed by DW with 83%, ESA with 86%, and ESRI with 82%. In the future, developing a spatial–temporal version of UISEM can support diverse urban applications globally. The datasets and (GEE and Python) codes are available at https://github.com/mnasarahmad/UISEM

Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding

Women in Bangladesh

Abstract The study aims to understand the food habit and dietary nutritional status of rural women in Bangladesh. The research is based on both primary and secondary data. Primary data collected from a structured questionnaire survey through interview and observation when some secondary data also collected from different sources. 384 respondents have been interviewed form nine villages of Ishwardi, Pabna; a North-Western district of Bangladesh. According to primary survey, 90% of our respondents are literate and 43% households earn less than monthly 16 thousand local currencies equivalent to around 200 USD. Every four out of five women are housewife or work in home and rest of them work outside. Rice is the staple food where 38.06% respondents took rice three times per day and 54.72% women have rice twice. Around 64% respondents took fruits daily but around 80% respondents have chicken on weekly basis. Even, 17.9% people took chicken monthly basis. Less than 2% women drink milk daily and 50.3% women drink on weekly basis. 50.52% respondents have normal body mass index (BMI) condition. The women from Hindu religious background are vegetarian in general. So they don't consume animal beef, meat or chicken. 63.3% women ate egg once in a week and 3.67% consume it daily. Though the overall dietary condition of women is improving in developing country like Bangladesh, but it is still not sufficient for many

Fever among the Ethnic Santal People in Bangladesh

Abstract The study tries to find out the scenario of black fever among the Santal people in Bangladesh. Santal patient health seeking behaviors related with their community people decision, free treatment consideration, preferable healthcare option. Those the entire thing is related with culture. The study is explorative and to some extent descriptive in nature that enforces to adopt mixed with qualitative and quantitative data as well as secondary and primary data. Research shows that 81% patient depend too much on treatment of indigenous physician (Kabiraj). Also barriers of accessing health care are the prevailing factor for health seeking behavior. 92% respondents said awareness and knowledge regarding black fever has too much impact. 43% people are influenced by church and Non-Governmental Organization (N.G.O) during decision making regarding treatment. 54% patients state that, skin turns into more black after taking medicine. Economic condition of lower class people has too much impact on health seeking behavior also. Santal people traditional practice is responsible attacked by black fever. If we will able to conscious ethnic people, dying and suffering regarding black fever will dissolve

Household COVID-19 secondary attack rate and associated determinants in Pakistan; A retrospective cohort study

Background COVID-19 household transmissibility remains unclear in Pakistan. To understand the dynamics of Severe Acute Respiratory Syndrome Coronavirus disease epidemiology, this study estimated Secondary Attack Rate (SAR) among household and close contacts of index cases in Pakistan using a statistical transmission model. Methodology A retrospective cohort study was conducted using an inclusive contact tracing dataset from the provinces of Punjab and Khyber-Pakhtunkhwa to estimate SAR. We considered the probability of an infected person transmitting the infection to close contacts regardless of residential addresses. This means that close contacts were identified irrespective of their relationship with the index case. We assessed demographic determinants of COVID-19 infectivity and transmissibility. For this purpose based on evolving evidence, and as CDC recommends fully vaccinated people get tested 5–7 days after close contact with a person with suspected or confirmed COVID-19. Therefore we followed the same procedure in the close contacts for secondary infection. Findings During the study period from 15th May 2020 to 15th Jan 2021, a total of 339 (33.9%) index cases were studied from 1000 cases initially notified. Among close contact groups (n = 739), households were identified with an assumed mean incubation period of 8.2+4.3 days and a maximum incubation period of 15 days. SAR estimated here is among the household contacts. 117 secondary cases from 739 household contacts, with SAR 11.1% (95% CI 9.0–13.6). All together (240) SAR achieved was 32.48% (95% CI; 29.12–37.87) for symptomatic and confirmed cases. The potential risk factors for SAR identified here included; old age group (\u3e45 years of age), male (gender), household members \u3e5, and residency in urban areas and for index cases high age group. Overall local reproductive number (R) based on the observed household contact frequencies for index/primary cases was 0.9 (95% CI 0.47–1.21) in Khyber Pakhtunkhwa and 1.3 (95% CI 0.73–1.56) in Punjab. Conclusions SAR estimated here was high especially in the second phase of the COVID-19 pandemic in Pakistan. The results highlight the need to adopt rigorous preventive measures to cut the chain of viral transmission and prevent another wave of COVID-19