Sustainable Development Of Land Resources Research Articles

Land use dynamics and ecosystem service valuation in the Sanmenxia Reservoir wetland of the Yellow River

The Sanmenxia Reservoir wetland (SRW) serves as a critical ecological buffer in the middle reaches of the Yellow River. Ongoing population growth and changes in land use have placed significant pressure on the wetland’s ecosystems. However, existing research has yet to establish a spatiotemporal analysis method to assess the impact of land use change on the ecosystem service value (ESV). This gap hinders the precise regulation and sustainable development of land resources. To gain a comprehensive understanding of the land use dynamics and ecological functions of the SRW, this study introduced a refined ESV evaluation method. This method revised the equivalent factors from spatial, temporal, and hydrological perspectives, with an emphasis on the impact of sediment discharge. Furthermore, the Patch-level Land Use Simulation (PLUS) model was employed to project the land use structure in 2030, and the corresponding ESV was analyzed. The study revealed the following: (1) Cropland constituted the primary land type in the SRW. Over the last two decades, the build-up area exhibited the most significant single land use dynamics, with the transformation of bare land to cropland spanning the widest range and largest area. (2) From 2000 to 2020, the ESV increased by 0.64 billion CNY, with the water body contributing almost all the increase. (3) The projected ESV for 2030 is 2.94 billion CNY, indicating an increase in ecological functions in the near future. Policy makers should recognizethe impact of land use change on ESV and implement measures to rebalance land use structure. While ensuring the preservation of cropland and the protection of lives and property, it is advisable to consider expanding the water body area to facilitate comprehensive sustainable development within SRW.

Potential and Influencing Factors of Urban Spatial Development under Natural Constraints: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area

As urbanization in China progresses, urban spatial development is transitioning from rapid expansion to more intensive and compact growth. This study examined the role of physical geography and environmental factors in shaping the urban spatial development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Based on the current natural conditions, we selected evaluation indices from topography, hydrogeology, climatic conditions, and natural disasters. These indices were used to create a carrying capacity and suitability evaluation system for development land under natural constraints. Finally, the spatial development potential of the city was finalized by taking into account the current state of the built-up area of the city. Meanwhile, we employed the Optimal Parameters-based Geographical Detector and assessed the impact of 14 natural factors on the spatial development of urban built-up areas. In 2020, the GBA had 52,168.77 km2 of land suitable for construction, of which 34,241.13 km2 was highly suitable (61.29%) and 17,927.64 km2 was moderately suitable (32.09%). At the Bay Area level, 90.15% of the development potential remains untapped; at the city level, Zhaoqing City has the highest potential at 99.56%, while Macao has the lowest at 26.83%. Key factors influencing urban development include silty sand content, annual average relative humidity, and cumulative temperature above 0 °C, with varying impacts across different urban scales. At the Bay Area level, the silty sand content, annual average relative humidity, and cumulative temperature above 0 °C are the main influencing factors on the spatial development of urban built-up areas; at the city level, the main factors are annual average relative humidity and cumulative active temperature above 0 °C. This study reveals the important influence of natural environmental factors on urban spatial development, which is conducive to promoting sustainable development of land resources in GBA.

INTERNATIONAL PRACTICE AND STANDARDS OF LAND VALUATION AND THEIR IMPACT ON THE LAND PLOTS MARKET PRICE

The article analyses international practices and land valuation standards, highlighting their significance in forming market prices for land plots. The authors consider the importance of harmonising national valuation standards with international ones, which will be an essential step toward ensuring transparency, fairness, and efficiency in the land market. The focus is on the impact of internationally recognised standards such as IVS, RICS, and TEGoVA on the land pricing process in different countries. The article discusses in detail the mechanisms through which standardised evaluation methods contribute to increasing the investment attractiveness of land resources, strengthening the confidence of investors and land owners, and improving the legal and economic basis of the land market. The authors emphasise that an adequate assessment of the land that meets international standards is critical for ensuring effective management of land resources, conducting sustainable land use, and preserving the ecological balance. The study reveals the potential advantages and challenges of adopting international assessment standards in Ukraine. In particular, we consider the issues of the need for retraining appraisers, the adaptation of national legislation and regulatory framework, and the potential for increasing transparency and stabilisation of the land market. The article points out the importance of international cooperation and exchange of experience for the successful integration of Ukraine into global market relations, boosting its investment attractiveness and fostering the sustainable development of land resources. The article provides a valuable contribution to understanding the main aspects of international experience in land valuation and its impact on market price formation mechanisms. It calls for further research and discussion of ways to adapt and implement international standards in Ukraine as a necessary prerequisite for developing an open, fair, and efficient land market. The study significantly contributes to the scientific understanding of the harmonisation processes of national approaches to land valuation with international standards and practices, which is relevant in the context of globalisation and market integration. Keywords: international valuation standard, land value, land plot, land cadastre.

Landscape Analysis and Ecological Risk Assessment during 1995–2020 Based on Land Utilization/Land Coverage (LULC) and Random Forest: A Case Study of the Fushun Open-Pit Coal Area in Liaoning, China

China’s Fushun open-pit mine is the largest century-old coal mine in Asia. Large-scale mining and the use of coal has caused dramatic changes in the regional urban landscape pattern, seriously affecting the ecological function and ecosystem stability of its surrounding landscape. Evaluating the ecological risks of the landscape in the urban areas of open-pit mines contributes to the risk management of regional ecosystems and the sustainable development of society. This study selected six-phase Landsat ETM/OLI remote sensing images from 1995 to 2020 and combined them with the random forest model to carry out an LULC classification of the open-pit mine and its surrounding areas and, on this basis, discusses the evolution of its landscape pattern and evaluates the ecological risks. It fills the gap in the research on the evolution of regional landscape patterns and ecological risks in the study area and improves the automatic classification efficiency of LULC for use in open-pit mines. The results show that the classification accuracy of LULC regarding open-pit mines based on image pixels and the random forest model can reach 30 m, and the rate of accuracy can reach 92–97%. From 1995 to 2020, the coverage area of forest land and building land in the study area has increased and is mainly composed of grassland and undeveloped land. The use of land was transferred to the mining area, the water body area maintained a relative dynamic balance, and the overall vegetation coverage of the mining area was greatly improved; the forest land began to expand from the surrounding area to the mining area in 2010, and the construction land began to move from the areas surrounding the mining area to the surrounding valleys in 2015. The landforms have extended radially, and the landscape sprawl index has increased, indicating the optimization of the ecological environment; the high- and medium-risk areas decreased by 75.51 km2, the low-risk areas expanded by 461.48 km2, and the overall ecological risk index decreased. From this, it is possible to conclude that the landscape restoration project adopted in the study area has achieved great results, and the improvement of the ecological environment also directly affects the increase of construction land. These research results can provide scientific guidance for the rational utilization and sustainable development of land resources in urban areas of open-pit mines.

THE USE OF GIS TECHNOLOGIES IN THE MANAGEMENT AND MONITORING OF LAND RESOURCES

This article explores the utilization of Geographic Information Systems (GIS) technologies in the management and monitoring of land resources. GIS has emerged as a powerful tool for analyzing spatial data, facilitating informed decision-making, and enhancing resource management practices. This article provides an overview of the key components of GIS technology and its applications in land resource management. By leveraging spatial data and analytical tools, GIS enhances decision-making processes, supports sustainable land use practices, and contributes to the conservation and sustainable development of land resources on a global scale.

Current Situation and Optimization Measures of Land Resource Management

In recent years, with the continuous expansion of urban scale, the population living in cities has been increasing, and land resources are becoming increasingly scarce. With the development of society, people's attention to land resources is also increasing. In this situation, it is important to recognize that in order to promote better economic development and overall social development. And it is necessary to optimize the management and allocation of land resources, and develop feasible optimization measures based on development issues to promote sustainable development of land resources.

Desertification Reversal Promotes the Complexity of Plant Community by Increasing Plant Species Diversity of Each Plant Functional Type

Desertification reversal is globally significant for the sustainable development of land resources. However, the mechanisms of desertification reversal at the level of plant community are still unclear. We hypothesized that desertification reversal has clear effects on plant community composition, plant functional types (PFTs), and other vegetation characteristics, including plant diversity and biomass, and their changes in the early stages of reversal are more dramatic than in later stages. We investigated the vegetation of four to five different stages of desertification reversal at each of seven large study sites in southwestern Mu Us Sandy Land, China. The results show that the dominant species in very severe desertification areas were replaced by perennial grasses in potential desertification areas. The importance values of annual forbs and perennial sub-shrubs decreased dramatically (from 42.59 and 32.98 to 22.13 and 5.54, respectively), whereas those of perennial grasses and perennial forbs increased prominently (from 13.26 and 2.71 to 53.94 and 11.79, respectively) with the reversal of desertification. Desertification reversal increased the complexity of plant community composition by increasing plant species in each PFT, and C3 plants replaced C4 plants to become the dominant PFT with reversal. Plant species richness and species diversity rose overall, and aboveground plant biomass significantly (p < 0.05) increased with the reversal of desertification. Most vegetation characteristics changed more strikingly in the early stages of desertification reversal than in later stages. Our results indicate that the type and composition of the plant community were dramatically affected by desertification reversal. Anthropogenic measures are more applicable to being employed in early stages than in later stages, and Amaranthaceae C4 plants are suggested to be planted in mobile dunes for the acceleration of desertification reversal. This study is useful for designing strategies of land management and ecological restoration in arid and semiarid regions.

An analysis of the spatiotemporal evolution and driving force of cultivated land green utilization in karst region of southwest China

Optimizing the green use efficiency of cultivated land in the karst region of southwest China is crucial for promoting sustainable development of land resources and achieving harmony between humans and nature. Based on the epsilon-based measure model and geographically and temporally weighted regression methods, this paper investigated the spatio-temporal variation and influencing factors of cultivated land green utilization efficiency. The results indicated that: i) Cultivated land green utilization efficiency had slightly increased from 0.842 in 2000 to 0.866 in 2020. ii) The spatial pattern of cultivated land green utilization efficiency showed a significant positive correlation, with a “hotspot-not significant-coldspot” from north to south. iii) Its spatial heterogeneity had increased during the observation period, with high-efficiency areas shifting from northeast to north. iv) The proportion of agricultural economy, general public budget revenue, per unit cultivated land value had a positive impact on the cultivated land green utilization efficiency, while urbanization rate, per capita cultivated land area, land replanting index, average altitude had a negative impact. Moreover, the driving factors exhibited significant spatiotemporal heterogeneity. Therefore, it is necessary to promote technological progress and scientific research, implement funds and policies, leverage the leading role of high-efficiency areas, formulate differentiated development strategies, and thereby improve the green use efficiency of cultivated land.

Characterizing land use transition in China by accounting for the conflicts underlying land use structure and function

Human activities and global changes have undergone significant transformations in land systems. It is imperative to comprehend the intricacies of land use transitions (LUT), as they serve as indicators of the evolving dynamics within the complex human-environmental relationship. In this study, we adopt the conflict-coordination theoretical framework to delve into the role of land-use conflicts (LUC) in driving LUT. Our investigation involves the quantification of LUT and the assessment of LUC within China, spanning from 1980 to 2020. Subsequently, we scrutinized the interplay between LUT and LUC using Spearman correlation analysis and the Wilcoxon test. Our findings reveal that greater diversity in land-use structure can be attributed to ecological land fragmentation and the restoration of unused land, while the expansion of urban and cropland areas contributes to a less diverse land-use pattern. Furthermore, there is a discernible upward trend in land use function. Although the overall degree of LUC in China is relatively mild, there is a noticeable upward trajectory over the past four decades. More pronounced conflicts within land-use patterns promote a diversified land-use pattern, while conflicts arising from competing land-use functions elevate the level of land-use functionality. Moreover, a dynamic interplay between land-use transitions and conflicts becomes evident, with intense conflicts within land-use patterns weakening land-use functionality, and imbalances in functionality driving greater diversity in land-use patterns. Our research introduces an innovative analytical framework that elucidates LUT through the lens of LUC, with the potential for broader applicability across different regions. This framework not only enhances our understanding of LUT but also provides valuable scientific guidance for the effective management of conflicts to promote the sustainable development of land resources.

Identification and Spatial Analysis of Land Salinity in China's Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery.

Precise identification and spatial analysis of land salinity in China's Yellow River Delta are essential for the rational utilization and sustainable development of land resources. However, the accurate retrieval model construction for monitoring land salinity remains challenging. This study constructed a land salinity retrieval framework using a harmonized UAV and Landsat-9 multi-spectral dataset. The Kenli district of the Yellow River Delta was selected as the case study area, and a land salinity monitoring index (LSMI) was proposed based on field survey data and UAV multi-spectral image and applied to the reflectance-corrected Landsat-9 OLI image. The land salinity distribution patterns were then mapped and spatially analyzed using Moran's I and Getis-Ord GI* analysis. The results demonstrated the following: (1) The LSMI-based method can accurately retrieve land salinity content with a validation determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of 0.75, 1.89, and 2.11, respectively. (2) Land salinization affected 93.12% of the cultivated land in the study area, and the severely saline soil grade (with a salinity content of 6-8 g/kg) covered 38.41% of the total cultivated land area and was widely distributed throughout the study area. (3) Saline land exhibited a positive spatial autocorrelation with a value of 0.311 at the p = 0.000 level; high-high cluster types occurred mainly in the Kendong and Huanghekou towns (80%), while low-low cluster types were mainly located in the Dongji, Haojia, Kenli, and Shengtuo towns (88.46%). The spatial characteristics of various salinity grades exhibit significant variations, and conducting separate spatial analyses is recommended for future studies.

Comprehensive Sustainability Indicator for Land Resource-Carrying Capacity in a Farming-Pastoral Region

The Northeast Farming-pastoral ecotone (NFPE) in China is crucial for farming and pastoralism. However, sustainable development of land resources faces several challenges from human activities and environmental degradation. Thus, assessing land resource-carrying capacity (LRCC) is crucial for effective land-use planning and management. This study proposes a comprehensive indicator system to evaluate LRCC in the NFPE. We integrate remote sensing, model simulation, and statistical data, employing Geographic Information System analysis and statistical techniques to assess spatiotemporal evolution characteristics of LRCC. Findings indicate increasing LRCC with spatial-temporal heterogeneity. The northern region exhibits relatively high and growing LRCC, while some eastern regions have low LRCC but a high growth trend. The southern region displays both low LRCC and growth. Balancing regional development and ecological protection requires considering spatial heterogeneity of LRCC in land-use planning and management decisions, facilitating differentiated land-use strategies for sustainable development.

Exploring the land ecological security and its spatio-temporal changes in the Yangtze River Economic Belt of China, 2000–2020

With the rapid development of new urbanization in the Yangtze River Economic Belt (YREB), it has hindered the sustainable development of land resources and posed a great threat to the regional land ecological security (LES). So, it is of great significance to explore the spatiotemporal evolution, and convergence characteristics of LES in the YREB. In this study, we chose the panel data of 11 provinces in the YREB from 2000 to 2020 to measure and analyze the levels of LES, the degree of regional differences, the sources of those differences, and the distribution dynamics using the entropy weight method, the Dagum Gini coefficient and the three-dimensional kernel density estimation method. The study also combines the coefficient of variation and a spatial convergence model to examine the convergence characteristics of LES in the context of new urbanization in the YREB. The results indicate that the levels of LES in the YREB and its three main regions (upstream, midstream, and downstream) are on an upward trend, showing a clear spatial pattern (high in the middle and low in the east and west), and that the absolute differences are decreasing. No polarization is evident, and the differences between the regions are the primary source of overall differences. The new urbanization has a significant positive effect on the LES of YREB and three regions. These results have a number of practical implications for the provinces of the YREB, which should take full account of their resources and environmental conditions, introduce land ecology protection policies tailored to local conditions, actively promote the synergistic management of the land ecology of the upstream, midstream, and downstream regions, and provide quantitative support for the coordinated promotion of new urbanization and the land ecological protection.

A Light-Weight Neural Network Using Multiscale Hybrid Attention for Building Change Detection

The study of high-precision building change detection is essential for the sustainable development of land resources. However, remote sensing imaging illumination variation and alignment errors have a large impact on the accuracy of building change detection. A novel lightweight Siamese neural network building change detection model is proposed for the error detection problem caused by non-real changes in high-resolution remote sensing images. The lightweight feature extraction module in the model acquires local contextual information at different scales, allowing it to fully learn local and global features. The hybrid attention module consisting of the channel and spatial attention can make full use of the rich spatiotemporal semantic information around the building to achieve accurate extraction of changing buildings. For the problems of large span of changing building scales, which easily lead to rough extraction of building edge details and missed detection of small-scale buildings, the multi-scale concept is introduced to divide the extracted feature maps into multiple sub-regions and introduce the hybrid attention module separately, and finally, the output features of different scales are weighted and fused to enhance the edge detail extraction capability. The model was experimented on the WHU-CD and LEVIR-CD public data sets and achieved F1 scores of 87.8% and 88.1%, respectively, which have higher change detection accuracy than the six comparison models, and only cost 9.15 G MACs and 3.20 M parameters. The results show that our model can achieve higher accuracy while significantly reducing the number of model parameters.

Concentrations and Stoichiometric Characteristics of C, N, and P in Purple Soil of Agricultural Land in the Three Gorges Reservoir Region, China

Soil stoichiometry is an essential tool for understanding soil nutrient balance and cycling. Previous studies have recognized that some relationships were observed between particle size and carbon and nitrogen parameters. This study attempted to evaluate nutrient element concentrations and their stoichiometric ratios of surface soil (0–10 cm) under different land use types (forest, sloping arable land, paddy fields, and orchards). and different particle sizes (<32 µm, <63 µm, and <125 µm) from a small typical hilly catchment (0.35 km2) in the Three Gorges Reservoir Region of China. The contents of soil organic carbon (SOC), total nitrogen (TN). and total phosphorus (TP) were measured, and the ratios of C:N, C:P, N:P were calculated. The results indicated that land use type and soil particle size have diverse impacts on the studied indexes (SOC, TN, TP, C:N, C:P, and N:P). Six indexes were significantly affected by land use type (p < 0.01), while only C:N ratio was statistically influenced by soil particle size (p < 0.05). Furthermore, several significant differences of studied parameters of four land use types grouped within three particle sizes were found. The concentrations of SOC (12.34~13.46 g kg−1), TN (1.27~1.59 g kg−1), and TP (0.71~0.92 g kg−1) in the study site were lower than the national average values of China. Moreover, the productivity in the study area was mainly limited by TN concentration. Additionally, the concentration of TP decreased obviously with the increase in particle size. Furthermore, various coupling relationships were validated by linear and nonlinear fitting among different indexes. At the small catchment scale, take forest as a reference, human activities have significant impact on C-N-P stoichiometry (p < 0.05). Especially, tillage may reduce SOC and TN contents, leading to a decline in soil quality. Overall, our findings can provide a basis for rational utilization and sustainable development of land resources.

Is land degradation worsening in Northern China? Quantitative evidence and enlightenment from satellites

AbstractLand degradation has become an urgent environmental issue globally. The complexity of land degradation hinders its quantification and monitoring, which is needed to realize the sustainable development of land resources. This study constructed a comprehensive index—land degradation status index (LDSI), which integrates in fractional vegetation cover (FVC), net primary productivity (NPP), albedo, and modified temperature vegetation drought index (MTVDI) based on spatial principal component analysis (SPCA). Land degradation was then identified by the dynamics of land degradation status during 2001–2018. This study investigated the spatio‐temporal process and driving mechanism of land degradation in Northern China. The result indicates that: (1) LDSI had a better monitoring performance compared with normalized difference vegetation index (NDVI), (2) although some degraded land had been effectively rehabilitated (17.11%), a highly clustered spatial distribution of land degradation status remained, challenging to break, (3) localized land degradation had expanded (1.063%), mainly in semiarid (0.481%), dry sub‐humid (0.289%), and humid (0.187%) regions, and (4) differences in climate, environmental backgrounds, and human activities were driven land degradation status and process. This study also assessed the effectiveness of ecological projects implemented by the Chinese government. The in‐depth understanding of the change regularity and influencing mechanism in land degradation status and process can provide a scientific basis for formulating ecological policies based on local conditions.

Intelligent classification model of land resource use using deep learning in remote sensing images

Accurate and effective remote sensing image classification and analysis methods can provide reliable support for the sustainable development of land resources, but the current methods can't support the precise processing of complex images. To solve this problem, this paper proposes a new classification and analysis method of remote sensing image land resources based on deep learning. Firstly, this method first uses ENVI software to realize image preprocessing on the collected data set, and realizes reliable data set; At the same time, based on the sparse auto-encoder network model, the sample features of the image data set are deeply mined to make a better reference for category determination; And based on ELM classifier network, convolution layer and sub sampling layer are alternately connected to extract the depth feature of remote sensing image, which can greatly reduce the number of network parameters and improve the computational. Finally, the actual collected data set is used for simulation verification, and the experimental results show that the accuracy of complex image analysis reaches 98.15%, which shows excellent performance of image feature classification and analysis.

Improving Typical Urban Land-Use Classification with Active-Passive Remote Sensing and Multi-Attention Modules Hybrid Network: A Case Study of Qibin District, Henan, China

The study of high-precision land-use classification is essential for the sustainable development of land resources. This study addresses the problem of classification errors in optical remote-sensing images under high surface humidity, cloud cover, and hazy weather. The synthetic aperture radar (SAR) images are sensitive to soil moisture, and the microwave can penetrate clouds, haze, and smoke. By using both the active and passive remote-sensing data, the Sentinel-1A SAR and Sentinel-2B multispectral (MS) images are combined synergistically. The full-band data combining the SAR + MS + spectral indexes is thus constructed. Based on the high dimensionality and heterogeneity of this data set, a new framework (MAM-HybridNet) based on two-dimensional (2D) and three-dimensional (3D) hybrid convolutional neural networks combined with multi-attention modules (MAMs) is proposed for improving the accuracy of land-use classification in cities with high surface humidity. In addition, the same training samples supported by All bands data (SAR + MS + spectral index) are selected and compared with k-Nearest Neighbors (KNN), support vector machine (SVM), 2D convolutional neural networks, 3D convolutional neural networks, and hybridSN classification models to verify the accuracy of the proposed classification model. The results show that (1) fusion classification based on Sentinel-2B MSI and Sentinel-1A SAR data produce an overall accuracy (OA) of 95.10%, a kappa coefficient (KC) of 0.93, and an average accuracy (AA) of 92.86%, which is better than the classification results using Sentinel-2B MSI and Sentinel-1A SAR images separately. (2) The classification accuracy improves upon adding the spectral index, and the OA, KC, and AA improve by 3.77%, 0.05, and 5.5%, respectively. (3) With the support of full-band data, the algorithm proposed herein produces better results than other classification algorithms, with an OA of 98.87%, a KC of 0.98, and an AA of 98.36%. These results indicate that the synergistic effect of active-passive remote-sensing data improves land-use classification. Additionally, the results verify the effectiveness of the proposed deep-learning classification model for land-use classification.

Study on Spatial Optimal Allocation of Land Use in Fenhe River Basin

Land is the basis for the survival and development of human society, and it is also an important factor that restricts the development of social economy. It has become an urgent problem to make scientific and efficient use of limited land resources, formulate a scientific land use optimization scheme and promote the efficient and sustainable development of land resources. In this paper, taking Fenhe River Basin as the research area, combining the current land use data, remote sensing data and social and economic data, ArcGIS is used to evaluate the land suitability, and based on the planning constraint index and land use allocation theory, the land use space is optimally allocated. The main conclusions of the optimization are as follows: (1) Through the analysis of the present situation of land use and the comparative analysis of the quantitative constraint indicators of various regions, the total area of cultivated land has decreased compared with the original situation of land use, but it has actually increased and decreased, mainly in rural residential areas with convenient irrigation conditions and low and flat terrain, which are distributed on both sides of Fenhe River and its tributaries in Fenhe Valley, mainly in areas with serious soil and water loss in Luliang Mountains. It is required to return farmland to forest and grassland, and to restore it to forestry land and grassland, so as to keep good land in Taiyuan Basin and Linfen Basin. (2) The total area of urban and rural construction land has increased compared with the original land use status, mainly in the general suitable areas of agricultural land affected by human beings around the existing towns. Qingxu County has a good development foundation, which can give play to the urban-rural linkage mechanism and establish a satellite city of Taiyuan. (3) The overall layout of ecological land has not changed much, mainly increasing the water source protection in ningwu county, the dam along the river in the scenic spot, and the construction of green belt along the river of Fenhe River and its tributaries.

Spatial-temporal dynamics and evolution of ecological security in a rapid urbanization city, Southwest China

The main goal of constructing ecological security patterns (ESPs) is to identify ecological sources, corridors and nodes that play significant roles in sustainable development on a regional scale. Although there are many studies on the construction of ESPs, there is no consensus in terms of research methodology and systematic frameworks for integrated landscape management. Based on land use data from 2000, 2010, and 2020 of Chongqing Municipality in southwest China, we evaluated the spatial-temporal variation of ESPs by integrating InVEST and Circuit Theory. Results showed that: (a) Habitat quality varied through space, with habitat quality being lower in the western and central regions and higher in the southeastern and northeastern regions. (b) The area of lower quality habitat across different time periods was more than 46%, and habitat quality over the last two decades has generally been low with no significant improvement. (c) From 2000 to 2020, ecological sources were primarily distributed in the mountainous areas with high habitat quality and fractional vegetation coverage in the northeast and southeast. The regions identified ecological sources in 2000, 2010, and 2020 accounted for 31.37, 33.53, and 32.7% of Chongqing Municipality, respectively. (d) The ESPs were composed of ecological sources dominated by forests, connected by continuous ecological corridors. The current ESPs of Chongqing Municipality included 20 ecological nodes, 17 continuous ecological corridors and 23 ecological sources. We strongly suggest the local governments strengthen the protection of the identified ecological nodes, ecological corridors, ecological sources, and protection gaps, and focus on strengthening the construction and management of the ecological corridor network system to promote species diffusion and gene exchange. Our findings are helpful for policy makers to introduce appropriate measures to objectively guide urban expansion via rational and sustainable development of land resources and improve the level of ecological security for Chongqing Municipality.

An integrated modeling approach for ecological risks assessment under multiple scenarios in Guangzhou, China

High-intensity human activities have caused dramatic changes in land-use structure in Guangzhou. It is essential to understand the spatiotemporal dynamics of land use and the induced ecological risks. In this research, a framework was developed for landscape ecological risk (LER) prediction based on a patch-generating land use simulation (PLUS) model through incorporating analysis of land expansion strategies, CA model based on multi-type random patch seeds and LER assessment model. The results showed: 1) the urbanization process of Guangzhou was obvious in the past 20 years. Proximity to railways, highways, and secondary roads have the primary impact on the expansion of cultivated land, grassland and construction land, respectively, 2) Guangzhou's future urban expansion is inevitable based on the PLUS model prediction, 3) during 2000–2020, the overall LER in Guangzhou was at a relatively low level. The higher ecological risk area was mainly distributed in the southern sea area, and 4) from 2020 to 2040, Guangzhou's higher ecological risk areas are on an overall decreasing trend under three scenarios. This research can provide a reference for the planning of regional ecological environmental protection and sustainable development of land resources, as well as LER prediction.