Expansion Of Area Research Articles

Conservation Imperatives: securing the last unprotected terrestrial sites harboring irreplaceable biodiversity

Ambitious biodiversity goals to protect 30% or more of the Earth’s surface by 2030 (30x30) require strategic near-term targets. To define areas that must be protected to prevent the most likely and imminent extinctions, we propose Conservation Imperatives—16,825 unprotected sites spanning ~164 Mha of the terrestrial realm that harbor rare and threatened species. We estimate that protecting the Conservation Imperatives would cost approximately US$169 billion (90% probability: US$146—US$228 billion). Globally, 38% of the 16,825 sites are either adjacent to or within 2.5 km of an existing protected area, potentially reducing land acquisition and management costs. These sites should be prioritized for conservation action over the next 5 years as part of a broader strategy to expand the global protected area network. The expansion of global protected areas between 2018 and 2023 incorporated only 7% of sites harboring range-limited and threatened species, highlighting a renewed urgency to conserve these habitats. Permanently protecting only 0.74% of land found in the tropics, where Conservation Imperatives are concentrated, could prevent the majority of predicted near-term extinctions once adequately resourced. We estimate this cost to be from US$29 billion to US$46 billion per year over the next 5 years. Multiple approaches will be required to meet long-term protection goals: providing rights and titles to Indigenous Peoples and Local Communities (IPLCs) conserving traditional lands, government designation of new protected areas on federal and state lands, and land purchase or long-term leasing of privately held lands.

Change Detection Using Multispectral Satellite Images for Sustainable Development in Regions of Al-Najaf Al-Ashraf, Iraq

The change detection in urban and surrounding areas, such as vegetation and bare, is a significant achievement for sustainable development, especially nowadays. The research conducted a supervisor classification of two images representing two time periods (1973 and 2023) using the method of land cover and land use (LCLU) using the ArcGIS 10.8.2 program. The classified pixels were converted into digital data to compare using the land cover method and land use change (LCLUG). The results showed a significant expansion in the urban areas of Najaf and Kufa districts, 5.72 km2 to 134.95 km2, with a decrease in the area of green areas, 17.26 km2 to 15.16 km2, and the absence of water areas, in addition to the transformation of most of the barren areas to urban areas 215.64 km2 to 88.51 km2, The study concluded that there was a significant decrease in the area of green areas in the study area and the absence of water bodies in urban areas, and these two indicators - along with the increase in urban areas - harm the environment of the region and the quality of environmental influences on the lives of the people who live in it. Which negatively affects its sustainable development plans.

Commercial processing of Oriental lilies affects bud opening and metabolic dynamics

Lilies are a high value cut flower typically producing 4–5 flowers per stem, but the opening of young buds of Oriental hybrid lilies is often affected in cut flowers. Commercial treatment includes harvesting of the stem when the oldest bud is closed and at turning colour, approximately 2 ds before it would open on the plant. Stems are then rehydrated, stored chilled for up to 72 h and transported dry. To understand the effect of commercial treatment onu the nutrient status metabolomes were compared throughout bud opening from different positions on the stem. At each developmental stage the metabolomic profile was affected by bud position and commercial treatment. Starch accumulated as long as buds remain closed; upon bud opening starch content declined. Reciprocally, sugar levels rose during flower opening and were affected by edge/ midrib location and commercial treatment. Glucose, fructose and sucrose levels remained higher in opened flowers still on the plant. AMY2 (amylase) transcript levels rose as did those of two sugar transporters (MST6 and SWEET7). Commercial processing therefore impacts on the metabolome and the ability to accumulate sugars in the opening flower bud. Commercial treatment delayed bud opening and the effect was dependent on the position of the bud on the stem. However, it had little impact on the rate of cell expansion during flower opening. Cell expansion in the different areas of the adaxial epidermis was unaffected by the commercial treatment. Furthermore, edge and adaxial tepal cells expanded faster during opening. Expression of cell expansion related genes (EXPA1 and LoPIP1) fell as flowers opened. This differential cell expansion in the tepal sectors could underpin the transition of a convex to a concave tepal shape during opening. In conclusion, commercial processing mainly affects the early stages of bud opening. Sugar and metabolite accumulation is compromised by commercial treatment, but this did not affect the capacity for cell expansion in the tepal. Furthermore, our data indicate that differential cell expansion in the different sectors of the tepals is important in lily flower opening, and that this is associated with starch breakdown and sugar accumulation.

Distribution of the black grouse, capercaillie, and hazel grouse (Tetraonidae) at the northern edge of their species range in the Nenets Autonomous Area

This paper presents data on the distribution of the black grouse Lyrurus tetrix (L., 1758), capercaillie Tetrao urogallus (L., 1758), and hazel grouse Tetrastes bonasia (L., 1758) in the Nenets Autonomous Area (the northeast of the European Russian Federation). The study has revealed trends indicating an expansion of the distribution range of these species to the north. According to our observations, black grouse, capercaillie, and hazel grouse in the Malozemelskaya Tundra are located further north than in the published data – about 40 km for black grouse, 20 km for capercaillie, and 20 km for hazel grouse. In the Malozemelskaya Tundra, the northernmost black grouse and capercaillie were found in spruce forest islands, while the hazel grouse inhabited riverine floodplain forests. In the Bolshezemelskaya Tundra, the capercaillie was the only grouse bird found further north compared to the published data, in the extremely northern islands of the forest within the Shapkina River valley. In the Bolshezemelskaya Tundra, new registrations of the grouse (200 km) and ruffed grouse (100 km) were noted to the east of the known data, indirectly indicating an expansion of the distribution area of these birds to the north in the regions adjacent to the Nenets Autonomous Area. It was noted that the trends of expansion of the range of distribution of black grouse to the north are unidirectional with positive trends in air temperature in the Nenets Autonomous Area and with the processes of gradual overgrowth of woody vegetation in treeless areas located between the forest tundra and northern spruce forest islands.

How does virtual water influence the water stress pattern in Africa? A research perspective from the perspectives of production and trade

Northern Africa has become the first region in the world to exhaust its water resources, with a 40 % decrease in per capita water availability south of the Sahara over the past decade. While adjusting production structures and consumption can regulate the supply-demand dynamics of water resources, the extent of the impact of virtual water-induced pressure on both the regional and national levels in Africa remains largely understudied. Applying the standard Penman formula, this research calculates the water footprint of eight cereal crops in 54 African countries from 1990 to 2021. By integrating corresponding data on cereal trade, the study analyzes changes in virtual water stress. The findings indicate a decline in the per-unit production and consumption water footprints for African cereals. However, the continuous expansion of cultivation areas contributes to a rising water stress. In comparison to 1990, the water stress for soybeans, sorghum, rice, maize, and cassava increased by 149.72 %, 146.88 %, 133.89 %, 123.30 %, and 90.8 %, respectively, in 2021. Only barley showed a reduction in water stress by 23.22 %. The study underscores the growing interconnectedness of virtual water trade (VWT) among African nations from 1990 to 2021, leading to a more balanced trade distribution. VWT has reduced water stress by 7.65 %, 2.08 %, and 1.8 % in Western, Central, and Northern Africa, respectively, while increasing pressure in Southern and Eastern Africa by 10.51 % and 1.01 %. The flow of virtual water in Africa is most influenced by spatial proximity, primarily occurring between adjacent countries or regions. Forecasts for water stress under the five scenarios of SSPs-RCP8.5 have been conducted, revealing a continuous increase in water stress across Africa. Furthermore, analysis of the SSP2-RCP8.5 scenario indicates that by 2030 and 2040, African cereal crops are projected to face virtual water resource stress increases of 7 % and 18.76 %, respectively, compared to 2020 levels. During the same period, Sierra Leone is anticipated to experience a growth rate in virtual water stress of approximately 1903.38 %. Consequently, altering crop cultivation structures and enhancing VWT are poised to alleviate water resource pressure, promoting the scientific management of agricultural water resources in Africa.

Urban Geomorphology Methods and Applications as a Guideline for Understanding the City Environment

Cities all over the world have developed on different geological-geomorphological substrates. Different kinds of human activities have operated for millennia as geomorphic agents, generating numerous and various erosion landforms and huge anthropogenic deposits. Considering the increasing demand for land and the expansion of the built-up areas involving and disturbing any kind of natural system inside and surrounding the actual urban areas, it is not negligible how important the dynamics of the urban environment and its physical evolution are. In this context, this manuscript addresses insights into eight case studies of urban geomorphological analyses of cities in Italy, Greece, and Brazil. The studies are based on surveying and mapping geomorphological processes and landforms in urban areas, supporting both geo-hazard assessment, historical evolution, and paleomorphologies, as well as disseminating knowledge of urban geoheritage and educating about the anthropogenic impact on urban sustainability. We hypothesize that urban geomorphological analysis of several case studies addresses the physical environment of modern cities in a multi-temporal, multidisciplinary, and critical way concerning global changes. Thus, this study aims to illustrate and propose a novel approach to urban geomorphological investigation as a model for the understanding and planning of the physical urban environment on a European and global scale.

A regional examination of the footprint of agriculture and urban cover on stream water quality

Freshwater systems in cold regions, including the Laurentian Great Lakes, are threatened by both eutrophication and salinization, due to excess nitrogen (N), phosphorus (P) and chloride (Cl−) delivered in agricultural and urban runoff. However, identifying the relative contribution of urban vs. agricultural development to water quality impairment is challenging in watersheds with mixed land cover, which typify most developed regions. In this study, a self-organizing map (SOM) analysis was used to evaluate the contributions of various forms of land cover to water quality impairment in southern Ontario, a population-dense, yet highly agricultural region in the Laurentian Great Lakes basin where urban expansion and agricultural intensification have been associated with continued water quality impairment. Watersheds were classified into eight spatial clusters, representing four categories of agriculture, one urban, one natural, and two mixed land use clusters. All four agricultural clusters had high nitrate-N concentrations, but levels were especially high in watersheds with extensive corn and soybean cultivation, where exceedances of the 3 mg L−1 water quality objective dramatically increased above a threshold of ‍∼30 % watershed row crop cover. Maximum P concentrations also occurred in the most heavily tile-drained cash crop watersheds, but associations between P and land use were not as clear as for N. The most urbanized watersheds had the highest Cl− concentrations and expansions in urban area were mostly at the expense of surrounding agricultural land cover, which may drive intensification of remaining agricultural lands. Expansions in tile-drained corn and soybean area, often at the expense of mixed, lower intensity agriculture are not unique to this area and suggest that river nitrate-N levels will continue to increase in the future. The SOM approach provides a powerful means of simplifying heterogeneous land cover characteristics that can be associated with water quality patterns and identify problem areas to target management.

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia.

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Evaluating coastal lagoon sustainability through the driver-pressure-state-impact-response approach: a study of Khenifiss Lagoon, southern Morocco

Coastal lagoons are valuable ecosystems, providing socioeconomic benefits and supporting human populations and biodiversity. However, these systems face several challenges, making them vulnerable to both natural and human factors. In this study, we apply the Driver-Pressure-State-Impact-Response (DPSIR) Approach to conduct a comprehensive socioeconomic and environmental assessment of the Khenifiss Lagoon to promote sustainable development and support decision-makers. Located on the southern Atlantic coast of Morocco, the lagoon was designated a natural reserve in 1962, a biological reserve in 1983, and a protected wetland under the Ramsar Convention since 1980. This study represents the initial endeavor to conduct a comprehensive global and multidisciplinary environmental assessment of the lagoon by using a wide range of data sources, including relevant publications and reports, satellite images and remote sensing data, field observations, and interviews, all analyzed under the DPSIR framework. Our findings show that both natural and human factors have an impact on the ecosystem. Natural Factors associated with the geomorphological features of the region likely contribute to the silting of the lagoon, possibly intensified by a large shipwreck stuck at its inlet. Meanwhile, human factors encompass population growth (at a rate of 2% per year), tourism, shellfish farming, fishing, shellfish harvesting, and salt extraction. Our results reveal significant changes in the lagoon’s condition in recent years, including a reduction in water body extent, a probable decrease in depth, and an increase in the accumulation of solid waste, plastics, and wastewater in three sectors spanning a total surface area of 464 ha (equivalent to 7% of the lagoon), a substantial expansion of the salt mining area encompassing 368 ha, and a remarkable loss of biodiversity, manifested in declining fish stocks and seabird populations. This study showed that the lagoon is positioned as a potential site for economic growth and serves to alert stakeholders and the local population to the ecosystem’s environmental issues. Based on the findings of this study, we highly recommend regulating human activities within the lagoon, the removal of the wreck at the entrance, proper waste management, community awareness programs, and strict monitoring and enforcement of regulations to protect the environment.

The synergy between protected area effectiveness and economic growth

Protected areas conserve biodiversity and ecosystem functions but might impede local economic growth. Understanding the global patterns and predictors of different relationships between protected area effectiveness and neighboring community economic growth can inform better implementation of the Kunming-Montreal Global Biodiversity Framework. We assessed 10,143 protected areas globally with matched samples to address the non-random location of protected areas. Our results show that protected areas resist human-induced land cover changes and do not limit nightlight increases in neighboring settlements. This result is robust, using different matching techniques, parameter settings, and selection of covariates. We identify four types of relationships between land cover changes and nightlight changes for each protected area: "synergy," "retreat," and two tradeoff relationships. About half of the protected areas (47.5%) retain their natural land cover and do so despite an increase of nightlights in the neighboring communities. This synergy relationship is the most common globally but varies between biomes and continents. Synergy is less frequent in the Amazon, Southeast Asia, and some developing areas, where most biodiversity resides and which suffer more from poverty. Smaller protected areas and those with better access to cities, moderate road density, and better baseline economic conditions have a higher probability of reaching synergy. Our results are promising, as the expansion of protected areas and increased species protection will rely more on conserving the human-modified landscape with smaller protected areas. Future interventions should address local development and biodiversity conservation together to achieve more co-benefits.

Spatial and Temporal Changes and Influencing Factors of Capital Cities in Five Provinces of the Underdeveloped Regions of Northwest China

In recent years, China’s economy has experienced rapid development, and its cities have undergone rapid expansion; however, the development of cities in the northwest region has been relatively slow due to various geographical and economic constraints. Studying the urban expansion in these regions is of significant importance for regional planning and development. This study selected the provincial capitals of five underdeveloped provinces in northwestern China as the research sample and used Landsat TM/OLI remote-sensing imagery as the primary data, supplemented by Digital Elevation Model (DEM), meteorological, and socio-economic data, the study extracted urban impervious surfaces using the ENDISI and MNDWI indices. It analyzed the spatial and temporal characteristics of urban impervious surfaces from 1990 to 2020 using indicators such as urban expansion intensity, compactness and fractal dimension, centroid migration, and standard deviation ellipse. Furthermore, the study quantified the influencing factors using Geodetectors. The findings reveal the following: (1) From 1990 to 2020, impervious surfaces in the five cities continued to expand, with Xi’an experiencing the largest expansion area at 549.94 km2 and Xining the smallest at only 132.83 km2, with an expansion intensity of merely 2.99%. However, significant disparities existed in expansion intensity and area across different periods. (2) Overall, the compactness of the cities decreased by 47.6% while the overall fractal dimension increased by 2.85%, indicating a trend towards more dispersed and complex urban forms. (3) Expansion directions varied among the cities, with Xi’an and Urumqi expanding towards the northwest, Lanzhou towards the north, Yinchuan primarily towards the east, and Xining mainly towards the west. (4) Economic, demographic, and investment factors were identified as the primary influencers of urban expansion, exhibiting changes over different periods. Analyzing the similarities and differences in city development can offer valuable insights into urban construction and sustainable development in underdeveloped areas.

Climate and urbanization drive changes in the habitat suitability of Schistosoma mansoni competent snails in Brazil

Schistosomiasis is a neglected tropical disease caused by Schistosoma parasites. Schistosoma are obligate parasites of freshwater Biomphalaria and Bulinus snails, thus controlling snail populations is critical to reducing transmission risk. As snails are sensitive to environmental conditions, we expect their distribution is significantly impacted by global change. Here, we used machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of forward-transmitting Biomphalaria hosts throughout Brazil. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability. Elucidating change in Biomphalaria distribution—while accounting for non-linearities that are difficult to detect from local case studies—can help inform schistosomiasis control strategies.

Monitoring LULC Dynamics and Landscape Pattern Analysis Using Landsat at Mongla and Surrounding Cities in Bangladesh

This study presents a thorough analysis of the temporal dynamics in land use and land cover (LULC) within the Mongla Port and its adjacent regions in Bangladesh, spanning from 2018 to 2022 and utilizing Landsat 8 satellite imagery. A seven-category classification system, including water bodies, trees, flooded vegetation, crops, built-up areas, bare ground, and rangeland, facilitated a detailed exploration of changing landscape patterns. Over the study period, a 1.87% reduction in tree cover was observed, contrasting with consistent expansion in built-up areas, which increased by 2.42%. Noteworthy fluctuations in agricultural lands revealed a net decrease of 165.95 km², while water bodies experienced fluctuations, ultimately recording a net increase of 142.29 km². The robust accuracy assessment, with overall accuracy ranging from 86.7% to 88.8%, underscores the reliability of the classification process. This research holds significant implications, offering valuable insights into sustainable development, conservation strategies, and disaster preparedness in a region undergoing transformative changes due to port establishment, especially considering the inclusion of the ecologically critical Sundarbans in the study area.

Predicting the Potential Distribution of Hypericum perforatum under Climate Change Scenarios Using a Maximum Entropy Model.

H. perforatum, as one of the Traditional Chinese Medicinal materials, possesses a variety of pharmacological activities and high medicinal value. However, in recent years, the wild resources of H. perforatum have been severely depleted due to global climate change and human activities, and artificial cultivation faces problems such as unstable yield and active ingredient content. This poses a serious obstacle to the development and utilization of its resources. Therefore, this experiment took H. perforatum as the research object and used 894 distribution records of H. perforatum and 36 climatic environmental factors, using the MaxEnt model and GIS technology to explore the main climatic factors affecting the distribution of H. perforatum. Additionally, by utilizing the principles of ecological niche theory, the potential suitable distribution regions of H. perforatum across past, present, and future timelines were predicted, which can ascertain the dynamics of its spatial distribution patterns and the trend of centroid migration. The results indicate that the main environmental factors affecting the geographical distribution of H. perforatum are solar radiation in April (Srad4), solar radiation in September (Srad9), mean temperature of driest quarter (Bio9), solar radiation in November (Srad11), annual mean temperature (Bio1), and annual precipitation (Bio12). Under future climate scenarios, there is a remarkable trend of expansion in the suitable distribution areas of H. perforatum. The centroid migration indicates a trend of migration towards the northwest direction and high-altitude areas. These results can provide a scientific basis for formulating conservation and sustainable use management strategies for H. perforatum resources.

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

The rapid and disordered expansion of artificial marine aquaculture areas has caused severe ecological and environmental problems. Accurate monitoring of offshore aquaculture areas is urgent and significant in order to support the scientific and sustainable management and protection of coastal marine resources. Artificial intelligence provides a valuable tool to improve marine resource monitoring. Deep learning methods have been widely used for marine object detection, but You Only Look Once (YOLO) models have not been employed for offshore aquaculture area monitoring. This study therefore evaluated the capacity of two well-known YOLO models, YOLOv5 and YOLOv7, to detect offshore aquaculture areas based on different high-resolution optical remote sensing imagery. Compared with YOLOv7 based on a satellite dataset, YOLOv5 increased the Precision value by approximately 3.29% (to 95.33%), Recall value by 3.02% (to 93.02%), mAP_0.5 by 2.03% (to 96.22%), and F1 score by 2.65% (to 94.16%). Based on the Google Earth dataset, YOLOv5 and YOLOv7 showed similar results. We found that the spatial resolution could affect the deep learning models’ performances. We used the Real-ESRGAN method to enhance the spatial resolution of satellite dataset and investigated whether super-resolution (SR) methods improved the detection accuracy of the YOLO models. The results indicated that despite improving the image clarity and resolution, the SR methods negatively affected the performance of the YOLO models for offshore aquaculture object detection. This suggests that attention should be paid to the use of SR methods before the application of deep learning models for object detection using remote sensing imagery.

Climate-informed flood risk mapping using a GAN-based approach (ExGAN)

This study develops a class of robust models for flood risk mapping in highly vulnerable regions by focusing on accurately depicting extreme precipitation patterns aligned with regional climates. By implementing sophisticated hydrodynamics modeling and advanced probabilistic approaches, the present work underscores the efficacy of physical-based methodologies in the flood risk assessment. We propose a machine learning based ExGAN to address the challenge of synthesizing extreme precipitation scenarios which faithfully capture the nuances of local climatology. It is expected that through refined temporal disaggregation, the ExGAN approach exhibits exceptional proficiency in replicating a diverse spectrum of extreme precipitation patterns specific to the vulnerable region under scrutiny. Therefore, using these synthesized scenarios as inputs in a meticulously calibrated hydrological model would enable a comprehensive and detailed flood risk mapping exercise. To demonstrate the robustness of the developed mode, we perform a rigorous testing and validation within the highly susceptible Martil river basin, situated in the northern Mediterranean region of Morocco. The obtained results confirm that extending return periods would provide invaluable insights into the expanding geographical expanse of at-risk areas, clarifying the evolving landscape of vulnerability rather than merely amplifying inherent risk levels. Comparisons against the conventional Monte-Carlo sampling are also carried out in this study and the obtained results highlight significant overestimations within the latter, emphasizing the imperative need to account for diverse uncertainties beyond the basic sampling strategies within the realm of hydrodynamic modeling.

Analysis of Urban Thermal Environment Improved by Blue-Green Spaces on the Landsat Data: A Case Study on Tianjin

China’s urbanization is characterized by rapid speed, large scale and high energy consumption, which causes a series of ecological problems. Among these problems, urban thermal environment is particularly prominent, and the most intuitive reflection is the heat island effect. It is of great significance to optimize the urban layout to give full play to the cooling effect of blue-green spaces for improving urban thermal environment. Compared with other methods, it is more extensive and feasible. However, the current research on urban thermal environment mostly focuses on revealing the driving effects of different land use, and lacks of in-depth discussions on blue-green spaces. Tianjin is located in the northeast of the North China Plain, and has a typical warm temperate sub-humid monsoon climate. This paper takes the six districts of Tianjin City as the research area, covering Heping District, Nankai District, Hongqiao District, Hebei District, Hexi District and Hedong District, with a total area of about 181.18 square kilometers. The typical climate and urbanization characteristics of Tianjin can provide reference for cities with the same characteristics. Firstly, this paper had adopted the Landsat changing scenario of the city gained during the periods of 2004, 2011 and 2017 to formulate the land surface temperature by Mono-window Algorithm, and divided the results into seven temperature zones by standard deviation classification method. In regard to blue-green spaces, this paper had identified Landsat satellite data by Maximum Likelihood Classifier, and classified land use into three categories: blue spaces, green spaces, and non-blue and green spaces. And, next, this paper had studied the correlation between the landscape pattern of blue-green spaces and urban thermal environment by Landscape Pattern Analysis, and used Moving-window Analysis to compare the relationship between thermal environment and blue-green spaces at different spatial scales. And so the research results prove that, the high-temperature regions of Tianjin has been expanding along with the expansion and agglomeration of the urban areas and across the river space restrictions, while the low-temperature regions of the city are mainly concentrated in the blue-green spaces, during the periods of 2004, 2011 and 2017. Furthermore, in the landscape pattern level, there is a significant negative correlation between patch perimeter index of blue-green spaces and land surface temperature, and a significant positive correlation between patch perimeter area ratio index of blue-green spaces and land surface temperature. In the multi-scale spatial pattern, the correlation between the Percent of Landscape of blue spaces patches and land surface temperature is the highest in the 1500 meters × 1500 meters sample, and the Percent of Landscape of green spaces patches and land surface temperature is the highest in the 300 meters × 300 meters sample. In addition, the improvement efficiency of thermal environment in blue spaces is higher. In the end, the optimization suggestions of adjusting the layout of blue-green spaces to effectively improve the urban thermal environment problem are proposed. By optimizing the blue-green space landscape patterns at the optimal scale, increasing the blue spaces in the northern high temperature range, widening and transforming the existing waterways, constructing riverside greenways and other strategies to effectively deal with the thermal environmental problems, improving the ecology and livability of the city. It is hoped to provide new ideas and references for the construction of ecological and livable city under the background of rapid urbanization. In the research process, due to the limitation of data sources, calculation methods and other factors, the relevant conclusion is still limited at the spatial scale. In the future research, the thermal environment and the pattern of blue-green spatial landscape at the fine scale will be studied.

Exploring land use dynamics in rural areas. An analysis of eight cases in the Global North

Rural areas are pivotal in the global economy, hence rural development planners and decision makers face significant technical and information management challenges, hindering their ability to assess sustainable development and formulate strategies. These challenges, coupled with shifts in traditional rural activities, aging populations, and climate change impacts, create inequalities and threaten rural communities. Therefore, developing approaches for the assessment of changes in rural areas are required. In recent years, remote sensing has emerged as a useful tool for monitoring the complex dynamics of rural regions, with a notable increase in the accessibility and use of freely available satellite data and platforms for tracking changes in land use and land cover (LULC). In light of this trend, this article analyzes the spatio-temporal trends in eight different development regions in Japan, Germany, Poland and Spain and integrates them with population data. This research relied on the utilization of openly available datasets - satellite imagery from Landsat and WorldPop population data, processed in Google Earth Engine (GEE). The time series of Landsat imagery were employed to analyze changes in LULC over a 20-year period from 2000 to 2020 in each test site. Unique combinations (i.e. each pixel history) were systematically examined and categorized based on established rules, with improbable scenarios marked as "uncertain." The comparison demonstrates that all eight regions have unique spatial, temporal and thematic characteristics. The most significant changes observed include the transformation of tree-covered areas into built-up areas in the case of Tsukuba and into agricultural lands in Shibushi. In the Bodzentyn and Połaniec communes in Poland, agricultural lands have transitioned into forested areas. In Spain, there is an increase in built-up areas, particularly at the outskirts of established towns. In Germany, the results indicate an expansion of built-up and agricultural areas in the Fürth region, while the Regen region experienced an increase in agricultural land. Incorporating population data reveals relationships between different types of LULC conversions and population trends, showing clear positive trends in areas transformed from agricultural and tree-covered land to built-up areas, with the most profound changes observed in Spanish cases and Tsukuba. The findings from automatic LULC classification highlight the usefulness of this tool in identifying socio-economic changes, especially when it is integrated with and compared to statistical data. Policy makers and planners at local levels should consider these specific values as a foundational reference point for shaping their plans related to sustainable development, land use, environmental preservation, and resource management.

Urbanization signature on hourly rainfall extremes of Kuala Lumpur

This study uses high-resolution satellite data to investigate the impact of urbanization on the local climate of Kuala Lumpur, Malaysia. Nonparametric statistical methods, including trend test, spatial correlation, and quantile regression, were employed to quantify interrelationships between satellite nighttime light (NTL), Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and Global Satellite Mapping of Precipitation (GSMaP) extreme rainfall events. The results revealed that urbanization increased Kuala Lumpur's LST by 3.8 to 4.1 °C and the 99th percentile rainfall by 10 to 43 % compared to surrounding areas. The correlation analysis showed an association (r = 0.62) between the trends in LST and rainfall extremes, with a significance level of p < 0.05. The NTL trend analysis showed a rapid expansion of the city, particularly in the west, and thus, an expansion of high LST areas and extreme rainfall amounts. Quantile regression results confirmed the influence of increased LST on rainfall extremes. The trends for hourly rainfall revealed increases mainly between 2pm and 9pm in the city, which is attributed to the intensification of convective rainfall caused by increased LST. The study highlights the importance of mitigating urban LST to reduce climatic extremes and promote sustainable urban development.

Urban expansion and vegetation dynamics: The role of protected areas in preventing vegetation loss in a growing mega city

The decrease in vegetation cover due to urban expansion poses serious challenge to urban sustainability. Protected areas (PAs) are the most effective tools to prevent the loss of urban vegetation cover and to control urban expansion. Hence, this study aims to assess the importance of PAs in protecting urban vegetation and the urban expansion in the mega city of Delhi. For this, Landsat datasets were used for land use and land cover (LULC) mapping and then land cover change rate (LCCR) and land cover intensity (LCI) were calculated. For assessing urban expansion dynamics, mean landscape expansion index (MLEI) and the area-weighted LEI (AWLEI) were calculated. To evaluate the significance of PAs in protecting vegetation cover, kernel density estimation (KDE) was applied to assess the spatial variation and concentration of vegetation cover under different PAs. The result shows that urban expansion in Delhi was initially characterized by edge expansion during 1991–2001, followed by outlying expansion of built-up area during 2001–2021, while infilling of open and vegetated areas by built-up area was consistent during 1991–2021. Vegetation cover on the other hand, has followed a fluctuating trend in the city, but has overall it has declined from 13.36% to 9.30% during 1991–2021. The vegetation cover has declined significantly in eastern, northern, and western parts of Delhi but has increased significantly in central and southern parts, especially during 2001–21. This is because the central and southern parts of Delhi are well planned and have several PAs while the western, northern, and eastern parts of Delhi are unplanned regions and have only a few PAs. The KDE chart shows that the PAs have played an important role in protecting the vegetation cover in Delhi with R2 value > 0.70. Hence, this study suggests to give special emphasis on preservation and expansion of PAs in urban planning for the long-term conservation of urban vegetation cover and sustainable urban development.