Eleonora Rivieccio, D. Fulgione, G. de Filippo, A. De Natale, Vincenzo Paturzo, Claudio Mineo, Stefania Passaretti, Anna Varriale, M. Buglione
The need to find a trade-off between protecting water-related ecosystems and increasing safe water-use for human society is recognized in the 2030 Agenda of the European Union. We assess the ecological status of a riverine system in order to mitigate human impacts, considering its importance for supplying drinking water to more than 4 million users in Rome. We used an integrated approach, analyzing animal and plant communities at riverbanks and the riverbed. A macrobenthos analysis revealed a well-structured community with a good ecology for all sampling stations. The highest value was found immediately upstream and downstream of the springs collection system, while the lowest richness value was where the river collects urban wastewater. A floristic inventory showed Hemicryptophytes composing almost 45% of all species, and prevalence of Euroasiatic (35%) and Orophilous (34%) chorotypes. A positive correlation between riverbed vegetation and the quality of the benthic community was revealed, while tree height seems to have a negative trend. Our data suggest a river stretch affected by resurgence and water abstraction did not highlight irreversible alterations to the landscape. Indeed, the composition of vegetation and correlated animal communities mirrored a clinal gradient expected for an Apennine river system. Our study has the potential to improve the approach used to monitor the impacts of humans on freshwater ecosystems, aiming at preserving the integrity of the water-related landscape.
{"title":"Better Safe Than Sorry: A Model to Assess Anthropic Impacts on a River System in Order to Take Care of the Landscape","authors":"Eleonora Rivieccio, D. Fulgione, G. de Filippo, A. De Natale, Vincenzo Paturzo, Claudio Mineo, Stefania Passaretti, Anna Varriale, M. Buglione","doi":"10.3390/land13071076","DOIUrl":"https://doi.org/10.3390/land13071076","url":null,"abstract":"The need to find a trade-off between protecting water-related ecosystems and increasing safe water-use for human society is recognized in the 2030 Agenda of the European Union. We assess the ecological status of a riverine system in order to mitigate human impacts, considering its importance for supplying drinking water to more than 4 million users in Rome. We used an integrated approach, analyzing animal and plant communities at riverbanks and the riverbed. A macrobenthos analysis revealed a well-structured community with a good ecology for all sampling stations. The highest value was found immediately upstream and downstream of the springs collection system, while the lowest richness value was where the river collects urban wastewater. A floristic inventory showed Hemicryptophytes composing almost 45% of all species, and prevalence of Euroasiatic (35%) and Orophilous (34%) chorotypes. A positive correlation between riverbed vegetation and the quality of the benthic community was revealed, while tree height seems to have a negative trend. Our data suggest a river stretch affected by resurgence and water abstraction did not highlight irreversible alterations to the landscape. Indeed, the composition of vegetation and correlated animal communities mirrored a clinal gradient expected for an Apennine river system. Our study has the potential to improve the approach used to monitor the impacts of humans on freshwater ecosystems, aiming at preserving the integrity of the water-related landscape.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141827659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jian Cao, Hongliang Qiu, Alastair M. Morrison, Yingzhi Guo
Despite the lengthy history of the research on destination image from various perspectives, how pro-environmental destination image promotes resident environmental citizenship behavior remains underexplored. Grounded in the cognition–affect–behavior (CAB) model, this research investigated the translation of pro-environmental destination image into resident environmental citizenship behavior via satisfaction and pride. Data were collected using an intercept survey approach from a tourism village recognized as one of the best in the world by UNWTO. The results indicated that a pro-environmental destination image has a positive impact on resident environmental citizenship behavior in the private and public domains. Furthermore, resident satisfaction and pride serve as mediators between pro-environmental destination image and resident environmental citizenship behavior. This research contributes to the literature on resident environmental citizenship behavior by considering the role of pro-environmental destination image. The findings provide practical implications for fostering environmental citizenship behavior through the presentation of pro-environmental images to residents and eliciting their positive emotions.
{"title":"The Effect of Pro-Environmental Destination Image on Resident Environmental Citizenship Behavior: The Mediating Roles of Satisfaction and Pride","authors":"Jian Cao, Hongliang Qiu, Alastair M. Morrison, Yingzhi Guo","doi":"10.3390/land13071075","DOIUrl":"https://doi.org/10.3390/land13071075","url":null,"abstract":"Despite the lengthy history of the research on destination image from various perspectives, how pro-environmental destination image promotes resident environmental citizenship behavior remains underexplored. Grounded in the cognition–affect–behavior (CAB) model, this research investigated the translation of pro-environmental destination image into resident environmental citizenship behavior via satisfaction and pride. Data were collected using an intercept survey approach from a tourism village recognized as one of the best in the world by UNWTO. The results indicated that a pro-environmental destination image has a positive impact on resident environmental citizenship behavior in the private and public domains. Furthermore, resident satisfaction and pride serve as mediators between pro-environmental destination image and resident environmental citizenship behavior. This research contributes to the literature on resident environmental citizenship behavior by considering the role of pro-environmental destination image. The findings provide practical implications for fostering environmental citizenship behavior through the presentation of pro-environmental images to residents and eliciting their positive emotions.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Longqing Liu, Shidong Zhang, Wenshu Liu, Hongjiao Qu, Luo Guo
Over the past two decades, due to the combined effects of natural and human factors, the ecological environment and resources of the Qinghai–Tibet Plateau (QTP) have faced serious threats, profoundly impacting its ecosystem and the lives of its residents. Therefore, the establishment of the ecological security pattern (ESP) is crucial to cope with climate change, maintain ecosystem function, and sustainable development. Based on the Pressure–State–Response (PSR) model, this study constructed an evaluation index system for the ecological security (ES) of the QTP, evaluated the ES of the QTP during 2000–2020, and predicted the ES of the QTP during 2025–2035 based on the deep learning model. Combined with the residents’ perception of ES, the ES of the QTP was evaluated comprehensively. The results showed that: (1) From 2000 to 2020, the ES value of the QTP continued to rise, the number of dangerous and sensitive counties decreased, and the number of other counties increased. The overall spatial distribution features higher values in the southeast and lower values in the northwest and central regions. (2) From 2000 to 2020, both hot spots and cold spots on the QTP decreased, with the hot spots mainly concentrated in the southeast of the QTP, represented by Yunnan Province, and the cold spots shifting from west to east, mainly concentrated in the central QTP, represented by Qinghai Province. (3) The Long Short-Term Memory (LSTM) model demonstrates high prediction accuracy. Based on the prediction of LSTM, the ES value of the QTP will continue to rise from 2025 to 2035, and the number of safe counties will reach the highest level in history. The spatial distribution is still higher in the southeast and lower in the northwest and central regions. (4) By analyzing residents’ perception of 25 potential factors that may affect the ES of the QTP, the results show that residents generally believe that these factors have an important impact on ES, and their evaluation is between “important” and “very important”. In addition, there is a significant correlation between these factors and the predicted values of ES. The results of the study will help to improve our understanding of the overall ecological environment of the QTP, provide accurate positioning and reasonable help for the government to formulate relevant protection strategies, and lay a methodological and practical foundation for the sustainable development of the QTP.
{"title":"Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning","authors":"Longqing Liu, Shidong Zhang, Wenshu Liu, Hongjiao Qu, Luo Guo","doi":"10.3390/land13071073","DOIUrl":"https://doi.org/10.3390/land13071073","url":null,"abstract":"Over the past two decades, due to the combined effects of natural and human factors, the ecological environment and resources of the Qinghai–Tibet Plateau (QTP) have faced serious threats, profoundly impacting its ecosystem and the lives of its residents. Therefore, the establishment of the ecological security pattern (ESP) is crucial to cope with climate change, maintain ecosystem function, and sustainable development. Based on the Pressure–State–Response (PSR) model, this study constructed an evaluation index system for the ecological security (ES) of the QTP, evaluated the ES of the QTP during 2000–2020, and predicted the ES of the QTP during 2025–2035 based on the deep learning model. Combined with the residents’ perception of ES, the ES of the QTP was evaluated comprehensively. The results showed that: (1) From 2000 to 2020, the ES value of the QTP continued to rise, the number of dangerous and sensitive counties decreased, and the number of other counties increased. The overall spatial distribution features higher values in the southeast and lower values in the northwest and central regions. (2) From 2000 to 2020, both hot spots and cold spots on the QTP decreased, with the hot spots mainly concentrated in the southeast of the QTP, represented by Yunnan Province, and the cold spots shifting from west to east, mainly concentrated in the central QTP, represented by Qinghai Province. (3) The Long Short-Term Memory (LSTM) model demonstrates high prediction accuracy. Based on the prediction of LSTM, the ES value of the QTP will continue to rise from 2025 to 2035, and the number of safe counties will reach the highest level in history. The spatial distribution is still higher in the southeast and lower in the northwest and central regions. (4) By analyzing residents’ perception of 25 potential factors that may affect the ES of the QTP, the results show that residents generally believe that these factors have an important impact on ES, and their evaluation is between “important” and “very important”. In addition, there is a significant correlation between these factors and the predicted values of ES. The results of the study will help to improve our understanding of the overall ecological environment of the QTP, provide accurate positioning and reasonable help for the government to formulate relevant protection strategies, and lay a methodological and practical foundation for the sustainable development of the QTP.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José A. Sobrino, Sergio Gimeno, Virginia Crisafulli, Álvaro Sobrino-Gómez
Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: ‘Urban Areas’, ‘Dense Vegetation’, ‘Sparse Vegetation’, ‘Water Bodies’, and Other’. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications.
{"title":"Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022","authors":"José A. Sobrino, Sergio Gimeno, Virginia Crisafulli, Álvaro Sobrino-Gómez","doi":"10.3390/land13071072","DOIUrl":"https://doi.org/10.3390/land13071072","url":null,"abstract":"Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: ‘Urban Areas’, ‘Dense Vegetation’, ‘Sparse Vegetation’, ‘Water Bodies’, and Other’. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maira Masood, Chunguang He, Shoukat Ali Shah, Syed Aziz Ur Rehman
Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region’s ecosystems and human communities.
{"title":"Land Use Change Impacts over the Indus Delta: A Case Study of Sindh Province, Pakistan","authors":"Maira Masood, Chunguang He, Shoukat Ali Shah, Syed Aziz Ur Rehman","doi":"10.3390/land13071080","DOIUrl":"https://doi.org/10.3390/land13071080","url":null,"abstract":"Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region’s ecosystems and human communities.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High-quality utilization of urban land (HUUL) is essential for optimizing urban land use and promoting high-quality development. Previous research has mainly focused on examining urban land use efficiency, neglecting the connection between urban land use and high-quality development. This study reveals the intrinsic association mechanism between high-quality development and urban land use, which can provide comprehensive theoretical and empirical support for high-quality land use and high-quality urban development. This study constructed an evaluation system for HUUL that comprehensively adopted the entropy method, kernel density estimation, and the geodetector model to analyze the spatio-temporal evolution and driving factors of the HUUL levels of 284 Chinese cities from 2006 to 2020. The measurement results showed that during 2006–2020, the HUUL level showed an apparent upward trend, and the eastern region > the central region > the overall region > the western region > the northeast region. From the kernel density map, there was a noticeable trend of varying degrees of increase in the difference of the main peak position of the HUUL level among cities in all regions except the west. Furthermore, some cities in the eastern and western regions had significantly higher HUUL levels than the others. According to the results of the factor analysis, it is evident that innovative use and open use are the internal primary factors that drive the enhancement of the HUUL level. Moreover, the level of economic development is the external primary factor that facilitates the improvement in HUUL level.
{"title":"Spatio-Temporal Evolution and Drivers of High-Quality Utilization of Urban Land in Chinese Cities","authors":"Jinhua Liu, Xiaozhou Huang","doi":"10.3390/land13071077","DOIUrl":"https://doi.org/10.3390/land13071077","url":null,"abstract":"High-quality utilization of urban land (HUUL) is essential for optimizing urban land use and promoting high-quality development. Previous research has mainly focused on examining urban land use efficiency, neglecting the connection between urban land use and high-quality development. This study reveals the intrinsic association mechanism between high-quality development and urban land use, which can provide comprehensive theoretical and empirical support for high-quality land use and high-quality urban development. This study constructed an evaluation system for HUUL that comprehensively adopted the entropy method, kernel density estimation, and the geodetector model to analyze the spatio-temporal evolution and driving factors of the HUUL levels of 284 Chinese cities from 2006 to 2020. The measurement results showed that during 2006–2020, the HUUL level showed an apparent upward trend, and the eastern region > the central region > the overall region > the western region > the northeast region. From the kernel density map, there was a noticeable trend of varying degrees of increase in the difference of the main peak position of the HUUL level among cities in all regions except the west. Furthermore, some cities in the eastern and western regions had significantly higher HUUL levels than the others. According to the results of the factor analysis, it is evident that innovative use and open use are the internal primary factors that drive the enhancement of the HUUL level. Moreover, the level of economic development is the external primary factor that facilitates the improvement in HUUL level.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wanqiu Zhang, Zeru Jiang, Huayang Dai, Gang Lin, Kun Liu, Ruiwen Yan, Yuanhao Zhu
Mining activities have significantly altered the land use patterns of mining areas, exacerbated the degree of landscape fragmentation, and thereby led to the loss of biodiversity. Ecological networks have been recognized as an essential component for enhancing habitat connectivity and protecting biodiversity. However, existing studies lack dynamic analysis at the landscape scale under multiple future scenarios for mining areas, which is adverse to the identification of ecological conservation regions. This study used the MOP-PLUS (multi-objective optimization problem and patch-level land use simulation) model to simulate the land use patterns in the balance of ecology and economy (EEB) scenario and ecological development priority (EDP) scenario for the Shendong coal base. Then, climate change and land use patterns were integrated into ecosystem models to analyze the dynamic changes in the ecological networks. Finally, the conservation priorities were constructed, and dynamic conservation hotspots were identified using landscape mapping methods. The following results were obtained: (1) From 2000 to 2020, large grassland areas were replaced by mining areas, while cultivated land was replenished. By 2030, the forest and grassland areas (967.00 km2, 8989.70 km2) will reach their peaks and the coal mine area (356.15 km2) will reach its nadir in the EDP scenario. (2) The fragmentation of ecological sources intensified (MPS decreased from 19.81 km2 to 18.68 km2) and ecological connectivity declined (in particular, α decreased by 6.58%) from 2000 to 2020. In 2030, the connectivity in the EDP scenario will increase, while the connectivity in the EEB scenario will be close to that of 2020. (3) The central and southeastern parts of the Shendong coal base have higher conservation priorities, which urgently need to be strengthened. This study offers guidance on addressing the challenges of habitat and biodiversity conservation in mining areas.
{"title":"Modelling Multi-Scenario Ecological Network Patterns and Dynamic Spatial Conservation Priorities in Mining Areas","authors":"Wanqiu Zhang, Zeru Jiang, Huayang Dai, Gang Lin, Kun Liu, Ruiwen Yan, Yuanhao Zhu","doi":"10.3390/land13071065","DOIUrl":"https://doi.org/10.3390/land13071065","url":null,"abstract":"Mining activities have significantly altered the land use patterns of mining areas, exacerbated the degree of landscape fragmentation, and thereby led to the loss of biodiversity. Ecological networks have been recognized as an essential component for enhancing habitat connectivity and protecting biodiversity. However, existing studies lack dynamic analysis at the landscape scale under multiple future scenarios for mining areas, which is adverse to the identification of ecological conservation regions. This study used the MOP-PLUS (multi-objective optimization problem and patch-level land use simulation) model to simulate the land use patterns in the balance of ecology and economy (EEB) scenario and ecological development priority (EDP) scenario for the Shendong coal base. Then, climate change and land use patterns were integrated into ecosystem models to analyze the dynamic changes in the ecological networks. Finally, the conservation priorities were constructed, and dynamic conservation hotspots were identified using landscape mapping methods. The following results were obtained: (1) From 2000 to 2020, large grassland areas were replaced by mining areas, while cultivated land was replenished. By 2030, the forest and grassland areas (967.00 km2, 8989.70 km2) will reach their peaks and the coal mine area (356.15 km2) will reach its nadir in the EDP scenario. (2) The fragmentation of ecological sources intensified (MPS decreased from 19.81 km2 to 18.68 km2) and ecological connectivity declined (in particular, α decreased by 6.58%) from 2000 to 2020. In 2030, the connectivity in the EDP scenario will increase, while the connectivity in the EEB scenario will be close to that of 2020. (3) The central and southeastern parts of the Shendong coal base have higher conservation priorities, which urgently need to be strengthened. This study offers guidance on addressing the challenges of habitat and biodiversity conservation in mining areas.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rural communities in ecologically sensitive areas are confronted with environmental challenges and land waste. The hollowing out of villages because of the steady loss of young people has brought these problems to the fore. Research on the remediation of rural settlements in ecologically sensitive regions is scarce. This paper focuses on Tong Yu County, a region located in the agricultural and pastoral intertwined zones of western Jilin, which is known for its ecological sensitivity. On the basis of the results of the geographical assessment of the area via hierarchical analysis (AHP method)and soil and water conservation capacity evaluation via the revised universal soil loss equation model(RUSLE) and the net primary production quantitative index model(NPP model), we propose a rural development program that divides the study area into three different zones with different development orientations, namely, potential enhancement, agglomeration development, and expansion restrictions. Moreover, we conceived a rural settlement remediation scheme using the kernel density estimation method and provided remediation strategies for rural settlements of various density levels. The rural settlement development program and the rural settlement remediation scheme led to the optimization of rural settlements. Notably, with increasing kernel density, the area of rural settlements tends to increase and then decrease. Our research helps save 5.059 km2 of land resources in the study area and offers guidance for improving the rural settlement layout in Tong Yu County.
{"title":"Rural Settlement Optimization for Ecologically Sensitive Area Evaluations Based on Geo-Proximity and the Soil–Water Conservation Capacity","authors":"Ruiyi Lou, Dongyan Wang","doi":"10.3390/land13071071","DOIUrl":"https://doi.org/10.3390/land13071071","url":null,"abstract":"Rural communities in ecologically sensitive areas are confronted with environmental challenges and land waste. The hollowing out of villages because of the steady loss of young people has brought these problems to the fore. Research on the remediation of rural settlements in ecologically sensitive regions is scarce. This paper focuses on Tong Yu County, a region located in the agricultural and pastoral intertwined zones of western Jilin, which is known for its ecological sensitivity. On the basis of the results of the geographical assessment of the area via hierarchical analysis (AHP method)and soil and water conservation capacity evaluation via the revised universal soil loss equation model(RUSLE) and the net primary production quantitative index model(NPP model), we propose a rural development program that divides the study area into three different zones with different development orientations, namely, potential enhancement, agglomeration development, and expansion restrictions. Moreover, we conceived a rural settlement remediation scheme using the kernel density estimation method and provided remediation strategies for rural settlements of various density levels. The rural settlement development program and the rural settlement remediation scheme led to the optimization of rural settlements. Notably, with increasing kernel density, the area of rural settlements tends to increase and then decrease. Our research helps save 5.059 km2 of land resources in the study area and offers guidance for improving the rural settlement layout in Tong Yu County.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of industrialization and urbanization, the issue of soil environmental pollution is becoming more and more prominent, especially concerning heavy metal contamination, which has garnered significant scholarly attention. The surface watershed formed by waterline is influenced by various factors such as topography, industrial emissions, and agricultural runoff, resulting in a complex process of migration and accumulation of heavy metal elements from multiple sources. In this study, the pollution characteristics and sources of heavy metal elements Hg, As, Pb, Ni, Cd, Cr, Cu and Zn in 165 surface soil samples from the Manghe River watershed in Jiyuan City were comprehensively analyzed using a variety of methods, including statistics, geostatistics, enriched factor analysis and the Positive Matrix Factorization Model (PMF). The results showed that the concentrations of Hg, Cd, As, Cu, Pb and Zn exceeded their corresponding background values with varying degrees of enrichment. Notably, the average contents of Cd, Hg and Pb were 26.70 times, 3.69 times and 4.49 times higher than those in Chinese soils on average, respectively, showing obvious enrichment characteristics. Moreover, there were distinct spatial distribution patterns for each heavy metal element; Ni and Cr exhibited similar trends mainly controlled by the parent material, while human activities significantly affect the other six elements forming high-value areas around mining and related industries. It is noteworthy that Cu, Hg and Zn were influenced by dominant wind direction in autumn and winter, forming sub-high-value zones in southern forested areas; meanwhile, Cu and Zn were also influenced by agricultural fertilizer application as well as surface runoff, leading to secondary high-value areas in the dryland areas. Further analysis revealed a significant positive correlation among these heavy metal elements, suggesting that they may share common sources. Through the PMF Model, four main factors were identified, with factor 2 (36.25%), factor 1 (23.00%), factor 3 (21.20%) and factor 4 (19.55%) ranked in descending order of contribution rate. The heavy metal pollution in the study area was attributed to anthropogenic activities and natural factors, accounting for 63.75% and 36.25%, respectively. Coal mining, chemical industry smelting, vehicle emissions and excessive use of agrochemicals were identified as the main sources of heavy metal pollution. These pollutants entered the soil through direct emissions, atmospheric deposition, transportation and agricultural activities, exerting a significant impact on the soil environment. Therefore, delving into the spatial distribution pattern of soil heavy metal pollution and precise analysis of its sources are of great importance for effective treatment and remediation of soil heavy metal pollution in small watersheds, maintaining healthy soil ecology and safeguarding human health.
{"title":"Contamination Characteristics and Source Apportionment of Heavy Metal in the Topsoil of a Small Watershed in South Taihang","authors":"Jiafu Liu, Yuxin Chen, Yingtao Shang, Hongbo Li, Quanlai Ma, Fengjie Gao","doi":"10.3390/land13071068","DOIUrl":"https://doi.org/10.3390/land13071068","url":null,"abstract":"With the rapid development of industrialization and urbanization, the issue of soil environmental pollution is becoming more and more prominent, especially concerning heavy metal contamination, which has garnered significant scholarly attention. The surface watershed formed by waterline is influenced by various factors such as topography, industrial emissions, and agricultural runoff, resulting in a complex process of migration and accumulation of heavy metal elements from multiple sources. In this study, the pollution characteristics and sources of heavy metal elements Hg, As, Pb, Ni, Cd, Cr, Cu and Zn in 165 surface soil samples from the Manghe River watershed in Jiyuan City were comprehensively analyzed using a variety of methods, including statistics, geostatistics, enriched factor analysis and the Positive Matrix Factorization Model (PMF). The results showed that the concentrations of Hg, Cd, As, Cu, Pb and Zn exceeded their corresponding background values with varying degrees of enrichment. Notably, the average contents of Cd, Hg and Pb were 26.70 times, 3.69 times and 4.49 times higher than those in Chinese soils on average, respectively, showing obvious enrichment characteristics. Moreover, there were distinct spatial distribution patterns for each heavy metal element; Ni and Cr exhibited similar trends mainly controlled by the parent material, while human activities significantly affect the other six elements forming high-value areas around mining and related industries. It is noteworthy that Cu, Hg and Zn were influenced by dominant wind direction in autumn and winter, forming sub-high-value zones in southern forested areas; meanwhile, Cu and Zn were also influenced by agricultural fertilizer application as well as surface runoff, leading to secondary high-value areas in the dryland areas. Further analysis revealed a significant positive correlation among these heavy metal elements, suggesting that they may share common sources. Through the PMF Model, four main factors were identified, with factor 2 (36.25%), factor 1 (23.00%), factor 3 (21.20%) and factor 4 (19.55%) ranked in descending order of contribution rate. The heavy metal pollution in the study area was attributed to anthropogenic activities and natural factors, accounting for 63.75% and 36.25%, respectively. Coal mining, chemical industry smelting, vehicle emissions and excessive use of agrochemicals were identified as the main sources of heavy metal pollution. These pollutants entered the soil through direct emissions, atmospheric deposition, transportation and agricultural activities, exerting a significant impact on the soil environment. Therefore, delving into the spatial distribution pattern of soil heavy metal pollution and precise analysis of its sources are of great importance for effective treatment and remediation of soil heavy metal pollution in small watersheds, maintaining healthy soil ecology and safeguarding human health.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An understanding of how land use efficiency and industrial clusters interact helps one to make informed decisions that balance economic benefits with sustainable urban development. The emergence of industrial clusters is a result of market behavior, while the determination of administrative boundaries is a result of government behavior. When these two are not consistent, it can lead to distortions in the allocation of land resources. However, current research on industrial development and land use efficiency is based on agglomeration within administrative regions rather than on industrial clusters. This study addresses this gap by identifying industrial clusters based on the spatial distribution of enterprises and analyzing their impact on land use efficiency. This study uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify industrial clusters, the convex hull algorithm to study their morphology, and spatial econometrics to measure the relationship between land use efficiency and the scale of industrial clusters. The results indicate the following: (1) the density of manufacturing industry (MI) clusters is significantly higher than that of information technology industry (ITI) clusters, and larger industrial clusters tend to be more circular in shape; (2) there is a positive correlation between the scale of industrial clusters and land use efficiency, and industrial clusters with varying levels of land use efficiency are interspersed throughout; (3) significant differences exist between the boundaries of industrial clusters and administrative regions, which could lead to biases when analyzing land use efficiency based on administrative regions. This study provides theoretical support for government policies on improving land use efficiency in China.
{"title":"A Model to Analyze Industrial Clusters to Measure Land Use Efficiency in China","authors":"Yanzhe Cui, Yingnan Niu, Yawen Ren, Shiyi Zhang, Lindan Zhao","doi":"10.3390/land13071070","DOIUrl":"https://doi.org/10.3390/land13071070","url":null,"abstract":"An understanding of how land use efficiency and industrial clusters interact helps one to make informed decisions that balance economic benefits with sustainable urban development. The emergence of industrial clusters is a result of market behavior, while the determination of administrative boundaries is a result of government behavior. When these two are not consistent, it can lead to distortions in the allocation of land resources. However, current research on industrial development and land use efficiency is based on agglomeration within administrative regions rather than on industrial clusters. This study addresses this gap by identifying industrial clusters based on the spatial distribution of enterprises and analyzing their impact on land use efficiency. This study uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify industrial clusters, the convex hull algorithm to study their morphology, and spatial econometrics to measure the relationship between land use efficiency and the scale of industrial clusters. The results indicate the following: (1) the density of manufacturing industry (MI) clusters is significantly higher than that of information technology industry (ITI) clusters, and larger industrial clusters tend to be more circular in shape; (2) there is a positive correlation between the scale of industrial clusters and land use efficiency, and industrial clusters with varying levels of land use efficiency are interspersed throughout; (3) significant differences exist between the boundaries of industrial clusters and administrative regions, which could lead to biases when analyzing land use efficiency based on administrative regions. This study provides theoretical support for government policies on improving land use efficiency in China.","PeriodicalId":508186,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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