Land Cover Change Research Articles

Effects of urbanization on river morphology in a Mediterranean coastal city (Genova, Italy)

From the geomorphological point of view, urbanization changes fluvial systems more drastically than any other single human activity. Its impact on rivers is of interest as it results in serious issues concerning geomorphic instability, ecosystem service reduction, and geo-hydrological risk increase. The present study provides a quantitative and qualitative assessment of landscape changes driven by urbanization, which occurred from the mid-19th century to the present day along the lower reaches of the Bisagno and Polcevera rivers and their tributaries in the Mediterranean coastal city of Genova (NW Italy). The bankfull channel morphological adjustments, the minor hydrographic network disappearance, the land use and land cover changes, and the current physical accessibility of river margins were investigated. This work is based on collection and review of historical data, photograph interpretation, GIS analysis, and field surveys. Urbanization essentially occurred between the mid-19th century and the late 1930s. The bankfull channel of the Bisagno and Polcevera rivers experienced large narrowing associated with widespread establishment of channelization structures. The minor hydrographic network essentially disappeared underground. After the 1930s, further minor anthropogenic interventions were implemented to accommodate urban expansion and consolidate channelization works; however, the overall geomorphological setting remained unchanged. The geomorphological evolution of the Bisagno and Polcevera rivers followed three different phases associated with the urban expansion process. Nowadays, the study area is completely urbanized, the fluvial stems flowing through the valley floor are totally disconnected from their adjacent areas, the main rivers show a relevant geomorphological stability induced by anthropogenic pressures, and the accessibility of river margins for people is rather limited. The outcomes from this study represent an essential knowledge base for technicians and policymakers to plan urban development and inform effective and sustainable management measures. Moreover, they provide insights into the medium- and short-term morphological evolution of urban rivers.

Narrowing down the drivers of flood risk in medium-sized sub-Saharan African cities: insights from the Greater Kumasi Metropolitan Area, Ghana

ABSTRACT Literature on the drivers of flooding in sub-Saharan Africa is mostly focused on large and megacities, despite the majority of cities in the region being medium- and small-sized. We conducted a systematic review using 27 peer-reviewed scientific papers selected following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols using the Greater Kumasi Metropolitan Area (GKMA), Ghana, as a case study, and validated the results in a workshop attended by 51 stakeholders. We found that the reported drivers of flood risk in GKMA are land cover changes (61.5%) (e.g. wetlands degradation); governance challenges (16.2%); challenges with drainage (13%); extreme rainfall (6.5%); and apathy towards nature (3.2%). We conclude that floods in GKMA are overwhelmingly human induced through unsustainable urbanization processes that are so rapid that city authorities are unable to properly control them. Also, a holistic understanding of flood risk is missing since majority of the included papers rather used qualitative methodologies.

Temporal Analysis of Riparian Buffer Zones and Demographic Trends in the Coastal Districts of Kastamonu

Aim of study: This study investigates the temporal changes of forest areas within and outside the Riparian Buffer Zones (RBZs) and examines their correlation with demographic trends in the coastal districts of Kastamonu from 1987, 2000, and 2016 to assess how population dynamics influence forest cover. Area of study: The study was conducted in four coastal districts in northern Kastamonu province, Türkiye, covering approximately 1000 square kilometers and focusing on 153 forest villages. Material and methods: Geographic Information Systems and remote sensing data delineated and analyzed land cover and population changes. The study used Landsat imagery and population data to explore spatial and temporal dynamics within the RBZs Main results: The study found a significant increase in forest area from 1987 to 2000 and a subsequent decrease from 2000 to 2016, with these changes showing a weak but significant negative correlation with the declining local population. Research highlihts: The findings suggest that declining rural populations may reduce anthropogenic pressures on forests and pose challenges to effective forest management and conservation. The research highlights the need for sustainable strategies integrating demographic change with forest conservation efforts to maintain ecosystem services and biodiversity.

Assessment of the spatial-temporal impact of the Longhai Railway transportation corridor on land cover changes and landscape patterns

IntroductionTransportation corridors, as special economic corridors, have a greater impact on land cover and landscape pattern changes.MethodsTherefore, 10 buffer zones were established at 1 km intervals on both sides of the Longhai Railway as the centerline to trace the impact of the railroad corridor on the land use change and regional landscape pattern change of the cities along the line from 1985 to 2020.ResultThe results show that: (1) The land cover changes along the railroad corridor during the 35 years are mainly characterized by the conversion between cropland, grassland, and construction land. Compared with 1985, in 2020, the construction land increased by 161.96%, the grassland area decreased by 11.83%, and the cropland area decreased by 15.83%. (2) The fragmentation of land patches and vegetation coverage is negatively and positively correlated with the buffer zone distance, respectively. In the same year, the comprehensive land-use dynamic degree is smaller as it is further away from the railway. The nighttime light index in the buffer zone is significantly correlated with the land aggregation index and average patch area, and the closer to the railroad, the higher the land aggregation index of construction land. (3) In terms of zoning, the intensity of land cover and landscape pattern changes in the eastern section is higher than that in the western section, with a higher degree of land fragmentation and more agglomeration of construction land, and the transportation corridor has a greater impact on the change of integrated land use motives in this region. The results of the study can provide a scientific basis for optimising the spatial pattern of land and improving the ecological environment in the construction of cross-regional transport corridors.

Analysis of short- and long-term controls on the variability of event-based runoff coefficient

Study regionThis study focuses on the Ohio Region, which spans 11 states in the eastern United States. Study focusRunoff coefficients are crucial in hydrology, indicating the relationship between rainfall and runoff. Understanding their controls and variability is essential for water resource assessment, management strategies, and land use planning. This research examines factors influencing runoff coefficients and their trends in the Ohio region using data from the North American Land Data Assimilation System phase-2 (NLDAS-2) Mosaic Land Surface Model, covering 2000–2020. The analysis considers short-term controls, such as climatic features (rainfall intensity, amount, and duration), hydrological factors (antecedent soil moisture, drainage density, and curve number), topographic factors (drainage area, land use, slope, elevation), and watershed shape. Additionally, the study investigates trends in runoff coefficients and their long-term controls, including climatic factors and land use changes. New hydrological insights for the regionThe findings indicate that runoff coefficients increase with antecedent soil moisture and rainfall intensity. Higher elevations show lower runoff coefficients due to forested land use. Larger watersheds have lower runoff coefficients at low rainfall intensity but higher ones when intensity is high. Long-term trends reveal soil moisture as the primary control, with land cover changes as a secondary factor. This research deepens understanding of runoff coefficient dynamics and controls in the Ohio region. Future studies could explore the impacts of urbanization, reservoirs, evapotranspiration, and snowmelt on runoff coefficients.

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025.

Devastation of the cerrado of mato grosso do sul and the advance of arenization in the pardo river watershed

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes.

The effects of land cover transition and its patch mosaics on soil erosion using geospatial technology in South Wollo, Ethiopia

Soil erosion has been a significant threat to agricultural productivity, dam sustainability, and ecosystem services in the highlands of Ethiopia. Despite some watershed-specific research on this problem, the spatiotemporal distribution of soil erosion risks is still rarely assessed in South Wollo. This study aims to monitor land cover changes, analyze landscape mosaics, and estimate soil loss in South Wollo from 1990 to 2020. The Revised Universal Soil Loss Equation (RUSLE) was utilized to estimate soil loss. Key informant interviews and field observations were conducted to gain further insights into land cover change and soil erosion. The results showed that agricultural land use was dominant, covering 50.75 % of the study area in 1990 and it continued to expand at an annual rate of 1.73 %, primarily on shrubland and grassland. The landscape structure was more heterogeneous in the north compared to the central and southern areas. In 1990, the mean soil loss was 21.13 t ha-1yr−1, totaling 41.53 million tons. By 2020, These figures had increased to 28.97 t ha-1yr−1 and 49.86 million tons, respectively. Areas experiencing very severe soil erosion (>50 t ha-1yr−1) expanded from 10.81 % (1990) to 13.57 % (2020). The magnitude of soil loss also differed among land cover types, with the highest rates observed in bare land (>85 t ha-1yr−1), followed by agricultural land (>30 t ha-1yr−1). Soil loss factors and landscape metrics exhibited a significant correlation with soil loss, though the strength and direction of these interactions varied. Land cover change and its associated soil erosion were primarily exacerbated by policy factors, including tenure insecurity and disregard for farmers’ indigenous conservation knowledge. This study offers valuable insights into the trends of land cover transitions, landscape structure, and soil loss. It will help guide sustainable land management, aimed at increasing green legacy and reducing soil loss.

Assessing Land-Cover Changes in the Natural Park ‘Fragas do Eume’ over the Last 25 Years: Insights from Remote Sensing and Machine Learning

The ‘Fragas do Eume’ Natural Park includes one of the best-preserved Atlantic forests in Europe. These forests are part of the Natura 2000 Network. This scientific study focuses on analysing land-cover changes in the ‘Fragas do Eume’ Natural Park (NW Spain) over a 25-year period, from 1997 to 2022, using machine learning techniques for the classification of satellite images. Several image processing operations were carried out to correct radiometry, followed by supervised classification techniques with previously defined training areas. Five multispectral indices were used to improve classification accuracy, and their correlation was evaluated. Land-cover changes were analysed, with special attention to the transitions between eucalyptus plantations and native deciduous forests. A significant increase in eucalyptus plantations (48.2%) (Eucalyptus globulus Labill.) was observed, while native deciduous forests experienced a decrease in their extent (17.6%). This transformation of the landscape affected not only these two habitats, but also cropland and scrubland areas, both of which increased. Our results suggest that the lack of effective conservation policies and the economic interest of fast-growing tree plantations could explain the loss of native deciduous forests. The results highlight the need to implement pro-active and sustainable management measures to protect these natural forest ecosystems in the ‘Fragas do Eume’ Natural Park.

Analysing Thermal Land Surface Characteristics and Urban Expansion in Kenitra City, Morocco, using Remote Sensing and GIS

Abstract This research investigates how land use and cover the affect land surface temperature in Kenitra. It employs Geographic Information System techniques and remote sensing methods to monitor and control surface temperature changes. The present study integrates remote sensing and geographic information system to process satellite images. Furthermore, it focuses, also, on analyzing land surface temperature and its correlation with land cover changes over three time periods (1989, 2000, 2020) using landsat satellite data. Thus, supervised classification is employed to map these changes and derive urban heat islands from thermal band data. This study provides a comprehensive evaluation of the relationship between land use and land surface temperature. It utilizese a landscape dynamics assessment based on multi-source and multi-sensor remote sensing technologies. Specifically, the study uses landsat satellite data (TM for 1989 and OLI-8 for 2002 and 2020) to assess the effects of land use and land cover changes on land surface temperature distribution in the Kenitra city. Spatial and statistical analyses are performed by comparing maps from remotely sensed data generated using geographic information system. Therefore, these analyses reveal various changes in the kenitra region between 1989 and 2020. The primary changes observed include an increase in built-up areas and bare ground with a decrease alongside in natural areas (vegetation, etc.). The average temperatures in 1989, 2002, and 2020 were 29°C, 31.05°C, and 32°C, respectively. The obtained results are valuable for planning and managing climate scientists, land-use planners, and researchers focusing on sustainable urbanization areas. The study’s findings will assist urban planners and policymakers in adopting appropriate measures for sustainable planning in the city to mitigate the adverse effects of land surface temperature.

Separation of the Urbanization Effects on Thermal Environment

Rapid urbanization leads to changes in the thermal characteristics of urban areas. Despite cities serving as natural laboratories to investigate responses to global change, it remains unclear how urbanization affects the thermal environment during global warming. Urbanization effects can be categorized as either the effect of urban climate (UCE) or land cover change (LCCE). This study proposes a conceptual framework for separating the urbanization effects on the thermal environment in 29 major Chinese cities. Results show that land surface temperature increases with impervious surface fractions in the urban area and decreases with vegetation fractions in rural areas, resulting in significant UCE and LCCE across cities, with LCCE approximately 1.3 to 5.7 times greater than UCE. These findings provide a comprehensive understanding of the thermal environment change caused by urbanization. To mitigate the negative effects of urbanization on the thermal environment, reducing land cover change would be more effective.

Prediction of Land Cover Change in Pangkah Subdistrict, Tegal Regency, Indonesia Using Cellular Automata-Markov

Abstract Pangkah Subdistrict, strategically located next to Tegal Regency’s capital, is experiencing steady population growth. The growing population and rising demand for agricultural products create competition for land use. By integrating remote sensing with geographic information systems (GIS), it becomes possible to evaluate land-cover changes over time, forming a foundation for forecasting future land use in Tegal Regency. This study examines land-cover changes in Pangkah Subdistrict for the years 2007, 2015, and 2023, while also predicting land-cover for 2045 using the Cellular Automata-Markov method. The findings indicate a continuous increase in built-up land due to population growth, while agricultural land decreases each year. The modeling results will inform evaluations of spatial planning in Pangkah Subdistrict.

Detecting small-scale landslides along electrical lines using robust satellite-based techniques

Robust Satellite Technique (RST) was applied to detect small-scale landslides along electrical lines in Sicily, Italy. To this end, electrical poles were selected as targets within the study area. The methodology, implemented in Google Earth Engine (GEE) environment, exploits the Copernicus Sentinel-2 platform to identify anomalous land cover variation, in terms of Normalized Difference Vegetation Index (NDVI), possibly related to small displacements affecting electric poles. Since the applied methodology is based on land cover change, dense vegetation plays an important role in detecting small-scale landslides. Therefore, we targeted months with the highest vegetation density, such as February, March, and April from 2016–2023. The results obtained reveal that out of the five targeted electrical poles, four of them exhibited anomalies > 2-sigma indicating significant changes in land cover possibly related to local ground movement as confirmed by aerial photos collected in the period 2015–2023. Our findings reveal anomalies of −2.17 and −2.36 on 7/17/2017 and 9/05/2017 for pole 1. For pole 2, the results show an anomaly of −2.02 on 8/11/2018. The results also indicate anomalies of −4.40 and −2.99 on 7/09/2021 and 9/27/2022 for pole 3. For pole 4, the findings show an anomaly of −3.10 on 1/18/2019.

The role of society in the expansion of oil palm plantation and the changes in land cover at Kuantan Singingi, Riau

The role of society in the expansion of oil palm plantation and the changes in land cover at Kuantan Singingi, Riau

Multi-temporal assessment of a wildfire chronosequence by remote sensing

The study aimed to develop a methodological framework to identify forest ecosystems affected by wildfires and evaluate their recovery chronologically. To do this remote sensing analysis, sites with burn scars were selected based on various criteria (fire severity, affected area, vegetation and soil type, slope, aspect, and one-time occurrence of wildfire in the last 23 years). Spectral vegetation indices (VIs) from satellite imagery were used to estimate burn severity and vegetation cover changes. Images of surface reflectance were obtained from the collection of Landsat 5 ETM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, available and processed on the Google Earth Engine Platform (GEE). Indices VIs (i) the normalized difference vegetation index (NDVI), (ii) the normalized burn ratio (NBR), and (iii) the differenced normalized burn ratio (dNBR) were calculated to classify burn severity. The one-time occurrence selection was performed using the LandTrendr algorithm to monitor changes in land cover and burned areas. To validate the selection, the chosen sites within the chronosequence were clustered on 4 seasons of soil properties and litter accumulation recovery. Our result can guide methodological comparisons and forest management practices on large surfaces by comparing parches of different time-affected ecosystems. Validation sites of the cluster chronosequence shows consistent recovery of soil properties as soil carbon, bulk density and litter accumulation through the studied years•The study developed a framework to identify wildfire-affected forest ecosystems and evaluate their recovery using remote sensing and local data.•Vegetation indices (NDVI, NBR, dNBR) from Landsat satellite imagery processed on the Google Earth Engine were used to assess burn severity and vegetation changes over time.•Selected sites were validated using the LandTrendr algorithm and monitored for seasonal changes in soil properties and litter accumulation.

Cloud removal in landsat images based on spatial-spectral-temporal random forests

Abstract The Landsat-8 Operational Land Imager (OLI), together with the Landsat-9 OLI2 sensors, provides important optical data for various applications. Due to frequent cloud cover, the obtained Landsat images always contain different degrees of spatial information loss. Although great efforts have been made to remove clouds in remote sensing images in recent decades, there still exists a large challenge, that is, the land cover changes between the target cloudy and the auxiliary cloud-free images, which limits greatly the accuracy of cloud removal. To cope with this issue, a spatial-spectral-temporal random forest (SSTRF) algorithm was proposed in this paper. The SSTRF model aims at removing clouds in Landsat images with the assistance of a remote sensing image with spatially complete cover from another satellite, that is, the 8-day MODIS surface reflectance product (MOD09A1). By compositing the valid data from 8 separate days, the MOD09A1 product has a great possibility to provide cloud-free land cover change information for the cloud removal of Landsat images. Through experiments with simulated and real clouds, SSTRF was demonstrated to produce greater accuracy than two benchmark cloud removal methods and has great potential to be applied to practical use.

Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil)

This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.

Removing thick clouds in landsat 8 OLI images with cloudy time-series auxiliary data

Abstract Thick cloud occlusion severely limits the subsequent application of optical remote-sensing images. Existing mainstream cloud removal methods often use cloud-free homologous temporal images to provide auxiliary information. However, due to the limitation of cloud occlusion and satellite revisit cycle, the time interval between the cloud-free auxiliary image and the target cloudy image is often too large, and the possible land cover changes during the period increase the uncertainty of effective information on the auxiliary images. In this case, more accurate reconstructions may be obtained by using temporally closer and partially cloud-contaminated images. In this paper, a deep network, convLSTM, is developed for cloud removal (i.e., CR-convLSTM), which makes full use of the complementary effective information on partially cloud-contaminated time-series images. Experiments based on simulated clouds indicated that CR-convLSTM can obtain more accurate predictions than the classical cloud removal method MNSPI. CR-convLSTM is an effective cloud removal method with high computational efficiency while ensuring predicting accuracy.

Ecohydrological Variables Underlie Local Moisture Recycling in Mediterranean‐Type Climates

AbstractMediterranean areas are projected to face increased water scarcity due to global changes. Because a relatively large fraction of the precipitation in Mediterranean areas originates locally, changes at the land surface may further dampen local precipitation. Here, we study the contribution of evaporation to local precipitation for the first time on a scale of approximately 50 km using local evaporation recycling (ELMR) and local precipitation recycling (PLMR), and make a comparison among five Mediterranean climate regions: South West Australia, South West US, central Chile, the Mediterranean Basin, and the Cape region of South Africa. Specifically, this study aims to understand the effects of ecohydrological (dependent on vegetation or the hydrological cycle) and non‐ecohydrological variables on ELMR and PLMR. We find that (a) on average, ecohydrological variables correlate more frequently and more strongly to ELMR and PLMR than non‐ecohydrological variables; (b) ELMR is large over wet areas and PLMR is large over dry areas; and (c) there are differences in underlying factors of ELMR and PLMR among the regions due to differences in wetness, topography, and land cover. The results suggest that in Mediterranean regions, changes in vegetation cover or the hydrological cycle may strengthen the local water cycle through enhancing ELMR. Finally, ELMR and PLMR help to identify where in Mediterranean regions we might enhance the local water cycle through land cover changes.

Spatio-temporal tendencies of urban land surface temperature on the Andean piedmont under climate change: A case study of Metropolitan Lima, Peru (1986–2024)

The increase in land surface temperature (LST) in megacities has contributed to global warming due to poor urban planning and high population concentration. This study analyzes spatio-temporal trend patterns of LST in the city of Metropolitan Lima, Peru, during the summers of 1986–2024. The Mann-Kendall tests and the Theil-Sen Slope method were used to analyze the spatiotemporal trends of LST, relating R2 and the Mann-Kendall p-value of the annual average LST in each district. A hierarchical cluster analysis was performed to group districts according to their average yearly LST. Urban heat islands were identified based on basin configuration and distance to the sea at 1 km intervals. The results reveal a significant increase in LST (p<0.001) related to El Niño-Southern Oscillation phenomena, in addition to urban growth and land cover change. The significant positive trend of LST showed a heterogeneous distribution in all districts. The districts were grouped into three clusters with statistically significant differences in LST (p<0.001), spatially configured radially from the sea to the foot of the Andes, up to 1179 m a.s.l. Urban heat islands did not correlate with significant positive trends in basins. Still, they showed a considerable increase in LST in areas of high economic activity, including dense commercial, industrial, and residential areas. This information is crucial to managing climate change adaptation and mitigation measures and contributing to sustainable urban planning focused on the population’s well-being.