Urban Land Cover Research Articles

Index-Based Change Detection, Spatial Zoning and Environmental Criticality of Urban Land Cover: A Spatiotemporal Study on Gazipur Sadar Upazila of Bangladesh

Urbanization has become a comprehensive phenomenon worldwide as well as in Bangladesh. This study has tried to analyze the urban land cover change pattern of Gazipur Sadar Upazila between 2001 and 2021. This study also has made an investigation to classify the urban land cover of Gazipur Sadar Upazila based on categorical spatial zoning and analyze how urbanization affects nearby green spaces to monitor environmental criticality. Multispectral Landsat satellite imageries, population census data, air quality index, and open-ended questionnaire data were used as the operational data. Spectral indices such as NDVI, and IBI, were performed from remotely sensed satellite data. NDVI was used to extract green cover and IBI was used to extract the built-up area. Spatial zoning was done based on existing literature, expert interviews, and RS analysis. Environmental criticality was analyzed by correlating population density and air quality data with the extracted green cover. Between 2001 and 2021, the built-up area expanded by around 77.7 km2 and green cover by about 74.79 km2. Although the green cover flourished, it decreased in Tongi and Konabari. Besides, the south-western region of Gazipur experiences rapid growth in built-up areas. Three classified zones e.g., industrial and manufacturing zone, forested zone, and neutral zone were extracted from criteria-based spatial zoning. The results of green space per capita analysis showed that from 2001 to 2021, green space in the study area decreased by 10-713 m2. Environmental criticality analysis illustrated that in 2001, two regions (Tongi and Konabari) were classified as highly critical, and by 2021, this number had escalated to five (Tongi, Konabari, Kasimpur, Basan, Gaccha). Furthermore, the study uncovered that the criticality in urbanized areas is growing rapidly.

Future changes in key plant traits across Central Europe vary with biogeographical status, woodiness, and habitat type

Many plant traits covary with environmental gradients, reflecting shifts in adaptive strategies and thus informing about potential consequences of future environmental change for vegetation and ecosystem functioning. Yet, the evidence of trait–environment relationships (TERs) remains too heterogeneous for reliable predictions, partially due to insufficient consideration of trait syndromes specific to certain growth forms and habitats. Moreover, it is still unclear whether non-native and native plants' traits align similarly along environmental gradients, limiting our ability to assess the impacts of future plant invasions. Using a Bayesian multilevel modelling framework, we assess TERs for native and non-native woody and herbaceous plants across six broad habitat types in Central Europe at a resolution of c. 130 km2 and use them to project trait change under future environmental change scenarios until 2081–2100. We model TERs between three key plant traits (maximum height, Hmax; specific leaf area, SLA; seed mass, SM) and individual environmental factors (7 climate variables and % urban land cover) and estimate trait change summed across all environmental effects. We also quantify the change in the average trait difference between native and non-native plants. Our models depict multiple TERs, with important differences attributed to biogeographical status and woodiness within and across habitat types. The overall magnitude of trait change is projected to be greater for non-native than native taxa and to increase under more extreme scenarios. Native woody plant assemblages may generally experience a future increase across all three traits, whereas woody non-natives may decline in Hmax and increase in SLA and SM. Herbaceous Hmax is estimated to increase and SLA to decrease in most habitats. The obtained trait projections highlight conditions of competitive advantage of non-native plants over natives and vice versa and can serve as starting points for projecting future changes in ecosystem functions and services.

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia.

Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines (SVM) for supervised classification and cellular automata-based artificial neural network (CA-ANN) models for prediction in the quantum geographic information systems (QGIS) plugin MOLUSCE. Multi-temporal spatial Landsat 5 Thematic Mapper (TM) imageries, Enhanced Thematic Mapper plus 7 (ETM+), and Landsat 8 Operational Land Imager (OLI) images were used to find the acute problem the watershed is facing. Accuracy was assessed using the confusion matrix in ArcGIS 10.4 produced from ground truth data and Google Earth Pro. The results acquired from kappa statistics for 1991, 2007, and 2021 were 0.78, 0.83, and 0.88 respectively. The change detection trend indicates that urban land cover has an increasing trend throughout the entire period. In the future trend, agriculture land may shoot up to 86.79% and 86.78% of land use class in 2035 and 2049. Grassland may attenuate by 0.03% but the forest land will substantially diminish by 0.01% from 2035 to 2049. The increase of land specifically was observed in agriculture from 3128.4 to 3130 km2. Judicious planning and proper execution may resolve the water management issues incurred in the basin to secure the watershed.

Evaluation of Land Use/Land Cover Changes due to Urban Sprawl in Bengaluru Rural, Karnataka, India

The availability of productive land is significantly impacted by the global phenomenon of urbanization. The amount of land available for food production and other essential activities decreases as cities grow because the urban perimeter encroaches on rural and natural areas. Conducting research on urban sprawl analysis and land use land cover (LULC) change assessment is essential in ensuring sustainable urban growth. Bengaluru, a rapidly expanding metropolitan city, has a significant impact on the area around it, making it a prime location for this kind of study. In this study, authors sought to assess how urban sprawl affected LULC in the Bengaluru rural district that surrounds the city of Bengaluru. The study evaluated changes in LULC over a two-decade period using remote sensing data and GIS tools. Five LULC classes were used to categorize the study area: settlement, waterbody, vegetation, agriculture, and barren land. The maximum likelihood technique was used to classify Landsat images from three different time periods using the supervised image classification method in the ERDAS software. Accuracy assessment was used to gauge the classified images’ accuracy. The study’s important findings showed how the LULC classes in the study area have been negatively impacted by the urban sprawl. The study emphasizes the significance of ongoing research in LULC change assessment and urban sprawl analysis to ensure sustainable urban growth and safeguard the availability of productive land.

Significant improvement in freshwater invertebrate biodiversity in all types of English rivers over the past 30 years

There remains a persistent concern that freshwater biodiversity is in decline and being threatened by pollution. As the UK, and particularly England, is a densely populated nation with rivers of modest dilution capacity, this location is very suitable to examine how freshwater biodiversity has responded to human pressures over the past 30 years. A long-term dataset of 223,325 freshwater macroinvertebrate records from 1989 to 2018 for England was retrieved and examined. A sub-set of approximately 200 sites per English Region (1515 sites in total with 62,514 samples), with the longest and most consistent records were matched with predicted wastewater exposure, upstream land cover and terrain characteristics (latitude, altitude, slope gradient and flow discharge). To understand changes in macroinvertebrate diversity and sensitivity with respect to these parameters, the biotic indices of (i) overall family richness, (ii) Ephemeroptera, Plecoptera, Trichoptera (EPT) family richness, and (iii) the Biological Monitoring Working Party (BMWP) scores of NTAXA (number of scoring taxa) and (iv) ASPT (average score per taxon) were selected. A review of how close the BMWP scores come to those expected at minimally impacted reference sites was included. For all latitudes, altitudes, channel slope, river size, wastewater exposure levels, and differing proportions of upstream woodland, seminatural, arable and urban land cover, all diversity or sensitivity indices examined improved over this period, although this improvement has slowed in some cases post 2003. Mean overall family richness has increased from 15 to 25 family groups, a 66 % improvement. The improvement in mean EPT family richness (3 to 10 families, >300 % improvement), which are considered to be particularly sensitive to pollution, implies macroinvertebrate diversity has benefited from a national improvement in critical components of water quality.

Assessment of landcover impacts on the groundwater quality using hydrogeochemical and geospatial techniques.

Groundwater quality is influenced by urbanization and land use land cover (LULC) changes. This study investigated their impact on groundwater quality in Quetta City, Pakistan, from 2015 to 2021. About 58 groundwater samples from monitoring wells were analyzed using hydrogeochemical and statistical methods. The water quality index (WQI), Wilcox, USSL, and various agricultural indices were employed to assess water quality trends. LULC analysis and NDVI using Sentinel-2 imagery revealed increased urban and agricultural areas and decreased barren land. Rapid urbanization was evident, with the buildup class expanding by 7.50% during this period. NDVI findings emphasized monitoring vegetation health and water quality for environmental assessments. The groundwater in Quetta was primarily classified as Cl-Ca·Mg, Cl-Ca, and Cl-Na according to the Piper diagram, with water-rock interactions and rock weathering evident. Most groundwater samples were suitable for irrigation according to the Wilcox and USSL diagrams. The WQI demonstrated overall safety for human consumption, but declining WQI values in northern parts due to urbanization are concerning. The results also revealed a moderate positive relationship between landcover classes and WQI values. It can be concluded that urbanization and excessive use of pesticides contributed to declining agricultural land quality. The spatial overlay of agricultural indices with landcover class suggested that barren land was most suitable, followed by build-up and agriculture were suitable for drinking and agriculture purposes. Moreover, agricultural indices moderately declined due to excessive fertilizers and pesticides in the agriculture landcover class. Thus, effective water resource management is crucial to address challenges. This comprehensive study serves as a baseline for future research and recommends larger-scale studies to implement efficient management strategies, urbanization planning, and safe irrigation and drinking water practices to prevent groundwater pollution.

Fleeting fields of Zadar (Croatia): characterizing millennial-scale urban landcover change, green space, and resilience into the twenty first century

Cities are a growing factor in global change today, but urbanization as a process has played a significant role in shaping our planet’s environments for millennia. Exploring the longevity or persistence of cityscapes can therefore reveal qualities that may have strengthened urban sustainability or resilience over long periods. In the Mediterranean, many ancient cities lie in ruin and are fully formed archaeological sites, while others reflect continuous growth and expansion into the modern era, often replacing what has traditionally been a rural mosaic of green space with a more homogenized urban landcover. Green spaces like cultivated lands, urban forests, recreational parks, and green belts are essential components of urban resilience, as they build adaptive capacity by improving human health and livelihoods, reducing surface runoff and erosion, and mitigating urban heat island effects, among others. Protection of green space in urban and peri-urban contexts also offers greater capacity to transform in the face of uncertain change. This paper centers on the ancient city of Zadar along Croatia’s Adriatic coast to characterize broad millennial-scale changes in urban landcover and green space. The results suggest that the distribution of urban landcover and green space appears to have been fairly stable for much of Zadar’s 3,000-year history, which arguably played a significant role in its persistence into the present era. However, the pace and scale of urban development, as well as the corresponding losses of green space, have accelerated from the mid-twentieth century onward, depleting a major source of socioecological resilience that has benefitted the city since the Iron Age. Archaeological and historical fields of study provide a deep temporal context to these contemporary challenges and are well-suited to identify and promote the locally and historically distinctive character of surviving green spaces. Land use legacies stemming from Roman surveying and historic field clearance practices around Zadar have resulted in one of the most distinctive and well-preserved physical manifestations of ancient and historic land use in the Mediterranean. Recognition of their cultural significance, even in their diminished state, would add further value for their protection and continued capacity toward urban resilience in the next century.

Watershed-Mediated Ecomorphological Variation: A Case Study with the Twin-Striped Clubtail Dragonfly (Hylogomphus geminatus).

Anthropogenic land-cover change is modifying ecosystems at an accelerating rate. Changes to ecomorphologically variable taxa within those ecosystems serve as early-warning signs that resources on which humans and other animals depend are being altered. One known ecomorphologically variable taxon is Hylogomphus geminatus, a species of dragonfly in the southeastern United States that shows pronounced variation in total body length across its limited geographic range. We measured total length of live as well as preserved museum specimens of H. geminatus and the sympatric species Progomphus obscurus (as a means for comparison). Both species showed significant size differences linked to HUC-8 watersheds in which they occur. H. geminatus showed additional significant differences on either side of the Apalachicola River, Florida, for all comparisons by sex. In overlapping watersheds, the species tended to show the same trends in length relative to their respective averages. Smaller body length was associated with more urban and agricultural land cover. These findings indicate that ecomorphological variation is tied to the watershed scale and point to significant variations on either side of the Apalachicola River. More thorough future analyses would be needed to verify trends in body length and identify the drivers behind them.

Urban Land Use and Land Cover Mapping

The Brazilian urbanization process produced a complex urban space, with a variety of urban land use and cover as a result. The study of these forms through a classification system is essential, but most current systems don’t capture this complexity, condensing it. Urban forms are difficult to distinguish and classify, resulting in the need for a system with a high degree of detail, for a more accurate urban planning. The objective of this work is to propose a classification system for urban land use and cover, which can demonstrate the multiplicities through remote sensing, using data from CBERS 04A satellite. The methodology surveys the visual elements of remote sensing images, through visual interpretation, relating them to each proposed use and cover class. With this, a classification system was developed that covers the urban space in 17 classes, being an effective way to raise information about the different urban forms.

Consistent metropolitan boundaries for the remote sensing of urban land

This paper introduces an internationally consistent definition of metropolitan areas to the literature regarding the remote sensing of urban land use or land cover. In the cross-comparison of land use or land cover for explicitly bounded urban areas, the observed ‘economic’ definition is argued to hold distinct potential merits over administrative or agglomeration-based boundaries, which typically underpin other studies. To illustrate the proposed merits as well as their implications for the remote sensing literature, the empirical analysis considers the case of 687 European metropolitan areas. Across these metropolitan areas, whose boundaries are defined jointly by the OECD and the European Commission, land cover and land use are segmented in a fusion of imagery from radar and optical sensors in Sentinel satellites. Segmentation is achieved using deep learning in a well-established model architecture. The analytical focus is on built-up areas that are in a residential use or in a commercial or industrial use. Map classifications and accuracy measures are obtained for cities as well as their respective commuting zones as these together embody metropolitan areas. The results underline that not only land use area estimates but also map classification accuracy vary widely across individual metropolitan areas. Whereas classification accuracy to some degree varies for metropolitan areas within as well as between countries, classification accuracy is positively associated with population size and built-up area density as regression analysis confirms. Additionally, the extent of built-up areas in distinct uses is shown to vary across different types of metropolitan (sub-)areas. This study's findings highlight the typically unobserved role that study area definition and selection may play in affecting outcomes in remote sensing studies in urban settings, as relevant to both studies of single as well as multiple urban areas. The consistent comparison of remote sensing outcomes across metropolitan areas may further promote generalization in a growing and global field and potentially supports better-informed policy making processes.

Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network

Reliable information of land cover dynamics in dryland cities is crucial for understanding the anthropogenic impacts on fragile environments. However, reduced classification accuracy of dryland cities often occurs in global land cover data. Although many advanced classification techniques (i.e., convolutional neural networks (CNN)) have been intensively applied to classify urban land cover because of their excellent performance, specific classification models focusing on typical dryland cities are still scarce. This is mainly attributed to the similar features between urban and non-urban areas, as well as the insufficient training samples in this specific region. To fill this gap, this study trained a CNN model to improve the urban land classification accuracy for seven dryland cities based on rigorous training sample selection. The assessment showed that our proposed model performed with higher overall accuracy (92.63%) than several emerging land cover products, including Esri 2020 Land Cover (75.55%), GlobeLand30 (73.24%), GLC_FCS30-2020 (69.68%), ESA WorldCover2020 (64.38%), and FROM-GLC 2017v1 (61.13%). In addition, the classification accuracy of the dominant land types in the CNN-classified data exceeded the selected products. This encouraging finding demonstrates that our proposed architecture is a promising solution for improving dryland urban land classification accuracy and compensating the deficiency of large-scale land cover mapping.

Assessing the potential impacts of LULC change on urban air quality in Dhaka city

Dhaka is among the top five metropolises with the most particulate pollution and polluted air alone, responsible for 17.6% of the risk of mortality and disability. As a result of recent global episodes of urban air pollution, policymakers and administration agencies are endeavoring to regulate urban land use and land cover change (LULC) and sustainably enhance air quality. This study aims to establish a relationship between alterations in PM2.5 concentration and LULC features. The Bangladesh Meteorological Department provided PM2.5 and meteorological data for 2012 and 2018, and RapidEye image data was used to classify land-use for 2012 and 2018. Between 2012 and 2018, waterbodies and vegetation fell by 2% and 5.3%, and construction and bare land increased by 4% and 3.4%, respectively. Reduced vegetation and waterbodies might result in an increase in barren land and built-up areas. Numerous industrial sites and the unlawful acquisition of canals may also contribute to the growth of barren land. In 2018, homogeneity of the landscape pattern is higher than 2011. The aggregation and contagion indices, landscape metrices, have inverse correlation of −0.01 and −0.158 respectively with PM2.5 concentrations. However, the landscape index has positive correlation of 0.06 with PM2.5 concentrations. On the other hand, the variability in PM2.5 concentrations is correlated with the altered characteristics of LULCs and related landscape indicators. According to the spatial correlation, PM2.5 levels have decreased as vegetation and waterbodies have increased, as the PM2.5 concentration is negatively correlated with vegetation and waterbodies by −0.212 and −0.028, respectively. Furthermore, meteorological indices are significantly correlated with PM2.5 concentration, which was used as an independent variable to estimate the annual average PM2.5 concentration levels for 2024 using an Artificial Neural Network, which range from 312 to 188 μg/m3. The government should take appropriate measures to curb polluting air emissions, ensuring a livable city in the future.

Detecting the interaction between urban elements evolution with population dynamics model

Exploring the evolution of urban elements can improve understanding of the developmental process of city and drive such development into a better direction. However, the non-linearity and complexity of changes in urban elements have brought great challenges to understanding this process. In this paper, we propose a cross-diffusion partial differential equation based on ecological dynamics to simulate the evolutionary process of urban elements from the microscopic viewpoint. The interaction between urban elements is simulated by constructing a non-linear and spatiotemporal change equation, and the main influence between elements is evaluated by the key parameters in the discussed equation. Our model is first experimented to time-series data on population density and housing prices to analyzes the interaction of these two elements in the evolution process. We then extend the model to label data, land cover data, to obtain a quantitative expression of the interaction between different land types in the process of urban land cover change.

Urban land use land cover classification based on GF-6 satellite imagery and multi-feature optimization

Urban land use/land cover (LULC) classification has long been a hotspot for remote sensing applications. With high spatio-temporal resolution and multispectral, the recently launched GF-6 satellite provides ideal open imagery for LULC mapping. In this study, we utilized multitemporal GF-6 images to generate six types of land features, including spectral bands, texture features, built-up, waterbody, vegetation, and red-edge indices. The minimum Redundancy Maximum Relevance (mRMR) algorithm was employed to optimize feature selection. Subsequently, Random Forest (RF) and Extreme Gradient Boosting (XGBT) were assessed using different feature selections. Besides, various feature configurations were designed for LULC classification and comparison. The results indicate that the mRMR-based RF method achieved the highest overall accuracy of 91.37%. The temporal red-edge indices were important features for urban LULC classification and contributed mainly to grassland and cropland. These results supplement existing classification methods and assist in improving LULC mapping in urban areas with complex landscapes.

Modeling the impact of urban land cover features and changes on the land surface temperature (LST): The case of Jordan

Higher land surface temperature (LST) in cities than their surrounding areas presents a major sustainability challenge for cities. Decision-makers and planners use the LST measurements to monitor the urban environment to reduce the urban climate’s main challenges. Therefore, there is an urgent need to examine the impacts of urban features and changes on the LST. This study focused on the relationship between LST and urban land changes and the impact of these changes on LST during different periods. Although a set of studies explored the relationship between landscape features and urban LST, several aspects still need further discussion. Here, the study aims to explore the influence of landscape features and land cover patterns on land surface temperature LST in two cities (Amman and Zarqa) in Jordan, and identify which of these features (vegetation cover, built-up and population density) has the most effective and influence on LST values. Therefore, this paper is the first study about land surface temperature LST and its relations with these aspects in Jordan. This study used a mixed method approach using quantitative method (GIS) and qualitative method (comparative case studies). This study revealed that the most important features affecting the LST values were: (1) Population density; (2) Built-up; and (3) Vegetation, and in descending order from the strongest to the least effective. It is also concluded that in the city in which the population density is high, the effect of built-up areas on the values of LST is as high as possible and positive more than the cities with medium and low population density. As for the city in which the population density is medium to low, the effect of vegetation cover on the values of LST is greater, and this can be more positive than in cities with a high population density. Therefore, the study contributes to improving the planners' and policymakers’ suitable future decisions for making sustainable cities.

Spatio-temporal changes in the Rishikesh agglomeration, Uttarakhand, India

Rishikesh is one of the fastest growing urban agglomerations in Uttarakhand state of India. Being a religious and historical city, it attracts millions of tourists across the globe, hence a huge pressure is created over the limited land space. To tackle the pressure of tourists and satisfying the increasing demand of the space resulted into the expansion of urban area of Rishikesh. The study deals with the spatio-temporal changes occurred in the city of Rishikesh. The urban land use and land cover changes are analysed with the help of geographic information system. The analysis of satellite data (Landsat series MSS, TM, ETM + and OLI science 1972-2020) reflects that the settlement growth was quite unplanned. The haphazard growth of city resulted into the reduction of agriculture land. Various hotspots of settlement growth were noticed in study region. The town has limitation to growth because the town is enclosed by forests and holy river Ganga. There is urgent need to check city’s carrying capacity, high concentration of settlements and to maintain the concept of sustainable development.

Habitat Use, Terrestriality and Feeding Behaviour of Javan Slow Lorises in Urban Areas of a Multi-Use Landscape in Indonesia

Urban areas are rapidly expanding into natural habitats worldwide. When species are threatened with habitat loss, it is vital to understand how they will respond or adapt to the change in their environment. One primate species threatened by habitat loss is the fully arboreal Javan slow loris (Nycticebus javanicus). This non-leaping species not only relies on canopy continuity but is also subject to capture for illegal wildlife trade, especially in anthropogenic landscapes where they are easier to catch. We examine the use of urban areas by Javan slow lorises in terms of habitat use as well as feeding and terrestriality behaviours in the municipality of Cipaganti in West Java, Indonesia. For this study, we observed Javan slow lorises from May 2018 to April 2020 for two dry periods and two wet periods. We combined home ranges and core areas with a land cover classification of the area to understand the composition of the loris habitat. We also included feeding and terrestriality data to determine the ratio of these activities within each land cover class. We found that approximately half of their territory falls into natural areas (bamboo patches: 45–60%), and the other half is in human land use areas (agriculture, shade gardens, urban areas, and fallow land). Urban areas ranged from 0 to 54% of an individual loris’s habitat, with the proportion of urban land cover being higher in some individuals’ core areas than in their home ranges. Only urban areas showed a variation between periods, with p-values of 0.06 and 0.002 for home ranges and core areas, respectively, showing a significant increase usage during dry periods. Of all feeding observations, 4% occurred in urban areas with nectar being the most common feeding item. We recorded thirteen different food species in urban areas with Calliandra calothrysus being the most frequent. We found that 7% of terrestriality events occurred in urban areas. The findings from this study show that human land use areas cover a significant portion of the Javan slow lorises habitat in this region, further emphasising the need to consider the needs of these Critically Endangered primates when developing natural habitats into those of human managed landscapes. The same principles are true for the large number of species that are able to persist in urban areas in a world increasingly dominated by humans.

Conservation of skinks in New Zealand cities

Urbanisation dramatically alters ecosystems, disrupting key processes and threatening species persistence. Despite growing knowledge of the impacts of cities on wildlife, particularly for birds and mammals, there remains a paucity of research on how urbanisation impacts other taxa, including reptiles. We examined how urbanisation affects skink communities across multiple New Zealand cities by undertaking pitfall trapping surveys in a range of habitats. Of the 20 endemic species thought to have been present historically in the cities, only four were captured during surveys, Oligosoma aeneum in Hamilton; O. polychroma, O. aeneum, and O. ornatum in Wellington; O. polychroma in Nelson; and O. aff. polychroma Clade 5 in Dunedin. Our results reveal highly variable patterns of occupancy, abundance, and complex associations with key threats and habitat modifications. Sites in Nelson and Wellington were much more likely to record skinks than Hamilton and Dunedin, and modelling showed abundance of O. polychroma was positively related to rat tracking rates when grass cover was low but showed a negative relationship when grass cover was high. O. polychroma body condition was negatively associated with the proportion of urban land cover (built up areas or transport infrastructure). The low number of captures of several species is concerning and long-term monitoring is needed to assess the trajectories of these vulnerable populations. Effective conservation management of urban wildlife requires knowledge of where populations exist and how urban processes affect persistence. For understudied urban-dwelling taxa such as reptiles, the lack of this information may be putting populations at risk.

Impacts of urban land use and land cover change on wetland dynamics in Jimma city, southwestern Ethiopia

Abstract Wetland has been substantially decreased by anthropogenic activities worldwide, requiring urgent conservation action. The present study aimed to analyze the effects of land use land cover change on wetland dynamics from 1992 to 2022 in Jimma City, Southwestern Ethiopia. Landsat TM of 1992, ETM+ of 2000, ETM+ of 2009 and OLI/TIRS of 2022 were used in this study. Landsat images were classified by using supervised classification with maximum likelihood algorithm. The results revealed that grassland, wetland and vegetation land cover classes declined by 2,304.9 ha, 1410.2 ha and 820.5 ha, respectively whereas farm land, settlement and bare land increased by 2,199 ha, 2,111 ha and 225.4 ha, respectively. Results show that the normalized difference water index (NDWI) has positive and negative strong relationship with normailized difference vegetation index (NDVI) and normalized difference built up index (NDBI) with the coefficent determination (R2) of 0.96 and 0.98, respectively. Due to rapid urbanization and declining vegetation cover in the study area, about 98% of wetland land cover in the study area has been lost over the past decades. Therefore, the governmental and non-governmental organizations should give special attention to wetland restoration and management in the study area.

Blue-Green Infrastructure for Jakarta’s Sustainable Planning and Resilience

The concept of a sustainable city that has been described in the Sustainable Development Goals (SDGs) requires all cities in the world to implement it to solve climate problems that if not handled are increasing every year. The impact of climate change, which is a global problem, can affect lives, such as hydrometeorological disasters especially floods. Flood problems can be overcome with the Blue Green Infrastructure (BGI) development approach, one of which is green open space with a retention pond as an effort to achieve the SDGs in the Sustainable City component. Based on this statement, this study will identify in determining the location of BGI development in this case green open space with retention ponds to support the concept of sustainable planning and resilience in one of the metropolitan cities in Indonesia, Jakarta, which is the most populous city that has high rainfall and has minimal open space. The construction of the BGI is carried out as an effort to reduce the impact of climate change in the form of flooding by structural mitigation in the conservation of water balance in urban development, improving water quality, and reducing rainwater runoff and peak flows. This research was carried out in stages in the form of identification of urban land cover changes with image crawling as justification for the massive addition of built-up land, identification of vegetation density and temperature with Land Surface Temperature (LST) and flood hazard, with identification of retention pond needs. Eventually, recommendations for BGI development locations were given based on changes in urban land, temperature, vegetation, and flood hazard as well as the fulfilment of supporting infrastructure needs.