Forest Landscape Pattern Research Articles

Top 10+1 indicators for assessing forest ecosystem conditions: A five-decade fragmentation analysis

Globally, land use change has consistently resulted in greater losses than gains in aboveground biomass (AGB). Forest fragmentation is a primary driver of biodiversity loss and the depletion of natural capital. Measuring landscape characteristics and analyzing changes in forest landscape patterns are essential for accounting for the contributions of forest ecosystems to the economy and human well-being. This study predicts national forest distribution for 2036 and 2054 using a Cellular Automata (CA) system and assesses ecosystem conditions through landscape metrics at the patch, class, and landscape levels. We calculated 130 metrics and applied a Variance Threshold method to remove features with low variance, testing different thresholds. The first filtered-out metrics were further analysed through Principal Component Analysis combined with a Feature Importance technique to select and rank the top 10 indicators: effective mesh size, splitting index, mean radius of gyration, largest patch index, mean core area, core area percentage, Simpson's evenness index, mutual information, Simpson's diversity index, and mean contiguity index. The eleventh selected indicator is the AGB density, a structural measurement for ecosystem condition and a proxy for forest carbon storage and sequestration assessments. From 2000 to 2018, the national AGB forest carbon stock decreased from 131.5 to 91.3 Megatons (Mt) with expected values for 2036 and 2054 being 71.8 and 55.3 Mt., respectively. Landscape measurements quantitatively describe forest dynamics, providing insights into the structure, configuration, and changes characterizing landscape evolution. This research underscores the capability of CA models to map large-scale forest resources and predict future development scenarios, offering useful information for conservation and environmental management decisions. Additionally, it provides measurements to support Ecosystem Accounting by assessing forest extent and indicators of its conditions.

Impact of Forest Landscape Patterns on Ecological Quality in Coastal Cities of Fujian, China, from 2000 to 2020

The Fujian coastal zone, a key region in China’s coastal belt, has experienced significant landscape and ecological changes due to intense human activities. Understanding the relationship between landscape patterns and ecological quality is critical for sustainable development and ecological protection. Taking the coastal cities, including Fuzhou, Xiamen, and Ningde in Fujian Province of China, as a case, the spatio–temporal changes in landscape patterns and the remote sensing-based ecological index (RSEI) during 2000 and 2020 were explored by the Google Earth Engine (GEE) cloud platform, and then their spatial relationships were identified through Pearson correlation analysis and bivariate spatial autocorrelation analysis. The findings reveal that (1) forest land was the dominant landscape in Fuzhou and Ningde, while cropland prevailed in Xiamen. Significant changes occurred in the land use landscape patterns of the three cities, mainly due to a substantial increase in the built-up land and varying degrees of reduction in arable and forest land. At the landscape level, both Fuzhou and Xiamen exhibited increased landscape fragmentation, while Ningde showed a trend of landscape aggregation; at the class level, forest land in Fuzhou and Xiamen exhibited increased fragmentation, whereas in Ningde, it showed an aggregation trend. (2) Between 2000 and 2020, the ecological–environmental quality of Fuzhou and Ningde continuously improved, while the improvement in Xiamen was less significant. Poor and fair ecological environments in the three cities were mainly concentrated in city centers and coastal zones, and areas of ecological quality degradation were primarily concentrated in coastal zones. (3) Correlation analysis indicates that, whether at the landscape level or the class level, the ecological quality of the three cities is significantly negatively correlated with the fragmentation index and significantly positively correlated with the aggregation index. Moreover, the positive correlation between ecological quality and the forest landscape aggregation index, as well as the negative correlation with the forest landscape fragmentation index, are both significantly stronger than those at the landscape level. As urbanization progresses, forest landscape fragmentation intensifies, especially in city centers and coastal areas, having a significant negative impact on ecological quality. These results highlight the importance of landscape pattern management in maintaining ecological quality. This paper provides insights for coastal cities on balancing urban development with ecological preservation in the context of rapid urbanization.

Satellite Reveals a Coupling between Forest Displacement and Landscape Fragmentation across the Economic Corridor of the Eurasia Continent

Jointly building the Economic Corridor of the Eurasia Continent (ECEC), which is one of the most important parts of the Silk Road Economic Belt, is a pivotal initiative for fostering regional development. Forests, which serve as a green foundation of economic resilience, underpin this effort. However, there is an imbalance in ecological status due to differences in natural resources and the social economy along the economic corridor. This imbalance has led to alterations in landscapes, yet the specific changes and their underlying relationships are still much less understood. Here, we quantitatively detected changes in the forest landscape and its ecological efforts over the ECEC via widespread, satellite-based and long-term land cover maps released by the European Space Agency (ESA) Climate Change Initiative (CCI). Specifically, the coupling between changes in forest coverage and landscape patterns, e.g., diversity, was further examined. The results revealed that forest coverage fluctuated and declined over the ECEC from 1992 to 2018, with an overall reduction of approximately 9784.8 km2 (i.e., 0.25%). Conversions between forests and other land cover types were widely observed. The main displacements occurred between forests and grasslands/croplands (approximately 48%/21%). Moreover, the landscape diversity in the study area increased, as measured by the effective diversity index (EDI), during the study period, despite obvious spatial heterogeneity. Notably, this pattern of landscape diversity was strongly associated with forest displacement and local urban development through coupling analysis, consequently indicating increasing fragmentation rather than biological diversity. This study highlights the coupled relationship between quantitative and qualitative changes in landscapes, facilitating our understanding of environmental protection and policy management.

The Impact of Urban Forest Landscape on Thermal Environment Based on Deep Learning: A Case of Three Main Cities in Southeastern China

Accelerated urbanization has exacerbated the urban heat island phenomenon, and urban forests have been recognized as an effective strategy for modulating thermal environments. Nevertheless, there remains a dearth of systematic investigations into the nonlinear associations between the detailed spatial configurations of urban forests and thermal conditions. We proposed a deep learning-based approach to extract forest data, utilizing multisource high-resolution remote sensing data with relative radiometric correction. Subsequently, we employed deep neural networks (DNNs) to quantify the linkages between urban forest landscape patterns and land surface temperature (LST) in summer and winter across Fuzhou, Xiamen, and Zhangzhou in Fujian Province, China. Our findings indicate the following: (1) Our extraction approach outperforms DeepLabv3+, FCN_8S, and SegNet in terms of extraction precision and adaptability, achieving an overall accuracy (OA) of 87.57%; furthermore, the implementation of relative radiometric correction enhances both the extraction precision and model generalizability, improving OA by 0.05%. (2) Geographic and seasonal differences influence the urban forests’ cooling effects, with more pronounced cooling in summer, particularly in Zhangzhou. (3) The significance of forest landscape composition and configuration in affecting the thermal environment varies seasonally; landscape configuration plays a more pivotal role in modulating surface temperatures across the three cities, with a more critical role in winter than in summer. (4) Seasonal and city-specific variations in forest spatial patterns influence LST. Adopting the appropriate forest structures tailored to specific seasons, cities, and scales can optimize cooling effects. These results offer quantitative insights into urban heat island dynamics and carry significant implications for urban planning strategies.

Study on the influence of landscape pattern on the spread of pine wilt disease from a multi-scale perspective

Since the introduction of pine wilt disease (PWD) into Chongqing, China, it has been continuously spreading, posing a significant threat to the local forest ecosystem dominated by pine forests. Utilizing data spanning the last two decades on the PWD epidemic subcompartment and host coniferous forest in Chongqing, this study employs a geographic detector model to scrutinize the impact of forest landscape patterns on PWD dissemination at the county, township, and 1 km grid scales. The findings reveal three distinct stages of the PWD epidemic in Chongqing from 2001 to 2020: diffusion, stabilization, and outbreak. The western and central regions of Chongqing experienced severe impacts, while the southern part exhibited relatively milder effects. Across the three spatial scales, various landscape pattern indices exhibit robust and consistent impacts on PWD occurrence. Notably, patch area emerges as the most influential factor, with an explanatory power reaching 0.228. Additionally, landscape shape index, number of patches, patch density, river and road exert substantial influence on PWD incidence. Conversely, the impact of edge density, landscape dominance, landscape separation, and landscape fragmentation on PWD occurrence is comparatively weak. Moreover, the interaction of different landscape pattern factors nonlinearly enhances the explanatory power of PWD occurrence. Among these interactions, the synergy between patch area and landscape dominance proves to have the highest explanatory power for the spatial differentiation of PWD at 0.718. This study explores the relationship between the invasion and spread of PWD and landscape patterns, offering useful insights for the prevention and early warning of PWD. Additionally, this research provides valuable reference points for promoting the healthy and sustainable development of forest resources in China.

A landscape index for indicating the spatio-temporal dynamics of carbon storage in an opencast coal mine

Coal is China’s main resource, with open-pit mining accounting for a significant portion of global production. However, this activity, including mining and ecological restoration, can have a definite impact on ecosystem carbon storage and its distribution; its associated factors are also unclear. In this paper, we quantify the carbon storage changes in Haerwusu coal mine, a typical large-scale coal mine in China, based on land use/land cover (LULC) characteristics, and analyze the impact factors of carbon density from 2007 to 2022 by integrating the InVEST model with the landscape ecological function contribution ratio and multiple regression model. The results are as follows. (1) Carbon storage decreased from 159.95 × 104 to 147.51 × 104 from 2007 to 2017 and then increased to 151.91 × 104 to 2022. (2) The degree of coordination between carbon storage forest and grassland area landscape pattern coupling ranged from 0.887 to 0.867 from 2007 to 2022, with the lowest point at 0.720 in 2012. (3) Carbon storage was significantly related to vegetation indices, temperature, and elevation, and these factors can explain 37.5% of the carbon storage spatial variability; stepwise regression analysis showed that the integration of landscape patterns, such as Shannon’s diversity index (SHEI) and the aggregation index (AI), could improve the explanation by 1.4%. (4) Based on the analysis of the landscape ecological function contribution ratio, the carbon storage-sensitive areas can be classified into three levels: extremely sensitive areas ranging 0 to 4 km from the mine, sensitive areas ranging 4 to 8 km, and insensitive areas ranging beyond 8 km. This study proposes a strategy for analyzing changes of carbon storage in coal mines, highlights the important role of landscape patterns in influencing carbon storage, and provides a reliable reference support for the ecological management of coal mines.

Landscape Ecological Risk Assessment of Saihanba under the Change in Forest Landscape Pattern

Examining the Saihanba Mechanical Forest Farm, this study utilized Landsat remote sensing data from 1987, 1997, 2001, 2013, and 2020 to interpret land use from the Support Vector Machine (SVM) method, and to decipher evolving land use patterns over the last four decades. Grounded in landscape ecology theory, an innovative evaluation index for landscape ecological risk was introduced, leading to the delineation of 382 ecological risk evaluation units. Employing landscape pattern indices and a method of spatial autocorrelation, we analyzed the spatial and temporal distribution characteristics and spatial correlation patterns of landscape ecological risk across five distinct periods. Geostatistical approaches were used to explore the driving factors of landscape risk. The results indicate that since 1987, there have been significant changes in land use types, especially in forest landscapes, their proportion increasing from 23.19% to 74.55%. In 1987, the proportion of high-risk areas was 72.30%, but in 2020, high-risk areas had significantly decreased and clustered in specific locations. The landscape ecological risks in each period of the study area showed a positive spatial correlation and tended to gather in space. After comprehensive exploration using a geographic detector, we found that landscape type, temperature, and vegetation coverage are the main risk factors. Among them, landscape type has the greatest impact on the landscape and works together with slope, aspect, and precipitation. In forest farm management, only the adaptation and adjustment of single factors are often paid attention to, while the compound effects of multiple factors are ignored. The results of this study bring important reference value to the operation and development of forest farms.

Effects of urban green spaces landscape pattern on carbon sink among urban ecological function areas at the appropriate scale: A case study in Xi’an

At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on regional or city-wide areas, often overlooking the nuanced effects of landscape pattern on carbon sink within specialized urban ecological function areas. Focusing on the city of Xi'an and emphasizing the forest within green spaces as the principal subject, this study investigated the spatial interplay between forest landscape pattern and carbon sink across the Ecological Control Area, the Urban Ecological Construction Buffer Area, and the Tsinling Mountains Ecological Protection Area. Grounded in field surveys, this study employed GF1-WFV remote sensing imagery to ascertain the 2021 land-use data in Xi'an and further classified forests into five distinct types: deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, coniferous and broad-leaved mixed forest, and evergreen-deciduous broad-leaved mixed forest. Employing the Carnegie-Ames-Stanford Approach model and landscape metrics methods, the study evaluated the carbon sink and landscape pattern at multi-scale. Through semi-variance analyses, we discerned the appropriate grid scales. Finally, multiple linear stepwise regression model elucidated the significant landscape metrics, while geographically weighted regression model were employed to scrutinize spatial relationships. Our findings suggested that grid scales of 5 km for the Ecological Control Area, 3 km for the Urban Ecological Construction Buffer Area, and 6 km for the Tsinling Mountains Ecological Protection Area are most appropriate for effective management and planning. Metrics related to area, edge, and fragmentation were pivotal in enhancing carbon sink, and their influence was spatially variable. This study furnished an in-depth analysis of how landscape pattern and carbon sink interact spatially within various urban ecological function areas at appropriate grid scales, thereby providing a scientific framework for the sustainable planning and management of green spaces.

Forest Fire Spread Hazard and Landscape Pattern Characteristics in the Mountainous District, Beijing

Objective: This study established an index system for assessing forest fire spread hazards and conducted a forest fire spread hazard assessment in the mountainous district of Beijing (including Fangshan, Mentougou, Changping, Yanqing, Huairou, Miyun, and Pinggu). The relationship between forested landscape spatial pattern and forest fire spread hazard was explored; this method provided the basis for the establishment of a landscape forest fire security guarantee system. Methods: The forest fire spread hazard assessment index system was constructed from four aspects: forest fuel, meteorological factors, topographic factors, and fire behavior. The weighted comprehensive evaluation method and area-weighted average method were used to calculate the forest fire spread hazard indices at the subcompartment scale and township scale. Moran’s I index was selected as the spatial autocorrelation index to analyze the autocorrelation degree and spatial distribution of the forest fire spread hazard index. Eleven representative landscape pattern indices were selected to analyze the main landscape spatial pattern affecting forest fire spread hazard by correlation analysis and principal component analysis. Results: (1) The areas with high, medium–high, medium-low, and low forest fire spread hazard grades accounted for 39.87%, 33.10%, 11.37%, and 15.66% of the study area, respectively, at the subcompartment scale and for 52.36%, 22.58%, 18.39%, and 6.67% of the study area, respectively, at the township scale. (2) The forest fire spread hazard index results obtained at the subcompartment and township scales in the Mountainous District of Beijing showed a spatial agglomeration distribution law. (3) The forest fire spread hazard was influenced mainly by landscape diversity (SHDI and PRD), landscape aggregation (AI, CONTAG, and PD), and landscape area (TA). Conclusions: The overall forest fire spread hazard in the mountainous district of Beijing showed a gradual increase from plains to mountainous areas. The land types of the high-spread hazard subcompartment mainly included general shrubbery and coniferous forestlands, and the dominant species in the high-spread hazard arbor forest subcompartment were mainly Platycladus orientalis, Pinus tabuliformis, and Quercus mongolica.

Coupling of Forest Carbon Densities with Landscape Patterns and Climate Change in the Lesser Khingan Mountains, Northeast China

This research investigated the effects of the forest landscape composition and spatial distribution and local climate change’s lag effects on the carbon density of stands and provides a reference for optimizing the stand structure and sustainable management of forest resources in Xinqing District, Yichun City, Heilongjiang Province, China. Using second-class forest resource survey data of the Xinqing Forestry Bureau for 2007 and 2017, the forest carbon density, landscape pattern index and local forest climate were quantified by ArcGIS10.7, Fragstats4.2 and SPSS25, and a coupling coordination degree model was constructed to reflect their correlations. The overall broadleaved mixed forest area was larger in the new green area, and the overall forest productivity had improved in the past ten years. Forest management gradually improved from nonforest to forest land, resulting in a high degree of fragmentation in the surrounding landscape. The coupling research on the forest carbon density and the forest landscape pattern index and local climate index showed that, overall, the landscape pattern and the impact of climate change on the forest carbon density had a positive interaction; adjustments and improvements can be made to the forest carbon density in the poor-condition area by combining specific situations of the landscape pattern and climate change.

The Influence of Traditional Ethnic Villages on Forest Structure Based on PLS-SEM: A Case Study of Miao Inhabited Area

The activity characteristics of the village and its symbiotic relationship with the environment play an important role in the sustainable development of the surrounding environment. Miao villages have a long history. In the process of long-term interaction with the surrounding natural environment, Miao villages have formed a unique forest culture, which has an important impact on the distribution of the surrounding forests and plays a crucial role in local forest management. In this study, we took the Miao villages of China that are distributed in Qiandongnan Miao and Dong Autonomous Prefecture of China as an example and constructed a research framework to study the interaction between the natural environment and human activities around the Miao villages and its impact on forest change based on partial least squares structural equation modeling (PLS-SEM) and geographically weighted regression modeling (GWR) methods. The validity and reliability evaluations showed that the PLS-SEM model was reasonable. The results showed that the Miao villages were randomly distributed within 0–2 km and clustered in the 8–10 km buffer zone. The temporal variation of the forest landscape around the Miao villages was small, and the spatial heterogeneity was obvious. Within the 0~2 km buffer zone, the proportion of closed-canopy forest was the largest, and with the increase in the buffer zone radius, the proportion of closed-canopy forest gradually decreased, the open-canopy forest gradually grew, and the proportion of shrubbery and other forests showed an upward trend first and then a downward trend. Temporally, the four forest landscapes did not change much, with closed and open forests sliding, and shrubbery and other forests increasing. Regarding the drivers of forest structure change, topographic factors and landscape patterns had a positive effect on forest structure, while human activities had a negative effect. The influence of topography on human activities in the Miao villages weakened the direct positive effect of topography on forests and promoted the positive effect of the landscape pattern on forests. There were significant spatial differences in the GWR regression coefficients of the effects of different factors on forest structure in 2020, with a pivotal negative correlation between NDVI and night-time light data. In addition, the higher the elevation, the more unfavorable the distribution of open-canopy forests and the better the distribution of closed-canopy forests. The higher the slope, the higher the NDVI but the more unfavorable the distribution of closed-canopy forests. In general, the impacts of Miao villages on forest structure are highly complex and interactive, with both direct and indirect effects. Different factors interact to affect the structure of the forest. The study on the effect of Miao villages on forests is helpful for more targeted forest protection and the formulation of laws and regulations, so as to find a balance between human activities and forest management, in order to maintain the ecological balance of local areas. This study introduced the PLS-SEM model to investigate the impacts of Miao villages on forest structure, which effectively enhanced our understanding of the drivers and impacts of forest change and provides theoretical support and a basis for forest protection and management.

Forest cover, landscape patterns, and water quality: a meta-analysis

Forest cover, landscape patterns, and water quality: a meta-analysis

Assessing Forest Landscape Stability through Automatic Identification of Landscape Pattern Evolution in Shanxi Province of China

The evolution of forest landscape patterns can reveal the landscape stability of forest dynamics undergoing complex ecological processes. Analysis of forest landscape dynamics in regions under ecological restoration can evaluate the impact of large-scale afforestation on habitat quality and provide a scientific basis for achieving sustainable eco-environment development. In this study, a method for assessing forest landscape stability by characterizing changes in forest landscape patterns was proposed. Toeplitz inverse covariance-based clustering (TICC) was used to automatically identify landscape pattern evolution by investigating the synergistic changes of two landscape indices—forest cover area (CA) and patch density (PD)—and to extract the short-term processes—degradation, restoration, and stable—that took place between 1987 and 2021. Four long-term evolution modes, no change, increase, decrease, and wave, based on the temporal distribution of short-term change processes, were also defined to assess landscape stability. Our results showed that (i) the forest’s short-term change processes have various forms. The restoration subsequence was the largest and accounted for 46% of the total subsequence and existed in 75% of the landscape units. The time distribution of these three change processes showed that more landscape units have begun to transition into a stable state. (ii) The long-term change modes showed an aggregation distribution law and indicated that 57% of the landscape units were stable and 6.7% were unstable. Therefore, our study can provide a new perspective for the dynamic analysis of landscape patterns and offer insights for formulating better ecological restoration strategies.

Evaluations on the Consequences of Fire Suppression and the Ecological Effects of Fuel Treatment Scenarios in a Boreal Forest of the Great Xing’an Mountains, China

With global warming, catastrophic forest fires have frequently occurred in recent years, posing a major threat to forest resources and people. How to reduce forest fire risk is a hot topic in forest management. Concerns regarding fire suppression and forest fuel treatments are rising. Few studies have evaluated the ecological effects of fuel treatments. In this study, we used the LANDIS PRO model to simulate the consequences of fire suppression and the ecological effects of fuel treatments in a boreal forest of the Great Xing’an Mountains, China. Four simulation scenarios were designed, focusing on whether to conduct fuel treatments or not under two fire-control policies (current fire suppression policy and no fire suppression policy). Each scenario contains nine fuel treatment plans based on the combinations of different treatment methods (coarse woody debris reduction, prescribed burning, coarse woody debris reduction plus prescribed burning), treatment frequency (low, medium, and high), and treatment area (large, medium, and small). The ecological effects of the fuel treatments were evaluated according to the changes in fire regimes, species succession, and forest landscape patterns to find a forest fuel management plan that is suitable for the Great Xing’an Mountains. The results showed that long-term fire suppression increases fuel loads and the probability of high-intensity forest fires. The nine fuel management plans did not show significant differences in terms of species succession and forest landscape patterns while lowering forest fire intensity, and none of them were able to restore historical vegetation structure and composition. Our results consolidate the foundation for the practical performance of forest fuel treatments in fire-prone forest landscapes. We suggest a suitable fuel treatment plan for the Great Xing’an Mountains, with a low treatment frequency (20 years), large treatment area (10%), and coarse woody debris reduction, plus the prescribed burning measure.

气候变化情景下天宝岩国家级自然保护区森林景观演替长期动态模拟

PDF HTML阅读 XML下载 导出引用 引用提醒 气候变化情景下天宝岩国家级自然保护区森林景观演替长期动态模拟 DOI: 10.5846/stxb202202170376 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目(31370624);福建农林大学林学高峰学科建设项目(72202200205);福建江夏学院科研人才培育项目(JXS2020008) Long-term dynamic simulation of forest landscape succession in Tianbaoyan National Nature Reserve under climate change scenarios Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:气候变化将会对森林树种结构、空间结构以及林龄结构等产生重大影响,准确预测森林景观演替对未来气候变化的响应,不仅能够为科学管理森林生态系统提供理论依据,而且对制定生物多样性保护与珍稀物种保护策略也具有重要意义。本文运用LANDIS Pro 7.0与LINKAGES模型,模拟天宝岩国家级自然保护区8个树种在2种不同气候变化情景(RCP4.5和RCP8.5)下未来300年的森林植被演替动态,分析森林景观格局变化特征及其对气候变化的响应。结果表明:毛竹、马尾松、猴头杜鹃、长苞铁杉以及杉木的潜在面积分布与景观格局指数对气候变化的响应较为显著。在气候变化情景下,各树种的景观分维度均介于1.03-1.08,保护区内各景观斑块相对简单规则。毛竹、猴头杜鹃和杉木聚集度下降趋势明显而斑块密度显著上升,长苞铁杉随演替进行面积逐渐减少而聚集度相对较高且斑块密度剧增,马尾松斑块密度缓慢增加而聚集度先降后升,随气候变化这些树种的景观完整度都遭到了不同程度的破坏,且在RCP8.5气候情景下景观破碎化更严重。而气候变化对阔叶林与柳杉的影响则较小,且阔叶林在演替期间斑块密度下降而聚集度稳中有增,潜在面积分布呈现出良好的发展势头。随气候变化,天宝岩自然保护区森林景观的演替最终将朝着"常绿阔叶林为主、针阔叶混交林为辅"的群落组成演变,保护区森林景观格局对气候变化的响应较为敏感。 Abstract:Climate change will have a significant impact on forest tree species structure, spatial structure and forest age structure. Accurately predicting the response of forest landscape succession to future climate change not only provides a theoretical basis for scientific management of forest ecosystems, but also plays an important role in formulating strategies for biodiversity conservation and rare species conservation. In this study, LANDIS Pro 7.0 and LINKAGES models were used to simulate forest landscape succession dynamics of eight tree species in Tianbaoyan National Nature Reserve under two different climate change scenarios (RCP4.5 and RCP8.5) over the next 300 years, so as to analyze the forest vegetation landscape pattern change characteristics and its response to climate change. The results showed that the potential area distribution and landscape pattern index of Phyllostachys hetericycla, Pinus massoniana, Rhododendron simiarum, Tsuga longibracteata, and Cunninghamia lanceolata had significant responses to climate change. Under the climate change scenario, the fractal dimension index of each species ranged from 1.03 to 1.08, indicating that each landscape patch in the reserve was relatively simple and regular. For Phyllostachys hetericycla, Rhododendron simiarum and Cunninghamia lanceolate, the contagion index decreased while the patch density increased significantly. For Tsuga longibracteata, the contagion index was relatively high and patch density increased with the gradual decrease of area during succession. For Pinus massoniana, patch density increased slowly while contagion index first decreased and then increased. These results indicated that landscape integrity of these species was damaged to varying degrees with climate change, and landscape fragmentation was more serious under RCP8.5 climate scenario. However, climate change had no significant effect on broad-leaved forest and Cryptomeria fortunei. And for broad-leaved forest, the patch density decreased during succession while the patch aggregation increased steadily, and the potential area distribution showed a good momentum of development. Therefore, with the change of climate, the forest landscape succession in Tianbaoyan Nature Reserve will eventually evolve towards the community composition of evergreen broad-leaved forest, supplemented by mixed coniferous broad-leaved forest. And the forest landscape pattern of the nature reserve is more sensitive to climate change. 参考文献 相似文献 引证文献

Causes of Changing Woodland Landscape Patterns in Southern China

Forests are composed of landscape spatial units (patches) of different sizes, shapes, and characteristics. The forest landscape pattern and its trends are closely related to resistance to disturbance, restoration, stability, and the biodiversity of the forest landscape and directly influence the benefits and sustainable exploitation of forest landscape resources. Therefore, forest landscape patterns and the driving forces have increasingly attracted the attention of researchers. The present study analyzed the spatial and temporal dynamics of woodland landscape patterns in typical hilly mountainous areas in southern China using ArcGIS, landscape pattern index, and morphological spatial pattern analysis. Meanwhile, a logistic regression model was used to analyze the drivers of woodland change in Anyuan County from three aspects: natural, geographic location, and socio-economic conditions. The total area of woodland decreased during the 10-year study period, with a net decrease of 4959.27 ha, mainly due to conversion into cultivated land, garden land, and construction land. Patch density, edge density, and aggregation index of woodlands increased over time, indicating enhanced fragmentation, stable and complex patch edges, and increased patch connectivity. Conversely, the highest patch index values exhibited decreasing trends, indicating decreases in the dominant patch type. Morphological spatial pattern analysis results showed that the core area was dominant and the islet area increased over time, which also indicates enhanced fragmentation. Forest landscape change is the result of environmental change, ecological processes, and human disturbance, with geographical location and social economy having greater influences on forest landscape change. Human activities such as navel orange cultivation, returning cultivated land to forest, and land occupation for construction were the major factors driving woodland change. The results provide reference that could facilitate forest management and sustainable forest resource utilization.

Assessing the Fragmentation, Canopy Loss and Spatial Distribution of Forest Cover in Kakamega National Forest Reserve, Western Kenya

Kakamega National Forest Reserve is a tropical forest ecosystem at high risk of irreplaceable biodiversity loss due to persistent human-induced pressures. The aim of this paper is to assess the effect of fragmentation and forest cover loss on forest ecosystems in Kakamega National Forest Reserve, with the objectives: (1) to quantify the forest cover loss and analyse fragmentation in the Kakamega forest ecosystem and (2) to analyse the effect of forest cover loss on the spatial distribution of the Kakamega forest ecosystem at different timescales. Hansen global forest change data was used as an input training dataset on the Google Earth Engine platform (GEE) to estimate the area of forest cover loss by aggregating the sum of pixel values, and to provide a time series visualization of forest change by the extent of cover loss using Sentinel-2 and Landsat 7 false colour composites (RBG) in QGIS software. Fragmentation analysis was performed using reclassified forest loss and distribution data from the Hansen product as binary raster input in Guidos software. Total forest cover loss over 20 years was estimated at 826.60 ha. The first decade (2000–2010) accounted for 146.31 ha of forest cover loss, and the second decade (2010–2020) accounted for 680.29 ha of forest cover loss. Forest area density (FAD) analysis depicted an increase in the dominant layer by 8.5% and a 2.5% decrease in the interior layer. Morphological spatial pattern analysis (MSPA) illustrated a change in the core layer of 96% and a 14% increase in the openings class layer. Therefore, this study demonstrates that forest cover loss and landscape pattern alteration changed the dynamics of species interaction within ecological communities. Fragmented habitats adversely affected the ecosystem’s ability to recover the loss of endemic species, which are at risk of extinction in the backdrop of climate change. Anthropogenic drivers i.e., the clearing of natural forest and conversion of forest land for non-forest use, have contributed significantly to the loss of forest cover in the study area.

Characterizing Forest Cover and Landscape Pattern Using Multi-Source Remote Sensing Data with Ensemble Learning

Accurate information on forest distribution is an essential basis for the protection of forest resources. Recent advances in remote sensing and machine learning have contributed to the monitoring of forest-cover distribution cost-effectively, but reliable methods for rapid forest-cover mapping over mountainous areas are still lacking. In addition, the forest landscape pattern has proven to be closely related to the functioning of forest ecosystems, yet few studies have explicitly measured the forest landscape pattern or revealed its driving forces in mountainous areas. To address these challenges, we developed a framework for forest-cover mapping with multi-source remote sensing data (Sentinel-1, Sentinel-2) and an automated ensemble learning method. We also designed a scheme for forest landscape pattern evaluation and driver attribution based on landscape metrics and random forest regression. Results in the Qilian Mountains showed that the proposed framework and scheme could accurately depict the distribution and pattern of forest cover. The overall accuracy of the obtained level-1 and level-2 forest-cover maps reached 95.49% and 78.05%, respectively. The multi-classifier comparison revealed that for forest classification, the ensemble learning method outperformed base classifiers such as LightGBM, random forests, CatBoost, XGBoost, and neural networks. Integrating multi-dimensional features, including spectral, phenological, topographic, and geographic information, helped distinguish forest cover. Compared with other land-cover products, our mapping results demonstrated high quality and rich spatial details. Furthermore, we found that forest patches in the Qilian Mountains were concentrated in the eastern regions with low-to-medium elevations and shady aspects. We also identified that climate was the critical environmental determent of the forest landscape pattern in the Qilian Mountains. Overall, the proposed framework and scheme have strong application potential for characterizing forest cover and landscape patterns. The mapping and evaluation results can further support forest resource management, ecological assessment, and regional sustainable development.

Indexing coordination of ecosystem and urban economic vitality in coastal cities: An observation in yangtze river delta

Advanced geographic technologies provide an opportunity to understand the urban forest landscape and guide the governance of the urban ecosystem. However, only few studies stressed the importance of data techniques in understanding urban sustainability, especially urban forest landscape. Therefore, this study makes an analysis of urban forest resources in a city of Yangtze River Delta with the help of multi-source data techniques and further data analysis of different forest landscape pattern indices in the study area with the help of SPSS (Statistical Product and Service Solutions). The following conclusions are drawn: 1) According to the visual analysis, the spatial distribution of forest patches in the study area has a great difference. 2) All the seven landscape pattern indices are positively correlated with the distribution density of POI (Point of Interest), which represents the urban economic vitality. The correlation coefficients are NP (R2 = 0.3063), PD (R2 = 0.0079), ED (R2 = 0.3955), AREA (R2 = 0.5408), CONTIG (R2 = 0.0323), PAFRAC (R2 = 0.3662) and AI (R2 = 0.2014), respectively. This indicates that the higher the economic vitality is, the more fragmented and complex the urban forest patches are. 3) According to the geographically weighted regression model, the goodness of fit between the spatial distribution density of POI and NP, PD, ED, and AI reaches 0.804, 0.771, 0.634, and 0.619, respectively, and the explanatory power of the model is more than twice that of the corresponding linear regression model. The data illustrates that the correlation between economic vitality and urban forest landscape pattern indices has significant spatial heterogeneity.

The dynamics of tea plantation encroachment into forests and effect on forest landscape pattern during 1991–2021 through time series Landsat images

Tea plantations encroachment into forests has occurred in the major tea planting area of Yunnan province, China in the past decades. However, the dynamics of tea plantation encroachment into forests and the effect on forest landscape pattern is not clear. In this study, we proposed a method to evaluate the dynamic effects of tea plantation encroachment on forest landscape pattern change by using time series Landsat 5/7/8 data from 1991 to 2021 on the Google Earth Engine (GEE) platform. First, a pixel- and phenology-based algorithm was applied to generate tea plantations encroachment into forests maps for seven historical periods. Second, three indices including the tea plantations encroachment index (TPEI), the growth area of tea plantations encroachment into forests (GATEF), and the growth index of tea plantations encroachment into forests (GITEF) were used to quantify the tea plantations encroachment into forests of seven historical periods. Third, the effects of forests landscape pattern resulting from tea plantations encroachment were evaluated in class metrics and landscape metrics respectively. Our results show a total of 13,721 ha of GATEF from the early 1990s to the early 2020s. The GITEF increased from the early 1990s to the early 2010s and decreased from the early 2010s to the early 2020s in our study area. And the TPEI indicates that the edge encroachment mode dominated in our study period. Correlation analysis indicates that tea plantations encroachment level had significant correlation coefficients with landscape pattern indices (such as CONTAG (r = −0.91), ED (r = 0.95), DIVISION (r = 0.85), and SHEI (r = 0.86)), which indicates that the level of tea plantations encroachment affects significantly the forest landscape pattern. This study provides a promising approach to quantifying the effect of forest landscape pattern results from tea plantations encroachment.