Community-based assessment of the dynamics of urban landscape characteristics and ecosystem services in the rainforest and guinea savanna ecoregions of Nigeria

Understanding the dynamics of urban landscapes and their impacts on ecological well-being is crucial for developing sustainable urban management strategies in times of rapid urbanisation. This study assesses the nature and drivers of the changing urban landscape and ecosystem services in cities located in the rainforest (Akure and Owerri) and guinea savannah (Makurdi and Minna) of Nigeria using a combination of remote sensing and socioeconomic techniques. Landsat 8 datasets provided spatial patterns of the normalised difference vegetation index (NDVI) and normalised difference built-up index (NDBI). A household survey involving the administration of a semi-structured questionnaire to 1552 participants was conducted. Diminishing NDVI and increasing NDBI were observed due to the rising trend of urban expansion, corroborating the perception of over 54% of the respondents who noted a decline in landscape ecological health. Residential expansion, agricultural practices, transport and infrastructural development, and fuelwood production were recognised as the principal drivers of landscape changes. Climate variability/change reportedly makes a 28.5%-34.4% (Negelkerke R2) contribution to the changing status of natural landscapes in Akure and Makurdi as modelled by multinomial logistic regression, while population growth/in-migration and economic activities reportedly account for 19.9%-36.3% in Owerri and Minna. Consequently, ecosystem services were perceived to have declined in their potential to regulate air and water pollution, reduce soil erosion and flooding, and mitigate urban heat stress, with a corresponding reduction in access to social services. We recommend that urban residents be integrated into management policies geared towards effectively developing and enforcing urban planning regulations, promoting urban afforestation, and establishing sustainable waste management systems.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Do Naturalistic Developmental Behavioral Interventions improve family quality of life? A systematic review and meta-analysis

LAY ABSTRACT

Naturalistic Developmental Behavioral Interventions have been described as culturally responsive and family-friendly interventions, with research demonstrating improvements in children's development following the receipt of these interventions. Given the child-directed nature of Naturalistic Developmental Behavioral Interventions and the intervention's integration within families' daily routines, many studies have examined the impact of Naturalistic Developmental Behavioral Interventions on family and family member quality of life. We conducted a systematic review and meta-analysis to explore the relationship between Naturalistic Developmental Behavioral Interventions and family quality of life. Results suggest that the provision of a Naturalistic Developmental Behavioral Intervention neither improved nor worsened family or family member quality of life. For those involved in delivering Naturalistic Developmental Behavioral Intervention services, there is an immediate need to convey to families that children's improvements will likely not translate into improvements in family quality of life.

Spatio-temporal analysis of changes occurring in land use and its impact on land surface temperature

This study shows how remote sensing and Geographic Information System (GIS) can extract land surface temperature (LST) from the Landsat 5, 7, and 8 datasets. In this research, LST over Kharun's lower catchment, located in Chhattisgarh, India, has been estimated. LST data from 2000, 2006, 2011, 2016, and 2021 were analyzed to see how the LULC pattern changed and how that changed LST. In 2000, the average temperature of the study region was 27.73 °C, whereas in 2021, it reached 33.47 °C. When the average temperature values for each class were determined, it was discovered that forest and adjacent waterbodies had the lowest values, with about 24.15 °C in 2000 and 27.65 °C in 2021, whereas urban regions had more variation in values, ranging from 30.15 °C in 2000 to 38.95 °C in 2021. There could be an increase in LST over time because cities are replacing the green cover. For example, there was a notable increase of 5.74 °C in the mean LST over the research area. The findings revealed that places with extensive urban sprawl had LST between 26 and 45°, which was greater than other natural land cover types, such as vegetation and waterbodies, which was between 24 and 35°. These findings support the suggested method's effectiveness for retrieving LST from the Landsat 5, 7, and 8 thermal bands when combined with integrated GIS approaches. So, the goal of this study is to look at Land Use Change (LUC) and changes in LST using Landsat data and figure out how they are related to LST, the Normalized Difference Vegetation Index (NDVI), and the Normalized Built-up Index (NDBI), which are used as major components.

Urbanization-induced land use/land cover change and its impact on surface temperature and heat fluxes over two major cities in Western Ethiopia

Much of the urbanization that occurs in Africa creates the potential for technological development and economic growth but is also a breeding ground for environmental and health problems. This study was undertaken to evaluate the urban-induced land use/land cover (LULC) change and its contribution to the land surface temperature (LST) and urban heat fluxes from 2001 to 2021. More specifically, the study analyzed different scenarios of LULC change and retrieved the LST to evaluate the trends of the urban heat flux (UHI) in response to the urban-induced LULC change. The analysis of LULC change from 2001 to 2021 indicated that built-up and bare land showed the highest rate of increase at the expense of declining open spaces, agricultural land, and vegetation areas. The built-up areas in Nekemte and Jimma City increased by 929.25 ha (172.75%) and 2285.64 ha (226.93%) over the investigated period, respectively. The highest changes in LULC are seen in built-up areas followed by agricultural land, while the smallest changes are shown by water body followed by bare land. Built-up areas showed the highest net gain, while agricultural land experienced the greatest loss. In areas where the vegetation cover is low, low LST was depicted, and high LST was shown in areas where built-up areas were concentrated in both cities. Due to the LULC changes, the average LST increased by 1.9 °C and 2.2 °C in Nekemte and Jimma City, respectively, over the last 21 years. The urbanization-induced LULC change does not only cause changes in the hydrological process but also changes in the thermal variations and urban heat stress of the two urban centers. The result indicates that the increases in vegetation and green areas are significant in improving the heat stress and thermal characteristics of urban areas. Overall, to achieve sustainable urban development, the integration of land use with urban planning policies could be critical to the resilience of local environment and urban ecosystem.

A geographic information systems and remote sensing-based approach to assess urban micro-climate change and its impact on human health in Bartin, Turkey

Increasing land surface temperature (LST) is one of the major urban climatology problems arising in urban development. In this paper, the impact of vegetation and built-up areas on the LST and impact of LST on human health are assessed using the Landsat thermal data in Bartin, Turkey. The results show that there is a constant change in the share of vegetation and built-up areas due to rapid urbanization in Bartin. Strong positive correlation has been found between NDBI and LST while strong negative correlation has been found between NDVI and LST, suggesting their strong impacts on land surface temperatures. Similarly, a strong positive correlation has been observed between LST, sleep deprivation, and heat stress. This study provides precise information on effects of urbanization and man-made activities, which cause major changes in micro-climate and human health in the city. This study can assist decision-makers or planners to plan future developments sustainably.

Reexamining Pathways Early Autism Intervention in Children Before and After the Third Birthday: A Randomized Control Trial

We reexamined the efficacy of Pathways early autism intervention using generalized measures of social communication and language skills administered by an unfamiliar adult in a novel environment. Generalized measures improve on sources of measurement bias. Sixty-seven autistic children blocked on age (under versus over 3 years) were randomly assigned to 15 weeks of Pathways or services-as-usual. Age moderated the effects of Pathways for social communication. Specifically, Pathways had a significantly large effect for children under 3 and a small effect that approached significance for children over 3. Pathways also had a small effect on expressive speech/language skills. Results replicate previous findings of the efficacy of Pathways on proximal and distal skills and support the importance of early intervention.

Use of cellular automata-based artificial neural networks for detection and prediction of land use changes in North-Western Dhaka City

The purpose of this study was to analyze the trend of change in land use land cover (LULC) and land surface temperature (LST) in Mirpur and its surrounding area over the last 30 years using Landsat satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, 5-year intervals of Landsat 4-5 TM and Landsat 8 OLI images were utilized to track the relationship between LULC changes and LST. The modeled LST was validated with MODIS-derived LST within the study area. Cellular automata-based artificial neural network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were analyzed to determine their link with LST. The relation between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature was increasing steadily for the last 30 years. For the year 2019, it was determined that roughly 86% of total land area has been converted to built-up area and that 89% of land area had an LST greater than 28 °C. According to the study, if the current trend continues, 72% of the Mirpur area is predicted to see temperatures near 32 °C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.

Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices

Over the recent two decades, land use/land cover (LULC) drastically changed in Estonia. Even though the population decreased by 11%, noticeable agricultural and forest land areas were turned into urban land. In this work, we analyzed those LULC changes by mapping the spatial characteristics of LULC and urban expansion in the years 2000-2019 in Estonia. Moreover, using the revealed spatiotemporal transitions of LULC, we simulated LULC and urban expansion for 2030. Landsat 5 and 8 data were used to estimate 147 spectral-textural indices in the Google Earth Engine cloud computing platform. After that, 19 selected indices were used to model LULC changes by applying the hybrid artificial neural network, cellular automata, and Markov chain analysis (ANN-CA-MCA). While determining spectral-textural indices is quite common for LULC classifications, utilization of these continues indices in LULC change detection and examining these indices at the landscape scale is still in infancy. This country-wide modeling approach provided the first comprehensive projection of future LULC utilizing spectral-textural indices. In this work, we utilized the hybrid ANN-CA-MCA model for predicting LULC in Estonia for 2030; we revealed that the predicted changes in LULC from 2019 to 2030 were similar to the observed changes from 2011 to 2019. The predicted change in the area of artificial surfaces was an increased rate of 1.33% to reach 787.04 km² in total by 2030. Between 2019 and 2030, the other significant changes were the decrease of 34.57 km² of forest lands and the increase of agricultural lands by 14.90 km² and wetlands by 9.31 km². These findings can develop a proper course of action for long-term spatial planning in Estonia. Therefore, a key policy priority should be to plan for the stable care of forest lands to maintain biodiversity.

Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management

Impervious surface is mainly defined as any surface which water cannot infiltrate the soil. Due to the impact of urban impervious surfaces (UIS) on environmental issues, the amount of impervious surfaces has been recognized as the most significant index of environmental quality. Detection and analysis of impervious surfaces within a watershed is one of the developing areas of scientific interest. This study evaluates and compares the accuracy and performance of five classification algorithms-supervised object-based nearest neighbour (NN) classifier, supervised pixel-based maximum likelihood classifier (MLC), supervised pixel-based spectral angle mapper (SAM), band ratioing normalized difference built-up index (NDBI), and normalized difference impervious index (NDII)-in extracting urban impervious surfaces. Our first aim was to identify the most effective method for mapping UIS using Sentinel-2A and Landsat-8 satellite data. The second aim was to compare and reveal the efficiency of the spatial and spectral resolution of Sentinel-2A and Landsat-8 data in extracting UIS. The results revealed that the supervised object-based NN approach using the visible and near-infrared bands of both satellite imagery produced the most homogenous and accurate map among the other methods. The object-based NN algorithm achieved an overall classification accuracy of 90.91% and 88.64%, and Kappa coefficient of 0.82 and 0.77 for Sentinel-2 and Landsat-8 images, respectively. The study also showed that the Sentinel-2 image yielded better results than the Landsat-8 pan-sharpened image in extracting detail and classification accuracy. Comparing these methods in the selected challenging study area can provide insight into the selection of the classification method for rapid and reliable extraction of UIS.

Monitoring of UN sustainable development goal SDG-9.1.1: study of Algerian "Belt and Road" expressways constructed by China

The proportion of the rural population who live within 2 km of an all-season road is an indicator of the United Nations' Sustainable Development Goals (SDGs) 9.1.1. This paper aims to calculate SDG indicator 9.1.1 in the proximity of five Algerian expressways. Three monitoring methods are proposed for different spatial regions based on the five expressways built by China's Belt and Road Initiative Project. These methods are based on remote sensing and WorldPop and The High Resolution Settlement Layer (HRSL) population data. The results indicate that (1) the WorldPop population statistics show that the five expressways built by China's Belt Project have increased the rural population of the 2 km buffer zone by 192,016 between the start of construction and eight years after its completion. By the end of 2019, the population increased by 329,291 accounting for 1.17% of the rural population. (2) Based on populations estimated form built-up index (NDBI) building areas, the rural populations within the 2 km buffer area of the Bejaia-Haniff Expressway in 2011, 2015, and 2019 were 273,118, 306,430, and 375,408, respectively. (3) HRSL population grid statistics indicate that, in 2015, the populations were: East-West Expressway = 911,549, Bejaia Expressway = 127,471, Tipaza Expressway = 71,411, North-South Expressway = 30,583, and Cherchell Ring Expressway = 41,657. (4) A visual interpretation method based on Google Earth imagery was used to count the number of buildings and number of building floors in the town of Tikhramtath. Based on the estimated population of each building and floor, the population of Tikhramtath town in 2011, 2015, 2017, and 2019 was estimated as 1,790, 2,785, 3,365, and 3,870, respectively. (5) Through analysis and accuracy assessment, the appropriate statistical methods for different regions were determined.

Quantifying Caregiver Change Across Early Autism Interventions Using the Measure of NDBI Strategy Implementation: Caregiver Change (MONSI-CC)

This study aimed to provide initial validity and reliability of the Measure of NDBI Strategy Implementation-Caregiver Change (MONSI-CC), a novel measure that captures changes in caregivers' implementation of NDBI strategies during early intervention. The MONSI-CC was applied to 119 observations of 43 caregiver-child dyads of preschoolers with autism spectrum disorders (ASD). The MONSI-CC showed high inter-rater and test-retest reliability and captured significant improvements in caregivers' implementation of NDBI strategies. Significant associations between improvements in caregiver NDBI implementation and improvements in the child's ASD symptoms also emerged. Our work shows promising evidence for the utility of the MONSI-CC to evaluate implementation of NDBI strategies by caregivers as a mediating and moderating factor for treatment effects on children with ASD.

Study protocol of the multi-centre, randomised controlled trial of the Frankfurt Early Intervention Programme A-FFIP versus early intervention as usual for toddlers and preschool children with Autism Spectrum Disorder (A-FFIP study)

BACKGROUND

Naturalistic developmental behavioural interventions (NDBI) have been shown to improve autism-specific symptoms in young children with Autism Spectrum Disorder (ASD). NDBI approaches, such as the ASD-specific Frankfurt Early Intervention Programme for ASD (A-FFIP), are based on ASD-specific developmental and learning aspects. A-FFIP is a low-intensity intervention which can easily be implemented in the local health care/social welfare system. The aim of the present study is to establish 1-year efficacy of the manualised early intervention programme A-FFIP in toddlers and preschool children with ASD. It is hypothesised that A-FFIP will result in improved ASD-specific symptoms compared to early intervention as usual (EIAU). Child- and family-specific secondary outcomes, as well as moderators and mediators of outcome, will be explored.

METHODS/DESIGN

A prospective, multi-centre, parallel-group, randomised controlled, phase-III trial comparing A-FFIP versus EIAU. A total of 134 children (A-FFIP: 67, EIAU: 67) aged 24-66 months at baseline assessment meeting the criteria for ASD (DSM-5) will be included. The primary outcome is the absolute change of the total score of the Brief Observation of Social Communication Change (BOSCC-AT) between baseline (T2) and 1-year follow-up (T6). The treatment effect will be tested, adjusted for relevant covariates applying a mixed model for repeated measures. Secondary outcomes are BOSCC social communication and repetitive-behaviour scores, single ASD symptoms, language, cognition, psychopathology, parental well-being and family quality of life. Predictors, moderators and mediating mechanisms will be explored.

DISCUSSION

If efficacy of the manualised A-FFIP early intervention is established, the current study has the potential to change clinical practice strongly towards the implementation of a low-intensity, evidence-based, natural early intervention in ASD. Early intervention in ASD requires specialist training, which subsequently needs to be developed or included into current training curricula.

TRIAL REGISTRATION

German Registry for Clinical Trials (Deutscher Register Klinischer Studien, DRKS); ID: 00016330. Retrospectively registered on 4 January 2019. URL: https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00016330.

Case-control study of the low intensive autism-specific early behavioral intervention A-FFIP: Outcome after one year

Abstracts: Objective: In current international research, early intervention in children with autism-spectrum disorder (ASD) focuses on naturalistic developmental behavioral interventions (NDBI). The manualized Frankfurt Early Intervention Program for preschool-aged children with ASD (A-FFIP) implements NDBI principles within a low-intensity approach of 2 h intervention/week. The present case-control study established effect sizes of change in autistic symptoms, comorbid behavioral problems as well as IQ after one year. Methodology: An intervention group (N = 20; age: 3.4-7.9 years) and a treatment-as-usual control group (N = 20; age: 3.2-7.3 years) of children with ASD were matched for developmental and chronological age. The outcome measures used were the ADOS severity score, the Child Behavior Checklist, and cognitive development. Results: After one year, the A-FFIP group showed a trend towards greater improvement in autistic symptoms (η2 = .087 [95 %-CI: .000-.159]) and significantly greater improvements in cognitive development (η2 = .206 [CI: .012-.252]) and global psychopathology (η2 = .144 [CI: .001-.205]) compared to the control group. Conclusion: The efficacy of A-FFIP should be established in a larger, sufficiently powered, randomized controlled study.

Time-series dataset on land surface temperature, vegetation, built up areas and other climatic factors in top 20 global cities (2000-2018)

Time-series datasets of Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built Index (NDBI) and other climatic factors are of significance due to their application in tracking climate change in cities. In this paper, new data processing methods are presented using the application of Google Earth Engine (GEE) and GIS. Different variables including LST (both daytime and nighttime), NDVI, NDBI, rainfall, wind speed, evapotranspiration, and surface soil moisture were computed for 18 years from 2000 to 2018 with of use of GEE platform. The study areas cover 20 top global cities which were mentioned in the global cities index report in 2018 [1]. The data sources used on GEE are: MODIS Terra LST and Emissivity 8-Day Global 1km; MODIS Terra Vegetation Indices 16-Day Global 1km; MODIS Terra Surface Reflectance 8-Day Global 500 m; TRMM Monthly Precipitation Estimate data; Terra Monthly Climate; MODIS Terra Net Evapotranspiration 8-Day Global 500 m; and NASA-USDA SMAP Global Soil Moisture Data. Also, to gather information regarding the global cities, United Nations (UN) population dataset, cities elevation and the A.T.Kerney report [1] was used. A short description of GEE functions to retrieve variables is provided. The dataset can be used to investigate the spatial-temporal relationships between LST, vegetation and built-up areas, as well as to provide the global perspective of climate and population change in various cities around the world.

The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis

Rapid and irregular urbanization is an essential issue in terms of environmental assessment and management. The dynamics of landscape patterns should be observed and analyzed by local authorities for a sustainable environment. The aim of this study is to determine which spectral urban index, originated from old Landsat missions, represents impervious area better when new generation Earth observation satellite Landsat 8 data are used. Two datasets of Landsat 8, acquired on 2 September 2013 and 10 September 2016, were utilized to investigate the consistency of the results. In this study, commonly used urban indices namely normalized difference built-up index (NDBI), index-based built-up index (IBI), urban index (UI), and enhanced built-up and bareness index (EBBI) were utilized to extract impervious areas. The accuracy assessment of urban indices was conducted by comparing the results with pan-sharpened images, which were classified using maximum likelihood classification (MLC) method. The kappa values of MLC, IBI, NDBI, EBBI, and UI for 2013 dataset were 0.89, 0.79, 0.71, 0.59, and 0.49, respectively, and the kappa values of MLC, IBI, NDBI, EBBI, and UI for 2016 dataset were 0.90, 0.78, 0.70, 0.56, and 0.47, respectively. In addition, area information was extracted from indices and classified images, and the obtained outcomes showed that IBI presented better results than the other urban indices, and UI extracted impervious areas worse than the other indices in both selected cases. Consequently, Landsat 8 satellite data can be considered as an important source to extract and monitor impervious surfaces for the sustainable development of cities.