Future scenarios impact on land use change and habitat quality in Lithuania

A protocol to model future land use scenarios using Dinamica-EGO

Land use changes are important drivers of ecosystem change. They depend on ecological, social, economic and political aspects. This work aims to develop a detailed protocol to forecast land use changes using Dinamica-Ego software. It includes the 1) time frame definition, 2) future scenarios definition, 3) identify the major driving forces of land use change, 4) collection and organize data for the modelling process, 5) calculation of landscape metrics for the base year, and 6) Dinamica-Ego modelling. Here, several sub-steps are described that involve calculating the transition matrix, preparing the raster cube, calculating the Weights of Evidence (WoE), assessing multicollinearity, revising the raster cube, validating the land use change model, adjusting the transition matrix and WoE and running the future land use simulation. The protocol explains how to simulate land use changes to 2050, showing scenarios 1) business as usual and 2) urbanization in Kaunas (Lithuania).•The protocol details a step-by-step approach to model land use change using Dinamica-Ego;•This protocol can be replicated in forecasting land use in any urban area;•The results obtained using this protocol were well-validated. Therefore, the reliability is high.

Coastal zones vulnerability evaluation in the southern Baltic Sea: Shoreline dynamics and land use/land cover changes over five decades

Over the past century, coastal zones have experienced significant population growth and rapid development, often conflicting with these environments' dynamic and sensitive nature. The present study investigated five decades (1972-2023) of shoreline dynamics and land-use/land-cover (LULC) transformations along three study sectors located on a 47 km stretch of the Southern Baltic coastline. The research employed eleven multispectral Landsat MSS/TM/OLI images within a geographic information system (GIS) framework to analyze coastline variations and LULC patterns. Results showed significant accretion in Sector I (Usedom), while Sectors II and III (Wolin) experienced marked erosion. Over the entire study period, 29.59 % (3.21 km), 39.90 % (4.51 km), and 67.54 % (9.45 km) of the shorelines in Sector-I, Sector-II, and Sector-III experienced erosion. The distance correlation showed that hydrometeorological variables associated with wind-wave dynamics, exerted a stronger influence on shoreline changes. The LULC change analysis highlighted a decline in forest cover (-846.86 ha) and increased built-up areas (+1137.86) across all sectors. These results enabled the identification of four coastal vulnerability zones-one in Usedom and three in Wolin-characterized by pronounced erosion, forest degradation, and urban expansion. These findings can inform coastal management strategies by identifying high-risk zones, guiding sustainable development practices, and prioritizing areas for conservation and intervention.

Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project

This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel-2 imagery, we observed an increase in tree cover from 25.02% in 2015 to 29.99% in 2023 and a decrease in barren land from 20.64% to 16.81%, with an accuracy above 85%. Hotspot and spatial clustering analyses revealed significant vegetation recovery, with high-confidence hotspots rising from 36.76% to 42.56%. A predictive model for the Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture and precipitation as primary drivers of vegetation growth, with the ANN model achieving an R 2 of 0.8556 and an RMSE of 0.0607 on the testing dataset. These results demonstrate the effectiveness of integrating machine learning with remote sensing as a framework to support data-driven afforestation efforts and inform sustainable environmental management practices.

Assessing carbon stock change for effective Nature-based Solutions implementation allocation: A framework

Mapping and assessing the carbon stock change (CSC) in urban areas can support the allocation of Nature-based Solutions (NbS) to mitigate climate change and advance urban sustainability. However, an effective framework concerning historical CSC and future simulation to support the allocation of NbS implementation is lacking. To fill this gap, we proposed a framework and applied it in the Zhejiang coastal region based on the assessment of historical (from 1990 to 2020) and predicted future (2030) CSC and local context analysis of urban and ecosystem challenges. Over the past three decades, the Zhejiang coastal region has experienced a considerable C stock loss of 20.34 Tg, predominantly owing to fast urbanization. The severest C stock reduction occurred from 2000 to 2010, with a slowdown in the following decade. Even so, more effective spatial management policies are urgent to mitigate further C stock depletion. Our framework identified 50.51% of the study area as the allocation area for NbS implementations where current and future C sequestration demand existed. Within the allocation area, six NbS types identified from literature were allocated or co-allocated, leading to eight tailored NbS implementations to tackle specific urban and ecosystem challenges of each location. The most widely allocated NbS implementations were "NbS1 × NbS2 × NbS3" and "NbS2 × NbS4", covering 42.86% and 34.69% of the allocation area. NbS2 covered nearly the entire allocation area (98.80%), with its primary role of habitat preservation and to control urban expansion. The proposed framework can be adapted to support various planning decisions regarding the prioritization and spatial allocation of NbS.

Growing mismatches of supply and demand of ecosystem services in the Netherlands

A sufficient supply of goods and services from ecosystems is vital to ensure human well-being. This study evaluates whether the demand for goods and services within the Netherlands is met by Dutch ecosystems, could otherwise be provided through technology and/or by imports, or remains (partially) unmet. Additionally, this study shows the dynamics of supply and demand of these services over time. The results reveal that no ecosystem service supply is fully meeting the total demand provided by Dutch ecosystems. Additionally, for the majority of the services (10 out of 17), the gap between supply and demand widened over the last two decades, indicating a growing mismatch. Imports and technology only partly close the gap between supply and demand. The growing mismatch between supply and demand is expected to lead to increasing negative impacts on human well-being, such as poor air and water quality, heat stress in urban areas, increasing flood risks, limiting opportunities for outdoor recreation and loss of biodiversity. Our findings show that current policy goals to maintain and restore ecosystem services are not on track in the Netherlands. Urgent action is necessary to enhance the sustainable utilization of natural resources and to optimize the balance between supply and demand. Priority should be given to goods and services facing unmet demand where imports or technological solutions are not feasible, particularly those where the gap between supply and demand is widening.

Mapping and assessing the future provision of lake ecosystem services in Lithuania

Lakes supply multiple ecosystem services (ES), key to supporting socio-ecologic systems and human well-being. In the context of future land use and climate changes, it is imperative to anticipate potential impacts on lake ES supply. Hence, studies that deal with future lake ES, such as mapping, are lacking. In this work, we mapped and assessed the future supply of three ES: (1) maintenance of nursery conditions (nursery ES), (2) maintenance of chemical conditions of freshwaters (nutrient regulation ES), and (3) direct and indirect cultural outputs (recreation ES) in Lithuania. Four future scenarios were utilised, integrating land use and climate changes: A0 - business as usual; A1-urbanisation; A2: land abandonment and afforestation; and A3 - agricultural intensification. The projected year was 2050, following the intermediate Representative Concentration Pathway (RCP 4.5). The future scenarios were simulated using the open-source software Dinamica EGO based on a 6-step modelling framework. Statistical differences among the scenarios and ES were analysed by applying a Kruskal-Wallis ANOVA test. Spatial analysis was done by performing a Moran's I and Getis-Ord (Gi∗) hotspot analysis. The results showed significant differences in nursery and nutrient regulation ES. The highest supply in nursery ES was observed for the A0 scenario. For nutrient regulation ES, the lowest ES supply was identified for the A1 scenario and for recreation ES, the highest was found in the A2 scenario. The eastern and northeastern regions of Lithuania showed a high ES supply. Hot spots were only identified in the eastern region. These regions are associated with a high area covered by forests and protected areas. The central region shows a low ES supply, identified as a cold spot where the agricultural landscape dominates. The results of the PCA analysis revealed an association between nursery and recreation ES. Nutrient regulation was not associated with the other two ES. Mapping and assessing the impact of future scenarios is vital to anticipating the potential dynamics of lake ES, especially in the context of climate change. This information is essential in the context of environmental management, helping decision-makers to ensure a sustainable ES supply and contributing to human wellbeing.

How has carbon storage changed in the Yili-Tianshan region over the past three decades and into the future? What has driven it to change?

Predicting future land use changes and assessing carbon storage remain challenging. Nowadays, how nature and socioeconomics drive changes in carbon storage is a hot topic in research. In this study, through the projection of land use type and the integration of the PLUS, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Geodetector models, we constructed a framework for assessing carbon storage in different land use scenarios. Utilizing this framework, it is possible to project land use change and estimate carbon storage based on different development scenarios. We applied the framework to the Yili Tianshan region and identified the main driving forces for carbon storage change. Further, we estimated the carbon storage in the Yili Tianshan region in 2035 under four scenarios (RE, NE, EP, and CLP). The results showed the following: 1) Between 1990 and 2020, there was an increase in the forest area and water bodies in the Yili-Tianshan region, mainly from bare land. 2) As shown on the time scale, carbon storage increases in the Yili-Tianshan region with a W-shaped fluctuation by converting grasslands and bare land into forests. On a spatial scale, the carbon storage was lower in the center and higher on both sides in the Yili-Tianshan region. 3) In 2035- RE, 2035-ND, and 2035-EP scenarios, the carbon storage was increased by 4.30 Tg, 6.67 Tg, and 12.08 Tg; in the 2035-CLP scenario, it was decreased by 14.63 Tg. The Yili-Tianshan region experienced a notable rise in carbon storage under the 2035-EP scenario compared to the other three scenarios. 4) Soil type played a significant role in the spatial differentiation of carbon storage in Yili-Tianshan (q value 0.5958), followed by population density (0.5394). The changes in carbon storage in the Yili-Tianshan region are the result of synergistic effects of multiple factors, in which the soil type∩soil erosion intensity are the most important. This research could provide a reference method for improving regional carbon storage.

Investigating the impact of urban growth on land use using spatial autocorrelation methods in Konya/Türkiye

Land use land cover (LULC) change, global environmental change, and sustainable change are frequently discussed topics in research at the moment. It is important to determine the historical LULC change process for effective environmental planning and the most appropriate use of land resources. This study analysed the spatial autocorrelation of the land use structure in Konya between 1990 and 2018. For this, Global and Local Moran's I indices based on land use data from 122 neighbourhoods and hot spot analysis (Getis-Ord Gi*) methods were applied to measure the spatial correlation of changes and to determine statistically significant hot and cold spatial clusters. According to the research results, the growth of urban areas has largely destroyed the most productive agricultural lands in the region. This change showed high spatial clustering both on an area and a proportional basis in the northern and southern parts of the city. On the other hand, the growth in the industrial area suppressed the pasture areas the most in the north-eastern region of the city, and this region showed high spatial clustering on both spatial and proportional scales.

Land degradation neutrality assessment and factors influencing it in China's arid and semiarid regions

The ecosystems in China's arid and semiarid regions are notably fragile and experiencing dramatic land degradation. At the 12th Conference of the Parties (COP12) to the United Nations Convention to Combat Desertification (UNCCD) in October 2015, a definition for land degradation neutrality (LDN) was proposed and subsequently integrated into the Sustainable Development Goals (SDGs). Research on LDN has developed in terms of conceptual framework constructions, quantitative assessments, and empirical studies. However, LDN and its drivers must be clarified in China's arid and semiarid regions since some representative processes have yet to be fully considered in the assessment. Here, we develop an LDN indicator system specialised for the area, assess their LDN status, and determine the impacts of human activities and climate change on LDN. Our research aims to refine the LDN indicator system tailored for China's arid and semiarid regions by incorporating the trends of wind and water erosion. We also identify the influence of human activity and climate change on LDN, which provides insightful strategies for ecological restoration and sustainable development in drylands with climate-sensitive ecosystems. The results show that: (1) In 2020, more than half of areas of China's arid and semiarid regions achieved LDN, with more pronounced success in the southeastern areas compared to the central regions. (2) For LDN drivers, elevation shows negligible influence on LDN, whereas increased temperature promotes LDN achievement. Conversely, factors like vapour pressure deficit and v-direction wind speed hinder it. In conclusion, China's arid and semiarid regions achieved LDN, and the dominant factor that substantially influences LDN varies across geographical zones, with higher wind speeds and elevated GDP levels generally obstructing LDN in most areas.

Mapping and assessment of ecosystem health in the Vilnius functional zone (Lithuania)

Urban expansion is a global phenomenon that impacts biodiversity loss and climate change. Soil sealing increases land degradation and the ecosystem services supply. This degradation also negatively affects ecosystem health, essential to make cities more sustainable and liveable. This work aims to study the ecosystem health spatiotemporal evolution (1990, 2000, 2006, 2012 and 2018) in the Vilnius (Lithuania) functional zone, using the vigour, organisation and resilience (VOR) method. The results showed that ecosystem health model validation was acceptable (r = -0.761; p < 0.01). Between 1990 and 2018, an increase (18.37 %) in ecosystem vigour was observed. The values were significantly higher in 2006, 2012 and 2018 than in 1990 and 2000. We identified a decrease between 1990 and 2018 regarding ecosystem organisation (7.15 %) and resilience (9.92 %). However, no significant differences between the years were identified. Ecosystem health decreased (11.49 %) between 1990 and 2018, mainly between 2012 and 2018. Ecosystem health values in 2018 were significantly lower than those identified in the previous years. The lowest values of ecosystem vigour, organisation and resilience were identified in the Vilnius city centre, while the highest was observed in the Vilnius functional zone. From 1990 to 2018, ecosystem vigour increased in some elderships located on the fringe of the studied area due to land abandonment and forest plantations. Simultaneously, a decrease in ecosystem organisation and resilience in the elderships located in Vilnius city centre was observed due to urban sprawl and the consequent landscape fragmentation. This negatively impacted ecosystem health, overshadowing the positive trend observed in ecosystem vigour. Different processes (e.g., urban sprawl, land abandonment, forest plantations) occurred in the Vilnius functional zone. It is essential to halt urban expansion and its adverse impacts on ecosystem health, city sustainability and liveability.

Measuring anthropogenic impact of the ox-bow lakes in moribund Ganges deltaic India

Measuring the anthropogenic impact score (AIS) of the ox-bow lakes in order to explore the present situation and future ways of restoration is very necessary, particularly in highly populated areas. The present work targeted to do this considering 68 contributing parameters under eight AIS constituting components like pollution impact score (PIS), habitat alteration impact score (HAIS), hydrological alteration impact score (HYAIS), landscape alteration impact score (LAIS), etc. and tried to explore the major determinants behind. Machine learning (ML) algorithms were applied for computing component level and overall, AIS. A supervised correlation attribute evaluator (CAE) was applied for detecting major determinants. The result revealed out of total 44 major ox-bow lakes 40.90 % to 59.09 % (9.97 km2 to 14.69 km2) were identified as highly impacted both at the component level and overall scale as per the best predicted Random Forest (RF) model. Hydrologically connected lakes were less impacted than isolated ones. Genetically main river (Bhagirathi-Hooghly) left ox-bow lakes are less affected than those of the off-shoot channel. Larger size lakes witnessed less impact than medium and smaller lakes. Pollution, habitat, and hydrological components were found as the most dominant components of AIS. Reclamation, pollution, and eutrophication factors were identified as the dominant factors. AIS is negatively associated with fish yield and positively associated with the livelihood vulnerability of the dependent fishermen community. Since hydrological connectivity is a big issue; maintenance of it could be a good approach to its sustainability.

Government drivers of gastric cancer prevention: The identification of risk areas and macro factors in Gansu, China

The threat of gastric cancer remains significant worldwide, especially in Gansu, located in northwestern China. However, the spatiotemporal distribution characteristics and the impacts of macro factors such as social-economic, climatic conditions, and healthcare resources allocation were less reported before. Based on the data from the medical big data platform of the Gansu Province Health Commission, Gansu Province Bureau of Statistics and some public databases, we conducted joinpoint regression analysis, spatial autocorrelation analysis, trend surface analysis, space scanning analysis, geographically and temporally weighted regression (GTWR) analysis with Joinpoint_5.0, ArcGIS_10.8, GeoDa, and SaTScanTM_10.1.1. Finally, we have found that the increasing trend of gastric cancer incidence in Gansu has reached a turning point and is now declining. Moreover, significant spatial heterogeneity exists in the distribution of gastric cancer across Gansu Province. The identified risk areas and the impacts of macro factors on gastric cancer and their temporal trends could provide evidence for governments to develop specific policies for gastric cancer prevention.

Spatiotemporal evolution of habitat quality and its response to landscape patterns in karst mountainous cities: a case study of Guiyang City in China

Habitat quality heterogeneity is one of the concrete manifestations of landscape pattern changes caused by human activities, which is of great significance to improve habitat quality by optimizing landscape pattern, thus scientifically protecting biodiversity and promoting ecological civilization construction. The coupling of rapid urbanization and ecological restoration measures has had a significant influence on the habitat quality of fragile and fragmented karst mountainous cities in recent years. In this study, spatiotemporal dynamics and heterogeneity of habitat quality and the impact of landscape patterns on habitat quality are analyzed in Guiyang, a typical karst mountain city in southwest China, mainly using the key methodologies such as the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, Exploratory Spatial Data Analysis (ESDA), and hierarchical partitioning (HP). We found that the habitat quality index of Guiyang City improved from 0.6643 to 0.6988 during 2000-2019; the distribution of habitat quality has significant spatiotemporal heterogeneity and spatial aggregation effect with the low values or the decreased areas concentrated in and around the built-up areas or urbanization expansion areas. Landscape composition had greater contribution than landscape configuration to habitat quality. The increased areas of natural habitat have had a positive effect on habitat quality. Moreover, each landscape configuration had a significant positive or negative correlation with the habitat quality. Therefore, implementing ecological protection and restoration measures in karst mountainous cities might be an effective strategy to improve habitat quality during rapid urbanization. Furthermore, optimizing habitat patterns, reducing the habitats loss, and protecting the natural habitat integrity are crucial to improving and maintaining biodiversity in the study area.

Land use and habitat quality change in the Yellow River Basin: A perspective with different CMIP6-based scenarios and multiple scales

Studying the spatial distribution of land use/land cover (LULC) and habitat quality (HQ), influenced by both climate change and socio-economic factors, holds immense importance for fostering ecological sustainability. The previous scale setting was based on changes in granularity and division of spatial ranges, without considering the differences in land quantity structure and spatial expansion under different spatial ranges. Therefore, this study is based on climate and economic data at different spatial scales to determine the various land demands of provinces (YRB-P) and integration of provinces (YRB-I) in the Yellow River Basin, and to limit the expansion of LULC in corresponding regions. At the same time, we have also established three future scenarios representing different development speeds based on the latest path of shared socio-economic development in CMIP6. We found exhibit significant characteristics in ecological responses under combinations of different scales and scenarios. Shandong and Henan Provinces are the main gathering (38.7-41.7%, 24.1-26.5%) and expansion (68.54-85.99 × 102km2, 18.89-34.12 × 102km2) provinces of built-up land under the YRB-P scale, and their HQ (0.260-0.397) are significantly lower than the average HQ (0.619-0.654). Forest land, grassland, and high value regions of HQ show "45°" distribution at two scales, with high and low values clearly clustered (Moran's I is 0.5440-0.580). The HQ evolution region is larger and more dispersed at the YRB-P scale, but accumulates in local areas at the YRB-I scale. In addition, the highest and lowest HQ mean values appear under the low speed development scenario at the YRB-P scale (0.721) and the rapid development scenario at the YRB-I scale (0.689), respectively. This study helps decision-makers control different scales and development scenarios to improve the ecological level of the study area.

Understanding supply-demand mismatches in ecosystem services and interactive effects of drivers to support spatial planning in Tianjin metropolis, China

Metropolitan areas are being challenged by the disparity between growing societal needs and dwindling natural resource provision. Understanding the supply-demand mismatches of ecosystem services (ES) and their drivers is essential for landscape planning and decision-making. However, integrating such information into spatial planning remains challenging due to the complex nature of urban ecosystems and their intrinsic interactions. In this study, we first assessed and mapped the supply, demand, and mismatches of six typical ES in Tianjin, China. We then clustered numerous townships based on their corresponding spatial characteristic of ES supply-demand mismatches. We also used Random Forest regression to examine the relative importance of drivers and applied Partial Least Squares structural equation modelling to decouple their interactions. The results showed that, the distribution of ES supply and demand showed obvious spatial heterogeneity, with a common surplus of ES supply in highly natural mountainous region and an excess of demand in urban centre. Additionally, all towns were classified into four spatial clusters with homogeneous states of supply-demand mismatches, serving as basic units for spatial optimization. Moreover, the interactions between drivers affected ES supply-demand mismatches in a coupled manner, including the direct effects of the socioeconomic factor (-0.821) and landscape composition (0.234), as well as the indirect effects of the biophysical factor (0.151) and landscape configuration (0.082). Finally, we discussed the utility of analysing the spatial mismatches between ES supply and demand for integrated territorial planning and coordinated decision-making.

Upgrading protected areas can improve or reverse the decline in conservation effectiveness: Evidence from the Tibetan Plateau, China

Protected areas (PAs) are considered essential for maintaining biodiversity. Several governments would like to strengthen the management levels of their PAs (as shorthand for a hierarchy in PA administrative governance) to consolidate their conservation effectiveness. This upgrade (e.g., from provincial- to national-level PAs) means stricter protection and increased funds for PA management. However, confirming whether such an upgrade can produce the expected positive outcomes is key given limited conservation funds. Here, we used the Propensity Score Matching (PSM) method to quantify the impacts of upgrading PAs (i.e., from provincial to national) on vegetation growth on the Tibetan Plateau (TP). We found that the impacts of PA's upgrading can be divided into two impact types: 1) curbed or reversed declines in conservation effectiveness and 2) rapidly increased conservation effectiveness before the upgrade. These results indicate that the PA's upgrading process (including the pre-upgrade operations) can improve PA effectiveness. Nevertheless, the gains did not always occur after the official upgrade. This study demonstrated that in comparison to other PAs, those with more resources or stronger management policies were more effective.

The Spatio-Temporal Patterns and Driving Forces of Land Use in the Context of Urbanization in China: Evidence from Nanchang City

Land use change has been one of the common problems in the context of urbanization in China. Social economy and land use interact with each other, and it is especially important for human society to adhere to sustainable development, and to deal with the contradictory relationship between the social-economic needs and land use change. The objectives of this study are: (1) Obtain time-series land-use classification data and its spatial distribution in Nanchang City; (2) Identify the characteristics and driving force of spatial-temporal land use changes in Nanchang City from 2000 to 2020; (3) Discuss the relationship between the urban expansion and social economy in Nanchang City. The results show that the spatial distribution of land use in Nanchang City has changed significantly from 2000 to 2020, and the largest area of land-use type in Nanchang City has been cropland. The cropland has continuously declined, and the urban area has increased significantly. A lot of cropland has been transformed into urban areas, and land use degree in Nanchang City has significantly increased. The spatial pattern of land use has greatly changed, and the city spatial pattern has become more aggregated, while the spatial distribution of cropland, forest and grassland has become more fragmented. Moreover, there has been an obvious correlation between social-economic development and the level of land use, and GDP has been the main driver of land use change. The central urban area of Nanchang city has been the main hotspot of land use change.

Habitat quality dynamics in China's first group of national parks in recent four decades: Evidence from land use and land cover changes

As the most biodiversity-rich part of the protected areas system, habitats within the pilot national parks have long been threatened by drastic human-induced land use and land cover changes. The growing concern about habitat loss has spurred China's national park project to shift from pilot to construction phase with the official establishment of China's first group of national parks (CFGNPs) in October 2021. But far too little attention has been paid to the synergistic work concerning the habitat quality (HQ) dynamics of all five national parks. Here, the InVEST model, combined with a satellite-derived land use and land cover product and a hot spot analysis (HSA) method, was used to investigate the HQ dynamics at the park- and pixel-scale within the CFGNPs. Our results demonstrate that the past ecological conservation practices within national parks have been unpromising, especially in Giant Panda National Park, Northeast China Tiger and Leopard National Park (NCTL), and Wuyi Mountain National Park (WYM), where HQ as a whole showed a significant decline. Furthermore, more than half of Hainan Tropical Rainforest National Park (87.2%), WYM (77.4%), and NCTL (52.9%) showed significant HQ degradation from 1980 to 2019. Besides, increasing trends in the area shares of HQ degraded pixels were observed in all five national parks from 1980-1999 to 2000-2019. The HSA implied that the hot spots of high HQ degradation rates tend to occur in areas closer to urban settlements or on the edge of national parks, where human activities are intensive. Despite these disappointing findings, we highlighted from the observed local successes and the HQ plateau that the construction of CFGNPs is expected to reverse the deteriorating HQ trends. Thus, we concluded our paper by proposing an HSA-based regulatory zoning scheme that includes five subzones to guide the future construction of China's national park system.

Spatial and Temporal Evolution of Land Use and the Response of Habitat Quality in Wusu, China

Understanding land use change and its impact on habitat quality (HQ) is conducive to land use management and ecological protection. We used the InVEST model and the GeoDetector model to explore the land use and HQ of Wusu from 1980 to 2020. We found that the spatial distribution of land use in Wusu had the most dramatic change from 2000 to 2010, and accordingly, the habitat quality deteriorated seriously from 2000 to 2010. Via correlation analysis, the response of HQ to land use change is obvious, among which the negative effect of forest land to construction land is the largest, and the positive effect of construction land to water is the largest. However, the overall HQ had the largest negative response to the change of grassland to arable land, and the largest positive response to the change of unused land to grassland. Of the driving factors that cause land use change and thus affect HQ, the human factors are the strongest, and the negative impact on HQ is more irreversible. This study can provide a scientific basis for land use management and ecological protection in Wusu, and can help to further promote the exploration of human activities and ecological responses in arid and semi-arid areas.

Effects of human activity intensity on habitat quality based on nighttime light remote sensing: A case study of Northern Shaanxi, China

Habitat quality is a crucial expression of the value of ecosystem services. Habitat quality issues caused by human activities are troubling dilemmas worldwide, and there is an urgent need to assess the impact of the large-scale human activity intensity on habitat quality. In this study, Northern Shaanxi, China, is used as an example to demonstrate how the impact on habitat quality can be explored by simulating the intensity of human activities using nighttime light remote sensing data from 2000 to 2020. Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data, Visible Infrared Imaging Radiometer Suite-Day-Night Band (VIIRS-DNB) data, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used in this study. The results showed that from 2000 to 2020, human activity intensity in Northern Shaanxi increased by 361.4 %, and the habitat quality decreased by 2.3 %. Habitat quality was more spatially clustered and spatially dependent than habitat degradation. Human activity intensity and habitat quality were significantly correlated. Habitat quality in Yulin city decreased significantly compared with that in Yan'an city. The differences in human activity types and economic structures of Yulin and Yan'an were an important factor for this phenomenon. The assessment of the impact of human activity intensity on habitat quality using nighttime light data is feasible and can be applied in other larger regions worldwide.

Mapping lake ecosystem services: A systematic review

Lake ecosystems are essential for human wellbeing and development for their capacity to provide multiple ecosystem services (ES). However, because of an anthropogenic-driven ecologic degradation, the sustainable supply of lake ES is at risk. It is, therefore, necessary to restore lake ecosystems and their ES for the sustainable support of future generations. There is a lack of knowledge about mapping lakes ES at the global level. This study conducts a systematic literature review focused on mapping lake ES. Initially, 617 studies were identified. However, only 30 studies were considered. The 30 studies identified were published between 2015 and 2021. Most of them were conducted in Asia, followed by Europe and America. The selected works covered all three Common International Classification of ES sections (regulating and maintenance, provisioning and cultural). We identified a similar number of works carried out in all the sections. Most of the studies were performed at a regional scale and focused on ES supply. Methodologically, an equal number of works followed qualitative and quantitative approaches. In total, 13 different methods were identified. The most used were the ES value (ESV) and biophysical modelling. Less than half of the works that applied the ESV method mapped all the ES analysed. Among the studies assessed, most were focused on environmental and socio-economic aspects. Only 1 study attempted to validate the results. Overall, 16 studies assessed the drivers of change impacts on ES supply. Nevertheless, only 1 considered climate change. The other 15 only considered land-use changes. This systematic review showed that a small number of works focused on lake ES mapping. Although challenging, there is an urgent global need for studies in this domain.

Projections of land use change and habitat quality assessment by coupling climate change and development patterns

Exploring future land use changes and assessing the habitat quality remains a challenging topic for watershed ecological sustainability. However, most studies ignore the effects of coupled climate change and development patterns. In this study, a framework for assessing habitat quality under the influence of future land use change is constructed based on exploring the driving forces of land use change factors and integrating the system dynamics (SD) model, future land use simulation (FLUS) model and InVest model. The framework enables the projection of land use change and the assessment of habitat quality in the context of future climate change and different development strategies. Applying the framework to the Weihe River Basin, the main driving forces of land-use change in the Weihe River Basin were identified based on geographical detectors, and habitat quality assessment was realized for the Weihe River Basin under the coupled scenarios of three typical shared socioeconomic pathways and future development patterns (SSP126-EP, SSP245-ND, SSP585-EG). The results show that 1) population, precipitation, and temperature are the major driving factors for land use change. 2) The coupling model of SD and FLUS can effectively simulate the future trend of land use change, the relative error is within 2 %, and the overall accuracy is 93.58 %. 3) Significant differences in habitat quality as a result of modifications in land use patterns in different contexts. Affected by ecological protection, the habitat quality in SSP126-EP was significantly better than that in SSP245-ND and SSP585-EG. This research can provide references for future watershed ecological management decisions.

Non-growing season drought legacy effects on vegetation growth in southwestern China

Water availability influences terrestrial ecosystems' composition, structure, and function. Recently, climate change increased drought periods frequency and length in many parts of the world, including southwestern China, a biodiversity hotspot. Although the drought impacts on ecosystems are well known, studies are scarce in subtropical areas of China. This work studied the drought legacy effects on vegetation growth in southwestern China using Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI), with a particular focus on non-growing season extreme drought events. Pervasive non-growing season drought legacy effects were found in the first growing season in most parts of southwestern China. The highest impacts were identified in forests, while the effects in grass were less severe. At the regional scale, horizontal and vertical spatial patterns of drought legacy effects were heterogeneous, and the highest impacts were found in warmer and wetter forests and alpine grasslands. Our study highlights that severe drought conditions may dramatically affect vegetation growth in southwestern China.

Estimating potential illegal land development in conservation areas based on a presence-only model

Conservation areas are facing increasing threats from anthropogenic land use activities. It is important to reasonably recognize and predict suspected illegal land development in advance. However, traditional methods easily suffer from selection bias due to the lack of accurate and reliable absence data. To tackle this problem, we have presented a novel method for estimating potential illegal land development based on the presence-only maximum entropy (MAXENT) model. The principle of MAXENT can guarantee that no additional unknown information (e.g., inaccurate pseudo-absence samples) will be introduced into the estimation procedure. This method was applied to the conservation areas in a fast-growing city, and the robustness of the MAXENT models was confirmed by the high AUC scores (over 0.80). The results indicated that the proposed method performs more effectively than the presence-absence random forest model. In addition, topographic conditions and proximity to transportation networks played dominant roles in the emergence of suspected illegal land development. Moreover, the probability map generated by MAXENT suggests that a considerable amount of forest, farmland, grassland, and water bodies will face a high degree of danger. Therefore, both superior and local governments should pay much more attention to regions with a higher potential for illegal land development. In summary, our findings are expected to support decision-making in the management and assessment of conservation areas in fast-growing regions. More importantly, the proposed method can be further applied to illegal land development estimation in many other regions.

Landscape pattern change and its correlation with influencing factors in semiarid areas, northwestern China

The West Liaohe Plain (WLP) is a typical crop-pastoral ecotone of the semiarid area in Northwestern, China. Land use/cover change (LUCC) of the WLP might endanger this ecosystem, triggering long-lasting environmental concerns. LUCC data for China (1980-2020) and hydrometeorological data were analyzed to reveal factors contributing to change and explore sustainable development opportunities. The results show that characteristics of the main land-use types in the WLP have changed significantly, especially cultivated land area, which increased by 15.2% and 6.79% during the periods 1980-1995 and 2000-2020, respectively. Response relationships were observed due to natural (precipitation, temperature, and runoff) and anthropogenic (economy) factors and LUCC. Between 2000 and 2020, the impact of anthropogenic factors on cultivated land was stronger than on grassland at the class and landscape level, using the landscape indices which were selected, including percent of landscape (PLAND), number of patches (NP), largest patch index (LPI), and Shannon's evenness index (SHEI). Expansion of cultivated land from 1990 to 1995 was not only related to anthropogenic factors but also to hydrological & climatic factors. The results of this study have the potential to influence sustainable land resource development.

The effects of landscape change on habitat quality in arid desert areas based on future scenarios: Tarim River Basin as a case study

Human activities have caused spatiotemporal patterns of land use and land cover (LULC) change. The LULC change has directly affected habitat quality (HQ) and ecosystem functions. Assessing, simulating, and predicting spatiotemporal changes and future trends under different scenarios of LULC-influenced HQ is beneficial to land use planners and decision-makers, helping them to formulate plans in a sustainable and responsible way. This study assesses and simulates the HQ of the Tarim River Basin (TRB) using the future land use simulation model (FLUS), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and partial least squares regression (PLSR). Since 2000, the TRB has experienced a declining trend in HQ from 0.449 to 0.444, especially in the lower elevations (740-2000m) and on sloped land (<10°). The decline will continue unless effective and sustainable plans are implemented to halt it. Agricultural and settlement areas have a lower HQ and a higher degree of habitat degradation than native habitats. This shows that the expansion of oasis agriculture (with an annual growth rate of 372.17 km²) and settlements (with an annual growth rate of 23.50 km²) has caused a decline in native habitat and subsequent habitat fragmentation. In other words, changes in LULC have caused a decline in the HQ. Moreover, there is a significant negative correlation between HQ and urbanization rate (p<0.01), and the PLSR also indicate that number of patches (NP), area-weighted mean fractal dimension index (FRAC_AM), percentage of landscape (PLAND), and largest patch index (LPI) were also important contributors to worsening the HQ. Therefore, the TRB urgently needs appropriate strategies to preserve its natural habitats into the future, based on the ecological priority scenario (EPS) and harmonious development scenario (HDS), which can help to maintain a high-quality habitat.

Classifying and Mapping Cultural Ecosystem Services Using Artificial Intelligence and Social Media Data

Quantifying and mapping cultural ecosystem services are complex because of their intangibility. Data from social media, such as geo-tagged photographs, has been proposed for mapping cultural use or appreciation of ecosystems. However, manual content analysis and classification of large numbers of photographs is time-consuming. The potential of deep learning for automating the analysis of crowdsourced social media content is still being explored in CES research. Here, we use a new deep learning model for automating the classification of natural and human elements relevant to CES from Flickr images. This approach applies a convolutional neural network architecture to analyze over 29,000 photographs from the Lithuanian coast and uses hierarchical clustering to group these photographs. The accuracy of the classification was assessed by comparison with manual classification. Over 37% of the photographs were taken for the landscape appreciation class, and 28% of the photographs were taken of nature, of animals or plants, which represent the nature appreciation class. The main clusters were identified in urban areas, more precisely in the main coastal cities of Lithuania. The distribution of the nature photographs was concentrated around particular natural attractions, and they were more likely to occur in parks and natural reserves with high levels of vegetation and animal cover. This approach that was developed for clustering the photographs was accurate and saved approximately 100 km of manual work. The method demonstrates how analyzing large numbers of digital photographs expands the analytical toolbox available to researchers and allows the quantification and mapping of CES at large geographical scales. Automated assessment and mapping of cultural ecosystem services could be used to inform urban planning and improve nature reserve management.

Is There a Spatial Relationship between Urban Landscape Pattern and Habitat Quality? Implication for Landscape Planning of the Yellow River Basin

The extent to which landscape spatial patterns can impact the dynamics and distribution of biodiversity is a key geography and ecology issue. However, few previous studies have quantitatively analyzed the spatial relationship between the landscape pattern and habitat quality from a simulation perspective. In this study, the landscape pattern in 2031 was simulated using a patch-generating simulation (PLUS) model for the Yellow River Basin. Then, the landscape pattern index and habitat quality from 2005 to 2031 were evaluated using the Fragstats 4.2 and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Furthermore, we analyzed the spatial distribution characteristics and spatial spillover effects of habitat quality using spatial autocorrelation analysis. Finally, the spatial association between the landscape pattern index and habitat quality was quantitatively revealed based on a spatial lag model. The simulation results showed that: (1) from 2005 to 2031, the landscape of the Yellow River Basin would be dominated by grassland and unused land, and the areas of construction land and water body will increase significantly, while the area of grassland will decrease; (2) patch density (PD) and Shannon's diversity index (SHDI) show significant increases, while edge density (ED), landscape shape index (LSI), mean patch area (AREA_MN), and contagion index (CONTAG) decrease; (3) from 2005 to 2031, habitat quality would decrease. The high-value areas of habitat quality are mainly distributed in the upper reaches of the Yellow River Basin, and the low-value areas are distributed in the lower reaches. Meanwhile, both habitat quality and its change rate present positive spatial autocorrelation; and (4) the spatial relationships of habitat quality with PD and COHESION are negative, while ED and LSI have positive impacts on habitat quality. Specifically, landscape fragmentation caused by high PD has a dominant negative influence on habitat quality. Therefore, this study can help decision makers manage future landscape patterns and develop ecological conservation policy in the Yellow River Basin.

Mapping and assessment of future changes in the coastal and marine ecosystem services supply in Lithuania

Assessing and mapping ecosystem services (ES) became an integral part of coastal and marine management practices. Hence, quantitative and validated approaches are lacking, especially to address future conditions. The objective of this study is to apply further existing and develop new methodological frameworks to quantitatively assess and map the current and future supply of 3 ES in the coastal zone of Lithuania: coastal flood protection, nutrient regulation, and maintenance of nursery conditions. For coastal flood ES modelling, 2 time periods (1990 and 2018) and 4 scenarios (A0, A1 A2, A3 - based on future socio-economic changes in Lithuania) were analysed. The coastal flood protection ES model was validated (r² = 0.30) using tree cover density. The results showed spatial differences among the analysed periods but no statistical differences. High supply areas are located in the southern coastal area, while the central part displays a low supply. For nutrient regulation and maintenance of nursery conditions, 7 time periods were analysed: a historical period and 6 scenarios based on Representative Concentration Pathway 4.5 and 8.5 and 3 Shared Socioeconomic Pathways. The nutrient regulation ES model was validated (r² = 0.85) using in situ nutrient. Statistical differences were observed for this ES, but a similar spatial distribution of high and low supply areas. A decrease in the supply was observed comparing the historical period and future scenarios. Maintenance of nursery conditions was validated (r² = 0.72) based on the protection status of the coastal zone. Results show no statistical differences and similar spatial patterns among the periods. Rocky and sandbank areas show a high supply for this ES. Limitations of our work are mainly related to the resolution of the utilised indicators. Nevertheless, the information obtained from our models can support spatial planning and decision-making processes.

Land Cover Changes in Selected Areas Next to Lagoons Located on the Southern Coast of the Baltic Sea, 1984–2021

The aim of the study is the evaluation of land cover changes in selected areas next to three lagoons (the Curonian Lagoon, the Vistula Lagoon and the Szczecin Lagoon) located on the southern coast of the Baltic Sea (in Lithuania, Russia, Poland and Germany) from 1984 to 2021. The changes are evaluated using multispectral (visible light—RGB and near infrared—NIR) satellite images from the Landsat 5 and Sentinel-2 sensors. Due to their high importance for ecosystem services, two main land cover types are evaluated, i.e., forest area and inland water reservoirs. The classification of the images is performed using a random forest algorithm. Areas of water bodies and forests are evaluated for the years 1984 and 2021. During period 1984–2021, positive changes in land cover are observed in all three regions included in the study. In almost all parts, with the exception of the Polish part of the area located next to the Szczecin Lagoon, of these regions, an increase in forest area is observed. The increase ranges from 0.1% (Poland, area next to the Vistula Lagoon) to 1.2% (Germany, area next to the Szczecin Lagoon). The area of inland water reservoirs has not changed significantly in the long term. Despite the global warming, no reduction in the area of these water reservoirs is observed, even new seminatural reservoirs have been created in some parts of the study area.

The Imprint of Built-Up Land Expansion on Cropland Distribution and Productivity in Shandong Province

Grain self-sufficiency is a national food security target of China. The way that built-up land expansion impacts upon cropland loss and food provision needs to be explored in the major grain producing areas. Shandong Province is an important agricultural food production region, which is also experiencing rapidly urbanizing. Here we assessed the spatiotemporal distribution of cropland loss due to built-up land expansion and landscape dynamics of cropland during 2000–2020, by using 30 m resolution land cover data. We also analyzed the potential yield change influenced by cropland loss. The results showed that the area of built-up land expanded by 5199 km2 from 2000–2010, and 11,949 km2 from 2010–2020. Approximately 95% of the new built-up land was from cropland during the two stages, and the primary mode of built-up land expansion was the edge expansion. The patch density and the patch size of cropland kept increasing and decreasing, respectively, and the aggregation index kept decreasing from 2000 to 2020, indicating increased cropland fragmentation. The proportion of occupied cropland with potential yield greater than 7500 kg/ha was 25% and 37% during the former and the latter period. Thus, higher quality cropland was encroached in the recent period. The findings could provide meaningful implications for making sustainable land use development strategies in the study area and other similar regions.

Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

The residential real estate market is very important because most people’s wealth is in this sector, and it is an indicator of the economy. Real estate market data in general and market transaction data, in particular, are inherently spatiotemporal as each transaction has a location and time. Therefore, exploratory spatiotemporal methods can extract unique locational and temporal insight from property transaction data, but this type of data are usually unavailable or not sufficiently geocoded to implement spatiotemporal methods. In this article, exploratory spatiotemporal methods, including a space-time cube, were used to analyze the residential real estate market at small area scale in the Dublin Metropolitan Area over the last decade. The spatial patterns show that some neighborhoods are experiencing change, including gentrification and recent development. The extracted spatiotemporal patterns from the data show different urban areas have had varying responses during national and global crises such as the economic crisis in 2008–2011, the Brexit decision in 2016, and the COVID-19 pandemic. The study also suggests that Dublin is experiencing intraurban displacement of residential property transactions to the west of Dublin city, and we are predicting increasing spatial inequality and segregation in the future. The findings of this innovative and exploratory data-driven approach are supported by other work in the field regarding Dublin and other international cities. The article shows that the space-time cube can be used as complementary evidence for different fields of urban studies, urban planning, urban economics, real estate valuations, intraurban analytics, and monitoring sociospatial changes at small areas, and to understand residential property transactions in cities. Moreover, the exploratory spatiotemporal analyses of data have a high potential to highlight spatial structures of the city and relevant underlying processes. The value and necessity of open access to geocoded spatiotemporal property transaction data in social research are also highlighted.

Sensitivity and future exposure of ecosystem services to climate change on the Tibetan Plateau of China

CONTEXT

Climate change has imposed tremendous impacts on ecosystem services. Recent attempts to quantify such impacts mainly focused on a basin or larger scale, or used limited time periods that largely ignore observations of long-term trends at a fine resolution, thereby affecting the recognition of climate change's effect on ecosystem services.

OBJECTIVES

This study conducts a detailed and spatially explicit recognition of climate change's effect on ecosystem services and provides an intuitive map for decision-making and climate change adaptation planning.

METHODS

We used long-term time series of ecosystem service assessments and various future climate scenarios to quantify the sensitivity and future exposure of ecosystem services to climate change on the Tibetan Plateau.

RESULTS

Carbon sequestration (CS) and habitat quality experience significant growth, while water retention did not show any trend. Sensitivity patterns of these ecosystem services vary largely. For CS, more than half of the pixels showed a positive sensitivity to climate change, even though the degree of sensitivity is not high. There is substantial spatial heterogeneity in the exposure of ecosystem services to future climate changes, and high levels of future climate change increase the intensity of exposure.

CONCLUSIONS

This study illustrates the complex spatial association between ecosystem services and climatic drivers, and these findings can help optimize local response strategies in the context of global warming. For example, the existing protected areas have notable conservation gaps for disturbance of future climate change on ecosystem services, especially in the southeastern part of the study area.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10980-021-01320-9.